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Qiu Y, Li ZT, Yang SX, Chen WS, Zhang Y, Kong QY, Chen LR, Huang J, Lin L, Xie K, Zeng W, Li SQ, Zhan YQ, Wang Y, Zhang JQ, Ye F. Early differential diagnosis models of Talaromycosis and Tuberculosis in HIV-negative hosts using clinical data and machine learning. J Infect Public Health 2025; 18:102740. [PMID: 40086140 DOI: 10.1016/j.jiph.2025.102740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2024] [Revised: 03/02/2025] [Accepted: 03/04/2025] [Indexed: 03/16/2025] Open
Abstract
BACKGROUND Talaromyces marneffei is an emerging pathogen, and the number of infections in HIV-negative individuals is increasing. In HIV-negative individuals, talaromycosis is usually misdiagnosed as another disease, especially tuberculosis (TB). METHODS We retrospectively extracted the clinical data of HIV-negative patients with Talaromyces marneffei infection from 2018 to 2023, analyzed the differences between TB patients and talaromycosis patients and attempted to establish differential diagnosis models utilizing clinical prediction models for these two diseases. RESULTS Overall, 718 patients, including 137 patients with talaromycosis and 581 patients with pulmonary tuberculosis (PTB), were enrolled in this study. According to the multivariate analysis, age > 65 years, expectoration, and PLT count were independent predictors for TB. Fever, chest pain, gasping, rash, lymphadenectasis, osteolysis, Neu count, EOS count, and ALB were independent predictors for talaromycosis. Receiver operating characteristic (ROC) curve analysis of the training set showed that the area under the curve (AUC) (95 % CI) of the clinical differential model based on logistic regression analysis was 0.918 (0.884-0.953). The model was verified in the validation set. ROC curve analysis of the validation set showed that the AUC (95 % CI) was 0.900 (0.841-0.959). CONCLUSION These new differential diagnosis models can calculate the probability of either talaromycosis or tuberculosis.
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Affiliation(s)
- Ye Qiu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120, China; Department of Respiratory and Critical Medicine, The Affiliated Tumour Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Zheng-Tu Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120, China
| | - Shi-Xiong Yang
- Department of Tuberculosis Ward, Guangxi Nanning Fourth People's Hospital, Nanning, Guangxi 530021, China
| | - Wu-Shu Chen
- Nanshan School of Guangzhou Medical University, Guangzhou, Guangdong 510120, China
| | - Yong Zhang
- Chongqing Nanpeng Artificial Intelligence Technology Research Institute Co., Ltd, Chongqing 401123, China
| | - Qun-Yu Kong
- Chongqing Nanpeng Artificial Intelligence Technology Research Institute Co., Ltd, Chongqing 401123, China
| | - Ling-Rui Chen
- Chongqing Nanpeng Artificial Intelligence Technology Research Institute Co., Ltd, Chongqing 401123, China
| | - Jie Huang
- Department of Tuberculosis Ward, Guangxi Nanning Fourth People's Hospital, Nanning, Guangxi 530021, China
| | - Lü Lin
- Department of Tuberculosis Ward, Guangxi Nanning Fourth People's Hospital, Nanning, Guangxi 530021, China
| | - Kan Xie
- Department of Tuberculosis Ward, Guangxi Nanning Fourth People's Hospital, Nanning, Guangxi 530021, China
| | - Wen Zeng
- Department of Respiratory and Critical Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Shao-Qiang Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120, China
| | - Yang-Qing Zhan
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120, China
| | - Yan Wang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120, China
| | - Jian-Quan Zhang
- Department of Respiratory and Critical Medicine, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, Guangdong 518000, China.
| | - Feng Ye
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120, China.
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Zhang J, Cao Q, Mao C, Xu J, Li Y, Mu Y, Huang G, Chen D, Deng X, Xu T, Zhou F, Wang X. Development and validation of a prediction model for gestational diabetes mellitus risk among women from 8 to 14 weeks of gestation in Western China. BMC Pregnancy Childbirth 2025; 25:385. [PMID: 40175970 PMCID: PMC11967024 DOI: 10.1186/s12884-025-07442-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 03/07/2025] [Indexed: 04/04/2025] Open
Abstract
OBJECTIVES To develop a clinically applicable and promotable prediction model for assessing the risk of gestational diabetes mellitus (GDM) within the context of primary healthcare institutions. METHODS The construction and the internal validation of the prediction model involved a cohort of 6,216 pregnant women observed from January 2019 to June 2019 in a Class A tertiary hospital in western China. External validation was subsequently conducted with 443 pregnant women from October 2020 to June 2021. Core characteristics were identified and the model was established using the least absolute shrinkage and selection operator (LASSO) regression. Internal validation was performed using the Bootstrap method. Model evaluation included discrimination and calibration tests, decision curve analysis (DCA), and the clinical impact curve. Visualization of the model was achieved through a static nomogram and a risk-scoring model. RESULTS The simplified prediction model possessed seven variables, including age, prepregnancy body mass index (BMI), polycystic ovary syndrome (PCOS), history of GDM, family history of diabetes, fasting plasma glucose (FPG), and urine glucose. This model exhibited a predictive accuracy, as reflected by a C-index of 0.736 (95% CI: 0.720 ~ 0.753) in the training set. The C-indexes were 0.735 and 0.694 in the internal and external testing set. Well-fitted calibration curves, the DCA curve, and the clinical impact curve demonstrated the feasibility of the simplified prediction model. For enhanced clinical application, the static nomogram and the risk-scoring model were employed to visualize the model. CONCLUSIONS This study developed a prediction model for assessing the risk of GDM among women from 8 to 14 weeks of gestation in western China. The model demonstrated moderate discriminatory ability, well-fitted calibration, and convenient visualization, suggesting its suitability for implementation and widespread adoption, particularly within the context of primary healthcare institutions.
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Affiliation(s)
- Jiani Zhang
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan, China
| | - Qi Cao
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan, China
- Department of Reproductive Medical Center, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Chihui Mao
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan, China
| | - Jinfeng Xu
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan, China
| | - Yaqian Li
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan, China
| | - Yi Mu
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan, China
- National Office for Maternal and Child Health Surveillance, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Guiqiong Huang
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan, China
| | - Daijuan Chen
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan, China
| | - Xixi Deng
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan, China
| | - Tingting Xu
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan, China
| | - Fan Zhou
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan, China
| | - Xiaodong Wang
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China.
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan, China.
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Chen J, Xing QC. Advancements and challenges in esophageal carcinoma prognostic models: A comprehensive review and future directions. World J Gastrointest Oncol 2025; 17:101379. [PMID: 39958560 PMCID: PMC11755996 DOI: 10.4251/wjgo.v17.i2.101379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2024] [Revised: 11/04/2024] [Accepted: 11/22/2024] [Indexed: 01/18/2025] Open
Abstract
In this article, we comment on the article published by Yu et al. By employing LASSO regression and Cox proportional hazard models, the article identified nine significant variables affecting survival, including body mass index, Karnofsky performance status, and tumor-node-metastasis staging. We firmly concur with Yu et al regarding the vital significance of clinical prediction models (CPMs), including logistic regression and Cox regression for assessment in esophageal carcinoma (EC). However, the nomogram's limitations and the complexities of integrating genetic factors pose challenges. The integration of immunological data with advanced statistics offers new research directions. High-throughput sequencing and big data, facilitated by machine learning, have revolutionized cancer research but require substantial computational resources. The future of CPMs in EC depends on leveraging these technologies to improve predictive accuracy and clinical application, addressing the need for larger datasets, patient-reported outcomes, and regular updates for clinical relevance.
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Affiliation(s)
- Jia Chen
- Department of Clinical Pharmacy, Xiangtan Central Hospital, Xiangtan 411100, Hunan Province, China
| | - Qi-Chang Xing
- Department of Clinical Pharmacy, Xiangtan Central Hospital, Xiangtan 411100, Hunan Province, China
- The Affiliated Hospital, Hunan University, Changsha 410082, Hunan Province, China
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Zhou T, Ni Z, Fan H, Huang H, Jin H. Utilizing innovative two curves in nomogram. Front Med (Lausanne) 2025; 11:1478603. [PMID: 39839611 PMCID: PMC11746040 DOI: 10.3389/fmed.2024.1478603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2024] [Accepted: 12/16/2024] [Indexed: 01/23/2025] Open
Abstract
Objective Nomograms are valuable tools in clinical research for predicting patient outcomes. Understanding threshold values within these models is crucial for assessing the model's effectiveness and practical application in clinical environments. Methods We developed two novel interpretive curves to enhance the utility of nomograms. These curves were designed to provide clear visualization of how clinical prediction models perform across various thresholds. The curves are applied to two case studies to demonstrate their practical application and efficacy. Results In both examples, the novel curves successfully highlighted critical threshold values and revealed changes in prediction accuracy across these thresholds. This enhanced the understanding of the nomogram's performance, providing clinicians with more informative decision-making tools. Conclusions The introduction of these interpretive curves allows for a more nuanced understanding of nomogram-based predictions, offering insights into threshold effects that can inform clinical decisions.
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Affiliation(s)
- Tianhan Zhou
- The Department of General Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhongkai Ni
- The Department of General Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Hao Fan
- School of Clinical Medicine, Zhejiang Chinese Medicine University, Hangzhou, China
| | - Hai Huang
- The Department of General Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Haimin Jin
- The Department of General Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
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Zhong Y, Wang H, Wang Y. Development and validation of a nomogram for predicting sleep disturbance in pregnant and postpartum women: A pilot study. Heliyon 2024; 10:e39750. [PMID: 39759350 PMCID: PMC11697565 DOI: 10.1016/j.heliyon.2024.e39750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Revised: 10/22/2024] [Accepted: 10/22/2024] [Indexed: 01/07/2025] Open
Abstract
Background Sleep disturbances are common in pregnant and postpartum women, impacting their health. Predictive tools for timely intervention are scarce. Objective To develop and validate a nomogram predicting sleep disturbance risk in this demographic. Methods We enrolled unipara with singleton pregnancies from Shenzhen hospitals in 2022, with complete data and survey cooperation. Data collected included demographics, pregnancy stage, systemic health, sleep status, and emotional state. Subjects were randomly assigned to training (70 %) and validation (30 %) groups. Risk factors were identified via logistic regression, and the nomogram was evaluated using calibration, ROC curves, and DCA. Results The study involved 727 women. Identified risk factors for sleep disturbance included education, income, and various systemic and emotional symptoms. The nomogram demonstrated strong predictive accuracy in both groups (AUC: 0.93 and 0.91), with calibration and DCA confirming its reliability. Conclusion The nomogram accurately predicts sleep disturbance risk, aiding early detection and improving sleep quality in pregnant and postpartum women. Its broader applicability will be confirmed in future studies.
