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Zhao JY, Dou JQ, Chen MW. Construction of a risk prediction model for hypertension in type 2 diabetes: Independent risk factors and nomogram. World J Diabetes 2025; 16:102141. [DOI: 10.4239/wjd.v16.i5.102141] [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: 10/09/2024] [Revised: 01/04/2025] [Accepted: 02/26/2025] [Indexed: 04/25/2025] Open
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) is a prevalent metabolic disorder increasingly linked with hypertension, posing significant health risks. The need for a predictive model tailored for T2DM patients is evident, as current tools may not fully capture the unique risks in this population. This study hypothesizes that a nomogram incorporating specific risk factors will improve hypertension risk prediction in T2DM patients.
AIM To develop and validate a nomogram prediction model for hypertension in T2DM patients.
METHODS A retrospective observational study was conducted using data from 26850 T2DM patients from the Anhui Provincial Primary Medical and Health Information Management System (2022 to 2024). The study included patients aged 18 and above with available data on key variables. Exclusion criteria were type 1 diabetes, gestational diabetes, insufficient data, secondary hypertension, and abnormal liver and kidney function. The Least Absolute Shrinkage and Selection Operator regression and multivariate logistic regression were used to construct the nomogram, which was validated on separate datasets.
RESULTS The developed nomogram for T2DM patients incorporated age, low-density lipoprotein, body mass index, diabetes duration, and urine protein levels as key predictive factors. In the training dataset, the model demonstrated a high discriminative power with an area under the receiver operating characteristic curve (AUC) of 0.823, indicating strong predictive accuracy. The validation dataset confirmed these findings with an AUC of 0.812. The calibration curve analysis showed excellent agreement between predicted and observed outcomes, with absolute errors of 0.017 for the training set and 0.031 for the validation set. The Hosmer-Lemeshow test yielded non-significant results for both sets (χ2 = 7.066, P = 0.562 for training; χ2 = 6.122, P = 0.709 for validation), suggesting good model fit.
CONCLUSION The nomogram effectively predicts hypertension risk in T2DM patients, offering a valuable tool for personalized risk assessment and guiding targeted interventions. This model provides a significant advancement in the management of T2DM and hypertension comorbidity.
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Affiliation(s)
- Jian-Yong Zhao
- Department of Endocrinology, First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui Province, China
- Department of Endocrinology, Chaohu Hospital of Anhui Medical University, Chaohu 238000, Anhui Province, China
| | - Jia-Qing Dou
- Department of Endocrinology, Chaohu Hospital of Anhui Medical University, Chaohu 238000, Anhui Province, China
| | - Ming-Wei Chen
- Department of Endocrinology, First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui Province, China
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Liang GZ, Li XS, Hu ZH, Xu QJ, Wu F, Wu XL, Lei HK. Development and validation of a nomogram model for predicting overall survival in patients with gastric carcinoma. World J Gastrointest Oncol 2025; 17:95423. [PMID: 39958550 PMCID: PMC11755997 DOI: 10.4251/wjgo.v17.i2.95423] [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: 04/10/2024] [Revised: 10/01/2024] [Accepted: 11/06/2024] [Indexed: 01/18/2025] Open
Abstract
BACKGROUND The prevalence and mortality rates of gastric carcinoma are disproportionately elevated in China, with the disease's intricate and varied characteristics further amplifying its health impact. Precise forecasting of overall survival (OS) is of paramount importance for the clinical management of individuals afflicted with this malignancy. AIM To develop and validate a nomogram model that provides precise gastric cancer prevention and treatment guidance and more accurate survival outcome prediction for patients with gastric carcinoma. METHODS Data analysis was conducted on samples collected from hospitalized gastric cancer patients between 2018 and 2020. Least absolute shrinkage and selection operator, univariate, and multivariate Cox regression analyses were employed to identify independent prognostic factors. A nomogram model was developed to predict gastric cancer patient outcomes. The model's predictability and discriminative ability were evaluated via receiver operating characteristic curves. To evaluate the clinical utility of the model, Kaplan-Meier and decision curve analyses were performed. RESULTS A total of ten independent prognostic factors were identified, including body mass index, tumor-node-metastasis (TNM) stage, radiation, chemotherapy, surgery, albumin, globulin, neutrophil count, lactate dehydrogenase, and platelet-to-lymphocyte ratio. The area under the curve (AUC) values for the 1-, 3-, and 5-year survival prediction in the training set were 0.843, 0.850, and 0.821, respectively. The AUC values were 0.864, 0.820, and 0.786 for the 1-, 3-, and 5-year survival prediction in the validation set, respectively. The model exhibited strong discriminative ability, with both the time AUC and time C-index exceeding 0.75. Compared with TNM staging, the model demonstrated superior clinical utility. Ultimately, a nomogram was developed via a web-based interface. CONCLUSION This study established and validated a novel nomogram model for predicting the OS of gastric cancer patients, which demonstrated strong predictive ability. Based on these findings, this model can aid clinicians in implementing personalized interventions for patients with gastric cancer.
