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
۱۲, 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.

Introduction

Survivors of intracerebral hemorrhage (ICH), the most morbid form of stroke, often require gastrostomy, a percutaneous feeding tube in the abdomen to provide nutrition.1 Reliably predicting gastrostomy after ICH is important because gastrostomy placement predicts the need for future healthcare services and patient dependence at follow-up, and unexplained racial disparities in gastrostomy have been noted.2 Like outcomes for ICH generally,3,4 ordinal predictive scores for gastrostomy have been validated,5,6 including variables that measure the severity of neurologic injury and other established risk factors. The GRAVo score is composed of categorical variables (age over 50 years, black race, Glasgow Coma Scale (GCS) 12 or less, and hematoma volume more than 30 mL), with higher GRAVo scores predicting increased odds of the patient undergoing gastrostomy in a logistic regression model.6 Other prediction models of gastrostomy after ICH have identified similar predictors.7 Predicting outcomes, including gastrostomy, with ordinal scores, however, has suboptimal accuracy. The GRAVo score may not distinguish between components that sum to the same score, but are different (eg, a black patient over 50 years may have the same score as a patient with reduced GCS and a large hematoma, but their outcomes may be different). Therefore, prediction methods other than regression are needed that may more accurately predict the likelihood of a patient undergoing gastrostomy after ICH. Techniques that improve prediction of gastrostomy may be broadly applicable to other procedures, and other diseases. Machine learning8 refers to a collection of techniques intended to predict a result from data; regression is the most commonly utilized technique in clinical medicine, typically using ordinal scales.9,10 Several machine learning techniques inherently account for nonlinear predictions, such as proximity-based methods (eg, kth nearest neighbors, which predicts a classification based on similar patients) and decision-tree based methods (eg, random forests, which “grows” decision trees and identifies the most significant class). Machine learning techniques have been utilized to predict cardiovascular events in asymptomatic patients11 and arteriovenous malformations,12,13 but have not been utilized after ICH. We sought to derive and validate machine learning techniques other than logistic regression to predict gastrostomy (the technique used for GRAVo score development and validation).6 In this study, we tested the hypothesis that machine learning techniques improve the accuracy of gastrostomy prediction derived from traditional logistic regression in patients with ICH using the components of the GRAVo score as independent variables.