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Affiliation(s)
- Yingyu Zhong
- Shenzhen Maternity and Child Healthcare Hospital, China
| | - He Wang
- Shenzhen Maternity and Child Healthcare Hospital, China
| | - Yueyun Wang
- Shenzhen Maternity and Child Healthcare Hospital, China
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Kamau S, Kigo J, Maina M, Gachohi J. External validation of an admission risk score for predicting inpatient paediatric mortality in two Kenyan public hospitals. Wellcome Open Res 2024; 9:732. [PMID: 40103631 PMCID: PMC11914872 DOI: 10.12688/wellcomeopenres.23471.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/17/2024] [Indexed: 03/20/2025] Open
Abstract
Background Early identification of children at risk of mortality during hospitalization is crucial in preventing mortality in low-and middle-income countries (LMICs). This study aimed to externally validate an admission risk score for predicting inpatient paediatric mortality in resource-limited settings. Methods This retrospective study utilized routine clinical data of children aged ≤12 years admitted to two Kenyan public hospitals between January 2017 and October 2023. The admission risk score includes 13 clinical predictors, each assigned a value. Aggregate values were used to predict inpatient pediatric mortality, with a higher score indicating a greater risk of death. Children with scores of 0, 1-4 and ≥5 were categorized as low, moderate and high-risk categories, respectively. Discrimination was assessed using area under the receiver operating characteristic curve (AUC). Sensitivity, specificity, and positive and negative predictive values were calculated at different cutoff points. Results A total of 15,606 children were included in the study. Majority of the participants were male (8,847, 56.7%) and aged 12 - 59 months (7,222, 46.3%). Children classified as high-risk had a higher mortality rate (23.4%) than those classified as low risk (2%). The risk score demonstrated moderate discrimination, with an AUC of 0.73 (95% confidence interval (CI): 0.71 - 0.75). A cutoff of ≥3 achieved a balance between sensitivity and specificity, with values of 63.8% (95% CI: 60.7%-66.9%) and 72.2% (95% CI: 71.5% - 72.9%), respectively, compared to other cutoff points. Conclusion The risk score performed moderately in predicting inpatient paediatric mortality in two Kenyan public hospitals. The risk score can be used with other clinical assessments to rapidly identify high-risk children and guide targeted interventions to prevent mortality.
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Affiliation(s)
- Stephen Kamau
- Health Services Unit, KEMRI-Wellcome Trust Research Programme Nairobi, Nairobi, Kenya
- School of Public Health, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
| | - Joyce Kigo
- Health Services Unit, KEMRI-Wellcome Trust Research Programme Nairobi, Nairobi, Kenya
| | - Michuki Maina
- Health Services Unit, KEMRI-Wellcome Trust Research Programme Nairobi, Nairobi, Kenya
| | - John Gachohi
- School of Public Health, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
- Washington State University Global Health Program-Kenya, Nairobi, Kenya
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Tao S, Yu L, Yang D, Huang L, Li J. Association of endothelial function and limb artery indices with coronary artery stenosis severity in patients with hypertension. Ann Med 2024; 56:2427369. [PMID: 39541433 PMCID: PMC11565676 DOI: 10.1080/07853890.2024.2427369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2024] [Accepted: 10/26/2024] [Indexed: 11/16/2024] Open
Abstract
BACKGROUND Hypertension is one of the major risk factors for cardiovascular events. This study aims to analyse the association of endothelial function and limb artery indices with coronary artery stenosis (CAS) severity in hypertension based on easily accessible and detailed clinical information, and to help accurately identify high-risk groups and avoid missed diagnosis and misdiagnosis. METHODS Admission data of 1,375 consecutive hypertensive patients complicated with suspected coronary atherosclerotic heart disease (CHD) from September 2020 to August 2021 in China-Japan Friendship Hospital were retrospectively assessed. All candidates underwent coronary angiography for screening. A total of 600 eligible patients were classified in the CHD group (n = 359) and non-CHD group (n = 241) based on their coronary angiography results. Subjects in the CHD group were further assigned to 'high stenosis' (n = 178) and 'low stenosis' (n = 181) subgroups based on the median value of Gensini score. Endothelial function and limb artery indicators, including brachial artery flow-mediated vasodilatation (FMD), ankle-brachial index (ABI) and brachial-ankle pulse velocity (baPWV), were examined and compared between subgroups. Multivariate logistic regression analysis and multiple linear regression analysis were carried out to select independent risk factors of CAS severity in hypertension. A predictive equation was conducted according to the results of multivariate logistic regression analysis to make clinical practice easier. As the receiver operating characteristic (ROC) curve had been plotted, the predictive ability of endothelial function and limb artery indicators in CAS severity in hypertension was detected by the area under the curve (AUC). RESULTS In patients with hypertension, the FMD (p = 0.023), ABI (p < 0.001) and baPWV (p < 0.001) of CHD patients appeared substantially different from the non-CHD patients. Furthermore, the ABI (p < 0.001) and baPWV (p = 0.032) both independently associated with CAS severity in hypertensive patients with CHD. Based on the results of multivariate logistic regression analysis with CAS severity as a dependent variable, a predictive equation of baPWV, ABI and FMD was developed: combined coefficient = Logit(p)=5.531-0.218*FMD-7.019*ABI + 0.244*baPWV. From the combined coefficients of baPWV, ABI and FMD, the largest AUC was 0.800, suggesting a powerful predictive value of CAS severity in hypertensive patients, followed by ABI (AUC = 0.747, 95%CI 0.693-0.796), baPWV (AUC = 0.704, 95%CI 0.648-0.756) and FMD (AUC = 0.588, 95%CI 0.529-0.645). CONCLUSION This study shows that baPWV, ABI and FMD are independent risk factors for CHD, of which, baPWV and ABI are strongly associated with CAS severity in hypertensive patients. The predictive ability of CHD in hypertensive patients may be enhanced through combining the three endothelial function and limb artery indicators. The results may help to facilitate clinical decision-making during treatment and management of coronary artery disease.
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Affiliation(s)
- Shiyi Tao
- Department of Cardiology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
| | - Lintong Yu
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
| | - Deshuang Yang
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, China
| | - Li Huang
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, China
| | - Jun Li
- Department of Cardiology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
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Wang H, Shi J, Hou S, Kang X, Yu C, Jin H, Yang B, Shi Y, Li F, Li W, Gu J, Lei M, Lin Y, Dang L, Lin J, Guo Q, Wang G, Liu X. A large-scale retrospective study in China explores risk factors for disease severity in plaque psoriasis. Sci Rep 2024; 14:25749. [PMID: 39468139 PMCID: PMC11519361 DOI: 10.1038/s41598-024-73408-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 09/17/2024] [Indexed: 10/30/2024] Open
Abstract
Severe psoriasis has a long course and poor outcome, and it has long been a problem for patients. Understanding the independent risk factors that contribute to patients with severe psoriasis is critical for the development of effective treatment strategies. This large, multicenter study recruited 2,109 plaque psoriasis patients from 12 hospitals across China (October 31, 2019 - May 31, 2022). The logistic regression model underwent internal validation and external validation using two independent cohorts over future time periods (June 1, 2022 - January 31, 2023). The discriminative power of our model was substantiated by a C-index of 0.863 (95% CI: 0.848-0.879) in internal validation, further affirmed through 1,000 bootstrap validation (C-index: 0.860, 95% CI: 0.836-0.885) and external validation cohorts, where the C-index reached up to 0.910 (95% CI: 0.868-0.953) and 0.951 (95% CI: 0.924-0.977) in 2 external validation cohorts. To enhance accessibility for clinicians, the model has been made available as a dynamic nomogram and QR code. Our study identified 10 risk factors (the "DELPHI" consensus dichotomy, the DLQI index, the extent of skin involvement as measured by body surface area, the age of the patient at the time of clinical visit, sex, body weight in kilograms, career, the presence of scalp involvement, facial involvement, and arthropathy) for the overall severity of psoriasis (PASI ≥ 10). "Nomogram-10" provides clinicians with a practical tool to develop personalized intervention strategies based on an individual's risk profile.Trial registration: Chinese Clinical Trial Registry: ChiCTR1900024852.
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Affiliation(s)
- Huiwei Wang
- Department of Dermatology, The University of Hong Kong-Shenzhen Hospital, No. 1 Haiyuan 1 Rd, Futian District, Shenzhen, 518053, Guangdong, China
| | - Jialiang Shi
- Department of Dermatology, Shenzhen University General Hospital, No. 1098 Xue Yuan Avenue, Xi Li University Town, Nanshan District, Shenzhen, 518055, Guangdong, China
| | - Suchun Hou
- Department of Dermatology, Shenzhen University General Hospital, No. 1098 Xue Yuan Avenue, Xi Li University Town, Nanshan District, Shenzhen, 518055, Guangdong, China
| | - Xiaojing Kang
- Department of Dermatology and Venereology, People's Hospital of Xinjiang Uygur Autonomous Region, No. 91 Tianchi Road, Tianshan District, Urumqi, 830001, Xinjiang, China
| | - Chen Yu
- Department of Dermatology, Xijing Hospital, The Fourth Military Medical University, No. 127 Changle West Road, Xi'an, 710032, Shaanxi, China
| | - Hongzhong Jin
- Department of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifu Garden, Dongcheng District, 100730, Beijing, China
| | - Bin Yang
- Department of Dermatology, Dermatology Hospital of Southern Medical University, No. 2 Lujing Road, Yuexiu District, Guangzhou City, 510091, Guangdong, China
| | - Yuling Shi
- Department of Dermatology, Shanghai Skin Disease Hospital, Tongji University School of Medicine, No. 1278 Baode Road, Jing'an District, 200443, Shanghai, China
| | - Fuqiu Li
- Department of Dermatology, The Second Norman Bethune Hospital of Jilin University, Jilin University, No. 218 Ziqiang Street, Nanguan District, Changchun, 130041, Jilin, China
| | - Wei Li
- Department of Dermatology, Rare Diseases Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu, 610041, Sichuan, China
| | - Jun Gu
- Department of Dermatology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, No. 301 Yanchang Road, Jing'an District, 200072, Shanghai, China
| | - Mingjun Lei
- Department of Dermatology, Affiliated Hospital of Hebei University of Chinese Medicine, No. 389 Zhongshan East Road, Shijiazhuang, 200072, Hebei, China
| | - Youkun Lin
- Department of Dermatology/Venerology, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, China
| | - Lin Dang
- Department of Dermatology, Longgang Central Hospital, No. 6082, Longgang Avenue, Longgang District, Shenzhen, 518116, Guangdong, China
| | - Jialin Lin
- Department of Dermatology, The University of Hong Kong-Shenzhen Hospital, No. 1 Haiyuan 1 Rd, Futian District, Shenzhen, 518053, Guangdong, China
| | - Qing Guo
- Department of Dermatology, The University of Hong Kong-Shenzhen Hospital, No. 1 Haiyuan 1 Rd, Futian District, Shenzhen, 518053, Guangdong, China
| | - Gang Wang
- Department of Dermatology, Xijing Hospital, The Fourth Military Medical University, No. 127 Changle West Road, Xi'an, 710032, Shaanxi, China.
| | - Xiaoming Liu
- Department of Dermatology, Huazhong University of Science and Technology Union Shenzhen Hospital (The 6th Affiliated Hospital of Shenzhen University Medical School), No. 89 Taoyuan Road, Nanshan District, Shenzhen, 518052, Guangdong, China.
