Key result
Severe cardiac dysfunction phenotype linked to ~3.2-fold higher risk of adverse clinical trajectories in HFpEF.
Why the study?
Unrecognized HFpEF frequently complicates hypercapnic COPD, and overlapping symptomatology poses significant diagnostic challenges that can delay intervention.
Does interpretable machine learning and unsupervised consensus clustering accurately identify concurrent HFpEF and stratify prognostic risk in patients with hypercapnic COPD?
Observational (n=441)
No
Does interpretable machine learning and unsupervised consensus clustering accurately identify concurrent HFpEF and stratify prognostic risk in patients with hypercapnic COPD?
In patients with hypercapnic COPD, an interpretable Random Forest model accurately screens for concurrent HFpEF (test AUC 0.940), while unsupervised clustering identifies a severe cardiac dysfunction phenotype with markedly elevated risk of adverse clinical trajectories (HR 3.20).
Answers are grounded in this paper and cite their sources.
May aid HFpEF screening in hypercapnic COPD; hypothesis-generating for severe-phenotype risk stratification pending validation.
Background Unrecognized heart failure with preserved ejection fraction (HFpEF) frequently complicates hypercapnic chronic obstructive pulmonary disease (COPD). The overlapping symptomatology poses significant diagnostic challenges, potentially leading to delayed interventions. Methods Clinical and echocardiographic parameters from 441 patients with hypercapnic COPD were retrospectively analyzed. Five machine learning algorithms were trained and evaluated to identify concurrent HFpEF. SHapley Additive exPlanations (SHAP) analysis was applied to assess mathematical feature associations rather than causal relationships. Additionally, unsupervised consensus clustering was utilized for clinical phenotypic stratification. Results The Random Forest model demonstrated the highest discrimination capability [(test AUC=0.940, 95% CI: 0.898–0.982; 10-times repeated 5-fold cross-validation mean AUC = 0.928 ± 0.019)]. SHAP analysis suggested complex non-linear associations, indicating a potential synergistic interaction between elevated PaCO2 and body mass index in being associated with an elevated predicted HFpEF risk. Furthermore, consensus clustering identified three distinct clinical sub-phenotypes. Subsequent survival analyses indicated that the severe cardiac dysfunction phenotype, characterized by markedly elevated NT-proBNP and substantial hypercapnia, was independently associated with a higher risk of adverse clinical trajectories (Hazard Ratio=3.20, P < 0.001). Conclusion In this exploratory single-center study, interpretable machine learning algorithms demonstrated potential for assisting in the screening of concurrent HFpEF among hypercapnic COPD patients. While integrating SHAP-derived statistical associations and unsupervised phenotyping offers hypothesis-generating insights into cardiopulmonary risk stratification, rigorous external validation in multicenter prospective cohorts is required before clinical implementation.
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Deng et al. (2026) conducted an observational in Hypercapnic chronic obstructive pulmonary disease (COPD) with or without concurrent heart failure with preserved ejection fraction (HFpEF) (n=441). Severe cardiac dysfunction phenotype (Phenotype 3: markedly elevated NT-proBNP and substantial hypercapnia) and Random Forest machine learning model vs. Milder hypercapnia/lowest NT-proBNP phenotype (Phenotype 1) and traditional Logistic Regression was evaluated on Adverse clinical trajectories (event-free survival) across unsupervised clinical phenotypes (HR 3.20, p=<0.001). Random Forest identified concurrent HFpEF with a test AUC of 0.940, while the severe cardiac dysfunction phenotype independently predicted adverse clinical trajectories (HR 3.20, P < 0.001).
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