Machine learning to predict incident atrial fibrillation in patients with acute stroke: FIND-AF STROKE

Abstract

Abstract Background Atrial fibrillation (AF)-related strokes have a high rate of recurrence, and are associated with substantial morbidity, disability, healthcare expenditure, and mortality.(1-3) Detection of AF promises opportunity to prevent recurrent stroke through conversion of antiplatelet to anticoagulant therapy,(4,5) but use of extended monitoring has cost implications.(6) Thus accurately identifying patients with stroke at high risk for AF is important but previous previous models have been developed in small, unrepresentative cohorts.(7) Purpose To develop and validate a simple novel decision support tool for prediction of incident AF after hospitalisation with stroke through machine learning. Methods We developed the model (FIND-AF STROKE) in UK secondary care and death certificate-linked primary care health record data from individuals aged ≥30 years without known AF hospitalized with a stroke or transient ischaemic attack (Jan 2, 1998 to Feb 28, 2022), randomly divided into training (80%) and testing (20%) datasets. We evaluated logistic regression and random forest models for prediction of 2-year AF risk in the testing dataset with internal bootstrap validation, and compared against the CHA2DS2VASC score. Youden index was used to set a threshold for higher and lower risk. Results Of 35 530 UK individuals (mean age 72.1 (SD 12.7) years, 50.9% women), 1742 (4.8%) had developed incident AF at 2 years. Using feature importance a parsimonious version of the model was created requiring only age, sex, ethnicity (white versus other) and five comorbidities. In the testing dataset prediction performance for the random forest algorithm (area under the receiver operating characteristic (AUROC) 0.710, 95% CI 0.685 – 0.736) was superior to the CHA2DS2VASC score (0.645, 95% CI 0.617-0.672) (Figure 1). The threshold for higher risk using the Youden index was 4.81% which resulted in a negative predictive value of 97.6%, sensitivity 0.712, specificity 0.583. Of the post-stroke patients 44% were labelled at higher risk of AF, who were older, had a higher prevalence of comorbidities, and a 3.3-fold higher incidence of AF post-stroke, than those identified as lower risk (Table 1). Conclusions The machine learning FIND-AF STROKE decision support tool can accurately identify individuals at risk of incident AF after stroke to enable a targeted and cost-effective approach to extended monitoring. Validation within cohorts that have undergone extended monitoring post-stroke will give a more accurate estimation of yield at different risk thresholds.Figure 1