Supervised machine learning including environmental factors to predict in-hospital outcomes in acute heart failure patients

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 (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.

Keywords: Acute heart failure; Carbon monoxide; Environmental factors; Intensive cardiac care unit; Machine learning; Recreational drugs.

Graphical Abstract

Prediction models using machine learning including environmental factors to assess the risk of in-hospital major adverse events in patients hospitalized for acute heart failure. ADDICT-ICCU, Addiction in intensive cardiac care unit; AHF, acute heart failure; CO, carbon monoxide; LASSO, least absolute shrinkage and selection operator; MAE, major adverse event; PR, precision-recall; TAPSE, tricuspid annular plane systolic excursion; TTE, transthoracic echocardiogram; LVOT VTI, left ventricular outflow tract velocity–time integral; XGBoost, extreme gradient boosting.

Figure 1

Flowchart of the study population. ICCU, intensive cardiac care unit; ML, machine learning.

Figure 2

Feature importance selected by least absolute shrinkage and selection operator. Least absolute shrinkage and selection operator was used to evaluate the worth of each variable by measuring the log-rank–based variable importance with respect to the outcome and then to rank the attributes by their individual evaluations (top to bottom). Only attributes resulting in log-rank based variable importance > 30 (above the vertical line) were subsequently used for the model building. CO, carbon monoxide; HF, heart failure; LASSO, least absolute shrinkage and selection operator; LVOT VTI, left ventricular outflow tract velocity–time integral; MAP, mean arterial; TAPSE, tricuspid annular plane systolic excursion.

Figure 3

Performance of machine learning models for prediction of in-hospital major adverse event. Receiver operating characteristic (area under the receiver operating curve, A) and precision-recall curves (precision-recall area under the curve, B) for prediction of in-hospital MAE were used to compare least absolute shrinkage and selection operator, random forest, logistic regression, and extreme gradient boosting, developed using the set of variables selected by least absolute shrinkage and selection operator. AUC, area under the curve; LASSO, least absolute shrinkage and selection operator; ROC, receiver operating curve; XGBoost, extreme gradient boosting.

Figure 4

Comparison of our model with ACUTE HF score. Receiver operating characteristic (area under the receiver operating curve, A) and precision-recall curves (precision-recall area under the curve, B) for prediction of in-hospital major adverse event were used to compare our machine learning model to the ACUTE HF score. AUC, area under the curve; PR, precision-recall; ROC, receiver operating curve.