Evidence map›Paper›PMID 40275202›Full record

ArticleBMC geriatrics2025

Development and validation of a clinical-friendly model for predicting 180-day mortality in the older with community-acquired pneumonia.

Bingxuan Weng, Lixue Huang, Wenshu Jiao, Yuanqi Wang, Mengyuan Wang, Xuefeng Zhong, Xunliang Tong, Jin Jin, Yanming Li

Abstract readValidation StudyMulticenter Study
In one paragraph

Article in BMC geriatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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.

Bingxuan WengDepartment of Pulmonary and Critical Care Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, 100730, Beijing, China.
Lixue HuangDepartment of Pulmonary and Critical Care Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, 100730, Beijing, China.
Wenshu JiaoDepartment of Pulmonary and Critical Care Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, 100730, Beijing, China.
Yuanqi WangDepartment of Pulmonary and Critical Care Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, 100730, Beijing, China.
Mengyuan WangDepartment of Pulmonary and Critical Care Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, 100730, Beijing, China.
Xuefeng ZhongDepartment of Pulmonary and Critical Care Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, 100730, Beijing, China.
Xunliang TongDepartment of Pulmonary and Critical Care Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, 100730, Beijing, China.
Jin JinDepartment of Pulmonary and Critical Care Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, 100730, Beijing, China.
Yanming LiDepartment of Pulmonary and Critical Care Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, 100730, Beijing, China. liyanming2632@bjhmoh.cn.

Funding

Capital Medical Research and Development Fund 2024-1-4054Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences under Grants 2021-I2M-1-044National High Level Hospital Clinical Research Funding BJ-2023-066;BJ-2023-159;BJ-2024-139
6 · The paper itself

Abstract

objectiveCurrently, there is no effective way to identify older patients with community-acquired pneumonia (CAP) at high risk of long-term death. We aimed to develop and validate a pneumonia scoring system to predict 180-day mortality, and compare its performance with the commonly used CURB-65 score.

methodsThe prospective cohort study enrolled patients aged 65 years and older with CAP from 10 medical centers in China between April 2021 and December 2023. The primary outcome was 180-day mortality. A Cox proportional hazards model was used to develop a new pneumonia scoring system, and the area under the time-dependent curve (AUC) was used to assess its discriminatory power. Internal validation was performed using both bootstrap resampling and 10-fold cross-validation. The model was visualized by a nomogram and a questionnaire. The optimal cutoff value of the nomogram was determined based on the maximum Youden index for the 180-day mortality prediction, dividing patients into high- and low-risk groups. The performance of model in predicting both short- and long-term mortality was compared with CURB-65 using AUC, sensitivity, specificity, negative predictive value and positive predictive value.

resultsA total of 619 patients, with a median age of 78 years (IQR: 70.5-85.0), were included in the analysis. The 180-day mortality was 6.9%. The model was developed using six variables, including age, the ratio of pulse oximetry saturation (SpO

conclusionsThis study developed and validated a prediction model for long-term mortality in older patients with CAP, showing better discriminatory power and accuracy over CURB-65.

Indexed as

Community-Acquired InfectionsPneumoniaAgedAged, 80 and overChinaCommunity-Acquired PneumoniaFemaleHumansMaleNomogramsPredictive Value of TestsProspective StudiesRisk AssessmentCommunity acquired pneumoniaLong-term prognosisPrediction modelThe older

Identifiers

PMID40275202
PMCPMC12023421

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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.