Machine learning models demonstrated moderate predictive capability, yielding a pooled AUROC of 0.79 for stroke prognosis in AF, 0.68 for stroke prediction in AF, and 0.75 for AF prediction post-stroke.
Meta-Analysis (n=7,391,645)
Do machine learning models accurately predict stroke prognosis and prediction in atrial fibrillation patients, and AF prediction in stroke patients?
Machine learning models demonstrate moderate predictive capability for stroke prognosis and AF prediction, but require further refinement and validation before widespread clinical adoption.
Effect estimate: Mean AUROC 0.75
BACKGROUND/OBJECTIVE: Atrial fibrillation (AF) complicates the management of acute ischemic stroke (AIS), necessitating precise predictive models to enhance clinical outcomes. This meta-analysis evaluates the efficacy of machine learning (ML) models in three key areas: stroke prognosis in AF patients, stroke prediction in AF patients, and AF prediction in stroke patients. The study aims to assess the accuracy and variability of ML models in forecasting AIS outcomes and detecting AF in stroke patients, while exploring the clinical benefits and limitations of integrating these models into practice. METHODS: We conducted a systematic search of PubMed, Embase, and Cochrane databases up to June 2024, selecting studies that evaluated ML accuracy in stroke prognosis and prediction in AF patients and AF prediction in stroke patients. Data extraction and quality assessment were performed independently by two reviewers, with random-effects modeling applied to estimate pooled accuracy metrics. RESULTS: The meta-analysis included twenty-four studies comprising 7,391,645 patients, categorized into groups for stroke prognosis in AF patients (eight studies), stroke prediction in AF patients (thirteen studies), and AF prediction in stroke patients (three studies). The pooled AUROC was 0.79 for stroke prognosis and 0.68 for stroke prediction in AF, with higher accuracy noted in short-term predictions. The mean AUROC across studies was 0.75, with models such as Extreme Gradient Boosting (XGB) and Random Forest (RF) showing superior performance. For stroke prognosis in AF, the mean AUROC was 0.78, whereas stroke prediction yielded a mean AUROC of 0.73. AF prediction post-stroke had an average AUROC of 0.75. These findings indicate moderate predictive capability of ML models, underscoring the need for further refinement and standardization. The absence of comprehensive sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) metrics limited the ability to conduct full meta-analytic modeling. CONCLUSIONS: While ML models demonstrate potential for enhancing stroke prognosis and AF prediction, they have yet to meet the clinical standards required for widespread adoption. Future efforts should focus on refining these models and validating them across diverse populations to improve their clinical utility.
Goh et al. (Sat,) conducted a meta-analysis in Atrial fibrillation and acute ischemic stroke (n=7,391,645). Machine learning models was evaluated on Accuracy (AUROC) for stroke prognosis, stroke prediction, and AF prediction (Mean AUROC 0.75). Machine learning models demonstrated moderate predictive capability, yielding a pooled AUROC of 0.79 for stroke prognosis in AF, 0.68 for stroke prediction in AF, and 0.75 for AF prediction post-stroke.