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Zhou X, Zhu F. Development and Validation of a Nomogram Model for Accurately Predicting Depression in Maintenance Hemodialysis Patients: A Multicenter Cross-Sectional Study in China. Risk Manag Healthc Policy 2024; 17:2111-2123. [PMID: 39246589 PMCID: PMC11380485 DOI: 10.2147/rmhp.s456499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 03/23/2024] [Indexed: 09/10/2024] Open
Abstract
Purpose Depression is a major concern in maintenance hemodialysis. However, given the elusive nature of its risk factors and the redundant nature of existing assessment forms for judging depression, further research is necessary. Therefore, this study was devoted to exploring the risk factors for depression in maintenance hemodialysis patients and to developing and validating a predictive model for assessing depression in maintenance hemodialysis patients. Patients and Methods This cross-sectional study was conducted from May 2022 to December 2022, and we recruited maintenance hemodialysis patients from a multicentre hemodialysis centre. Risk factors were identified by Lasso regression analysis and a Nomogram model was developed to predict depressed patients on maintenance hemodialysis. The predictive accuracy of the model was assessed by ROC curves, area under the ROC (AUC), consistency index (C-index), and calibration curves, and its applicability in clinical practice was evaluated using decision curves (DCA). Results A total of 175 maintenance hemodialysis patients were included in this study, and cases were randomised into a training set of 148 and a validation set of 27 (split ratio 8.5:1.5), with a depression prevalence of 29.1%. Based on age, employment, albumin, and blood uric acid, a predictive map of depression was created, and in the training set, the nomogram had an AUC of 0.7918, a sensitivity of 61.9%, and a specificity of 89.2%. In the validation set, the nomogram had an AUC of 0.810, a sensitivity of 100%, and a specificity of 61.1%. The bootstrap-based internal validation showed a c-index of 0.792, while the calibration curve showed a strong correlation between actual and predicted depression risk. Decision curve analysis (DCA) results indicated that the predictive model was clinically useful. Conclusion The nomogram constructed in this study can be used to identify depression conditions in vulnerable groups quickly, practically and reliably.
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Affiliation(s)
- Xinyuan Zhou
- Department of Nephrology, the First People's Hospital of Pinghu, Jiaxing, Zhejiang, People's Republic of China
- Jiaxing University Master Degree Cultivation Base, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People's Republic of China
| | - Fuxiang Zhu
- Department of Nephrology, Jiaxing Hospital of Traditional Chinese Medicine, Jiaxing, Zhejiang, People's Republic of China
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Zhou X, Han J, Zhu F. Development and validation of a nomogram model for accurately predicting severe fatigue in maintenance hemodialysis patients: A multicenter cross-sectional study in China. Ther Apher Dial 2024; 28:390-398. [PMID: 38444376 DOI: 10.1111/1744-9987.14113] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/29/2024] [Accepted: 02/07/2024] [Indexed: 03/07/2024]
Abstract
INTRODUCTION This study aims to analyze the risk factors for severe fatigue in maintenance hemodialysis (MHD) patients and develop a clinical prediction model to help doctors and patients prevent severe fatigue. METHODS Multicentre MHD patients were included in this study. The objective was to investigate the risk factors for severe fatigue in MHD patients and develop a prediction model. RESULTS A total of 243 MHD patients were included in the study, and the incidence of severe fatigue was found to be 20.99%. Using age, body mass index, total cholesterol, and albumin levels, a predictive nomogram for fatigue was constructed. In the training set, the nomogram had an area under the curve of 0.851, sensitivity of 82.86%, specificity of 81.76%, and c-index of 0.851. The nomogram was accurate in calibration and proved to be clinically useful. CONCLUSION The nomogram developed in this study is a practical and reliable tool for quickly identifying severe fatigue in MHD patients.
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Affiliation(s)
- Xinyuan Zhou
- Jiaxing University Master Degree Cultivation Base, Zhejiang Chinese Medical University, Jiaxing, Zhejiang, China
- Department of Nephrology, The First Hospital of Jiaxing Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang, China
| | - Jiahui Han
- Jiaxing University Master Degree Cultivation Base, Zhejiang Chinese Medical University, Jiaxing, Zhejiang, China
- Department of Nephrology, The First Hospital of Jiaxing Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang, China
| | - Fuxiang Zhu
- Jiaxing Hospital of Traditional Chinese Medicine, Jiaxing, Zhejiang, China
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11
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Tiruneh SA, Vu TTT, Moran LJ, Callander EJ, Allotey J, Thangaratinam S, Rolnik DL, Teede HJ, Wang R, Enticott J. Externally validated prediction models for pre-eclampsia: systematic review and meta-analysis. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2024; 63:592-604. [PMID: 37724649 DOI: 10.1002/uog.27490] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 08/29/2023] [Accepted: 09/08/2023] [Indexed: 09/21/2023]
Abstract
OBJECTIVE This systematic review and meta-analysis aimed to evaluate the performance of existing externally validated prediction models for pre-eclampsia (PE) (specifically, any-onset, early-onset, late-onset and preterm PE). METHODS A systematic search was conducted in five databases (MEDLINE, EMBASE, Emcare, CINAHL and Maternity & Infant Care Database) and using Google Scholar/reference search to identify studies based on the Population, Index prediction model, Comparator, Outcome, Timing and Setting (PICOTS) approach until 20 May 2023. We extracted data using the CHARMS checklist and appraised the risk of bias using the PROBAST tool. A meta-analysis of discrimination and calibration performance was conducted when appropriate. RESULTS Twenty-three studies reported 52 externally validated prediction models for PE (one preterm, 20 any-onset, 17 early-onset and 14 late-onset PE models). No model had the same set of predictors. Fifteen any-onset PE models were validated externally once, two were validated twice and three were validated three times, while the Fetal Medicine Foundation (FMF) competing-risks model for preterm PE prediction was validated widely in 16 different settings. The most common predictors were maternal characteristics (prepregnancy body mass index, prior PE, family history of PE, chronic medical conditions and ethnicity) and biomarkers (uterine artery pulsatility index and pregnancy-associated plasma protein-A). The FMF model for preterm PE (triple test plus maternal factors) had the best performance, with a pooled area under the receiver-operating-characteristics curve (AUC) of 0.90 (95% prediction interval (PI), 0.76-0.96), and was well calibrated. The other models generally had poor-to-good discrimination performance (median AUC, 0.66 (range, 0.53-0.77)) and were overfitted on external validation. Apart from the FMF model, only two models that were validated multiple times for any-onset PE prediction, which were based on maternal characteristics only, produced reasonable pooled AUCs of 0.71 (95% PI, 0.66-0.76) and 0.73 (95% PI, 0.55-0.86). CONCLUSIONS Existing externally validated prediction models for any-, early- and late-onset PE have limited discrimination and calibration performance, and include inconsistent input variables. The triple-test FMF model had outstanding discrimination performance in predicting preterm PE in numerous settings, but the inclusion of specialized biomarkers may limit feasibility and implementation outside of high-resource settings. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
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Affiliation(s)
- S A Tiruneh
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - T T T Vu
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - L J Moran
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - E J Callander
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
- School of Public Health, Faculty of Health, University of Technology Sydney, Sydney, NSW, Australia
| | - J Allotey
- World Health Organization (WHO) Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - S Thangaratinam
- World Health Organization (WHO) Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
- Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | - D L Rolnik
- Department of Obstetrics and Gynaecology, Monash University, Clayton, VIC, Australia
| | - H J Teede
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - R Wang
- Department of Obstetrics and Gynaecology, Monash University, Clayton, VIC, Australia
| | - J Enticott
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
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Gu Z, Yang C, Zhang K, Wu H. Development and validation of a nomogram for predicting sever cancer-related fatigue in patients with cervical cancer. BMC Cancer 2024; 24:492. [PMID: 38637740 PMCID: PMC11025233 DOI: 10.1186/s12885-024-12258-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 04/15/2024] [Indexed: 04/20/2024] Open
Abstract
OBJECTIVE Cancer-related fatigue (CRF) has been considered the biggest influencing factor for cancer patients after surgery. This study aimed to develop and validate a nomogram for severe cancer-related fatigue (CRF) patients with cervical cancer (CC). METHODS A cross-sectional study was conducted to develop and validate a nomogram (building set = 196; validation set = 88) in the Department of Obstetrics and Gynecology of a Class III hospital in Shenyang, Liaoning Province. We adopted the questionnaire method, including the Cancer Fatigue Scale (CFS), Medical Uncertainty in Illness Scale (MUIS), Medical Coping Modes Questionnaire (MCMQ), Multidimensional Scale of Perceived Social Support (MSPSS), and Sense of Coherence-13 (SOC-13). Binary logistic regression was used to test the risk factors of CRF. The R4.1.2 software was used to develop and validate the nomogram, including Bootstrap resampling method, the ability of Area Under Curve (AUC), Concordance Index (C-Index), Hosmer Lemeshow goodness of fit test, Receiver Operating Characteristic (ROC) curve, Calibration calibration curve, and Decision Curve Analysis curve (DCA). RESULTS The regression equation was Logit(P) = 1.276-0.947 Monthly income + 0.989 Long-term passive smoking - 0.952 Physical exercise + 1.512 Diagnosis type + 1.040 Coping style - 0.726 Perceived Social Support - 2.350 Sense of Coherence. The C-Index of the nomogram was 0.921 (95% CI: 0.877∼0.958). The ROC curve showed the sensitivity of the nomogram was 0.821, the specificity was 0.900, and the accuracy was 0.857. AUC was 0.916 (95% CI: 0.876∼0.957). The calibration showed that the predicted probability of the nomogram fitted well with the actual probability. The DCA curve showed when the prediction probability was greater than about 10%, the benefit of the nomogram was positive. The results in the validation group were similar. CONCLUSION This nomogram had good identifiability, accuracy and clinical practicality, and could be used as a prediction and evaluation tool for severe cases of clinical patients with CC.
