A machine learning model using repurposed routine blood data stratified acute ischemic stroke risk in hypertensive patients, achieving an AUROC of 0.68 (95% CI 0.64-0.72).
Cohort (n=4,230)
Yes
Does a machine learning model using routine blood data accurately stratify acute ischemic stroke risk in hypertensive patients?
A machine learning model using routine blood biomarkers showed moderate accuracy (AUROC 0.68) in stratifying near-term acute ischemic stroke risk among hypertensive patients.
Effect estimate: AUROC 0.68 (95% CI 0.64-0.72)
Abstract Background and aims Hypertensive patients are at risk of developing acute ischemic stroke (AIS). The absence of accessible tools for near-term risk stratification hinders prevention. An AIS risk stratification model is independently validated in a hypertensive cohort in this study. Methods A model for AIS risk stratification in hypertensive patients by repurposing demographic data and 19 routine blood biomarkers from the preceding 30 days (territory-wide cohort, n=273,780) was developed. It was then applied to an independent cohort of 4,230 hypertensive patients, comprising 151 AIS cases and 4,079 non-AIS controls. Performance was evaluated via AUROC. A dual-threshold method stratified patients into low-, medium-, and high-risk groups, using thresholds pre-defined in the development cohort at 97.5% sensitivity and 90% specificity. Results Cases were older (75.6 ± 12.4 vs. 70.2 ± 14.4 years; p-value 0.05) and had a similar male proportion (55.6% vs. 53.2%; p-value = 0.62) as controls. The model achieved an AUROC of 0.68 (95% CI: 0.64–0.72) and stratified patients into low- (n=619; 14.6%), medium- (n=2,787; 65.9%), and high-risk groups. The high-risk group identified 39.7% (60/151) of AIS events, with a positive predictive value of 7.3%. The low-risk group excluded 14.6% (619/4230) patients, achieved a negative predictive value of 98.9% and missed 7 (5%) AIS cases. Conclusions The model that repurposes routine blood test data within 30 days to stratify AIS risk in a 4k hypertensive cohort was tested. The practicality and scalability of the model for opportunistic screening in primary care is validated, enabling targeted preventive therapy and bridging a critical gap in patient management. Conflict of interest
Lau et al. (2026) conducted a cohort in Hypertension and Acute Ischemic Stroke (n=4,230). Machine learning model using repurposed routine blood data was evaluated on Acute ischemic stroke (AIS) risk stratification performance via AUROC (AUROC 0.68, 95% CI 0.64-0.72). A machine learning model using repurposed routine blood data stratified acute ischemic stroke risk in hypertensive patients, achieving an AUROC of 0.68 (95% CI 0.64-0.72).