Supervised Machine Learning including Environmental Factors to Predict In-Hospital Outcomes in Acute Heart Failure Patients

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Abstract 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 (MAE) in patients hospitalized for AHF. Methods and results In April 2021, a French national prospective multicentre study included all consecutive patients hospitalized in ICCU. 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 dataset (N=322): mean arterial pressure, ischemic 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 (AUROC=0.82, 95% CI [0.78-0.86], PR-AUC=0.48, 95% CI [0.42-0.54], F1-score=0.56). Our ML-model exhibited a higher AUROC compared with an existing score for prediction of MAE (AUROC for our ML-model: 0.82 vs Acute HF-score: 0.57; p<0.001). Conclusions 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. TRIAL REGISTRATION ClinicalTrials.gov Identifier NCT05063097