Luyan Zheng, Jing Yang, Lingzhu Zhao, Chen Li, Kailu Fang, Shuwen Li, Jie Wu, Min Zheng
{"title":"开发并验证用于预测急性肾损伤肝硬化患者院内死亡率的 PHM-CPA 模型。","authors":"Luyan Zheng, Jing Yang, Lingzhu Zhao, Chen Li, Kailu Fang, Shuwen Li, Jie Wu, Min Zheng","doi":"10.1016/j.dld.2024.09.012","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The presence of acute kidney injury (AKI) significantly increases in-hospital mortality risk for cirrhotic patients. Early prognosis prediction for these patients is crucial. We aimed to develop and validate a machine learning model for in-hospital mortality prediction for cirrhotic patients with AKI.</p><p><strong>Methods: </strong>Data from cirrhotic patients with AKI hospitalized at the First Affiliated Hospital of Zhejiang University between January 1, 2013, and December 31, 2020 were used to train and validate an extreme Gradient Boosting model to predict in-hospital mortality risk. The Boruta algorithm was used for variable selection. The optimal model was selected and named as PHM-CPA (Prediction of in-Hospital Mortality for Cirrhotic Patients with AKI). The PHM-CPA model was then externally validated in patients from eICU Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care III dataset (MIMIC). The predictive performance of PHM-CPA model was compared with that of logistic regression (LR) model and 25 previously reported models.</p><p><strong>Results: </strong>A total of 519 cirrhotic patients with AKI were enrolled in model training cohort, of whom 118 (23%) died during hospitalization. Fifteen variables from common laboratory tests were selected to develop the PHM-CPA model. The PHM-CPA model achieved an AUROC of 0.816 (95% CI, 0.763-0.861) in the internal validation cohort and 0.787 (95% CI, 0.745-0.830) in the external validation cohort. The PHM-CPA model consistently outperformed the LR model and 25 previously reported models.</p><p><strong>Conclusion: </strong>We developed and validated the PHM-CPA model, comprising readily available clinical variables, which demonstrated superior performance and calibration in predicting in-hospital mortality for cirrhotic patients with AKI.</p>","PeriodicalId":11268,"journal":{"name":"Digestive and Liver Disease","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and validation of the PHM-CPA model to predict in-hospital mortality for cirrhotic patients with acute kidney injury.\",\"authors\":\"Luyan Zheng, Jing Yang, Lingzhu Zhao, Chen Li, Kailu Fang, Shuwen Li, Jie Wu, Min Zheng\",\"doi\":\"10.1016/j.dld.2024.09.012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The presence of acute kidney injury (AKI) significantly increases in-hospital mortality risk for cirrhotic patients. Early prognosis prediction for these patients is crucial. We aimed to develop and validate a machine learning model for in-hospital mortality prediction for cirrhotic patients with AKI.</p><p><strong>Methods: </strong>Data from cirrhotic patients with AKI hospitalized at the First Affiliated Hospital of Zhejiang University between January 1, 2013, and December 31, 2020 were used to train and validate an extreme Gradient Boosting model to predict in-hospital mortality risk. The Boruta algorithm was used for variable selection. The optimal model was selected and named as PHM-CPA (Prediction of in-Hospital Mortality for Cirrhotic Patients with AKI). The PHM-CPA model was then externally validated in patients from eICU Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care III dataset (MIMIC). The predictive performance of PHM-CPA model was compared with that of logistic regression (LR) model and 25 previously reported models.</p><p><strong>Results: </strong>A total of 519 cirrhotic patients with AKI were enrolled in model training cohort, of whom 118 (23%) died during hospitalization. Fifteen variables from common laboratory tests were selected to develop the PHM-CPA model. The PHM-CPA model achieved an AUROC of 0.816 (95% CI, 0.763-0.861) in the internal validation cohort and 0.787 (95% CI, 0.745-0.830) in the external validation cohort. The PHM-CPA model consistently outperformed the LR model and 25 previously reported models.