Benjamin Sibilia, Solenn Toupin, Nabil Bouali, Jean-Baptiste Brette, Arthur Ramonatxo, Guillaume Schurtz, Kenza Hamzi, Antonin Trimaille, Emmanuel Gall, Nicolas Piliero, Alexandre Unger, Stéphane Andrieu, Trecy Gonçalves, Fabien Picard, Vincent Roule, François Roubille, Sonia Houssany-Pissot, Océane Bouchot, Victor Aboyans, Reza Rossanaly Vasram, Thomas Bochaton, Damien Logeart, Alain Cohen Solal, Jérôme Cartailler, Alexandre Mebazaa, Jean-Guillaume Dillinger, Patrick Henry, Théo Pezel
{"title":"包括环境因素在内的监督机器学习预测急性心力衰竭患者的住院结果。","authors":"Benjamin Sibilia, Solenn Toupin, Nabil Bouali, Jean-Baptiste Brette, Arthur Ramonatxo, Guillaume Schurtz, Kenza Hamzi, Antonin Trimaille, Emmanuel Gall, Nicolas Piliero, Alexandre Unger, Stéphane Andrieu, Trecy Gonçalves, Fabien Picard, Vincent Roule, François Roubille, Sonia Houssany-Pissot, Océane Bouchot, Victor Aboyans, Reza Rossanaly Vasram, Thomas Bochaton, Damien Logeart, Alain Cohen Solal, Jérôme Cartailler, Alexandre Mebazaa, Jean-Guillaume Dillinger, Patrick Henry, Théo Pezel","doi":"10.1093/ehjdh/ztae094","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims: </strong>While few traditional scores are available for risk stratification of patients hospitalized for acute heart failure (AHF), the potential benefit of machine learning (ML) is not well established. We aimed to assess the feasibility and accuracy of a supervised ML model including environmental factors to predict in-hospital major adverse events (MAEs) in patients hospitalized for AHF.</p><p><strong>Methods and results: </strong>In April 2021, a French national prospective multicentre study included all consecutive patients hospitalized in intensive cardiac care unit. Patients admitted for AHF were included in the analyses. A ML model involving automated feature selection by least absolute shrinkage and selection operator (LASSO) and model building with a random forest (RF) algorithm was developed. The primary composite outcome was in-hospital MAE defined by death, resuscitated cardiac arrest, or cardiogenic shock requiring assistance. Among 459 patients included (age 68 ± 14 years, 68% male), 47 experienced in-hospital MAE (10.2%). Seven variables were selected by LASSO for predicting MAE in the training data set (<i>n</i> = 322): mean arterial pressure, ischaemic aetiology, sub-aortic velocity time integral, E/e', tricuspid annular plane systolic excursion, recreational drug use, and exhaled carbon monoxide level. The RF model showed the best performance compared with other evaluated models [area under the receiver operating curve (AUROC) = 0.82, 95% confidence interval (CI) (0.78-0.86); precision-recall area under the curve = 0.48, 95% CI (0.42-0.5), <i>F</i>1 score = 0.56). Our ML model exhibited a higher AUROC compared with an existing score for the prediction of MAE (AUROC for our ML model: 0.82 vs. ACUTE HF score: 0.57; <i>P</i> < 0.001).</p><p><strong>Conclusion: </strong>Our ML model including in particular environmental variables exhibited a better performance than traditional statistical methods to predict in-hospital outcomes in patients admitted for AHF.</p><p><strong>Study registration: </strong>ClinicalTrials.gov identifier: NCT05063097.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 2","pages":"190-199"},"PeriodicalIF":4.4000,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914725/pdf/","citationCount":"0","resultStr":"{\"title\":\"Supervised machine learning including environmental factors to predict in-hospital outcomes in acute heart failure patients.\",\"authors\":\"Benjamin Sibilia, Solenn Toupin, Nabil Bouali, Jean-Baptiste Brette, Arthur Ramonatxo, Guillaume Schurtz, Kenza Hamzi, Antonin Trimaille, Emmanuel Gall, Nicolas Piliero, Alexandre Unger, Stéphane Andrieu, Trecy Gonçalves, Fabien Picard, Vincent Roule, François Roubille, Sonia Houssany-Pissot, Océane Bouchot, Victor Aboyans, Reza Rossanaly Vasram, Thomas Bochaton, Damien Logeart, Alain Cohen Solal, Jérôme Cartailler, Alexandre Mebazaa, Jean-Guillaume Dillinger, Patrick Henry, Théo Pezel\",\"doi\":\"10.1093/ehjdh/ztae094\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aims: </strong>While few traditional scores are available for risk stratification of patients hospitalized for acute heart failure (AHF), the potential benefit of machine learning (ML) is not well established. We aimed to assess the feasibility and accuracy of a supervised ML model including environmental factors to predict in-hospital major adverse events (MAEs) in patients hospitalized for AHF.