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Affiliation(s)
- Guan-Zhong Liang
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing 400030, China
| | - Xiao-Sheng Li
- Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing 400030, China
| | - Zu-Hai Hu
- Department of Health Statistics, School of Public Health, Chongqing Medical University, Chongqing 400016, China
| | - Qian-Jie Xu
- Department of Health Statistics, School of Public Health, Chongqing Medical University, Chongqing 400016, China
| | - Fang Wu
- Research Center for Medicine and Social Development, School of Public Health, Chongqing Medical University, Chongqing 400016, China
| | - Xiang-Lin Wu
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing 400030, China
| | - Hai-Ke Lei
- The Research Center of Big Data, Chongqing University Cancer Hospital, Chongqing 400030, China
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Xu Q, Li X, Yuan Y, Hu Z, Liang G, Wang Y, Zhang W, Liu Y, Wang W, Lei H. Development and validation of a predictive risk tool for VTE in women with breast cancer under chemotherapy: a cohort study in China. Breast Cancer 2025; 32:154-165. [PMID: 39549222 DOI: 10.1007/s12282-024-01646-7] [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: 07/29/2024] [Accepted: 10/19/2024] [Indexed: 11/18/2024]
Abstract
OBJECTIVE The incidence of venous thromboembolism (VTE) is significantly elevated in breast cancer patients, with a three-to-fourfold increase, and further escalates to sixfold in those undergoing chemotherapy. This study aims to identify the risk factors for VTE and develop a Nomogram risk prediction model distinct from the traditional Khorana score. METHODS Univariate Cox regression analysis assessed the impact of each variable on the occurrence of VTE, while stepwise multivariate Cox regression analysis identified independent predictors. Based on these results, we constructed a Nomogram model. The model's performance was validated using the C-index, receiver-operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA). Comparisons were made with the Khorana score to evaluate the practical application value. RESULTS Out of the 903 patients, 108 (11.96%) developed VTE. Cox regression analysis identified that Stage, undergoing surgery, age, white blood cells (WBC), D-dimer, and carcinoembryonic antigen (CEA) were significant risk factors for VTE (P < 0.05). The Nomogram model's C-index was 0.77 (95% CI 0.72-0.83) in the training set and 0.76 (95% CI 0.69-0.84) in the validation set. The model demonstrated excellent predictive accuracy and generalizability on the receiver-operating characteristic (ROC) curves and calibration curves. Compared to the traditional Khorana score, the Nomogram model showed superior predictive accuracy and greater clinical benefit. CONCLUSIONS This study established a VTE risk prediction model for breast cancer patients undergoing chemotherapy. The model is characterized by its intuitive and straightforward application, making it highly suitable for rapid VTE risk assessment in clinical practice.
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Affiliation(s)
- Qianjie Xu
- Chongqing Cancer Multi-Omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Xiaosheng Li
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Yuliang Yuan
- Chongqing Cancer Multi-Omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Zuhai Hu
- Department of Health Statistics, School of Public Health, Chongqing Medical University, Chongqing, 400016, China
| | - Guanzhong Liang
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Ying Wang
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Wei Zhang
- Chongqing Cancer Multi-Omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Ya Liu
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, 400030, China.
| | - Wei Wang
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, 400030, China.
| | - Haike Lei
- Chongqing Cancer Multi-Omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, 400030, China.
