Evidence map›Paper›PMID 37940154›Full record

ArticleBMJ open2023

Development and validation of a multimorbidity risk prediction nomogram among Chinese middle-aged and older adults: a retrospective cohort study.

Xiao Zheng, Benli Xue, Shujuan Xiao, Xinru Li, Yimin Chen, Lei Shi, Xiaoyan Liang, Feng Tian, Chichen Zhang

Abstract readValidation Study
In one paragraph

Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Article
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.

Xiao ZhengDepartment of Health Management, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde, Foshan), Foshan, China.
Benli XueSchool of Health Management, Southern Medical University, Guangzhou, China.
Shujuan XiaoSchool of Health Management, Southern Medical University, Guangzhou, China.
Xinru LiSchool of Health Management, Southern Medical University, Guangzhou, China.
Yimin ChenSchool of Health Management, Southern Medical University, Guangzhou, China.
Lei ShiKey Laboratory of Philosophy and Social Sciences of Guangdong Higher Education Institutions for Collaborative Innovation of Health Management Policy and Precision Health Service, Guangzhou, China.ORCID 0000-0002-8924-0734
Xiaoyan LiangDepartment of Health Management, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde, Foshan), Foshan, China.
Feng TianDepartment of Health Management, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde, Foshan), Foshan, China zhangchichen@sina.com qqtina@smu.edu.cn.
Chichen ZhangSchool of Health Management, Southern Medical University, Guangzhou, China zhangchichen@sina.com qqtina@smu.edu.cn.ORCID 0000-0003-1095-9939

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe aim of this study is to establish a self-simple-to-use nomogram to predict the risk of multimorbidity among middle-aged and older adults.

designA retrospective cohort study.

participantsWe used data from the Chinese Longitudinal Healthy Longevity Survey, including 7735 samples.

main outcome measuresSamples' demographic characteristics, modifiable lifestyles and depression were collected. Cox proportional hazard models and nomogram model were used to estimate the risk factors of multimorbidity.

resultsA total of 3576 (46.2%) participants have multimorbidity. The result showed that age, female (HR 0.80, 95% CI 0.72 to 0.89), chronic disease (HR 2.59, 95% CI 2.38 to 2.82), sleep time (HR 0.78, 95% CI 0.72 to 0.85), regular physical activity (HR 0.88, 95% CI 0.81 to 0.95), drinking (HR 1.27 95% CI 1.16 to 1.39), smoking (HR 1.40, 95% CI 1.26 to 1.53), body mass index (HR 1.04, 95% CI 1.03 to 1.05) and depression (HR 1.02, 95% CI 1.01 to 1.03) were associated with multimorbidity. The C-index of nomogram models for derivation and validation sets were 0.70 (95% CI 0.69 to 0.71, p=0.006) and 0.71 (95% CI 0.70 to 0.73, p=0.008), respectively.

conclusionsWe have crafted a user-friendly nomogram model for predicting multimorbidity risk among middle-aged and older adults. This model integrates readily available and routinely assessed risk factors, enabling the early identification of high-risk individuals and offering tailored preventive and intervention strategies.

Indexed as

MultimorbidityNomogramsAgedChinaEast Asian PeopleFemaleHumansMiddle AgedRetrospective StudiesRisk Factorsagingchinachronic disease

Identifiers

PMID37940154
PMCPMC10632863

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