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      Machine Learning for Mortality Prediction in Patients With Heart Failure With Mildly Reduced Ejection Fraction

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          Abstract

          Background

          Machine‐learning‐based prediction models (MLBPMs) have shown satisfactory performance in predicting clinical outcomes in patients with heart failure with reduced and preserved ejection fraction. However, their usefulness has yet to be fully elucidated in patients with heart failure with mildly reduced ejection fraction. This pilot study aims to evaluate the prediction performance of MLBPMs in a heart failure with mildly reduced ejection fraction cohort with long‐term follow‐up data.

          Methods and Results

          A total of 424 patients with heart failure with mildly reduced ejection fraction were enrolled in our study. The primary outcome was all‐cause mortality. Two feature selection strategies were introduced for MLBPM development. The “All‐in” (67 features) strategy was based on feature correlation, multicollinearity, and clinical significance. The other strategy was the CoxBoost algorithm with 10‐fold cross‐validation (17 features), which was based on the selection result of the “All‐in” strategy. Six MLBPMs with 5‐fold cross‐validation based on the “All‐in” and the CoxBoost algorithm with 10‐fold cross‐validation strategy were developed by the eXtreme Gradient Boosting, random forest, and support vector machine algorithms. The logistic regression model with 14 benchmark predictors was used as a reference model. During a median follow‐up of 1008 (750, 1937) days, 121 patients met the primary outcome. Overall, MLBPMs outperformed the logistic model. The “All‐in” eXtreme Gradient Boosting model had the best performance, with an accuracy of 85.4% and a precision of 70.3%. The area under the receiver‐operating characteristic curve was 0.916 (95% CI, 0.887–0.945). The Brier score was 0.12.

          Conclusions

          The MLBPMs could significantly improve outcome prediction in patients with heart failure with mildly reduced ejection fraction, which would further optimize the management of these patients.

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          Most cited references34

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          2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure

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            2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC)Developed with the special contribution of the Heart Failure Association (HFA) of the ESC.

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              A Unified Approach to Interpreting Model Predictions

              Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is a unique solution in this class with a set of desirable properties. The new class unifies six existing methods, notable because several recent methods in the class lack the proposed desirable properties. Based on insights from this unification, we present new methods that show improved computational performance and/or better consistency with human intuition than previous approaches. To appear in NIPS 2017
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                Author and article information

                Contributors
                fwzhangjian62@126.com
                yuhuizhangjoy@163.com
                Journal
                J Am Heart Assoc
                J Am Heart Assoc
                10.1002/(ISSN)2047-9980
                JAH3
                ahaoa
                Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
                John Wiley and Sons Inc. (Hoboken )
                2047-9980
                10 June 2023
                20 June 2023
                : 12
                : 12 ( doiID: 10.1002/jah3.v12.12 )
                : e029124
                Affiliations
                [ 1 ] Heart Failure Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science, Peking Union Medical College Beijing China
                [ 2 ] Key Laboratory of Clinical Research for Cardiovascular Medications, National Health Committee Beijing China
                Author notes
                [*] [* ]Correspondence to: Jian Zhang, MD, PhD, and Yuhui Zhang, MD, PhD, Heart Failure Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science, Peking Union Medical College, Key Laboratory of Clinical Research for Cardiovascular Medications, National Health Committee, No. 167 Fuwai Hospital, Beilishi Rd. Xicheng District, 10037 Beijing, China. Email: fwzhangjian62@ 123456126.com , yuhuizhangjoy@ 123456163.com
                Author information
                https://orcid.org/0000-0002-3188-8693
                https://orcid.org/0000-0002-7927-560X
                https://orcid.org/0000-0003-0748-8777
                https://orcid.org/0000-0002-0086-8948
                https://orcid.org/0000-0001-5967-2889
                https://orcid.org/0000-0002-5093-4325
                https://orcid.org/0000-0002-2382-8869
                Article
                JAH38517 JAHA/2022/029124
                10.1161/JAHA.122.029124
                10356044
                37301744
                26b9bba9-aaec-4cbc-9ebf-d86dfdf78069
                © 2023 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 07 December 2022
                : 10 May 2023
                Page count
                Figures: 5, Tables: 3, Pages: 15, Words: 7251
                Funding
                Funded by: National Science and Technology Pillar Program
                Award ID: 2011BAI11B08
                Funded by: National Science and Technology 6 Pillar Program
                Award ID: 2017YFC1308305
                Award ID: 2017YFC1308300
                Funded by: Chinese Academy of Medical Science Innovation Fund for Medical Science
                Award ID: 2021‐CXGC08
                Categories
                Original Research
                Original Research
                Heart Failure
                Custom metadata
                2.0
                20 June 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.9 mode:remove_FC converted:20.06.2023

                Cardiovascular Medicine
                hfmref,machine learning,mortality,prediction,heart failure
                Cardiovascular Medicine
                hfmref, machine learning, mortality, prediction, heart failure

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