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Affiliation(s)
- ZhiHui Gu
- Department of Social Medicine, School of Health Management, China Medical University, No.77 PuHe Road, Shenyang North New District, 110122, Shenyang, Liaoning, People's Republic of China
| | - ChenXin Yang
- Department of Social Medicine, School of Health Management, China Medical University, No.77 PuHe Road, Shenyang North New District, 110122, Shenyang, Liaoning, People's Republic of China
| | - Ke Zhang
- Department of Social Medicine, School of Health Management, China Medical University, No.77 PuHe Road, Shenyang North New District, 110122, Shenyang, Liaoning, People's Republic of China
| | - Hui Wu
- Department of Social Medicine, School of Health Management, China Medical University, No.77 PuHe Road, Shenyang North New District, 110122, Shenyang, Liaoning, People's Republic of China.
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Li W, Wang Y, Zhou S, Liu S, Di L, Chen W, Lv H. Development and validation of predictive nomogram for postoperative non-union of closed femoral shaft fracture. Sci Rep 2024; 14:3543. [PMID: 38347044 PMCID: PMC10861573 DOI: 10.1038/s41598-024-53356-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 01/31/2024] [Indexed: 02/15/2024] Open
Abstract
Closed femoral shaft fracture is caused by high-energy injuries, and non-union exists after operation, which can significantly damage patients' body and mind. This study aimed to explore the factors influencing postoperative non-union of closed femoral shaft fractures and establish a predictive nomogram. Patients with closed femoral shaft fractures treated at Hebei Medical University Third Hospital between January 2015 and December 2021 were retrospectively enrolled. A total of 729 patients met the inclusion criteria; of them, those treated in 2015-2019 comprised the training cohort (n = 617), while those treated in 2020-2021 comprised the external validation cohort (n = 112). According to multivariate logistic regression analysis, complex fractures, bone defects, smoking, and postoperative infection were independent risk factors. Based on the factors, a predictive nomogram was constructed and validated. The C-indices in training and external validation cohorts were 0.818 and 0.781, respectively; and the C-index of internal validation via bootstrap resampling was 0.804. The Hosmer-Lemeshow test showed good fit of the nomogram (P > 0.05) consistent with the calibration plot results. The clinical effectiveness was best at a threshold probability of 0.10-0.40 in decision curve analysis. The risk prediction for patients with fractures using this nomogram may aid targeted prevention and rehabilitation programs.
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Affiliation(s)
- Wenjing Li
- Hebei Provincial Key Laboratory of Orthopaedic Biomechanics, Hebei Orthopaedic Research Institute, No. 139 Ziqiang Road, Shijiazhuang, 050051, China
- School of Public Health, Hebei Medical University, No.361 Zhongshan East Road, Shijiazhuang, 050017, China
- Trauma Emergency Center, Hebei Medical University Third Hospital, No. 139 Ziqiang Road, Shijiazhuang, 050051, China
| | - Yan Wang
- Hebei Provincial Key Laboratory of Orthopaedic Biomechanics, Hebei Orthopaedic Research Institute, No. 139 Ziqiang Road, Shijiazhuang, 050051, China
- School of Public Health, Hebei Medical University, No.361 Zhongshan East Road, Shijiazhuang, 050017, China
- Trauma Emergency Center, Hebei Medical University Third Hospital, No. 139 Ziqiang Road, Shijiazhuang, 050051, China
| | - Shuai Zhou
- Hebei Provincial Key Laboratory of Orthopaedic Biomechanics, Hebei Orthopaedic Research Institute, No. 139 Ziqiang Road, Shijiazhuang, 050051, China
- School of Public Health, Hebei Medical University, No.361 Zhongshan East Road, Shijiazhuang, 050017, China
- Trauma Emergency Center, Hebei Medical University Third Hospital, No. 139 Ziqiang Road, Shijiazhuang, 050051, China
| | - Shihang Liu
- Hebei Provincial Key Laboratory of Orthopaedic Biomechanics, Hebei Orthopaedic Research Institute, No. 139 Ziqiang Road, Shijiazhuang, 050051, China
- School of Public Health, Hebei Medical University, No.361 Zhongshan East Road, Shijiazhuang, 050017, China
- Trauma Emergency Center, Hebei Medical University Third Hospital, No. 139 Ziqiang Road, Shijiazhuang, 050051, China
| | - Luqin Di
- Trauma Emergency Center, Hebei Medical University Third Hospital, No. 139 Ziqiang Road, Shijiazhuang, 050051, China
| | - Wei Chen
- Hebei Provincial Key Laboratory of Orthopaedic Biomechanics, Hebei Orthopaedic Research Institute, No. 139 Ziqiang Road, Shijiazhuang, 050051, China.
- Trauma Emergency Center, Hebei Medical University Third Hospital, No. 139 Ziqiang Road, Shijiazhuang, 050051, China.
| | - Hongzhi Lv
- Hebei Provincial Key Laboratory of Orthopaedic Biomechanics, Hebei Orthopaedic Research Institute, No. 139 Ziqiang Road, Shijiazhuang, 050051, China.
- School of Public Health, Hebei Medical University, No.361 Zhongshan East Road, Shijiazhuang, 050017, China.
- Trauma Emergency Center, Hebei Medical University Third Hospital, No. 139 Ziqiang Road, Shijiazhuang, 050051, China.
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Guixue G, Yifu P, Yuan G, Xialei L, Fan S, Qian S, Jinjin X, Linna Z, Xiaozuo Z, Wen F, Wen Y. Progress of the application clinical prediction model in polycystic ovary syndrome. J Ovarian Res 2023; 16:230. [PMID: 38007488 PMCID: PMC10675861 DOI: 10.1186/s13048-023-01310-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 11/05/2023] [Indexed: 11/27/2023] Open
Abstract
Clinical prediction models play an important role in the field of medicine. These can help predict the probability of an individual suffering from disease, complications, and treatment outcomes by applying specific methodologies. Polycystic ovary syndrome (PCOS) is a common disease with a high incidence rate, huge heterogeneity, short- and long-term complications, and complex treatments. In this systematic review study, we reviewed the progress of clinical prediction models in PCOS patients, including diagnosis and prediction models for PCOS complications and treatment outcomes. We aimed to provide ideas for medical researchers and clues for the management of PCOS. In the future, models with poor accuracy can be greatly improved by adding well-known parameters and validations, which will further expand our understanding of PCOS in terms of precision medicine. By developing a series of predictive models, we can make the definition of PCOS more accurate, which can improve the diagnosis of PCOS and reduce the likelihood of false positives and false negatives. It will also help discover complications earlier and treatment outcomes being known earlier, which can result in better outcomes for women with PCOS.
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Affiliation(s)
- Guan Guixue
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China
| | - Pu Yifu
- Laboratory of Genetic Disease and Perinatal Medicine, Key laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Gao Yuan
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China
| | - Liu Xialei
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China
| | - Shi Fan
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China
| | - Sun Qian
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China
| | - Xu Jinjin
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China
| | - Zhang Linna
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China
| | - Zhang Xiaozuo
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China
| | - Feng Wen
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China
| | - Yang Wen
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China.
- Xuzhou Medical University affiliated hospital of Lianyungang, Lianyungang, Jiangsu, 222002, China.
- The first affiliated hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222002, China.
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Tao S, Yu L, Yang D, Yao R, Zhang L, Huang L, Shao M. Development and validation of a clinical prediction model for detecting coronary heart disease in middle-aged and elderly people: a diagnostic study. Eur J Med Res 2023; 28:375. [PMID: 37749613 PMCID: PMC10521501 DOI: 10.1186/s40001-023-01233-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 07/16/2023] [Indexed: 09/27/2023] Open
Abstract
OBJECTIVE To develop and validate a multivariate prediction model to estimate the risk of coronary heart disease (CHD) in middle-aged and elderly people and to provide a feasible method for early screening and diagnosis in middle-aged and elderly CHD patients. METHODS This study was a single-center, retrospective, case-control study. Admission data of 932 consecutive patients with suspected CHD were retrospectively assessed from September 1, 2020 to December 31, 2021 in the Department of Integrative Cardiology at China-Japan Friendship Hospital. A total of 839 eligible patients were included in this study, and 588 patients were assigned to the derivation set and 251 as the validation set at a 7:3 ratio. Clinical characteristics of included patients were compared between derivation set and validation set by univariate analysis. The least absolute shrinkage and selection operator (Lasso) regression analysis method was performed to avoid collinearity and identify key potential predictors. Multivariate logistic regression analysis was used to construct a clinical prediction model with identified predictors for clinical practice. Bootstrap validation was used to test performance and eventually we obtained the actual model. And the Hosmer-Lemeshow test was carried out to evaluate the goodness-fit of the constructed model. The area under curve (AUC) of receiver operating characteristic (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were plotted and utilized with validation set to comprehensively evaluate the predictive accuracy and clinical value of the model. RESULTS A total of eight indicators were identified as risk factors for the development of CHD in middle-aged and elderly people by univariate analysis. Of these candidate predictors, four key parameters were defined to be significantly related to CHD by Lasso regression analysis, including age (OR 1.034, 95% CI 1.002 ~ 1.067, P = 0.040), hemoglobin A1c (OR 1.380, 95% CI 1.078 ~ 1.768, P = 0.011), ankle-brachial index (OR 0.078, 95% CI 0.012 ~ 0.522, P = 0.009), and brachial artery flow-mediated vasodilatation (OR 0.848, 95% CI 0.726 ~ 0.990, P = 0.037). The Hosmer-Lemeshow test showed a good calibration performance of the clinical prediction model (derivation set, χ2 = 7.865, P = 0.447; validation set, χ2 = 11.132, P = 0.194). The ROCs of the nomogram in the derivation set and validation set were 0.722 and 0.783, respectively, suggesting excellent predictive power and suitable performance. The clinical prediction model presented a greater net benefit and clinical impact based on DCA and CIC analysis. CONCLUSION Overall, the development and validation of the multivariate model combined the laboratory and clinical parameters of patients with CHD, which could be beneficial to the individualized prediction of middle-aged and elderly people, and helped to facilitate clinical assessments and decisions during treatment and management of CHD.
