Development and internal validation of multimodal machine learning models for predicting eligibility for mechanical thrombectomy in suspected stroke patients using routinely collected clinical and imaging data

Background: Mechanical thrombectomy (MT) eligibility for acute ischemic stroke (AIS) patients depends upon clinical and advanced imaging assessments like CT perfusion (CTP). Assessment complexities and limited access to advanced imaging investigations are known challenges. We developed machine-learning models using routinely collected clinical and imaging data to predict MT eligibility. Methods: Age, National-Institutes-of-Health-Stroke-Scale-Score (NIHSS), last-known-well-time (LKWT), noncontrast-CT (NCCT) scan and CT-angiography (CTA) report from consecutive cohort of 260 AIS-suspected patients treated at a stroke centre during Apr’20 to Dec’23 were retrospectively collected. 160 underwent MT for anterior-circulation large vessel occlusion (LVOa); rest were MT ineligible. MT eligibility was determined based on clinical and imaging investigations including CTP during routine-care. The dataset was split into train:test sets (50:50 split). A commercially available artificial-intelligence algorithm calculated infarct volume and ASPECT score (ASPECTSq) from the NCCTs. We developed two supervised models using Gradient-Boosting-Machines. MODEL1 utilized age, NIHSS, LKWT, ASPECTSq and infarct volume as inputs; MODEL2 additionally included the presence/absence of LVOa as input. The target/response variable used for our supervised learning methods was whether the patients were MT eligible or not as determined during routine-care. Performance of the models were investigated using the test set. Results: Among 130 patients (mean age ± standard-deviation: 67.4 ± 14.2 years; 61 males) in test set, 80 (61.5%) were MT eligible; rest were ineligible. The area-under-the-receiver-operating-characteristics-curve, sensitivity and specificity of MODEL1 were 0.76 (95% CI: 0.67–0.85), 85% (75.6–91.2) and 60% (46.2–72.4), respectively. They were 0.92 (0.88–0.96), 82.5% (72.7–89.3) and 82% (69.2–90.2), respectively, for MODEL2. Conclusions: The models showed promising results, demonstrating that NCCT, potentially with CTA, could be sufficient for MT eligibility determination. Such models can enable faster referrals of patients to higher centers.