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Yang J, Zhang X, Chen J, Hou X, Shi M, Yin L, Hua L, Wang C, Han X, Zhao S, Kang G, Mai P, Jiang R, Tian H. Development and validation of an integrated model for the diagnosis of liver cirrhosis with portal vein thrombosis combined with endoscopic characters and blood biochemistry data: a retrospective propensity score matching (PSM) cohort study. Ann Med 2025; 57:2457521. [PMID: 39881530 PMCID: PMC11784028 DOI: 10.1080/07853890.2025.2457521] [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: 02/07/2024] [Revised: 12/30/2024] [Accepted: 01/14/2025] [Indexed: 01/31/2025] Open
Abstract
BACKGROUND Liver cirrhosis complicated by portal vein thrombosis (PVT) is a fatal complication with no specific manifestations but often misdiagnosed, it crucially increases the mortality worldwide. This study aimed to identify risk factors and establish a predictive model for diagnosis of venous thrombosis clinical by routine blood tests and endoscopic characteristics. METHODS Patients from Gansu Provincial Hospital from October 2019 to December 2023 were enrolled. The retrospective modelling cohort was screened by propensity score matching (PSM) at a 1:1 ratio from the baseline characteristics before endoscopic diagnosis. Variables were collected from blood test and endoscopic signs using machine learning method (ML). Logistic regression determined risk factors. The predictive performance was evaluated by receiver operation curve (ROC), calibration curve, clinical decision analysis (DCA) and influence curve (CIC). Furthermore, external cohort was used for validation, an online nomogram was established. RESULTS A total of 1,058 patients were enrolled, and 470 patients were included after PSM 1: 1. The model identified 7 factors, including splenectomy, blood urea nitrogen (BUN), serum sodium, activated partial thromboplastin time (APTT), thrombin time (TT), D-dimer, and degree of oesophageal varices. The area under the curve (AUC) was 0.907 (95% CI, 0.877-0.931). The calibration curve, decision and clinical impact curves showed the model demonstrated a good predictive accuracy and clinical benefits. The validation got an AUC of 0.890 (95% CI, 0.831-0.934), A nomogram tool was finally established for application. CONCLUSION Blood test combined endoscopic characters could preliminarily predict the liver cirrhosis with portal vein thrombosis for cirrhotic patients undergoing endoscopic examination.
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Affiliation(s)
- Jie Yang
- Endoscopic Diagnosis and Treatment Center, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Xu Zhang
- Endoscopic Diagnosis and Treatment Center, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Jia Chen
- Endoscopic Diagnosis and Treatment Center, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Xianghong Hou
- Endoscopic Diagnosis and Treatment Center, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Minghong Shi
- Endoscopic Diagnosis and Treatment Center, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Longlong Yin
- Endoscopic Diagnosis and Treatment Center, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Longchun Hua
- Endoscopic Diagnosis and Treatment Center, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Cheng Wang
- Endoscopic Diagnosis and Treatment Center, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Xiaolong Han
- Endoscopic Diagnosis and Treatment Center, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Shuyan Zhao
- Department of Gastroenterology, Third People’s Hospital of Yuzhong County, Lanzhou, Gansu, China
| | - Guolan Kang
- Endoscopic Diagnosis and Treatment Center, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Ping Mai
- Endoscopic Diagnosis and Treatment Center, Gansu Provincial Hospital, Lanzhou, Gansu, China
- Department of Gastroenterology, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Rui Jiang
- Endoscopic Diagnosis and Treatment Center, Gansu Provincial Hospital, Lanzhou, Gansu, China
- Department of Gastroenterology, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Hongwei Tian
- Endoscopic Diagnosis and Treatment Center, Gansu Provincial Hospital, Lanzhou, Gansu, China
- Department of First General Surgery, Gansu Provincial Hospital, Lanzhou, Gansu, China
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Qu D, Dai D, Li G, Zhou R, Dong C, Zhao J, An L, Song X, Zhu J, Li ZF. Development of an explainable prediction model for portal vein system thrombosis post-splenectomy in patients with cirrhosis. BMJ Health Care Inform 2025; 32:e101319. [PMID: 40216454 PMCID: PMC11987112 DOI: 10.1136/bmjhci-2024-101319] [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: 10/17/2024] [Accepted: 03/23/2025] [Indexed: 04/14/2025] Open
Abstract