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Affiliation(s)
- Shiyi Tao
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
| | - Lintong Yu
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
| | - Deshuang Yang
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, China
| | - Ruiqi Yao
- Department of Internal Medicine, Shenzhen Nanshan Chinese Medicine Hospital, Guangdong, China
| | - Lanxin Zhang
- Department of Oncology, Guang'anmenHospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Li Huang
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, China
| | - Mingjing Shao
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, China.
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Ran Q, Zhao X, Tian J, Gong S, Zhang X. A nomogram model for predicting malnutrition among older hospitalized patients with type 2 diabetes: a cross-sectional study in China. BMC Geriatr 2023; 23:565. [PMID: 37715131 PMCID: PMC10503093 DOI: 10.1186/s12877-023-04284-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 09/06/2023] [Indexed: 09/17/2023] Open
Abstract
BACKGROUND Malnutrition remains a pervasive issue among older adults, a prevalence that is markedly higher among those diagnosed with diabetes. The primary objective of this study was to develop and validate a risk prediction model that can accurately identify instances of malnutrition among elderly hospitalized patients with type 2 diabetes mellitus (T2DM) within a Chinese demographic. METHODS This cross-sectional study was conducted between August 2021 and August 2022, we enrolled T2DM patients aged 65 years and above from endocrinology wards. The creation of a nomogram for predicting malnutrition was based on risk factors identified through univariate and multivariate logistic regression analyses. The predictive accuracy of the model was evaluated by the receiver operating characteristic curve (ROC),the area under the ROC (AUC), the concordance index (C-index), and calibration curves. RESULTS The study included a total of 248 older T2DM patients, with a recorded malnutrition prevalence of 26.21%. The identified critical risk factors for malnutrition in this cohort were body mass index, albumin, impairment in activities of daily living, dietary habits, and glycosylated hemoglobin. The AUC of the nomogram model reached 0.914 (95% CI: 0.877-0.951), with an optimal cutoff value of 0.392. The model demonstrated a sensitivity of 80.0% and a specificity of 88.5%. Bootstrap-based internal verification results revealed a C-index of 0.891, while the calibration curves indicated a strong correlation between the actual and predicted malnutrition risks. CONCLUSIONS This study underscores the critical need for early detection of malnutrition in older T2DM patients. The constructed nomogram represents a practical and reliable tool for the rapid identification of malnutrition among this vulnerable population.
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Affiliation(s)
- Qian Ran
- Department of Endocrinology, Jiangnan Campus, the Second Affiliated Hospital of Chongqing Medical University, Tianwen Street, Nanan District, Chongqing, 401336, People's Republic of China
| | - Xili Zhao
- Department of Endocrinology, Jiangnan Campus, the Second Affiliated Hospital of Chongqing Medical University, Tianwen Street, Nanan District, Chongqing, 401336, People's Republic of China.
| | - Jiao Tian
- Department of Endocrinology, Jiangnan Campus, the Second Affiliated Hospital of Chongqing Medical University, Tianwen Street, Nanan District, Chongqing, 401336, People's Republic of China
| | - Siyuan Gong
- Department of Neurology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, 401336, People's Republic of China
| | - Xia Zhang
- Department of Endocrinology, Chongqing City Hospital of Traditional Chinese Medicine, Chongqing, 400011, People's Republic of China
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Sajjadian M, Uher R, Ho K, Hassel S, Milev R, Frey BN, Farzan F, Blier P, Foster JA, Parikh SV, Müller DJ, Rotzinger S, Soares CN, Turecki G, Taylor VH, Lam RW, Strother SC, Kennedy SH. Prediction of depression treatment outcome from multimodal data: a CAN-BIND-1 report. Psychol Med 2023; 53:5374-5384. [PMID: 36004538 PMCID: PMC10482706 DOI: 10.1017/s0033291722002124] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 05/04/2022] [Accepted: 06/20/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Prediction of treatment outcomes is a key step in improving the treatment of major depressive disorder (MDD). The Canadian Biomarker Integration Network in Depression (CAN-BIND) aims to predict antidepressant treatment outcomes through analyses of clinical assessment, neuroimaging, and blood biomarkers. METHODS In the CAN-BIND-1 dataset of 192 adults with MDD and outcomes of treatment with escitalopram, we applied machine learning models in a nested cross-validation framework. Across 210 analyses, we examined combinations of predictive variables from three modalities, measured at baseline and after 2 weeks of treatment, and five machine learning methods with and without feature selection. To optimize the predictors-to-observations ratio, we followed a tiered approach with 134 and 1152 variables in tier 1 and tier 2 respectively. RESULTS A combination of baseline tier 1 clinical, neuroimaging, and molecular variables predicted response with a mean balanced accuracy of 0.57 (best model mean 0.62) compared to 0.54 (best model mean 0.61) in single modality models. Adding week 2 predictors improved the prediction of response to a mean balanced accuracy of 0.59 (best model mean 0.66). Adding tier 2 features did not improve prediction. CONCLUSIONS A combination of clinical, neuroimaging, and molecular data improves the prediction of treatment outcomes over single modality measurement. The addition of measurements from the early stages of treatment adds precision. Present results are limited by lack of external validation. To achieve clinically meaningful prediction, the multimodal measurement should be scaled up to larger samples and the robustness of prediction tested in an external validation dataset.
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Affiliation(s)
- Mehri Sajjadian
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Rudolf Uher
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Keith Ho
- University Health Network, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada
- Unity Health Toronto, St. Michael's Hospital, 193 Yonge Street, 6th floor, Toronto, ON, M5B 1M4, Canada
| | - Stefanie Hassel
- Department of Psychiatry and Mathison Centre for Mental Health Research and Education, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, AB, T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Roumen Milev
- Departments of Psychiatry and Psychology, Queen's University, Providence Care Hospital, Kingston, ON, Canada
| | - Benicio N. Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
- Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | - Faranak Farzan
- eBrain Lab, School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC, Canada
| | - Pierre Blier
- The Royal's Institute of Mental Health Research, 1145 Carling Avenue, Ottawa, ON, K1Z 7K4, Canada
- Department of Cellular and Molecular Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada
- Department of Psychiatry, University of Ottawa, 1145 Carling Avenue, Ottawa, ON, K1Z 7K4, Canada
| | - Jane A. Foster
- Department of Psychiatry & Behavioural Neurosciences, St Joseph's Healthcare, Hamilton, ON, Canada
| | - Sagar V. Parikh
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Daniel J. Müller
- Campbell Family Mental Health Research Institute, Center for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Susan Rotzinger
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, St Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | - Claudio N. Soares
- Department of Psychiatry, Queen's University School of Medicine, Kingston, ON, Canada
| | - Gustavo Turecki
- Department of Psychiatry, Douglas Institute, McGill University, Montreal, QC, Canada
| | - Valerie H. Taylor
- Department of Psychiatry, Foothills Medical Centre, University of Calgary, Calgary, AB, Canada
| | - Raymond W. Lam
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Stephen C. Strother
- Rotman Research Center, Baycrest, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Sidney H. Kennedy
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, St Michael's Hospital, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University Health Network, Toronto, Ontario, Canada
- Krembil Research Centre, University Health Network, University of Toronto, Toronto, Canada
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Li W, Wang Y, Zhang Z, Chen W, Lv H, Zhang Y. A risk prediction model for postoperative recovery of closed calcaneal fracture: a retrospective study. J Orthop Surg Res 2023; 18:612. [PMID: 37608314 PMCID: PMC10463340 DOI: 10.1186/s13018-023-04087-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 08/08/2023] [Indexed: 08/24/2023] Open
Abstract
OBJECTIVE To explore the risk factors for postoperative recovery of closed calcaneal fracture and develop a prediction model. METHODS We retrospectively enrolled patients with closed calcaneal fracture from January 1, 2017 to December 31, 2020. Patients treated from 2017 to 2019 were selected as a training cohort and those in 2020 as a validation cohort. The outcome variable was the postoperative recovery evaluated by the Creighton-Nebraska calcaneal fracture scoring system. Multivariate logistic regression analysis was used to screen the risk factors of postoperative recovery. A risk prediction model was constructed in the training cohort and the corresponding nomogram was drawn. The model was validated internally using bootstrapping and externally by calculating the performance in the validation cohort. RESULTS A total of 659 patients with closed calcaneal fracture met the inclusion and exclusion criteria, which were divided into the training cohort (n = 509) and the validation cohort (n = 150). 540 cases (81.9%) patients recovered well after calcaneal fracture surgery. According to multivariate logistic regression analysis, female (OR = 2.525, 95% CI 1.283-4.969), > 60 years (OR = 6.644, 95% CI 1.243-35.522), surgery within 8-14 days after fracture (OR = 2.172, 95% CI 1.259-3.745), postoperative infection (OR = 4.613, 95% CI 1.382-15.393), and weight-bearing time longer than 3 months after surgery (4-6 months, OR = 2.885, 95% CI 1.696-4.907; 7-12 months, OR = 3.030, 95% CI 1.212-7.578; > 12 months, OR = 15.589, 95% CI 3.244-74.912) were independent risk factors for postoperative recovery of calcaneal fractures. The C-indices were 0.750(95% CI 0.692-0.808) in the training cohort and 0.688(95% CI 0.571-0.804) in the external validation cohort, and the C-index of internal validation was 0.715. The Hosmer-Lemeshow test showed good fitting of the model (all P > 0.05), which was consistent with the results of the calibration plots. Decision Curve Analysis indicated that the clinical effectiveness was the best when the threshold probability was between 0.10 and 0.45. CONCLUSIONS Patients with female, > 60 years, surgery within 8-14 days after fracture, postoperative infection, and weight-bearing time longer than 3 months after surgery are more likely to have poor postoperative recovery. The risk prediction of fracture patients through this model might be translated into clinical guidance and application. Trial registration This study was registered on the Chinese Clinical Trial Registry (Registration number: ChiCTR-EPR-15005878).
