# Predicting Ischemic Stroke Patients to Transfer for Endovascular Thrombectomy Using Machine Learning: A Case Study

## TL;DR

This study explores using machine learning to better select ischemic stroke patients for transfer to hospitals offering endovascular thrombectomy, aiming to reduce unnecessary transfers.

## Contribution

The novel contribution is applying machine learning models to predict suitable patients for EVT transfer, potentially improving decision-making in stroke care.

## Key findings

- Random forest and decision tree models achieved the lowest futile transfer rates of 0% and 18.9%, respectively.
- The study highlights the need for larger datasets to validate and generalize machine learning models for EVT patient selection.
- Ensemble methods did not outperform individual models in terms of accuracy or futile transfer rates.

## Abstract

Introduction: Endovascular thrombectomy (EVT) is highly effective for ischemic stroke patients with a large vessel occlusion. EVT is typically only offered at urban hospitals; therefore, patients are transferred for EVT from hospitals that solely offer thrombolysis. There is uncertainly around patient selection for transfer, which results in a large number of futile transfers. Machine learning (ML) may be able to provide a model that better predicts patients to transfer for EVT. Objective: The objective of the study is to determine if ML can provide decision support to more accurately select patients to transfer for EVT. Methods: This is a retrospective study. Data from Nova Scotia, Canada from 1 January 2018 to 31 December 2022 was used. Four supervised binary classification ML algorithms were applied, as follows: logistic regression, decision tree, random forest, and support vector machine. We also applied an ensemble method using the results of these four classification algorithms. The data was split into 80% training and 20% testing, and five-fold cross-validation was employed. Missing data was accounted for by the k-nearest neighbour’s algorithm. Model performance was assessed using accuracy, the futile transfer rate, and the false negative rate. Results: A total of 5156 ischemic stroke patients were identified during the time period. After exclusions, a final dataset of 93 patients was obtained. The accuracy of logistic regression, decision tree, random forest, support vector machine, and ensemble models was 68%, 79%, 74%, 63%, and 68%, respectively. The futile transfer rate with random forest and decision tree was 0% and 18.9%, respectively, and the false negative rate was 5.37 and 4.3%, respectively Conclusions: ML models can potentially reduce futile transfer rates, but future studies with larger datasets are needed to validate this finding and generalize it to other systems.