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

• Published in: PloS one Vol. 19; no. 6; p. e0296616
• Main Authors: Misra, Shubham, Kawamura, Yuki, Singh, Praveen, Sengupta, Shantanu, Nath, Manabesh, Rahman, Zuhaibur, Kumar, Pradeep, Kumar, Amit, Aggarwal, Praveen, Srivastava, Achal K., Pandit, Awadh K., Mohania, Dheeraj, Prasad, Kameshwar, Mishra, Nishant K., Vibha, Deepti
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 03.06.2024; Public Library of Science (PLoS)
• Subjects: Acids; Aged; Algorithms; Analysis; Biological markers; Biology and Life Sciences; Biomarkers; Biomarkers - blood; Cerebral Hemorrhage - blood; Cerebral Hemorrhage - diagnosis; Cerebral Hemorrhage - mortality; Computer and Information Sciences; Confidence intervals; Data mining; Female; Gelatinase A; Gelatinase B; Haptoglobin; Health aspects; Health care access; Hemorrhage; Humans; India; Insulin-like growth factor-binding protein 3; Intracerebral hemorrhage; Learning algorithms; Machine Learning; Male; Medical prognosis; Medical research; Medicine and Health Sciences; Medicine, Experimental; Middle Aged; Mortality; Peptides; Physical Sciences; Prediction models; Predictions; Prognosis; Protein gene product 9.5; Proteins; Proteomics; Proteomics - methods; Regression analysis; Regression models; Research and Analysis Methods; Statistical analysis; Stroke; Variables; α2-Macroglobulin; India
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0296616

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 (adjusted OR 9.23; 95%CI 2.41–35.33), alpha-2-macroglobulin (aOR 5.57; 95%CI 1.26–24.59), and Serpin-A11 (aOR 9.33; 95%CI 1.09–79.94) were independent predictors of 90-day poor outcome; MMP-2 (aOR 6.32; 95%CI 1.82–21.90) was independent predictor of 180-day poor outcome. In multivariable Cox regression models, IGFBP-3 (aHR 2.08; 95%CI 1.24–3.48) predicted 90-day and MMP-9 (aOR 1.98; 95%CI 1.19–3.32) predicted 180-day mortality. Machine learning identified additional predictors, including haptoglobin for poor outcomes and UCH-L1, APO-C1, and MMP-2 for mortality prediction. 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 multi-omics platform and temporal profiling is needed to explore additional biomarkers and refine predictive models for ICH prognosis.