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Affiliation(s)
- Wenjing Li
- Hebei Provincial Key Laboratory of Orthopaedic Biomechanics, Hebei Orthopaedic Research Institute, No. 139 Ziqiang Road, Shijiazhuang, 050051 China
| | - Yan Wang
- Hebei Provincial Key Laboratory of Orthopaedic Biomechanics, Hebei Orthopaedic Research Institute, No. 139 Ziqiang Road, Shijiazhuang, 050051 China
| | - Zenglei Zhang
- Rehabilitation Center, The Third Hospital of Hebei Medical University, No. 139 Ziqiang Road, Shijiazhuang, 050051 China
| | - Wei Chen
- Hebei Provincial Key Laboratory of Orthopaedic Biomechanics, Hebei Orthopaedic Research Institute, No. 139 Ziqiang Road, Shijiazhuang, 050051 China
- Trauma Emergency Center, The Third Hospital of Hebei Medical University, No. 139 Ziqiang Road, Shijiazhuang, 050051 China
| | - Hongzhi Lv
- Hebei Provincial Key Laboratory of Orthopaedic Biomechanics, Hebei Orthopaedic Research Institute, No. 139 Ziqiang Road, Shijiazhuang, 050051 China
- Trauma Emergency Center, The Third Hospital of Hebei Medical University, No. 139 Ziqiang Road, Shijiazhuang, 050051 China
| | - Yingze Zhang
- Hebei Provincial Key Laboratory of Orthopaedic Biomechanics, Hebei Orthopaedic Research Institute, No. 139 Ziqiang Road, Shijiazhuang, 050051 China
- Trauma Emergency Center, The Third Hospital of Hebei Medical University, No. 139 Ziqiang Road, Shijiazhuang, 050051 China
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Luo X, Sun J, Pan H, Zhou D, Huang P, Tang J, Shi R, Ye H, Zhao Y, Zhang A. Establishment and health management application of a prediction model for high-risk complication combination of type 2 diabetes mellitus based on data mining. PLoS One 2023; 18:e0289749. [PMID: 37552706 PMCID: PMC10409378 DOI: 10.1371/journal.pone.0289749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 07/26/2023] [Indexed: 08/10/2023] Open
Abstract
In recent years, the prevalence of T2DM has been increasing annually, in particular, the personal and socioeconomic burden caused by multiple complications has become increasingly serious. This study aimed to screen out the high-risk complication combination of T2DM through various data mining methods, establish and evaluate a risk prediction model of the complication combination in patients with T2DM. Questionnaire surveys, physical examinations, and biochemical tests were conducted on 4,937 patients with T2DM, and 810 cases of sample data with complications were retained. The high-risk complication combination was screened by association rules based on the Apriori algorithm. Risk factors were screened using the LASSO regression model, random forest model, and support vector machine. A risk prediction model was established using logistic regression analysis, and a dynamic nomogram was constructed. Receiver operating characteristic (ROC) curves, harrell's concordance index (C-Index), calibration curves, decision curve analysis (DCA), and internal validation were used to evaluate the differentiation, calibration, and clinical applicability of the models. This study found that patients with T2DM had a high-risk combination of lower extremity vasculopathy, diabetic foot, and diabetic retinopathy. Based on this, body mass index, diastolic blood pressure, total cholesterol, triglyceride, 2-hour postprandial blood glucose and blood urea nitrogen levels were screened and used for the modeling analysis. The area under the ROC curves of the internal and external validations were 0.768 (95% CI, 0.744-0.792) and 0.745 (95% CI, 0.669-0.820), respectively, and the C-index and AUC value were consistent. The calibration plots showed good calibration, and the risk threshold for DCA was 30-54%. In this study, we developed and evaluated a predictive model for the development of a high-risk complication combination while uncovering the pattern of complications in patients with T2DM. This model has a practical guiding effect on the health management of patients with T2DM in community settings.
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Affiliation(s)
- Xin Luo
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jijia Sun
- Department of Mathematics and Physics, School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Hong Pan
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Dian Zhou
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ping Huang
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jingjing Tang
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Rong Shi
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Hong Ye
- Department of Mathematics and Physics, School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ying Zhao
- Department of Mathematics and Physics, School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - An Zhang
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Ji Z, Li X, Lei S, Xu J, Xie Y. A pooled analysis of the risk prediction models for mortality in acute exacerbation of chronic obstructive pulmonary disease. THE CLINICAL RESPIRATORY JOURNAL 2023; 17:707-718. [PMID: 36945821 PMCID: PMC10435958 DOI: 10.1111/crj.13606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/17/2023] [Accepted: 03/13/2023] [Indexed: 03/23/2023]
Abstract
OBJECTIVE The prognosis for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is not optimistic, and severe AECOPD leads to an increased risk of mortality. Prediction models help distinguish between high- and low-risk groups. At present, many prediction models have been established and validated, which need to be systematically reviewed to screen out more suitable models that can be used in the clinic and provide evidence for future research. METHODS We searched PubMed, EMBASE, Cochrane Library and Web of Science databases for studies on risk models for AECOPD mortality from their inception to 10 April 2022. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST). Stata software (version 16) was used to synthesize the C-statistics for each model. RESULTS A total of 37 studies were included. The development of risk prediction models for mortality in patients with AECOPD was described in 26 articles, in which the most common predictors were age (n = 17), dyspnea grade (n = 11), altered mental status (n = 8), pneumonia (n = 6) and blood urea nitrogen (BUN, n = 6). The remaining 11 articles only externally validated existing models. All 37 studies were evaluated at a high risk of bias using PROBAST. We performed a meta-analysis of five models included in 15 studies. DECAF (dyspnoea, eosinopenia, consolidation, acidemia and atrial fibrillation) performed well in predicting in-hospital death [C-statistic = 0.91, 95% confidence interval (CI): 0.83, 0.98] and 90-day death [C-statistic = 0.76, 95% CI: 0.69, 0.82] and CURB-65 (confusion, urea, respiratory rate, blood pressure and age) performed well in predicting 30-day death [C-statistic = 0.74, 95% CI: 0.70, 0.77]. CONCLUSIONS This study provides information on the characteristics, performance and risk of bias of a risk model for AECOPD mortality. This pooled analysis of the present study suggests that the DECAF performs well in predicting in-hospital and 90-day deaths. Yet, external validation in different populations is still needed to prove this performance.
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Affiliation(s)
- Zile Ji
- Department of Respiratory DiseasesThe First Affiliated Hospital of Henan University of Chinese MedicineZhengzhouChina
- Co‐Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of P.R. ChinaHenan University of Chinese MedicineZhengzhouChina
| | - Xuanlin Li
- Department of Respiratory DiseasesThe First Affiliated Hospital of Henan University of Chinese MedicineZhengzhouChina
- Co‐Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of P.R. ChinaHenan University of Chinese MedicineZhengzhouChina
| | - Siyuan Lei
- Department of Respiratory DiseasesThe First Affiliated Hospital of Henan University of Chinese MedicineZhengzhouChina
- Co‐Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of P.R. ChinaHenan University of Chinese MedicineZhengzhouChina
| | - Jiaxin Xu
- Department of Respiratory DiseasesThe First Affiliated Hospital of Henan University of Chinese MedicineZhengzhouChina
- Co‐Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of P.R. ChinaHenan University of Chinese MedicineZhengzhouChina
| | - Yang Xie
- Department of Respiratory DiseasesThe First Affiliated Hospital of Henan University of Chinese MedicineZhengzhouChina
- Co‐Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of P.R. ChinaHenan University of Chinese MedicineZhengzhouChina
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21
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Lv H, Li W, Wang Y, Chen W, Yan X, Yuwen P, Hou Z, Wang J, Zhang Y. Prediction model for tibial plateau fracture combined with meniscus injury. Front Surg 2023; 10:1095961. [PMID: 37396296 PMCID: PMC10312001 DOI: 10.3389/fsurg.2023.1095961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 06/05/2023] [Indexed: 07/04/2023] Open
Abstract
PURPOSE To investigate a prediction model of meniscus injury in patients with tibial plateau fracture. METHODS This retrospective study enrolled patients with tibial plateau fractures who were treated in the Third Hospital of Hebei Medical University from January 1, 2015, to June 30, 2022. Patients were divided into a development cohort and a validation cohort based on the time-lapse validation method. Patients in each cohort were divided into a group with meniscus injury and a group without meniscus injury. Statistical analysis with Student's t-test for continuous variables and chi square test for categorical variables was performed for patients with and without meniscus injury in the development cohort. Multivariate logistic regression analysis was used to screen the risk factors of tibial plateau combined with meniscal injury, and a clinical prediction model was constructed. Model performance was measured by examining discrimination (Harrell's C-index), calibration (calibration plots), and utility [decision analysis curves (DCA)]. The model was validated internally using bootstrapping and externally by calculating their performance in a validation cohort. RESULTS Five hundred patients (313 [62.6%] males, 187 [37.4%] females) with a mean age of 47.7 ± 13.8 years were eligible and were divided into development (n = 262) and validation (n = 238) cohorts. A total of 284 patients had meniscus injury, including 136 in the development cohort and 148 in the validation cohort We identified high-energy injuries as a risk factor (OR = 1.969, 95%CI 1.131-3.427). Compared with blood type A, patients with blood type B were more likely to experience tibial plateau fracture with meniscus injury (OR = 2.967, 95%CI 1.531-5.748), and office work was a protective factor (OR = 0.279, 95%CI 0.126-0.618). The C-index of the overall survival model was 0.687 (95% CI, 0.623-0.751). Similar C-indices were obtained for external validation [0.700(0.631-0.768)] and internal validation [0.639 (0.638-0.643)]. The model was adequately calibrated and its predictions correlated with the observed outcomes. The DCA curve showed that the model had the best clinical validity when the threshold probability was 0.40 and 0.82. CONCLUSIONS Patients with blood type B and high-energy injuries are more likely to have meniscal injury. This may help in clinical trial design and individual clinical decision-making.
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Affiliation(s)
| | | | | | | | | | | | | | - Juan Wang
- Department of Orthopedic Surgery, The Third Hospital of Hebei Medical University, ShijiazhuangChina
| | - Yingze Zhang
- Department of Orthopedic Surgery, The Third Hospital of Hebei Medical University, ShijiazhuangChina
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22
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Chen X, Lin F, Xu X, Chen C, Wang R. Development, validation, and visualization of a web-based nomogram to predict the effect of tubular microdiscectomy for lumbar disc herniation. Front Surg 2023; 10:1024302. [PMID: 37021092 PMCID: PMC10069648 DOI: 10.3389/fsurg.2023.1024302] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 01/27/2023] [Indexed: 04/07/2023] Open
Abstract
Objective The purpose of this study was to retrospectively collect the relevant clinical data of lumbar disc herniation (LDH) patients treated with the tubular microdiscectomy (TMD) technique, and to develop and validate a prediction model for predicting the treatment improvement rate of TMD in LDH patients at 1 year after surgery. Methods Relevant clinical data of LDH patients treated with the TMD technology were retrospectively collected. The follow-up period was 1 year after surgery. A total of 43 possible predictors were included, and the treatment improvement rate of the Japanese Orthopedic Association (JOA) score of the lumbar spine at 1 year after TMD was used as an outcome measure. The least absolute shrinkage and selection operator (LASSO) method was used to screen out the most important predictors affecting the outcome indicators. In addition, logistic regression was used to construct the model, and a nomogram of the prediction model was drawn. Results A total of 273 patients with LDH were included in this study. Age, occupational factors, osteoporosis, Pfirrmann classification of intervertebral disc degeneration, and preoperative Oswestry Disability Index (ODI) were screened out from the 43 possible predictors based on LASSO regression. A total of 5 predictors were included while drawing a nomogram of the model. The area under the ROC curve (AUC) value of the model was 0.795. Conclusions In this study, we successfully developed a good clinical prediction model that can predict the effect of TMD for LDH. A web calculator was designed on the basis of the model (https://fabinlin.shinyapps.io/DynNomapp/).
