An evidence-based analysis of machine learning prediction models for cognitive impairment in cerebral small vessel...

Early identification of cerebral small vessel disease (CSVD) patients with a higher risk of developing cognitive impairment is essential for timely intervention and improved patient outcomes. Machine learning (ML) has emerged as a promising technique for cognitive impairment in CSVD. This study aims to conduct a thorough meta-analysis and comparison of published ML prediction models for cognitive impairment in patients with CSVD. Relevant studies were retrieved from four databases: PubMed, Embase, Web of Science, and the Cochrane Library. A meta-analysis of the C-index was performed using a random-effects model, while a bivariate mixed-effects model was used to calculate the pooled sensitivity and specificity. In addition, to limit the influence of heterogeneity, we also performed sensitivity analyses, a meta-regression, and subgroup analysis. Included for analysis were 13 studies involving 3444 patients. The pooled C-index, sensitivity, and specificity were 0.84 (95% CI 0.79-0.90), 0.83 (95% CI 0.78-0.88), and 0.80 (95% CI 0.71-0.86), respectively. As one of the most commonly used ML methods, logistic regression achieved a total merged C-index of 0.81, while non logistic regression models performed better with a total merged C-index of 0.86. Our findings indicate that ML models holds significant promise in forecasting the risk of cognitive impairment in patients with CSVD. However, future high-quality research that externally validates the algorithm through prospective studies with larger, more diverse cohorts is needed.