Friedman’s Gradient-Boosting Algorithm Predicts Lactate-Pyruvate Ratio Predicts Trends in Cases of Intracerebral Hemorrhages

Objective The local effects of an intracerebral hemorrhage (ICH) on surrounding brain tissue can be detected bedside using multimodal brain monitoring techniques. The aim of this study is to design a gradient boosting regression model using the R package boostmtree with the ability to predict lactate-pyruvate (L/P) ratio measurements in ICH. Methods We performed a retrospective analysis of 6 spontaneous ICH (sICH) and 6 traumatic ICH (tICH) patients who underwent surgical removal of the clot with microdialysis catheters placed in the perihematomal zone. Predictors of glucose, lactate, pyruvate, age, sex, diagnosis, and operation status were used to design our model. Results In a holdout analysis, the model forecasted L/P ratio trends in a representative in-sample testing set. We anticipate that boostmtree could be applied to designs of similar regression models to analyze trends in other MM features across other types of acute brain injury. Conclusion The model successfully predicted hourly L/P ratios in sICH and tICH cases after the hemorrhage evacuation and displayed significantly better performance than linear models. Our results suggest that boostmtree may be a powerful tool in developing more advanced mathematical models to assess other MM parameters for cases in which the perihematomal environment is monitored.