Key result
Machine learning with environmental factors outperforms ACUTE HF score in predicting major in-hospital HF events.
Why the study?
Few traditional scores exist for risk stratification in patients hospitalized for acute heart failure, and the potential benefit of machine learning remains not well established.
Does a machine learning model including environmental factors improve the prediction of in-hospital major adverse events in patients admitted for acute heart failure compared to traditional scores?
Observational (n=459)
Yes
Does a machine learning model including environmental factors improve the prediction of in-hospital major adverse events in patients admitted for acute heart failure compared to traditional scores?
Effect estimate: AUROC (95% CI 0.78-0.86)
Absolute Event Rate: 0.82% vs 0.57%
p-value: p=<0.001
A machine learning model incorporating environmental variables outperformed traditional risk scores in predicting in-hospital major adverse events for acute heart failure patients.
May enhance acute HF risk stratification with environmental data; hypothesis-generating and requires prospective validation before clinical use.
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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Sibilia et al. (2024) conducted an observational in Acute heart failure (n=459). Random forest machine learning model vs. ACUTE HF score was evaluated on In-hospital major adverse events (death, resuscitated cardiac arrest, or cardiogenic shock requiring assistance) (AUROC, 95% CI 0.78-0.86, p=<0.001). A machine learning model including environmental factors predicted in-hospital major adverse events in acute heart failure better than the ACUTE HF score (AUROC 0.82 vs 0.57; P<0.001).
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