</p><p><strong>Conclusion: </strong>We developed and validated the PHM-CPA model, comprising readily available clinical variables, which demonstrated superior performance and calibration in predicting in-hospital mortality for cirrhotic patients with AKI.</p>\",\"PeriodicalId\":11268,\"journal\":{\"name\":\"Digestive and Liver Disease\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Digestive and Liver Disease\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.dld.2024.09.012\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GASTROENTEROLOGY & HEPATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digestive and Liver Disease","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.dld.2024.09.012","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
背景:急性肾损伤(AKI)的出现大大增加了肝硬化患者的院内死亡风险。对这些患者进行早期预后预测至关重要。我们旨在开发并验证一种机器学习模型,用于预测 AKI 肝硬化患者的院内死亡率:我们使用浙江大学附属第一医院 2013 年 1 月 1 日至 2020 年 12 月 31 日期间住院的 AKI 肝硬化患者的数据,训练并验证了预测院内死亡风险的极端梯度提升模型。变量选择采用 Boruta 算法。选出的最优模型被命名为 PHM-CPA(肝硬化 AKI 患者院内死亡率预测)。随后,PHM-CPA 模型在来自 eICU 合作研究数据库(eICU-CRD)和重症监护医学信息市场 III 数据集(MIMIC)的患者中进行了外部验证。PHM-CPA模型的预测性能与逻辑回归(LR)模型和之前报道的25个模型进行了比较:共有 519 名肝硬化 AKI 患者加入模型训练队列,其中 118 人(23%)在住院期间死亡。PHM-CPA模型选取了常见实验室检测中的15个变量。PHM-CPA 模型在内部验证队列中的 AUROC 为 0.816(95% CI,0.763-0.861),在外部验证队列中的 AUROC 为 0.787(95% CI,0.745-0.830)。PHM-CPA模型的表现一直优于LR模型和之前报道的25种模型:我们开发并验证了 PHM-CPA 模型,该模型由现成的临床变量组成,在预测 AKI 肝硬化患者的院内死亡率方面表现出卓越的性能和校准性。
Development and validation of the PHM-CPA model to predict in-hospital mortality for cirrhotic patients with acute kidney injury.
Background: The presence of acute kidney injury (AKI) significantly increases in-hospital mortality risk for cirrhotic patients. Early prognosis prediction for these patients is crucial. We aimed to develop and validate a machine learning model for in-hospital mortality prediction for cirrhotic patients with AKI.
Methods: Data from cirrhotic patients with AKI hospitalized at the First Affiliated Hospital of Zhejiang University between January 1, 2013, and December 31, 2020 were used to train and validate an extreme Gradient Boosting model to predict in-hospital mortality risk. The Boruta algorithm was used for variable selection. The optimal model was selected and named as PHM-CPA (Prediction of in-Hospital Mortality for Cirrhotic Patients with AKI). The PHM-CPA model was then externally validated in patients from eICU Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care III dataset (MIMIC). The predictive performance of PHM-CPA model was compared with that of logistic regression (LR) model and 25 previously reported models.
Results: A total of 519 cirrhotic patients with AKI were enrolled in model training cohort, of whom 118 (23%) died during hospitalization. Fifteen variables from common laboratory tests were selected to develop the PHM-CPA model. The PHM-CPA model achieved an AUROC of 0.816 (95% CI, 0.763-0.861) in the internal validation cohort and 0.787 (95% CI, 0.745-0.830) in the external validation cohort. The PHM-CPA model consistently outperformed the LR model and 25 previously reported models.
Conclusion: We developed and validated the PHM-CPA model, comprising readily available clinical variables, which demonstrated superior performance and calibration in predicting in-hospital mortality for cirrhotic patients with AKI.
期刊介绍:
Digestive and Liver Disease is an international journal of Gastroenterology and Hepatology. It is the official journal of Italian Association for the Study of the Liver (AISF); Italian Association for the Study of the Pancreas (AISP); Italian Association for Digestive Endoscopy (SIED); Italian Association for Hospital Gastroenterologists and Digestive Endoscopists (AIGO); Italian Society of Gastroenterology (SIGE); Italian Society of Pediatric Gastroenterology and Hepatology (SIGENP) and Italian Group for the Study of Inflammatory Bowel Disease (IG-IBD).
Digestive and Liver Disease publishes papers on basic and clinical research in the field of gastroenterology and hepatology.
Contributions consist of:
Original Papers
Correspondence to the Editor
Editorials, Reviews and Special Articles
Progress Reports
Image of the Month
Congress Proceedings
Symposia and Mini-symposia.