BACKGROUND Portal vein system thrombosis (PVST) is a common and potentially life-threatening complication following splenectomy plus pericardial devascularisation (SPDV) in patients with cirrhosis and portal hypertension. Early prediction of PVST is critical for timely intervention. This study aimed to develop a machine learning-based prediction model for PVST occurrence within 3 months after splenectomy. METHODS 392 patients with cirrhosis who underwent splenectomy at the Second Affiliated Hospital of Xi'an Jiaotong University between 1 July 2016 and 31 December 2022 were enrolled in this study and followed up for 3 months. The predictive model integrated 37 candidate predictors based on accessible clinical data, including demographic characteristics, disease features, imaging results, laboratory values, perioperative details and postoperative prophylactic therapies, and finally, eight predictors were selected for model construction. The five machine learning algorithms (logistic regression, Gaussian Naive Bayes, decision tree, random forest and AdaBoost) were employed to train the predictive models for assessing risks of PVST, which were validated using five fold cross-validation. Model discrimination and calibration were estimated using receiver operating characteristic curves(ROC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value and Brier scores. The outcome of the predictive model was interpreted using SHapley Additive exPlanations (SHAP), which provided insights into the factors influencing PVST risk prediction. RESULTS During the 3-month follow-up, a total of 144 (36.73%) patients developed PVST. The AdaBoost model demonstrated the highest discriminative ability, with a mean area under the receiver operating characteristic curve (AUROC) of 0.72 (95% CI 0.60 to 0.84). Important features for predicting PVST included albumin, platelet addition, the diameter of the portal vein, γ-glutamyl transferase, length of stay, activated partial thromboplastin time, D-dimer level and history of preoperative gastrointestinal bleeding, as revealed by SHAP analysis. CONCLUSIONS The machine learning-based prediction models can provide an initial assessment of 3-month PVST risk after SPDV in patients with cirrhosis and portal hypertension. The AdaBoost model demonstrates moderate discriminative ability in distinguishing between high-risk and low-risk patients, with an AUROC of 0.72 (95% CI 0.60 to 0.84). By incorporating SHAP analysis, the model can offer transparent explanations for personalised risk predictions, facilitating targeted preventive interventions and reducing excessive interventions across the entire patient population.
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Affiliation(s)
- Dou Qu
- Institute for Precision Medicine, Xi'an Jiaotong University Second Affiliated Hospital, Xi'an, Shaanxi, China
- Hepatobiliary, splenic and pancreatic surgery, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Shaanxi Provincial Clinical Medical Research Center for Liver and Spleen Diseases, Xi'an, China
- National-Local Joint Engineering Research Center of Biodiagnosis & Biotherapy, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Duwei Dai
- Shaanxi Provincial Clinical Medical Research Center for Liver and Spleen Diseases, Xi'an, China
- Institute of Medical Artificial Intelligence, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Guodong Li
- Institute for Precision Medicine, Xi'an Jiaotong University Second Affiliated Hospital, Xi'an, Shaanxi, China
- Shaanxi Provincial Clinical Medical Research Center for Liver and Spleen Diseases, Xi'an, China
- National-Local Joint Engineering Research Center of Biodiagnosis & Biotherapy, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Rui Zhou
- Hepatobiliary, splenic and pancreatic surgery, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Shaanxi Provincial Clinical Medical Research Center for Liver and Spleen Diseases, Xi'an, China
| | - Caixia Dong
- Shaanxi Provincial Clinical Medical Research Center for Liver and Spleen Diseases, Xi'an, China
- Institute of Medical Artificial Intelligence, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Junxia Zhao
- Shaanxi Provincial Clinical Medical Research Center for Liver and Spleen Diseases, Xi'an, China
| | - Lingbo An
- Hepatobiliary, splenic and pancreatic surgery, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Shaanxi Provincial Clinical Medical Research Center for Liver and Spleen Diseases, Xi'an, China
| | - Xiaojie Song
- Shaanxi Provincial Clinical Medical Research Center for Liver and Spleen Diseases, Xi'an, China
| | - Jiazhen Zhu
- Hepatobiliary, splenic and pancreatic surgery, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- National-Local Joint Engineering Research Center of Biodiagnosis & Biotherapy, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Zong Fang Li
- Institute for Precision Medicine, Xi'an Jiaotong University Second Affiliated Hospital, Xi'an, Shaanxi, China
- Hepatobiliary, splenic and pancreatic surgery, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Shaanxi Provincial Clinical Medical Research Center for Liver and Spleen Diseases, Xi'an, China
