ArticleChest2020
Machine Learning and Prediction of All-Cause Mortality in COPD.
Article in Chest, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 63 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
63 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of artificial intelligence in the health management of chronic disease: bibliometric analysis.Frontiers in medicine · 2024Pooled it
- Translating machine learning predictions into meaningful risk estimates to support clinical decisions: a post hoc analysis of chronic obstructive pulmonary disease adverse outcomes using unified auto clinical scores.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Causality-Guided Machine Learning for Retinoblastoma Survival Prediction: Development and Comparative Evaluation Using SEER.Medical sciences (Basel, Switzerland) · 2026Article
- Objective dyspnea measurement in real time using a forehead wearable and artificial intelligence.NPJ primary care respiratory medicine · 2026Article
- Wearable Sleep Measures May Improve Machine Learning Prediction of Home-Based Pulmonary Rehabilitation Engagement Among Patients With Chronic Obstructive Pulmonary Disease: A Proof-of-Concept Study.Mayo Clinic proceedings. Digital health · 2026Article
- Predictive models for mortality readmission events and cardiovascular complications in patients with COPD: a systematic review and meta-analysis.ERJ open research · 2026Article
- The vascular fingerprint of the lungs: why pulmonary vessel count matters in COPD.The European respiratory journal · 2026Article
- Integrative multi-omic analyses identify major axes of heterogeneity in chronic obstructive pulmonary disease and uncover their molecular contributors.medRxiv : the preprint server for health sciences · 2026Article
- Predicting the frequent exacerbator phenotype in COPD: development and validation of a multicenter real-world prediction model.BMC medical informatics and decision making · 2025Article
- Risk prediction for acute kidney disease and adverse outcomes in patients with chronic obstructive pulmonary disease: an interpretable machine learning approach.Renal failure · 2025Article
- Artificial Intelligence for the Identification of Vascular Imaging Biomarkers in COPD: Redefining Phenotypes and Enabling Precision Care.Journal of clinical medicine · 2025Article
- Machine learning assisted immune profiling of COPD identifies a unique emphysema subtype independent of GOLD stage.iScience · 2025Article
- Oxidative Stress and Inflammation in Hypoxemic Respiratory Diseases and Their Comorbidities: Molecular Insights and Diagnostic Advances in Chronic Obstructive Pulmonary Disease and Sleep Apnea.Antioxidants (Basel, Switzerland) · 2025Review
- Association between life's crucial 9 and lung health: a population-based study.BMC pulmonary medicine · 2025Article
- Machine learning in solid organ transplantation: Charting the evolving landscape.World journal of transplantation · 2025Article
- Comparison of Predictive Models for Keloid Recurrence Based on Machine Learning.Journal of cosmetic dermatology · 2025Article
- Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning.Scientific reports · 2025Article
- CT-derived functional imaging biomarkers combined with FEV1 for predicting 10-year all-cause mortality in COPDGene cohort.npj biomedical innovations · 2025Article
- Machine Learning for Enhanced COPD Diagnosis: A Comparative Analysis of Classification Algorithms.Diagnostics (Basel, Switzerland) · 2024Article
- Neural network based estimates of the climate impact on mortality in Germany: application to storyline climate simulations.Scientific reports · 2024Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
- Erratum issued
Authors and funding
22 authors.
Funding
Abstract
backgroundCOPD is a leading cause of mortality. RESEARCH QUESTION: We hypothesized that applying machine learning to clinical and quantitative CT imaging features would improve mortality prediction in COPD. STUDY DESIGN AND
methodsWe selected 30 clinical, spirometric, and imaging features as inputs for a random survival forest. We used top features in a Cox regression to create a machine learning mortality prediction (MLMP) in COPD model and also assessed the performance of other statistical and machine learning models. We trained the models in subjects with moderate to severe COPD from a subset of subjects in Genetic Epidemiology of COPD (COPDGene) and tested prediction performance in the remainder of individuals with moderate to severe COPD in COPDGene and Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE). We compared our model with the BMI, airflow obstruction, dyspnea, exercise capacity (BODE) index; BODE modifications; and the age, dyspnea, and airflow obstruction index.
resultsWe included 2,632 participants from COPDGene and 1,268 participants from ECLIPSE. The top predictors of mortality were 6-min walk distance, FEV
interpretationAn MLMP-COPD model outperformed four existing models for predicting all-cause mortality across two COPD cohorts. Performance of machine learning was similar to that of traditional statistical methods. The model is available online at: https://cdnm.shinyapps.io/cgmortalityapp/.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.