Machine Learning and Prediction of All-Cause Mortality in COPD
Matthew Moll1, Dandi Qiao2, Elizabeth A Regan3
1Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA.
Chest
|May 1, 2020
Summary
A new machine learning model for COPD mortality prediction (MLMP-COPD) significantly outperformed existing models. This tool improves risk stratification for patients with chronic obstructive pulmonary disease.
Area of Science:
- Pulmonary Medicine
- Data Science
- Medical Informatics
Background:
- Chronic Obstructive Pulmonary Disease (COPD) is a major global cause of death.
- Accurate mortality prediction is crucial for managing COPD patients.
Purpose of the Study:
- To develop and validate a machine learning model (MLMP-COPD) for improved mortality prediction in COPD.
- To compare the MLMP-COPD model against established clinical indices.
Main Methods:
- Utilized a random survival forest with 30 clinical, spirometric, and CT imaging features.
- Developed the MLMP-COPD model using Cox regression on COPDGene data.
- Validated the model in independent COPDGene and ECLIPSE cohorts.
Main Results:
- The MLMP-COPD model achieved a C-index ≥ 0.7 in both cohorts, outperforming BODE and other indices (P < .05).
- Key predictors included 6-min walk distance, FEV1% predicted, age, and pulmonary artery-to-aorta ratio.
- The highest risk group identified by MLMP-COPD had significantly higher mortality (77%) than the highest BODE group (64%).
Conclusions:
- The MLMP-COPD model offers superior all-cause mortality prediction in COPD patients compared to existing tools.
- Machine learning approaches show promise in enhancing COPD prognostication.
- An online tool for the MLMP-COPD model is publicly available.
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