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Affiliation(s)
| | | | | | - Chunmei Chen
- Department of Neurosurgery, Pingtan Comprehensive Experimental Zone Hospital, Union Hospital, Fujian Medical University, Fuzhou, China
| | - Rui Wang
- Department of Neurosurgery, Pingtan Comprehensive Experimental Zone Hospital, Union Hospital, Fujian Medical University, Fuzhou, China
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Xu Y, Meng Y, Qian X, Wu H, Liu Y, Ji P, Chen H. Prediction model for delirium in patients with cardiovascular surgery: development and validation. J Cardiothorac Surg 2022; 17:247. [PMID: 36183105 PMCID: PMC9526933 DOI: 10.1186/s13019-022-02005-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 09/24/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The aim of this study was to construct a nomogram model for discriminating the risk of delirium in patients undergoing cardiovascular surgery. METHODS From January 2017 to June 2020, we collected data from 838 patients who underwent cardiovascular surgery at the Affiliated Hospital of Nantong University. Patients were randomly divided into a training set and a validation set at a 5:5 ratio. A nomogram model was established based on logistic regression. Discrimination and calibration were used to evaluate the predictive performance of the model. RESULTS The incidence of delirium was 48.3%. A total of 389 patients were in the modelling group, and 449 patients were in the verification group. Logistic regression analysis showed that CPB duration (OR [Formula: see text] 1.004, 95% CI: 1.001-1.008, [Formula: see text] 0.018), postoperative serum sodium (OR [Formula: see text] 1.112, 95% CI: 1.049-1.178, [Formula: see text] 0.001), age (OR [Formula: see text] 1.027, 95% CI: 1.006-1.048, [Formula: see text] 0.011), and postoperative MV (OR [Formula: see text] 1.019, 95% CI: 1.008-1.030, [Formula: see text] 0.001) were independent risk factors. The results showed that AUC[Formula: see text] was 0.712 and that the 95% CI was 0.661-0.762. The Hosmer-Lemeshow goodness of fit test showed that the predicted results of the model were in good agreement with the actual situation ([Formula: see text] 6.200, [Formula: see text] 0.625). The results of verification showed that the AUC[Formula: see text] was 0.705, and the 95% CI was 0.657-0.752. The Hosmer-Lemeshow goodness of fit test results were [Formula: see text] 8.653 and [Formula: see text] 0.372, indicating that the predictive effect of the model is good. CONCLUSIONS The establishment of the model provides accurate and objective assessment tools for medical staff to start preventing postoperative delirium in a purposeful and focused manner when a patient enters the CSICU after surgery.
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Affiliation(s)
- Yanghui Xu
- Departments of Cardiovascular Surgery Intensive Care Unit, Affiliated Hospital of Nantong University, Nantong, China
| | - Yunjiao Meng
- Departments of Cardiovascular Surgery Intensive Care Unit, Affiliated Hospital of Nantong University, Nantong, China
| | - Xuan Qian
- Departments of Cardiovascular Surgery Intensive Care Unit, Affiliated Hospital of Nantong University, Nantong, China
| | - Honglei Wu
- Departments of Cardiovascular Surgery Intensive Care Unit, Affiliated Hospital of Nantong University, Nantong, China
| | - Yanmei Liu
- Departments of Cardiovascular Surgery Intensive Care Unit, Affiliated Hospital of Nantong University, Nantong, China
| | - Peipei Ji
- Departments of Cardiovascular Surgery Intensive Care Unit, Affiliated Hospital of Nantong University, Nantong, China
| | - Honglin Chen
- School of Public Health, Nantong University, No.9, Sik Yuan Road, Nantong, China.
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Zhu Y, Xu W, Wan C, Chen Y, Zhang C. Prediction model for the risk of ESKD in patients with primary FSGS. Int Urol Nephrol 2022; 54:3211-3219. [PMID: 35776256 DOI: 10.1007/s11255-022-03254-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 06/11/2022] [Indexed: 11/27/2022]
Abstract
The purpose of this study is to build a prediction model for accurate assessment of the risk of end-stage kidney disease (ESKD) in individuals with primary focal segmental glomerulosclerosis (FSGS) by integrating clinical and pathological features at biopsy. The prediction model was created based on a retrospective study of 99 patients with biopsy-proven primary FSGS diagnosed at our hospital between December 2012 and December 2019. We assessed discriminative ability and predictive accuracy of the model by C-index and calibration plot. Internal validation of the prediction model was performed with 1000-bootstrap procedure. Eight patients (8.1%) progressed to ESKD before 31 March 2021. Univariate analysis revealed that disease duration before biopsy, hematuria, hemoglobin, eGFR, and percentages of sclerosis and global sclerosis were associated with renal outcome. In multivariate analysis, three predictors were included in final prediction model: eGFR, hematuria, and percentage of sclerosis. The C-index of the model was 0.811 and 5-year calibration plot showed good agreement between predicted renal survival probability and actual observation. A nomogram and an online risk calculator were built on the basis of the prediction model. In conclusion, we constructed and internally validated the first prediction model for risk of ESKD in primary FSGS, which showed good discriminative ability and calibration performance. The prediction model provides an accurate and simple strategy to predict renal prognosis which may help to identify patients at high risk of ESKD and guide the management for patients with primary FSGS in clinical practice.
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Affiliation(s)
- Yuting Zhu
- Department of Nephrology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Wenchao Xu
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Cheng Wan
- Department of Nephrology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Yiyuan Chen
- Department of Nephrology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Chun Zhang
- Department of Nephrology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
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Xin F, Fu L, Yang B, Liu H, Wei T, Zou C, Bai B. Development and validation of a nomogram for predicting stroke risk in rheumatoid arthritis patients. Aging (Albany NY) 2021; 13:15061-15077. [PMID: 34081620 PMCID: PMC8221354 DOI: 10.18632/aging.203071] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 04/29/2021] [Indexed: 01/08/2023]
Abstract
We developed and validated a nomogram to predict the risk of stroke in patients with rheumatoid arthritis (RA) in northern China. Out of six machine learning algorithms studied to improve diagnostic and prognostic accuracy of the prediction model, the logistic regression algorithm showed high performance in terms of calibration and decision curve analysis. The nomogram included stratifications of sex, age, systolic blood pressure, C-reactive protein, erythrocyte sedimentation rate, total cholesterol, and low-density lipoprotein cholesterol along with the history of traditional risk factors such as hypertensive, diabetes, atrial fibrillation, and coronary heart disease. The nomogram exhibited a high Hosmer–Lemeshow goodness-for-fit and good calibration (P > 0.05). The analysis, including the area under the receiver operating characteristic curve, the net reclassification index, the integrated discrimination improvement, and clinical use, showed that our prediction model was more accurate than the Framingham risk model in predicting stroke risk in RA patients. In conclusion, the nomogram can be used for individualized preoperative prediction of stroke risk in RA patients.
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Affiliation(s)
- Fangran Xin
- Department of Clinical Epidemiology and Evidence-Based Medicine, The First Affiliated Hospital, China Medical University, Shenyang, China
| | - Lingyu Fu
- Department of Clinical Epidemiology and Evidence-Based Medicine, The First Affiliated Hospital, China Medical University, Shenyang, China.,Department of Medical Record Management Center, The First Affiliated Hospital, China Medical University, Shenyang, China
| | - Bowen Yang
- Department of Medical Record Management Center, The First Affiliated Hospital, China Medical University, Shenyang, China
| | - Haina Liu
- Department of Rheumatology, The First Affiliated Hospital, China Medical University, Shenyang, China
| | - Tingting Wei
- Department of Clinical Epidemiology and Evidence-Based Medicine, The First Affiliated Hospital, China Medical University, Shenyang, China
| | - Cunlu Zou
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Bingqing Bai
- Department of Clinical Epidemiology and Evidence-Based Medicine, The First Affiliated Hospital, China Medical University, Shenyang, China
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Li F, Wei RB, Wang Y, Su TY, Li P, Huang MJ, Chen XM. Nomogram prediction model for renal anaemia in IgA nephropathy patients. Open Med (Wars) 2021; 16:718-727. [PMID: 34013043 PMCID: PMC8111477 DOI: 10.1515/med-2021-0284] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 03/18/2021] [Accepted: 04/07/2021] [Indexed: 12/29/2022] Open
Abstract
In this study, we focused on the influencing factors of renal anaemia in patients with IgA nephropathy and constructed a nomogram model. We divided 462 patients with IgA nephropathy diagnosed by renal biopsy into anaemic and non-anaemic groups. Then, the influencing factors of renal anaemia in patients with IgA nephropathy were analysed by least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression, and a nomogram model for predicting renal anaemia was established. Eventually, nine variables were obtained, which are easy to apply clinically. The areas under the receiver operating characteristic (ROC) curve and precision-recall (PR) curve reached 0.835 and 0.676, respectively, and the C-index reached 0.848. The calibration plot showed that the model had good discrimination, accuracy, and diagnostic efficacy. In addition, the C-index of the model following internal validation reached 0.823. Decision curve analysis suggested that the model had a certain degree of clinical significance. This new nomogram model of renal anaemia combines the basic information, laboratory findings, and renal biopsy results of patients with IgA nephropathy, providing important guidance for predicting and clinically intervening in renal anaemia.