- National-Local Joint Engineering Research Center of Biodiagnosis & Biotherapy, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Institute of Medical Artificial Intelligence, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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Nie GL, Yan J, Li Y, Zhang HL, Xie DN, Zhu XW, Li X. Predictive model for non-malignant portal vein thrombosis associated with cirrhosis based on inflammatory biomarkers. World J Gastrointest Oncol 2024; 16:1213-1226. [PMID: 38660630 PMCID: PMC11037040 DOI: 10.4251/wjgo.v16.i4.1213] [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: 10/08/2023] [Revised: 01/18/2024] [Accepted: 02/23/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Portal vein thrombosis (PVT), a complication of liver cirrhosis, is a major public health concern. PVT prediction is the most effective method for PVT diagnosis and treatment. AIM To develop and validate a nomogram and network calculator based on clinical indicators to predict PVT in patients with cirrhosis. METHODS Patients with cirrhosis hospitalized between January 2016 and December 2021 at the First Hospital of Lanzhou University were screened and 643 patients with cirrhosis who met the eligibility criteria were retrieved. Following a 1:1 propensity score matching 572 patients with cirrhosis were screened, and relevant clinical data were collected. PVT risk factors were identified using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis. Variance inflation factors and correlation matrix plots were used to analyze multicollinearity among the variables. A nomogram was constructed to predict the probability of PVT based on independent risk factors for PVT, and its predictive performance was verified using a receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA). Finally, a network calculator was constructed based on the nomograms. RESULTS This study enrolled 286 cirrhosis patients with PVT and 286 without PVT. LASSO analysis revealed 13 variables as strongly associated with PVT occurrence. Multivariate logistic regression analysis revealed nine indicators as independent PVT risk factors, including etiology, ascites, gastroesophageal varices, platelet count, D-dimer, portal vein diameter, portal vein velocity, aspartate transaminase to neutrophil ratio index, and platelet-to-lymphocyte ratio. LASSO and correlation matrix plot results revealed no significant multicollinearity or correlation among the variables. A nomogram was constructed based on the screened independent risk factors. The nomogram had excellent predictive performance, with an area under the ROC curve of 0.821 and 0.829 in the training and testing groups, respectively. Calibration curves and DCA revealed its good clinical performance. Finally, the optimal cutoff value for the total nomogram score was 0.513. The sensitivity and specificity of the optimal cutoff values were 0.822 and 0.706, respectively. CONCLUSION A nomogram for predicting PVT occurrence was successfully developed and validated, and a network calculator was constructed. This can enable clinicians to rapidly and easily identify high PVT risk groups.
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Affiliation(s)
- Guo-Le Nie
- The First School of Clinical Medicine, Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Jun Yan
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Ying Li
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Hong-Long Zhang
- The First School of Clinical Medicine, Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Dan-Na Xie
- The First School of Clinical Medicine, Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Xing-Wang Zhu
- The First School of Clinical Medicine, Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Xun Li
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
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Zhong X, Li S, Hu J, Lu J, Wang W, Hu M, Sun Q, Zhang S, Yang X, Yang C, Zhong L. Development and external validation of prognostic scoring models for portal vein thrombosis: a multicenter retrospective study. Thromb J 2023; 21:9. [PMID: 36691024 PMCID: PMC9869608 DOI: 10.1186/s12959-023-00455-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 01/18/2023] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Portal vein thrombosis is a common complication of liver cirrhosis and hepatocellular carcinoma; however, few studies have reported its long-term clinical prognosis. This study aimed to establish and validate easy-to-use nomograms for predicting gastrointestinal bleeding, portal vein thrombosis resolution, and mortality of patients with portal vein thrombosis. METHODS This multicenter retrospective cohort study included 425 patients with portal vein thrombosis who were divided into training (n = 334) and validation (n = 91) sets. Prediction models were developed using multivariate Cox regression analysis and evaluated using the consistency index and calibration plots. RESULTS Predictors of gastrointestinal bleeding included a history of gastrointestinal bleeding, superior mesenteric vein thrombosis, red color sign observed during endoscopy, and hepatic encephalopathy. Meanwhile, predictors of resolution of portal vein thrombosis included a history of abdominal infection, C-reactive protein and hemoglobin levels, and intake of thrombolytics. Predictors of death included abdominal infection, abdominal surgery, aspartate aminotransferase level, hepatic encephalopathy, and ascites. All models had good discriminatory power and consistency. Anticoagulation therapy significantly increased the probability of thrombotic resolution without increasing the risk of bleeding or death. CONCLUSIONS We successfully developed and validated three prediction models that can aid in the early evaluation and treatment of portal vein thrombosis.
