Evidence map›Paper›PMID 42191200›Full record

ArticleBMJ open2026

Multicentre prospective cohort study to develop and validate a machine learning-based model for predicting 6-month all-cause mortality in elderly patients with advanced chronic obstructive pulmonary disease in China: study protocol.

Hongshan Pu, Liheng Liu, Yiting Chang, Lin Su, Xiaofeng Zeng, Wenwu Cheng, Yan Jiang, Jinhan He, Li Mo

Abstract readMulticenter StudyValidation Study
In one paragraph

Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Hongshan Pu *The Center of Gerontology and Geriatrics, National Clinical Research Center of Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China.ORCID http://orcid.org/0000-0001-9336-5006
Liheng Liu *The Center of Gerontology and Geriatrics, National Clinical Research Center of Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Yiting ChangThe Center of Gerontology and Geriatrics, National Clinical Research Center of Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Lin SuThe Center of Gerontology and Geriatrics, National Clinical Research Center of Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Xiaofeng ZengThe Center of Gerontology and Geriatrics, National Clinical Research Center of Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Wenwu ChengDepartment of Integrated Therapy, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China.
Yan JiangEvidence-Based Nursing Research Laboratory, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, Sichuan, China.ORCID http://orcid.org/0000-0002-9813-3738
Jinhan HeDepartment of Pharmacy, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
Li MoThe Center of Gerontology and Geriatrics, National Clinical Research Center of Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China molleyhe@hotmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionChronic obstructive pulmonary disease (COPD) has an unpredictable clinical course, causing difficulties in short-term mortality prediction, overtreatment and delayed palliative care. Existing prediction models are limited and lack applicability to Chinese elderly patients with advanced COPD. Given the heavy disease burden and limited palliative care in China, we designed this multicentre cohort study to develop a 6-month mortality prediction model for elderly patients with advanced COPD to aid risk stratification, timely palliative care and efficient healthcare resource allocation. METHODS AND ANALYSIS: Patient recruitment has been ongoing since May 2024 and will be completed by December 2026, with a 12-month follow-up to be completed by December 2027. Eligible patients are being enrolled, and multidimensional baseline data including demographic characteristics, clinical indicators, laboratory results, comprehensive geriatric assessment and COPD-specific prognostic factors are being systematically collected. All participants will receive 12 months of standardised follow-up (monthly for the first 6 months and quarterly thereafter) to monitor 6-month all-cause mortality (primary outcome), as well as survival duration, end-of-life healthcare utilisation and do-not-resuscitate status (secondary outcomes). After completion of data collection, we will employ multiple machine learning algorithms to develop and internally validate a 6-month mortality prediction model with pre-specified centres reserved for external validation. Model performance will be evaluated by discrimination and calibration and head-to-head comparisons with the Body Mass Index, Airflow Obstruction, Dyspnoea and Exercise Capacity (BODE) and Age, Dyspnoea and Airflow Obstruction (ADO) indices will be conducted to verify its clinical value. The findings will provide a China-specific prediction tool for elderly patients with advanced COPD to guide clinical intervention, palliative care referral and healthcare resource allocation. ETHICS AND DISSEMINATION: This study was approved by the Biomedical Ethics Review Committee of West China Hospital, Sichuan University (No. 2024-2662) and registered at ChiCTR2500100351. Informed consent is being obtained from all participants. Results will be published in peer-reviewed journals and presented at academic conferences. TRIAL REGISTRATION NUMBER: ChiCTR2500100351.

Indexed as

Machine LearningPulmonary Disease, Chronic ObstructiveAgedAged, 80 and overChinaFemaleGeriatric AssessmentHumansMalePalliative CarePredictive Learning ModelsPrognosisProspective StudiesResearch DesignRisk AssessmentAgedMachine LearningMortalityProtocols & guidelinesPulmonary Disease, Chronic Obstructive

Identifiers

PMID42191200
PMCPMC13218155

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Registered trials

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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.