Prognostic biomarkers of intracerebral hemorrhage identified using targeted proteomics and machine learning algorithms

Early prognostication of patient outcomes in intracerebral hemorrhage (ICH) is critical for patient care. We aim to investigate protein biomarkers role in prognosticating outcomes in ICH patients. We assessed 22 protein biomarkers using targeted proteomics in serum samples obtained from the ICH patient dataset (N=150). We defined poor outcomes as modified Rankin scale score of 3-6. We incorporated clinical variables and protein biomarkers in regression models and random forest-based machine learning algorithms to predict poor outcomes and mortality. We report Odds Ratio (OR) or Hazard Ratio (HR) with 95% Confidence Interval (CI). We used five-fold cross-validation and bootstrapping for internal validation of prediction models. We included 149 patients for 90-day and 144 patients with ICH for 180-day outcome analyses. In multivariable logistic regression, UCH-L1 (aOR 9.23; 95%CI 2.41-35.33), alpha-2-macroglobulin (5.57; 1.26-24.59), and Serpin-A11 (9.33; 1.09-79.94) were independent predictors of 90-day poor outcome; MMP-2 (6.32; 1.82-21.90) was independent predictor of 180-day poor outcome. In multivariable Cox regression models, IGFBP-3 (aHR 2.08; 1.24-3.48) predicted 90-day and MMP-9 (1.98; 1.19-3.32) predicted 180-day mortality. Using machine learning, UCH-L1 and APO-C1 predicted 90-day mortality, and UCH-L1, MMP-9, and MMP-2 predicted 180-day mortality. Overall, random forest models outperformed regression models for predicting 180-day poor outcomes (AUC 0.89), and 90-day (AUC 0.81) and 180-day mortality (AUC 0.81). Serum biomarkers independently predicted short-term poor outcomes and mortality after ICH. Further research utilizing a multiomics platform and temporal profiling is needed to explore additional biomarkers and refine predictive models for ICH prognosis.