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Machine learning versus conventional methods for prehospital detection of stroke due to large vessel occlusion or
Bjarty L Garcia1,2,3, Luuk Dekker4,5, Rene Bekker2
1Department of Public Health and Primary Care, Leiden University Medical Center (LUMC), Leiden, The Netherlands.
Introduction:
Adequate prehospital triage of anterior-circulation LVO (aLVO) or ICH enables direct allocation to appropriate stroke centres. Traditional triage based on clinical scales or logistic regression may miss complex predictor interactions, whereas machine learning approaches may improve diagnostic accuracy. We compared 3 prehospital triage methods to detect a composite outcome of aLVO or ICH using internal-external cross-validation.
Patients And Methods:
We analysed emergency medical services-activated stroke codes (2018-2019) from the Leiden Prehospital Stroke Study and the Prehospital Triage of Patients With Suspected Stroke Study. We compared XGBoost (supervised machine learning [sML]), conventional logistic regression (cLR) and the Rapid Arterial oCclusion Evaluation (RACE) scale using 2-fold internal-external cross-validation. Two variable sets were evaluated: (1) routine prehospital variables (demographics, symptom duration, vital signs, glucose, neurological deficits) and (2) an extended set including medical history, anticoagulant use and point-of-care international normalised ratio. Discrimination and calibration were assessed using balanced accuracy, sensitivity, specificity, Brier scores and calibration plots.
Results:
Among 3320 patients, 538 (16%) had aLVO or ICH. Using routine variables, mean balanced accuracy was 82.1% for sML, 79.0% for cLR and 74.5% for RACE (P < .01). With extended variables, performance improved to 84.4% for sML and 80.7% for cLR (P < .01). The sML model achieved the highest sensitivity (76.7%) and specificity (92.1%). Calibration was good for both models, although Brier scores were lower for cLR.
Conclusion:
XGBoost showed modestly higher discriminative performance than cLR or RACE, while cLR showed better calibration. These findings suggest that machine learning may improve prehospital classification in selected settings, but the absolute gains were limited and require further external validation and prospective implementation studies.