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Zhang W, Shi J, Wang Y, Li E, Yan D, Zhang Z, Zhu M, Yu J, Wang Y. Risk factors and clinical prediction models for low-level viremia in people living with HIV receiving antiretroviral therapy: an 11-year retrospective study. Front Microbiol 2024; 15:1451201. [PMID: 39552647 PMCID: PMC11563986 DOI: 10.3389/fmicb.2024.1451201] [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: 06/18/2024] [Accepted: 10/15/2024] [Indexed: 11/19/2024] Open
Abstract
Objective This study explores the risk factors for low-level viremia (LLV) occurrence after ART and develops a risk prediction model. Method Clinical data and laboratory indicators of people living with HIV (PLWH) at Hangzhou Xixi Hospital from 5 April 2011 to 29 December 2022 were collected. LASSO Cox regression and multivariate Cox regression analysis were performed to identify laboratory indicators and establish a nomogram for predicting LLV occurrence. The nomogram's discrimination and calibration were assessed via ROC curve and calibration plots. The concordance index (C-index) and decision curve analysis (DCA) were used to evaluate its effectiveness. Result Predictive factors, namely, age, ART delay time, white blood cell (WBC) count, baseline CD4+ T-cell count (baseline CD4), baseline viral load (baseline VL), and total bilirubin (TBIL), were incorporated into the nomogram to develop a risk prediction model. The optimal model (which includes 6 variables) had an AUC for LLV after 1-year, 3-year, and 5-year of listing of 0.68 (95% CI, 0.61-0.69), 0.69 (95% CI, 0.65-0.70), and 0.70 (95% CI, 0.66-0.71), respectively. The calibration curve showed high consistency between predicted and actual observations. The C-index and DCA indicated superior prediction performance of the nomogram. There was a significant difference in CD4 levels between LLV and non-LLV groups during the follow-up time. The dynamic SCR, ALT, TG and BG levels and occurrence of complications differed significantly between the high- and low-risk groups. Conclusion A simple-to-use nomogram containing 6 routinely detected variables was developed for predicting LLV occurrence in PLWH after ART.
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Affiliation(s)
- Wenhui Zhang
- Department of Infection, Hangzhou Xixi Hospital, Zhejiang Chinese Medical University, Hangzhou, China
- Department of Nursing, Hangzhou Xixi Hospital, Zhejiang Chinese Medical University, Hangzhou, China
| | - Jinchuan Shi
- Department of Infection, Hangzhou Xixi Hospital, Zhejiang Chinese Medical University, Hangzhou, China
| | - Ying Wang
- Medical Laboratory, Hangzhou Xixi Hospital, Zhejiang Chinese Medical University, Hangzhou, China
| | - Er Li
- Department of Nursing, Hangzhou Xixi Hospital, Zhejiang Chinese Medical University, Hangzhou, China
| | - Dingyan Yan
- Department of Infection, Hangzhou Xixi Hospital, Zhejiang Chinese Medical University, Hangzhou, China
- Department of Nursing, Hangzhou Xixi Hospital, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhongdong Zhang
- Department of Infection, Hangzhou Xixi Hospital, Zhejiang Chinese Medical University, Hangzhou, China
| | - Mingli Zhu
- Medical Laboratory, Hangzhou Xixi Hospital, Zhejiang Chinese Medical University, Hangzhou, China
| | - Jianhua Yu
- Department of Infection, Hangzhou Xixi Hospital, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yi Wang
- Department of Infection, Hangzhou Xixi Hospital, Zhejiang Chinese Medical University, Hangzhou, China
- Clinical Research Laboratory, Hangzhou Xixi Hospital, Zhejiang University of Traditional Chinese Medicine, Hangzhou, China
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Jelicic J, Larsen TS, Andjelic B, Juul-Jensen K, Bukumiric Z. Should we use nomograms for risk predictions in diffuse large B cell lymphoma patients? A systematic review. Crit Rev Oncol Hematol 2024; 196:104293. [PMID: 38346460 DOI: 10.1016/j.critrevonc.2024.104293] [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: 02/17/2023] [Revised: 01/24/2024] [Accepted: 02/07/2024] [Indexed: 02/24/2024] Open
Abstract
Models based on risk stratification are increasingly reported for Diffuse large B cell lymphoma (DLBCL). Due to a rising interest in nomograms for cancer patients, we aimed to review and critically appraise prognostic models based on nomograms in DLBCL patients. A literature search in PubMed/Embase identified 59 articles that proposed prognostic models for DLBCL by combining parameters of interest (e.g., clinical, laboratory, immunohistochemical, and genetic) between January 2000 and 2024. Of them, 40 studies proposed different gene expression signatures and incorporated them into nomogram-based prognostic models. Although most studies assessed discrimination and calibration when developing the model, many lacked external validation. Current nomogram-based models for DLBCL are mainly developed from publicly available databases, lack external validation, and have no applicability in clinical practice. However, they may be helpful in individual patient counseling, although careful considerations should be made regarding model development due to possible limitations when choosing nomograms for prognostication.
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Affiliation(s)
- Jelena Jelicic
- Department of Hematology, Sygehus Lillebaelt, Vejle, Denmark; Department of Hematology, Odense University Hospital, Odense, Denmark.
| | - Thomas Stauffer Larsen
- Department of Hematology, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Bosko Andjelic
- Department of Haematology, Blackpool Victoria Hospital, Lancashire Haematology Centre, Blackpool, United Kingdom
| | - Karen Juul-Jensen
- Department of Hematology, Odense University Hospital, Odense, Denmark
| | - Zoran Bukumiric
- Department of Statistics, Faculty of Medicine, University of Belgrade, Serbia
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