Prehospital stroke-scale machine-learning model predicts the need for surgical intervention

Abstract

While the development of prehospital diagnosis scales has been reported in various regions, we have also developed a scale to predict stroke type using machine learning. In the present study, we aimed to assess for the first time a scale that predicts the need for surgical intervention across stroke types, including subarachnoid haemorrhage and intracerebral haemorrhage. A multicentre retrospective study was conducted within a secondary medical care area. Twenty-three items, including vitals and neurological symptoms, were analysed in adult patients suspected of having a stroke by paramedics. The primary outcome was a binary classification model for predicting surgical intervention based on eXtreme Gradient Boosting (XGBoost). Of the 1143 patients enrolled, 765 (70%) were used as the training cohort, and 378 (30%) were used as the test cohort. The XGBoost model predicted stroke requiring surgical intervention with high accuracy in the test cohort, with an area under the receiver operating characteristic curve of 0.802 (sensitivity 0.748, specificity 0.853). We found that simple survey items, such as the level of consciousness, vital signs, sudden headache, and speech abnormalities were the most significant variables for accurate prediction. This algorithm can be useful for prehospital stroke management, which is crucial for better patient outcomes.

Figure 1

Area under the receiver operating characteristic curves of machine learning models. The receiver operating characteristic curve of prehospital prediction algorithms for stroke requiring surgical intervention is depicted with 1-specificity on the x-axis and sensitivity on the y-axis using the training cohort (a) and the test cohort (b). The 95% confidence interval of the AUROC is also shown. AUROC area under the receiver operating characteristic curve.

Figure 2

SHAP value of stroke surgical intervention. The impact of the features on the model output was expressed as the SHAP value. The features are placed in descending order according to their importance. The association between the feature value and SHAP value indicates a positive or negative impact of the predictors. The extent of the value is depicted as red (high) or blue (low) plots. SHAP SHapley Additive exPlanation.

Figure 3

Feature importance. The impact of the features on the model output is expressed as the average of the absolute SHAP value. The larger the value, the more important is the feature for predicting stroke surgical intervention. SHAP SHapley Additive exPlanation.