Abstract: Background and Objective: Length of stay is an important factor for managing the limited resources of a hospital. The early, accurate prediction of hospital length of stay leads to optimized disposition of resources, particularly in complex stroke treatment. In the present study, we evaluated different machine learning techniques in their ability to predict the length of stay of patients with stroke of the anterior circulation who were treated with thrombectomy. Materials and Methods: This retrospective study evaluated four algorithms (support vector machine, generalized linear model, K-nearest neighbor, and random forest) to predict the length of hospitalization of 113 patients with acute stroke who were treated with thrombectomy. Input variables encompassed baseline data at admission, as well as peri-procedural and imaging data. Tenfold cross-validation was used to estimate accuracy. The accuracy of the algorithms was checked with a validation dataset. In addition to a regression analysis, we performed a binary classification analysis to identify patients who stayed longer than the mean length of stay. Results: The median length of stay was 10.6 days (interquartile range 6–15). The sensitivity of the best performing random forest model was 0.8, the specificity was 0.68, and the area under the curve was 0.73 in the classification analysis. The mean absolute error of the best performing random forest model was 4.6 days in the validation dataset in the regression analysis. Conclusion: The length of stay of patients with acute ischemic stroke of the anterior circulation can be predicted with machine learning. Machine learning could aid in optimal resource allocation in clinical routine.
Feyen et al. (2026) studied this question.