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Affiliation(s)
- Xuan Zhong
- Present Address: Department of Gastroenterology, Shanghai East Hospital, Tongji University School of Medicine, 150, Jimo Road, Pudong New Area, Shanghai, 200120 China
| | - Shan Li
- Present Address: Department of Gastroenterology, Shanghai East Hospital, Tongji University School of Medicine, 150, Jimo Road, Pudong New Area, Shanghai, 200120 China
| | - Jiali Hu
- Present Address: Department of Gastroenterology, Shanghai East Hospital, Tongji University School of Medicine, 150, Jimo Road, Pudong New Area, Shanghai, 200120 China
| | - Jinlai Lu
- Present Address: Department of Gastroenterology, Shanghai East Hospital, Tongji University School of Medicine, 150, Jimo Road, Pudong New Area, Shanghai, 200120 China
| | - Wei Wang
- Present Address: Department of Gastroenterology, Shanghai East Hospital, Tongji University School of Medicine, 150, Jimo Road, Pudong New Area, Shanghai, 200120 China
| | - Miao Hu
- Present Address: Department of Gastroenterology, Shanghai East Hospital, Tongji University School of Medicine, 150, Jimo Road, Pudong New Area, Shanghai, 200120 China
| | - Qinjuan Sun
- Present Address: Department of Gastroenterology, Shanghai East Hospital, Tongji University School of Medicine, 150, Jimo Road, Pudong New Area, Shanghai, 200120 China
| | - Shuo Zhang
- Present Address: Department of Gastroenterology, Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiaoqing Yang
- Present Address: Department of Gastroenterology, Shanghai East Hospital, Tongji University School of Medicine, 150, Jimo Road, Pudong New Area, Shanghai, 200120 China
| | - Changqing Yang
- Present Address: Department of Gastroenterology, Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Lan Zhong
- Present Address: Department of Gastroenterology, Shanghai East Hospital, Tongji University School of Medicine, 150, Jimo Road, Pudong New Area, Shanghai, 200120 China
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Liu GH, Lei P, Liao CS, Li J, Long JW, Huan XS, Chen J. Establishment and verification a nomogram for predicting portal vein thrombosis presence among admitted cirrhotic patients. Front Med (Lausanne) 2023; 9:1021899. [PMID: 36687401 PMCID: PMC9852861 DOI: 10.3389/fmed.2022.1021899] [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: 08/17/2022] [Accepted: 11/22/2022] [Indexed: 01/09/2023] Open
Abstract
Background Portal vein thrombosis (PVT) is an increasingly recognized complication of cirrhosis and possibly associated with mortality. This study aims to evaluate provoking factors for PVT, then establish a concise and efficient nomogram for predicting PVT presence among admitted cirrhotic patients. Materials and methods All cirrhotic patients admitted in Hunan Provincial People's Hospital between January 2010 and September 2020 were retrospectively reviewed, the clinical and laboratory data were collected. Multivariate logistic regression analysis and the least absolute shrinkage and selection operator regression method were used for screening the independent predictors and constructing the nomogram. The calibration curve was plotted to evaluate the consistent degree between observed outcomes and predicted probabilities. The area under the receiver operating characteristics curve was used to assess the discriminant performance. The decision curve analysis (DCA) was carried out to evaluate the benefits of nomogram. Results A total of 4,479 patients with cirrhosis were enrolled and 281 patients were identified with PVT. Smoking history, splenomegaly, esophagogastric varices, surgical history, red blood cell transfusion, and D-dimer were independent risk factors for PVT in cirrhosis. A nomogram was established with a good discrimination capacity and predictive efficiency with an the area under the curve (AUC) of 0.704 (95% CI: 0.664-0.745) in the training set and 0.685 (95% CI: 0.615-0.754) in the validation set. DCA suggested the net benefit of nomogram had a superior risk threshold probability. Conclusion A concise and efficient nomogram was established with good performance, which may aid clinical decision making and guide best treatment measures.
