Abstract Background Stroke is a leading cause of death worldwide and leaves most survivors with permanent disability. 12.2 million people suffer strokes annually, of which 70% occur in low- and middle-income countries (LMIC). Stroke presents diagnostic challenges which limits access to effective, but time-sensitive, reperfusion therapies. Currently, stroke is diagnosed by the combination of clinical symptoms and signs and brain imaging. The aim of this study was to develop clinical prediction tools, for differentiating acute ischemic stroke (AIS) and intracerebral haemorrhage (ICH) in the pre-hospital setting without neuroimaging, utilising machine learning (ML). Methods We included 5349 patients with confirmed acute stroke who presented within 6 hours of symptom onset, from the INTERSTROKE case-control study. We compared predictions using six commonly used ML algorithms for classification i.e. multivariate logistic regression (MLR), k-nearest neighbour (KNN), support vector machine (SVM), neural networks (NN), random forests (RF), XGBoost. Algorithm inputs included medical history, symptoms and signs, and point-of-care blood tests, with an output of final diagnosis. Results The training cohort included 4280 patients with 3084 ischemic stroke and 1196 ICH cases, and the test cohort included 1069 patients with 771 ischemic stroke and 298 ICH cases. From 174 potential variables, we used univariate analyses to select 52 candidate variables for inclusion in the ML algorithms. These were selected based on their ability to predict (p < 0.20) stroke subtype. The AUCs in the test cohort for each of the ML classification models were: MLR 0.819, KNN 0.795, SVM 0.802, NN 0.814, RF 0.814, and XGBoost 0.807. Conclusion ML models showed great potential to predict stroke type at the prehospital stage and have comparable predictive abilities to previously developed ML prediction tools. This may facilitate point-of-care decisions and management of stroke in the pre-hospital setting in the future.
Read full abstract