{"title":"Predicting the risk of mortality and rehospitalization in heart failure patients: A retrospective cohort study by machine learning approach","authors":"Marzieh Ketabi MSc, Aref Andishgar MD, Zhila Fereidouni PhD, Maryam Mojarrad Sani MD, MPH, Ashkan Abdollahi MD, Mohebat Vali PhD, Abdulhakim Alkamel MD, Reza Tabrizi PhD","doi":"10.1002/clc.24239","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Heart failure (HF) is a global problem, affecting more than 26 million people worldwide. This study evaluated the performance of 10 machine learning (ML) algorithms and chose the best algorithm to predict mortality and readmission of HF patients by using The Fasa Registry on Systolic HF (FaRSH) database.</p>\n </section>\n \n <section>\n \n <h3> Hypothesis</h3>\n \n <p>ML algorithms may better identify patients at increased risk of HF readmission or death with demographic and clinical data.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Through comprehensive evaluation, the best-performing model was used for prediction. Finally, all the trained models were applied to the test data, which included 20% of the total data. For the final evaluation and comparison of the models, five metrics were used: accuracy, F1-score, sensitivity, specificity and Area Under Curve (AUC).</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Ten ML algorithms were evaluated. The CatBoost (CAT) algorithm uses a series of decision tree models to create a nonlinear model, and this CAT algorithm performed the best of the 10 models studied. According to the three final outcomes from this study, which involved 2488 participants, 366 (14.7%) of the patients were readmitted to the hospital, 97 (3.9%) of the patients died within 1 month of the follow-up, and 342 (13.7%) of the patients died within 1 year of the follow-up. The most significant variables to predict the events were length of stay in the hospital, hemoglobin level, and family history of MI.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>The ML-based risk stratification tool was able to assess the risk of 5-year all-cause mortality and readmission in patients with HF. ML could provide an explicit explanation of individualized risk prediction and give physicians an intuitive understanding of the influence of critical features in the model.</p>\n </section>\n </div>","PeriodicalId":10201,"journal":{"name":"Clinical Cardiology","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10894620/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Cardiology","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/clc.24239","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Background
Heart failure (HF) is a global problem, affecting more than 26 million people worldwide. This study evaluated the performance of 10 machine learning (ML) algorithms and chose the best algorithm to predict mortality and readmission of HF patients by using The Fasa Registry on Systolic HF (FaRSH) database.
Hypothesis
ML algorithms may better identify patients at increased risk of HF readmission or death with demographic and clinical data.
Methods
Through comprehensive evaluation, the best-performing model was used for prediction. Finally, all the trained models were applied to the test data, which included 20% of the total data. For the final evaluation and comparison of the models, five metrics were used: accuracy, F1-score, sensitivity, specificity and Area Under Curve (AUC).
Results
Ten ML algorithms were evaluated. The CatBoost (CAT) algorithm uses a series of decision tree models to create a nonlinear model, and this CAT algorithm performed the best of the 10 models studied. According to the three final outcomes from this study, which involved 2488 participants, 366 (14.7%) of the patients were readmitted to the hospital, 97 (3.9%) of the patients died within 1 month of the follow-up, and 342 (13.7%) of the patients died within 1 year of the follow-up. The most significant variables to predict the events were length of stay in the hospital, hemoglobin level, and family history of MI.
Conclusions
The ML-based risk stratification tool was able to assess the risk of 5-year all-cause mortality and readmission in patients with HF. ML could provide an explicit explanation of individualized risk prediction and give physicians an intuitive understanding of the influence of critical features in the model.
期刊介绍:
Clinical Cardiology provides a fully Gold Open Access forum for the publication of original clinical research, as well as brief reviews of diagnostic and therapeutic issues in cardiovascular medicine and cardiovascular surgery.
The journal includes Clinical Investigations, Reviews, free standing editorials and commentaries, and bonus online-only content.
The journal also publishes supplements, Expert Panel Discussions, sponsored clinical Reviews, Trial Designs, and Quality and Outcomes.