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Affiliation(s)
- Guang-hua Liu
- Department of Blood Transfusion, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China,Laboratory of Hematology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Ping Lei
- Department of Blood Transfusion, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China,Laboratory of Hematology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Chu-shu Liao
- Department of Blood Transfusion, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China,Laboratory of Hematology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Jing Li
- Department of Clinical Laboratory, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Jiang-wen Long
- Department of Blood Transfusion, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China,Laboratory of Hematology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Xi-sha Huan
- Department of Blood Transfusion, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China,Laboratory of Hematology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Jie Chen
- Department of Clinical Laboratory, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China,*Correspondence: Jie Chen
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Nomogram for Predicting Postoperative Portal Venous Systemic Thrombosis in Patients with Cirrhosis Undergoing Splenectomy and Esophagogastric Devascularization. Can J Gastroenterol Hepatol 2022; 2022:8084431. [PMID: 36387035 PMCID: PMC9652084 DOI: 10.1155/2022/8084431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 06/20/2022] [Accepted: 07/21/2022] [Indexed: 11/06/2022] Open
Abstract
OBJECTIVES The aim of the study is to develop a nomogram for predicting postoperative portal venous systemic thrombosis (PVST) in patients with cirrhosis undergoing splenectomy and esophagogastric devascularization. METHODS In total, 195 eligible patients were included. Demographic characteristics were collected, and the results of perioperative routine laboratory investigations and ultrasound examinations were also recorded. Blood cell morphological traits, including the red cell volume distribution width (RDW), mean platelet volume, and platelet distribution width, were identified. Univariate and multivariate logistic regressions were implemented for risk factor filtration, and an integrated nomogram was generated and then validated using the bootstrap method. RESULTS A color Doppler abdominal ultrasound examination on a postoperative day (POD) 7 (38.97%) revealed that 76 patients had PVST. The results of the multivariate logistic regression suggested that a higher RDW on POD3 (RDW3) (odds ratio (OR): 1.188, 95% confidence interval (CI): 1.073-1.326), wider portal vein diameter (OR: 1.387, 95% CI: 1.203-1.642), history of variceal hemorrhage (OR: 3.407, 95% CI: 1.670-7.220), and longer spleen length (OR: 1.015, 95% CI: 1.001-1.029) were independent risk parameters for postoperative PVST. Moreover, the nomogram integrating these four parameters exhibited considerable capability in PVST forecasting. The nomogram's receiver operating characteristic curve reached 0.83 and achieved a sensitivity and specificity of 0.711 and 0.848, respectively, at its cutoff. The nomogram's calibration curve demonstrated that it was well calibrated. CONCLUSION The nomogram exhibited excellent performance in PVST prediction and might assist surgeons in identifying vulnerable patients and administering timely prophylaxis.
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Li J, Wu QQ, Zhu RH, Lv X, Wang WQ, Wang JL, Liang BY, Huang ZY, Zhang EL. Machine learning predicts portal vein thrombosis after splenectomy in patients with portal hypertension: Comparative analysis of three practical models. World J Gastroenterol 2022; 28:4681-4697. [PMID: 36157936 PMCID: PMC9476873 DOI: 10.3748/wjg.v28.i32.4681] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 05/25/2022] [Accepted: 08/01/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND For patients with portal hypertension (PH), portal vein thrombosis (PVT) is a fatal complication after splenectomy. Postoperative platelet elevation is considered the foremost reason for PVT. However, the value of postoperative platelet elevation rate (PPER) in predicting PVT has never been studied.
AIM To investigate the predictive value of PPER for PVT and establish PPER-based prediction models to early identify individuals at high risk of PVT after splenectomy.
METHODS We retrospectively reviewed 483 patients with PH related to hepatitis B virus who underwent splenectomy between July 2011 and September 2018, and they were randomized into either a training (n = 338) or a validation (n = 145) cohort. The generalized linear (GL) method, least absolute shrinkage and selection operator (LASSO), and random forest (RF) were used to construct models. The receiver operating characteristic curves (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the robustness and clinical practicability of the GL model (GLM), LASSO model (LSM), and RF model (RFM).
RESULTS Multivariate analysis exhibited that the first and third days for PPER (PPER1, PPER3) were strongly associated with PVT [odds ratio (OR): 1.78, 95% confidence interval (CI): 1.24-2.62, P = 0.002; OR: 1.43, 95%CI: 1.16-1.77, P < 0.001, respectively]. The areas under the ROC curves of the GLM, LSM, and RFM in the training cohort were 0.83 (95%CI: 0.79-0.88), 0.84 (95%CI: 0.79-0.88), and 0.84 (95%CI: 0.79-0.88), respectively; and were 0.77 (95%CI: 0.69-0.85), 0.83 (95%CI: 0.76-0.90), and 0.78 (95%CI: 0.70-0.85) in the validation cohort, respectively. The calibration curves showed satisfactory agreement between prediction by models and actual observation. DCA and CIC indicated that all models conferred high clinical net benefits.
CONCLUSION PPER1 and PPER3 are effective indicators for postoperative prediction of PVT. We have successfully developed PPER-based practical models to accurately predict PVT, which would conveniently help clinicians rapidly differentiate individuals at high risk of PVT, and thus guide the adoption of timely interventions.
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Affiliation(s)
- Jian Li
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Qi-Qi Wu
- Department of Trauma Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Rong-Hua Zhu
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Xing Lv
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Wen-Qiang Wang
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Jin-Lin Wang
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Bin-Yong Liang
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Zhi-Yong Huang
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Er-Lei Zhang
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
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