Machine learning reveals chronic graft-versus-host disease phenotypes and stratifies survival after stem cell transplant for hematologic malignancies
AUTHORS
- PMID: 30237265 [PubMed].
ABSTRACT
The application of machine learning in medicine has been productive in multiple fields, and has not previously been applied to analyze the complexity of chronic graft-versus-host disease organ involvement. Chronic graft-versus-host disease is classified by an overall composite score of mild, moderate or severe, which may overlook clinically relevant patterns in organ involvement. Here we applied a novel computational approach to chronic graft-versus-host disease with the goal of identifying phenotypic groups based on the subcomponents of the National Institutes of Health (NIH) Consensus Criteria. Computational analysis revealed 7 distinct patient groups with contrasting clinical risks. The high-risk group had an inferior overall survival compared to the low-risk group (HR 2.24 [95% CI 1.36-3.68]), and was independent of severity as measured by the NIH criteria. To test clinical applicability, knowledge was translated into a simplified clinical prognostic decision tree. Groups identified by the decision tree also stratified outcomes and closely matched those from the original analysis. Patients in the high and intermediate-risk decision tree groups had significantly shorter overall survival than those in the low-risk group (HR 2.79 [1.58-4.91] and HR 1.78 [1.06-3.01], respectively). Machine learning and other computational analyses may better reveal biomarkers and stratify risk than the current approach based on cumulative severity. This approach could now be explored in other disease models with complex clinical phenotypes. External validation must be completed prior to clinical application. Ultimately, this approach has the potential to reveal distinct pathophysiological mechanisms that may underlie clusters. Clinicaltrials.gov identifier: NCT00637689.
Tags: Publications, Student Publications