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Published on: March 17, 2020
Machine learning reveals chronic graft-versus-host disease phenotypes and stratifies survival after stem cell
Jocelyn S Gandelman1,2,3,4, Michael T Byrne1, Akshitkumar M Mistry3,5
1Department of Medicine, Division of Hematology/Oncology, Vanderbilt University Medical Center, Nashville, TN.
Insights
Machine learning identified seven patient groups in chronic graft-versus-host disease, revealing distinct clinical risks and survival outcomes independent of current severity scores. This computational approach offers improved risk stratification for better patient management.
Area of Science:
- Computational biology
- Medical informatics
- Oncology
Background:
- Chronic graft-versus-host disease (cGVHD) classification by composite scores may miss nuanced organ involvement patterns.
- Machine learning (ML) has not been previously applied to analyze cGVHD complexity.
Purpose of the Study:
- To apply a novel computational approach to identify phenotypic groups in cGVHD based on NIH Consensus Criteria subcomponents.
- To develop a clinical prognostic decision tree for cGVHD risk stratification.
Main Methods:
- Applied ML to analyze cGVHD patient data using NIH Consensus Criteria subcomponents.
- Developed a simplified clinical prognostic decision tree based on computational findings.
Main Results:
- Identified seven distinct patient groups with contrasting clinical risks.
- High-risk group showed significantly inferior overall survival (HR 2.24) independent of NIH severity.
- Decision tree groups also stratified outcomes, with high- and intermediate-risk groups having shorter survival.
Conclusions:
- ML and computational analyses can reveal biomarkers and stratify risk more effectively than current cumulative severity methods for cGVHD.
- This approach has potential for identifying distinct pathophysiological mechanisms and can be explored in other complex diseases.
- External validation is required before clinical application.
Abstract:
The application of machine learning in medicine has been productive in multiple fields, but has not previously been applied to analyze the complexity of organ involvement by chronic graft-versus-host disease. Chronic graft-versus-host disease is classified by an overall composite score as 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 Consensus Criteria. Computational analysis revealed seven distinct groups of patients with contrasting clinical risks. The high-risk group had an inferior overall survival compared to the low-risk group (hazard ratio 2.24; 95% confidence interval: 1.36-3.68), an effect that was independent of graft-versus-host disease severity as measured by the National Institutes of Health 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 (hazard ratio 2.79; 95% confidence interval: 1.58-4.91 and hazard ratio 1.78; 95% confidence interval: 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.
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