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Affiliation(s)
- Fei Li
- School of Medicine, Nankai University, No. 94 Weijin Road, Nankai District, Tianjin 300073, People's Republic of China
| | - Ri-Bao Wei
- School of Medicine, Nankai University, No. 94 Weijin Road, Nankai District, Tianjin 300073, People's Republic of China.,Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, No. 28 Fuxing Road, Haidian District, Beijing 100853, People's Republic of China
| | - Yang Wang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, No. 28 Fuxing Road, Haidian District, Beijing 100853, People's Republic of China
| | - Ting-Yu Su
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, No. 28 Fuxing Road, Haidian District, Beijing 100853, People's Republic of China
| | - Ping Li
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, No. 28 Fuxing Road, Haidian District, Beijing 100853, People's Republic of China
| | - Meng-Jie Huang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, No. 28 Fuxing Road, Haidian District, Beijing 100853, People's Republic of China
| | - Xiang-Mei Chen
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, No. 28 Fuxing Road, Haidian District, Beijing 100853, People's Republic of China
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Wang Z, Wang Y, Yang Y, Luo Y, Liu J, Xu Y, Liu X. A competing-risk nomogram to predict cause-specific death in elderly patients with colorectal cancer after surgery (especially for colon cancer). World J Surg Oncol 2020; 18:30. [PMID: 32019568 PMCID: PMC7001222 DOI: 10.1186/s12957-020-1805-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 01/23/2020] [Indexed: 12/15/2022] Open
Abstract
Background Clinically, when the diagnosis of colorectal cancer is clear, patients are more concerned about their own prognosis survival. Special population with high risk of accidental death, such as elderly patients, is more likely to die due to causes other than tumors. The main purpose of this study is to construct a prediction model of cause-specific death (CSD) in elderly patients using competing-risk approach, so as to help clinicians to predict the probability of CSD in elderly patients with colorectal cancer. Methods The data were extracted from Surveillance, Epidemiology, and End Results (SEER) database to include ≥ 65-year-old patients with colorectal cancer who had undergone surgical treatment from 2010 to 2016. Using competing-risk methodology, the cumulative incidence function (CIF) of CSD was calculated to select the predictors among 13 variables, and the selected variables were subsequently refined and used for the construction of the proportional subdistribution hazard model. The model was presented in the form of nomogram, and the performance of nomogram was bootstrap validated internally and externally using the concordance index (C-index). Results Dataset of 19,789 patients who met the inclusion criteria were eventually selected for analysis. The five-year cumulative incidence of CSD was 31.405% (95% confidence interval [CI] 31.402–31.408%). The identified clinically relevant variables in nomogram included marital status, pathological grade, AJCC TNM stage, CEA, perineural invasion, and chemotherapy. The nomogram was shown to have good discrimination after internal validation with a C-index of 0.801 (95% CI 0.795–0.807) as well as external validation with a C-index of 0.759 (95% CI 0.716–0.802). Both the internal and external validation calibration curve indicated good concordance between the predicted and actual outcomes. Conclusion Using the large sample database and competing-risk analysis, a postoperative prediction model for elderly patients with colorectal cancer was established with satisfactory accuracy. The individualized estimates of CSD outcome for the elderly patients were realized.
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Affiliation(s)
- Zhengbing Wang
- Department of Gastrointestinal Surgery, Affiliated Hospital of Yangzhou University, Yangzhou, 225100, People's Republic of China.
| | - Yawei Wang
- Department of Gastrointestinal Surgery, Northern Jiangsu People's Hospital, Clinical Medical School, Affiliated Hospital of Yangzhou University, Yangzhou, 225002, People's Republic of China.,Department of General Surgery, Jiangsu Provincial Hospital of Integrated Traditional and Western Medicine, Nanjing, 210046, People's Republic of China
| | - Yan Yang
- Department of Gastrointestinal Surgery, Northern Jiangsu People's Hospital, Clinical Medical School, Affiliated Hospital of Yangzhou University, Yangzhou, 225002, People's Republic of China
| | - Yi Luo
- Department of Gastrointestinal Surgery, Northern Jiangsu People's Hospital, Clinical Medical School, Affiliated Hospital of Yangzhou University, Yangzhou, 225002, People's Republic of China
| | - Jiangtao Liu
- Department of Gastrointestinal Surgery, Northern Jiangsu People's Hospital, Clinical Medical School, Affiliated Hospital of Yangzhou University, Yangzhou, 225002, People's Republic of China
| | - Yingjie Xu
- Department of Gastrointestinal Surgery, Northern Jiangsu People's Hospital, Clinical Medical School, Affiliated Hospital of Yangzhou University, Yangzhou, 225002, People's Republic of China
| | - Xuan Liu
- Department of Gastrointestinal Surgery, Northern Jiangsu People's Hospital, Clinical Medical School, Affiliated Hospital of Yangzhou University, Yangzhou, 225002, People's Republic of China
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Zhou ZR, Wang WW, Li Y, Jin KR, Wang XY, Wang ZW, Chen YS, Wang SJ, Hu J, Zhang HN, Huang P, Zhao GZ, Chen XX, Li B, Zhang TS. In-depth mining of clinical data: the construction of clinical prediction model with R. ANNALS OF TRANSLATIONAL MEDICINE 2019; 7:796. [PMID: 32042812 DOI: 10.21037/atm.2019.08.63] [Citation(s) in RCA: 194] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
This article is the series of methodology of clinical prediction model construction (total 16 sections of this methodology series). The first section mainly introduces the concept, current application status, construction methods and processes, classification of clinical prediction models, and the necessary conditions for conducting such researches and the problems currently faced. The second episode of these series mainly concentrates on the screening method in multivariate regression analysis. The third section mainly introduces the construction method of prediction models based on Logistic regression and Nomogram drawing. The fourth episode mainly concentrates on Cox proportional hazards regression model and Nomogram drawing. The fifth Section of the series mainly introduces the calculation method of C-Statistics in the logistic regression model. The sixth section mainly introduces two common calculation methods for C-Index in Cox regression based on R. The seventh section focuses on the principle and calculation methods of Net Reclassification Index (NRI) using R. The eighth section focuses on the principle and calculation methods of IDI (Integrated Discrimination Index) using R. The ninth section continues to explore the evaluation method of clinical utility after predictive model construction: Decision Curve Analysis. The tenth section is a supplement to the previous section and mainly introduces the Decision Curve Analysis of survival outcome data. The eleventh section mainly discusses the external validation method of Logistic regression model. The twelfth mainly discusses the in-depth evaluation of Cox regression model based on R, including calculating the concordance index of discrimination (C-index) in the validation data set and drawing the calibration curve. The thirteenth section mainly introduces how to deal with the survival data outcome using competitive risk model with R. The fourteenth section mainly introduces how to draw the nomogram of the competitive risk model with R. The fifteenth section of the series mainly discusses the identification of outliers and the interpolation of missing values. The sixteenth section of the series mainly introduced the advanced variable selection methods in linear model, such as Ridge regression and LASSO regression.
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Affiliation(s)
- Zhi-Rui Zhou
- Department of Radiotherapy, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
| | - Wei-Wei Wang
- Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University & Yunnan Provincial Tumor Hospital, Kunming 650118, China
| | - Yan Li
- Department of Anesthesiology, The Fourth Affiliated Hospital, Harbin Medical University, Harbin 150001, China
| | - Kai-Rui Jin
- Department of Radiation Oncology, Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai 200040, China
| | - Xuan-Yi Wang
- Department of Radiation Oncology, Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai 200040, China
| | - Zi-Wei Wang
- Department of Urology, Changhai Hospital, The Second Military Medical University, Shanghai 200040, China
| | - Yi-Shan Chen
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing Institute of Traditional Chinese Medicine, Beijing 100010, China
| | - Shao-Jia Wang
- Department of Gynecologic Oncology, The Third Affiliated Hospital of Kunming Medical University & Yunnan Provincial Tumor Hospital, Kunming 650118, China
| | - Jing Hu
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing Institute of Traditional Chinese Medicine, Beijing 100010, China
| | - Hui-Na Zhang
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing Institute of Traditional Chinese Medicine, Beijing 100010, China
| | - Po Huang
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing Institute of Traditional Chinese Medicine, Beijing 100010, China
| | - Guo-Zhen Zhao
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing Institute of Traditional Chinese Medicine, Beijing 100010, China
| | - Xing-Xing Chen
- Department of Radiation Oncology, Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai 200040, China
| | - Bo Li
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing Institute of Traditional Chinese Medicine, Beijing 100010, China
| | - Tian-Song Zhang
- Internal Medicine of Traditional Chinese Medicine Department, Jing'an District Central Hospital, Fudan University, Shanghai 200040, China
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Bomane A, Gonçalves A, Ballester PJ. Paclitaxel Response Can Be Predicted With Interpretable Multi-Variate Classifiers Exploiting DNA-Methylation and miRNA Data. Front Genet 2019; 10:1041. [PMID: 31708973 PMCID: PMC6823251 DOI: 10.3389/fgene.2019.01041] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 09/30/2019] [Indexed: 12/27/2022] Open
Abstract
To address the problem of resistance to paclitaxel treatment, we have investigated to which extent is possible to predict Breast Cancer (BC) patient response to this drug. We carried out a large-scale tumor-based prediction analysis using data from the US National Cancer Institute’s Genomic Data Commons. These data sets comprise the responses of BC patients to paclitaxel along with six molecular profiles of their tumors. We assessed 10 Machine Learning (ML) algorithms on each of these profiles and evaluated the resulting 60 classifiers on the same BC patients. DNA methylation and miRNA profiles were the most informative overall. In combination with these two profiles, ML algorithms selecting the smallest subset of molecular features generated the most predictive classifiers: a complexity-optimized XGBoost classifier based on CpG island methylation extracted a subset of molecular factors relevant to predict paclitaxel response (AUC = 0.74). A CpG site methylation-based Decision Tree (DT) combining only 2 of the 22,941 considered CpG sites (AUC = 0.89) and a miRNA expression-based DT employing just 4 of the 337 analyzed mature miRNAs (AUC = 0.72) reveal the molecular types associated to paclitaxel-sensitive and resistant BC tumors. A literature review shows that features selected by these three classifiers have been individually linked to the cytotoxic-drug sensitivities and prognosis of BC patients. Our work leads to several molecular signatures, unearthed from methylome and miRNome, able to anticipate to some extent which BC tumors respond or not to paclitaxel. These results may provide insights to optimize paclitaxel-therapies in clinical practice.
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Affiliation(s)
- Alexandra Bomane
- Cancer Research Center of Marseille, CRCM, INSERM, Institut Paoli-Calmettes, Aix-Marseille Univ, CNRS, Paris, France
| | - Anthony Gonçalves
- Cancer Research Center of Marseille, CRCM, INSERM, Institut Paoli-Calmettes, Aix-Marseille Univ, CNRS, Paris, France
| | - Pedro J Ballester
- Cancer Research Center of Marseille, CRCM, INSERM, Institut Paoli-Calmettes, Aix-Marseille Univ, CNRS, Paris, France
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Tomlinson F, Willis TA. What are medical students’ attitudes to clinical risk-scoring tools? An exploratory study. EDUCATION FOR PRIMARY CARE 2019; 30:355-360. [DOI: 10.1080/14739879.2019.1653227] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
| | - Thomas A Willis
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
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