</p><p><strong>Methods and results: </strong>In April 2021, a French national prospective multicentre study included all consecutive patients hospitalized in intensive cardiac care unit. Patients admitted for AHF were included in the analyses. A ML model involving automated feature selection by least absolute shrinkage and selection operator (LASSO) and model building with a random forest (RF) algorithm was developed. The primary composite outcome was in-hospital MAE defined by death, resuscitated cardiac arrest, or cardiogenic shock requiring assistance. Among 459 patients included (age 68 ± 14 years, 68% male), 47 experienced in-hospital MAE (10.2%). Seven variables were selected by LASSO for predicting MAE in the training data set (<i>n</i> = 322): mean arterial pressure, ischaemic aetiology, sub-aortic velocity time integral, E/e', tricuspid annular plane systolic excursion, recreational drug use, and exhaled carbon monoxide level. The RF model showed the best performance compared with other evaluated models [area under the receiver operating curve (AUROC) = 0.82, 95% confidence interval (CI) (0.78-0.86); precision-recall area under the curve = 0.48, 95% CI (0.42-0.5), <i>F</i>1 score = 0.56). Our ML model exhibited a higher AUROC compared with an existing score for the prediction of MAE (AUROC for our ML model: 0.82 vs. ACUTE HF score: 0.57; <i>P</i> < 0.001).</p><p><strong>Conclusion: </strong>Our ML model including in particular environmental variables exhibited a better performance than traditional statistical methods to predict in-hospital outcomes in patients admitted for AHF.</p><p><strong>Study registration: </strong>ClinicalTrials.gov identifier: NCT05063097.</p>\",\"PeriodicalId\":72965,\"journal\":{\"name\":\"European heart journal. Digital health\",\"volume\":\"6 2\",\"pages\":\"190-199\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914725/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European heart journal. 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Supervised machine learning including environmental factors to predict in-hospital outcomes in acute heart failure patients.
Aims: While few traditional scores are available for risk stratification of patients hospitalized for acute heart failure (AHF), the potential benefit of machine learning (ML) is not well established. We aimed to assess the feasibility and accuracy of a supervised ML model including environmental factors to predict in-hospital major adverse events (MAEs) in patients hospitalized for AHF.
Methods and results: In April 2021, a French national prospective multicentre study included all consecutive patients hospitalized in intensive cardiac care unit. Patients admitted for AHF were included in the analyses. A ML model involving automated feature selection by least absolute shrinkage and selection operator (LASSO) and model building with a random forest (RF) algorithm was developed. The primary composite outcome was in-hospital MAE defined by death, resuscitated cardiac arrest, or cardiogenic shock requiring assistance. Among 459 patients included (age 68 ± 14 years, 68% male), 47 experienced in-hospital MAE (10.2%). Seven variables were selected by LASSO for predicting MAE in the training data set (n = 322): mean arterial pressure, ischaemic aetiology, sub-aortic velocity time integral, E/e', tricuspid annular plane systolic excursion, recreational drug use, and exhaled carbon monoxide level. The RF model showed the best performance compared with other evaluated models [area under the receiver operating curve (AUROC) = 0.82, 95% confidence interval (CI) (0.78-0.86); precision-recall area under the curve = 0.48, 95% CI (0.42-0.5), F1 score = 0.56). Our ML model exhibited a higher AUROC compared with an existing score for the prediction of MAE (AUROC for our ML model: 0.82 vs. ACUTE HF score: 0.57; P < 0.001).
Conclusion: Our ML model including in particular environmental variables exhibited a better performance than traditional statistical methods to predict in-hospital outcomes in patients admitted for AHF.
Study registration: ClinicalTrials.gov identifier: NCT05063097.
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