Machine learning versus conventional methods for prehospital detection of stroke due to large vessel occlusion or intracranial haemorrhage
European Stroke Journal, vol. 11
Abstract
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.
Authors 20
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Leiden University Medical Center · Vrije Universiteit Amsterdam
Affiliation as printed
Department of Mathematics, Vrije Universiteit Amsterdam , Amsterdam ,
Department of Public Health and Primary Care, Leiden University Medical Center (LUMC) , Leiden ,
National eHealth Living Lab, Leiden University Medical Center (LUMC) , Leiden ,
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Leiden University Medical Center · Haga Hospital
Affiliation as printed
Department of Neurology, Haga Hospital , The Hague ,
Department of Neurology, Leiden University Medical Center (LUMC) , Leiden ,
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Affiliation as printed
Department of Mathematics, Vrije Universiteit Amsterdam , Amsterdam ,
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University Medical Center Groningen · Leiden University Medical Center
Affiliation as printed
Department of Neurology, Leiden University Medical Center (LUMC) , Leiden ,
Department of Neurology, University Medical Center Groningen (UMCG) , Groningen ,
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Leiden University Medical Center
Affiliation as printed
Department of Neurology, Leiden University Medical Center (LUMC) , Leiden ,
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Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences, Leiden University Medical Center (LUMC) , Leiden ,
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Regionale Ambulancevoorziening Hollands Midden
Affiliation as printed
Department of Research and Development, Regional Ambulance Services Hollands Midden (RAVHM) , Leiden ,
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Leiden University Medical Center · Medisch Centrum Haaglanden · Stichting Haaglanden Medisch Centrum
Affiliation as printed
Department of Neurology, Haaglanden Medical Center , The Hague ,
Department of Radiology, Leiden University Medical Center (LUMC) , Leiden ,
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Medisch Centrum Haaglanden · Stichting Haaglanden Medisch Centrum
Affiliation as printed
Department of Neurology, Haaglanden Medical Center , The Hague ,
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Affiliation as printed
Department of Neurology, Groene Hart Ziekenhuis , Gouda ,
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Affiliation as printed
Department of Neurology, Reinier de Graaf Gasthuis , Delft ,
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Affiliation as printed
Department of Neurology, Alrijne Ziekenhuis , Leiderdorp ,
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Affiliation as printed
Department of Neurology, Albert Schweitzer Ziekenhuis , Dordrecht ,
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Affiliation as printed
Department of Neurology, Erasmus MC University Medical Center , Rotterdam ,
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Affiliation as printed
Department of Neurology, Erasmus MC University Medical Center , Rotterdam ,
Department of Public Health, Erasmus MC University Medical Center , Rotterdam ,
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Affiliation as printed
Department of Neurology, Franciscus Gasthuis & Vlietland , Rotterdam ,
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Niels Henrik Chavannes Aachen Department of Public Health and Primary Care National eHealth Living Lab
Leiden University Medical Center
Affiliation as printed
Department of Public Health and Primary Care, Leiden University Medical Center (LUMC) , Leiden ,
National eHealth Living Lab, Leiden University Medical Center (LUMC) , Leiden ,
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Affiliation as printed
Department of Neurology, Erasmus MC University Medical Center , Rotterdam ,
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Nyika D. Kruyt corresponding Aachen Department of Neurology, Leiden University Medical Center (LUMC)
Leiden University Medical Center
Affiliation as printed
Department of Neurology, Leiden University Medical Center (LUMC) , Leiden ,
University Neurovascular Center (UNVC) Leiden-Den Haag, Leiden University Medical Center (LUMC) , Leiden ,
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