What is the clinical evidence from this study?

Study design: Cross-Sectional. Population: Cardiopulmonary dysfunction-related diseases (n=6000). Intervention: Multidimensional evaluation and AI-driven predictive models. Primary outcome: Establishment of reference ranges, early-warning models, and severity grading standard models for cardiopulmonary function.

July 1, 2025Open Access

Early warning and stratification of the elderly cardiopulmonary dysfunction-related diseases: multicentre prospective study protocol

Q: What does this research mean for the field?

A multicentre clinical imaging database and multimodal artificial intelligence models can be established to provide early warning and stratification of cardiopulmonary dysfunction in the elderly Chinese population. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Resultado clave

A multicentre prospective study will enroll over 6000 elderly participants in China to develop AI-driven early warning and stratification models for cardiopulmonary dysfunction and related diseases.

Diseño del estudio

Tipo

Cross-Sectional (n=6,000)

Multicéntrico

Sí

PICO estructurado

Can multimodal clinical and imaging data integrated with artificial intelligence establish reference ranges and early-warning models for cardiopulmonary dysfunction in elderly adults?

Población

Over 6000 elderly participants (≥ 60 years) from five regions across China (Shanghai, Shenyang, Guangdong, Qingdao, and Ningxia). Participants are categorized into three groups: healthy cardiopulmonary function, functional decrease, and established cardiopulmonary diseases (COPD, coronary syndromes, pulmonary hypertension, and heart failure).

Intervención

Comprehensive multimodal assessments including low-dose chest CT scans, echocardiography, laboratory examinations, and cardiopulmonary exercise testing (CPET), integrated using artificial intelligence to develop early-warning and severity grading models.

Resultado

Establishment of reference ranges for cardiopulmonary function in the elderly population, and development of early-warning models and severity grading standard models for cardiopulmonary function decline and cardiopulmonary diseases.

This multicenter prospective study protocol outlines the development of an AI-integrated clinical imaging database to establish reference ranges and early-warning models for cardiopulmonary dysfunction in elderly Chinese adults.

Limitaciones

The study population primarily consists of older adults from China, and the generalisability of the findings may be limited by ethnic differences.
The planned follow-up period of at least 3 years may be insufficient for assessing the natural progression of certain cardiopulmonary dysfunctions or the long-term effects of interventions.
The complexity of CPET equipment and procedures limits its use in large-scale studies, resulting in a small sample size of only 500 participants.
There may be lost follow-up during the observation process.
Multicentre research may not be completely consistent in terms of machine models.

Resumen

INTRODUCTION: In China, there is a lack of standardised clinical imaging databases for multidimensional evaluation of cardiopulmonary diseases. To address this gap, this study protocol launched a project to build a clinical imaging technology integration and a multicentre database for early warning and stratification of cardiopulmonary dysfunction in the elderly. METHODS AND ANALYSIS: This study employs a cross-sectional design, enrolling over 6000 elderly participants from five regions across China to evaluate cardiopulmonary function and related diseases. Based on clinical criteria, participants are categorized into three groups: a healthy cardiopulmonary function group, a functional decrease group and an established cardiopulmonary diseases group. All subjects will undergo comprehensive assessments including chest CT scans, echocardiography, and laboratory examinations. Additionally, at least 50 subjects will undergo cardiopulmonary exercise testing (CPET). By leveraging artificial intelligence technology, multimodal data will be integrated to establish reference ranges for cardiopulmonary function in the elderly population, as well as to develop early-warning models and severity grading standard models. ETHICS AND DISSEMINATION: The study has been approved by the local ethics committee of Shanghai Changzheng Hospital (approval number: 2022SL069A). All the participants will sign the informed consent. The results will be disseminated through peer-reviewed publications and conferences.

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