What is the clinical evidence from this study?

Study design: Cohort. Population: Pre-cardiopulmonary dysfunction (n=2370). Intervention: Pre-CPD Index (simplified three-feature model) vs. Existing clinical concepts. Primary outcome: Predictive accuracy in the cardiopulmonary event-driven test set.

What does this research mean for the field?

A simplified three-feature Pre-Cardiopulmonary Dysfunction (Pre-CPD) index model, comprising MEF25 %pred, FEV1/FVC, and ejection fraction, achieves 89.03% predictive accuracy for cardiopulmonary events in asymptomatic individuals, outperforming existing clinical concepts. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to identify pre-cardiopulmonary dysfunction in individuals without existing cardiopulmonary diseases.

May 20, 2026

B110-05 Early Risk Stratification of Pre-Cardiopulmonary Dysfunction Using Functional Indicators and Unsupervised Clustering: A Retrospective Cohort Study

Key Result

A simplified three-feature Pre-CPD index model (MEF25 %pred, FEV1/FVC, and EF) achieved 89.03% predictive accuracy for cardiopulmonary events, outperforming existing clinical concepts.

Key Points

The aim is to identify pre-cardiopulmonary dysfunction in individuals without existing cardiopulmonary diseases.
Retrospective cohort with 2370 individuals without baseline cardiopulmonary disease.
Utilized constrained k-means clustering on 3080 feature combinations.
Developed predictive models using logistic regression and validated in an event-driven test set.
Identified four cardiopulmonary phenotypes with varying event rates: preserved function (1.5%), high lung volume (2.49%), isolated dysfunction (3.32%), and combined impairment (5.69%).
The simplified three-feature predictive model achieved an accuracy of 89.03% in the test set, outperforming existing methods.

Study Design

Type

Cohort (n=2,370)

Structured PICO

Does a machine learning-derived Pre-Cardiopulmonary Dysfunction (Pre-CPD) index accurately predict cardiopulmonary events in asymptomatic individuals without baseline disease?

Population

2,370 individuals without baseline cardiopulmonary disease from the Xiangya Cardiopulmonary Health and Disease Cohort (XY-CPHDC)

Intervention

Pre-Cardiopulmonary Dysfunction (Pre-CPD) Index, a machine learning-derived model using three features (MEF25 %pred, FEV1/FVC, and EF)

Comparator

Existing clinical concepts

Outcome

Predictive accuracy for cardiopulmonary events in a test set

A machine learning-derived Pre-Cardiopulmonary Dysfunction (Pre-CPD) index using three functional features accurately identifies high-risk asymptomatic individuals, offering a potential tool for early prevention.

Abstract

Abstract Background Cardiovascular and respiratory diseases are leading global causes of death worldwide, and functional decline in these systems often begins decades before clinical diagnosis. Thus, intervention during this subclinical stage is critical. However, existing early identification concepts are largely limited to single organ/system, overlooking the interaction and frequent co-impairment of cardiopulmonary function. Therefore, there is an urgent need to characterize pre-cardiopulmonary dysfunction (Pre-CPD). Methods This study utilized unsupervised clustering and supervised learning models on 2370 individuals without baseline cardiopulmonary disease from the Xiangya Cardiopulmonary Health and Disease Cohort (XY-CPHDC). Constrained k-means clustering was applied to 3,080 combinations of features across four modules (pulmonary function, echocardiography, electrocardiogram, and serum lipids) to identify clinically heterogeneous phenotypes. Cluster labels were used as supervised targets to build logistic regression models of varying complexity, thereby developing the Pre-CPD Index. The model was validated in a cardiopulmonary event-driven test set (n = 237) and compared with existing clinical concepts. Findings Among 2,133 participants in the train and validation sets, systematic evaluation of multidimensional physiological indices identified an optimal feature set comprising seven key indicators and revealed four heterogeneous cardiopulmonary phenotypes:Cluster 1:Preserved cardiopulmonary function (event rate 1.5%);Cluster 2: High lung volume with preserved cardiac function (event rate 2.49%);Cluster 3: Isolated small airway dysfunction with compensatory cardiac enhancement (event rate 3.32%);Cluster 4: Combined cardiopulmonary impairment (event rate 5.69%).Cluster 4 showed the lowest overall functional metrics (e.g., EF 60.0%, FVC %pred 102.1%), whereas Cluster 3 demonstrated markedly reduced small airway function (MEF25 %pred 63.3%) but elevated cardiac performance (EF 70.0%). Based on these phenotypes, multiple Pre-CPD index models were developed and evaluated. The simplified three-feature model, consisting of MEF25 %pred, FEV1/FVC, and EF, achieved the highest predictive accuracy (89.03%) in the test set (n = 237), substantially outperforming existing clinical concepts. Interpretation We build a concise and quantitative tool, pre-CPD index, to quantify high-risk asymptomatic individuals with cardiopulmonary function deline through machine learning approach, which has the potention to be utilized for early prevention and precision intervention in cardiopulmonary diseases. This abstract is funded by: This work was supported by the National Key Research and Development Program (No. 2022YFC3601001) and National Multidisciplinary Cooperative Diagnosis and Treatment Capacity Building Project for Major Diseases (Lung Cancer) (No. z027002)

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