Prehospital Stroke-Scale Machine-Learning Model Predicts Need for Surgery in Scientific Reports

A new machine-learning model has been developed to predict the need for surgical intervention for stroke patients. The model, developed by researchers from a secondary medical care area, was created using data obtained from vitals and neurological symptoms of adult stroke patients suspected by paramedics. The results showed that the algorithm was able to predict surgery with high accuracy, with an area under the receiver operating characteristic curve of 0.802. The researchers found that simple survey items such as vital signs and sudden headaches were the most significant factors for accurate predictions. The importance of pre-hospital stroke management has become increasingly critical to improve patient outcomes. The development of prehospital diagnosis scales has been regularly reported, and machine learning has been shown to improve pre-hospital diagnosis scales. The American Stroke Association has also provided guidelines and recommendations for recognizing strokes accurately and activating emergency medical services (EMS) to make sure patients are transported to the right medical facility. However, prehospital diagnosis of stroke can be complex due to the similarity of symptoms between different types of strokes, potentially causing delays in hospital arrival or misdiagnosis. This machine-learning algorithm can greatly benefit prehospital stroke management by allowing EMS personnel to diagnose patients with consistent accuracy, improving patient outcomes by enabling appropriate and timely transport of patients requiring stroke surgical treatment.