Prehospital stroke-scale machine-learning model predicts the need for surgical intervention.
Scientific Reports · 2023-06-05 · Method & validation
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.
Abstract © the authors or publisher, via PubMed. Reuse and removal
Tags
Decision support tools · Mortality & survival · Paramedics & EMTs · Triage instruments
Related articles
- Clinical Prediction Rules to Classify Types of Stroke at Prehospital Stage. (Stroke, 2018)
- Real-World Field Performance of the Los Angeles Motor Scale as a Large Vessel Occlusion Screen: A Prospective Muticentre Study. (Cerebrovasc Dis, 2021)
- Accuracy of Stroke Identification by Paramedics in a Metropolitan Prehospital Setting: A Cohort Study (Paramedicine, 2017)
- Prehospital-Stroke-Scale Parameterized Hospital Selection Protocol for Suspected Stroke Patients Considering Door-to-Treatment Durations. (J Am Heart Assoc, 2022)
- Large Vessel Occlusion Scales Increase Delivery to Endovascular Centers Without Excessive Harm From Misclassifications. (Stroke, 2017)
- Methodology of the Field Administration of Stroke Therapy - Magnesium (FAST-MAG) phase 3 trial: Part 1 - rationale and general methods. (Int J Stroke, 2014)