Multi-Omic Admission-Based Prognostic Biomarkers Identified by Machine Learning Algorithms Predict Patient Recovery and 30-Day Survival in Trauma Patients

Sultan S. Abdelhamid, Jacob Scioscia, Yoram Vodovotz, Junru Wu, Anna Rosengart, Eunseo Sung, Syed Rahman, Robert Voinchet, Jillian Bonaroti, Shimena Li, Jennifer L. Darby, Upendra K. Kar, Matthew D. Neal, Jason Sperry, Jishnu Das*, Timothy R. Billiar*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Admission-based circulating biomarkers for the prediction of outcomes in trauma patients could be useful for clinical decision support. It is unknown which molecular classes of biomolecules can contribute biomarkers to predictive modeling. Here, we analyzed a large multi-omic database of over 8500 markers (proteomics, metabolomics, and lipidomics) to identify prognostic biomarkers in the circulating compartment for adverse outcomes, including mortality and slow recovery, in severely injured trauma patients. Admission plasma samples from patients (n = 129) enrolled in the Prehospital Air Medical Plasma (PAMPer) trial were analyzed using mass spectrometry (metabolomics and lipidomics) and aptamer-based (proteomics) assays. Biomarkers were selected via Least Absolute Shrinkage and Selection Operator (LASSO) regression modeling and machine learning analysis. A combination of five proteins from the proteomic layer was best at discriminating resolvers from non-resolvers from critical illness with an Area Under the Receiver Operating Characteristic curve (AUC) of 0.74, while 26 multi-omic features predicted 30-day survival with an AUC of 0.77. Patients with traumatic brain injury as part of their injury complex had a unique subset of features that predicted 30-day survival. Our findings indicate that multi-omic analyses can identify novel admission-based prognostic biomarkers for outcomes in trauma patients. Unique biomarker discovery also has the potential to provide biologic insights.

Original languageEnglish
Article number774
JournalMetabolites
Volume12
Issue number9
DOIs
StatePublished - Sep 2022
Externally publishedYes

Keywords

  • biomarkers
  • machine learning
  • multi-omics
  • prognostic
  • proteomics
  • trauma

Fingerprint

Dive into the research topics of 'Multi-Omic Admission-Based Prognostic Biomarkers Identified by Machine Learning Algorithms Predict Patient Recovery and 30-Day Survival in Trauma Patients'. Together they form a unique fingerprint.

Cite this