The Atrial Fibrillation In Critically Ill patients (AFICILL) studies: validation and implemetation of topological data analysis and machine learning techniques in the prediction of atrial-fibrillation related outcomes in patients admitted to medical sub-intensive care units

Abstract

Non-valvular atrial fibrillation (NVAF) is the most common sustained arrhythmia observed in critically ill patients, linked to a higher risk of embolic and haemorrhagic events. Conventional tools, such as CHADS2, CHA2DS2-VASc, and HAS-BLED scores, are ineffective for risk stratification and do not offer guidance for anticoagulation strategies in this population. Recently, we engineered new machine-learning (ML) models retrospective cohorts, with promising results; in this work, we aim to validate our ML models in a larger cohort. We performed a retrospective analysis of all consecutive critically ill patients admitted to our step-down unit over a 10-year period who had a history of NVAF. We calculated classical risk scores and trained our ML models on pre-specified outcomes: the main outcome (MO) which was a composite of in-hospital death or intensive care unit (ICU) transfer, stroke/TIA, and major bleeding (MB) during the admission. After eliminating trauma and non-critical patients, we obtained 2105 subjects, with 314 MO, 134 cardioembolic stroke/TIA and 227 MB. Classical risk scores (APACHE-II for MO, CHADS2 and CHA2DS2-VASc for stroke/TIA, HAS-BLED for MB) performed poorly, while ML confirmed its accuracy in predicting outcomes also in this extended cohort (AUC APACHE-II:0.6397; 95%CI:0.6064-0.6729; AUC MO-ML:0.96; 95%CI:94.6-97.2; p<0.0001; AUC CHADS2:0.5775; 95%CI:0.5332-0.6218; p<0.0001; AUC CHA2DS2-VASc:0.5793; 95%CI:0.5357-0.6228; AUC stroke/TIA-ML:0.95; 95%CI:94.3- 96.6; p<0.0001; AUC HAS-BLED:0.5089 95%CI:0.4786-0.5392; AUC MB-ML:0.973 95%CI 95.5–98.1; p<0.0001). ML models can be considered as potential candidates in this setting to guide anticoagulant therapy. Multicenter, prospective cohorts will be necessary to establish their applicability in clinical practice.

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