Improving the Accuracy of Scores to Predict Gastrostomy after Intracerebral Hemorrhage with Machine Learning

Abstract

Background: Gastrostomy placement after intracerebral hemorrhage indicates the need for continued medical care and predicts patient dependence. Our objective was to determine the optimal machine learning technique to predict gastrostomy.

Methods: We included 531 patients in a derivation cohort and 189 patients from another institution for testing. We derived and tested predictions of the likelihood of gastrostomy placement with logistic regression using the GRAVo score (composed of Glasgow Coma Scale ≤12, age >50 years, black race, and hematoma volume >30 mL), compared to other machine learning techniques (kth nearest neighbor, support vector machines, random forests, extreme gradient boosting, gradient boosting machine, stacking). Receiver Operating Curves (Area Under the Curve, [AUC]) between logistic regression (the technique used in GRAVo score development) and other machine learning techniques were compared. Another institution provided an external test data set.

Results: In the external test data set, logistic regression using the GRAVo score components predicted gastrostomy (P < 0.001), however, with a lower AUC (0.66) than kth nearest neighbors (AUC 0.73), random forests (AUC 0.74), Gradient boosting machine (AUC 0.77), extreme gradient boosting (AUC 0.77), (P < 0.01 for all compared to logistic regression). Results from the internal test set were similar.

Conclusions: Machine learning techniques other than logistic regression (eg, random forests, extreme gradient boost, and kth nearest neighbors) were significantly more accurate for predicting gastrostomy using the same independent variables. Machine learning techniques may assist clinicians in identifying patients likely to need interventions.

Keywords: Intracerebral hemorrhage—gastrostomy—outcomes—machine learning.