Evidence map›Paper›PMID 37528213›Full record

Observational studyScientific reports2023

Establishment of risk model for elderly CAP at different age stages: a single-center retrospective observational study.

Chunxin Lv, Teng Pan, Wen Shi, Weixiong Peng, Yue Gao, Abdul Muhith, Yang Mu, Jiayi Xu, Jinhai Deng, Wei Wei

Erratum issuedOpen access · goldAbstract readObservational Study
In one paragraph

Observational study in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 28% of its field
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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors at 6 institutions in 2 countries.

Chunxin Lv *Oncology Department, Shanghai Punan Hospital of Pudong New District, No 279, Linyi Road, Pudong, Shanghai, China.
Teng Pan *Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, China.
Wen ShiDepartment of Dermatology, Shanghai Punan Hospital of Pudong New District, No 279, Linyi Road, Shanghai, China.
Weixiong PengHunan Zixing Artificial Intelligence Technology Group Co., Ltd., Hunan Province, Changsha City, China.
Yue GaoHunan Zixing Artificial Intelligence Technology Group Co., Ltd., Hunan Province, Changsha City, China.
Abdul MuhithDepartment of Oncology, Royal Marsden Hospital, London, UK.
Yang MuHunan Zixing Artificial Intelligence Technology Group Co., Ltd., Hunan Province, Changsha City, China.
Jiayi XuGeriatric Department, Minhang Hospital, Fudan University, No 170, Xinsong Road, Shanghai, China.
Jinhai DengHunan Zixing Artificial Intelligence Technology Group Co., Ltd., Hunan Province, Changsha City, China. jinhai.deng@kcl.ac.uk.
Wei WeiOncology Department, Shanghai Punan Hospital of Pudong New District, No 279, Linyi Road, Pudong, Shanghai, China. wwei0104@163.com.
Chongqing Three Gorges University · CNPunan Hospital · CNChongqing University · CNMinhang District Central Hospital · CNRoyal Marsden Hospital · GBShenzhen Maternity and Child Healthcare Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Community-acquired pneumonia (CAP) is one of the main reasons of mortality and morbidity in elderly population, causing substantial clinical and economic impacts. However, clinically available score systems have been shown to demonstrate poor prediction of mortality for patients aged over 65. Especially, no existing clinical model can predict morbidity and mortality for CAP patients among different age stages. Here, we aimed to understand the impact of age variable on the establishment of assessment model and explored prognostic factors and new biomarkers in predicting mortality. We retrospectively analyzed elderly patients with CAP in Minhang Hospital, Fudan University. We used univariate and multiple logistic regression analyses to study the prognostic factors of mortality in each age-based subgroup. The prediction accuracy of the prognostic factors was determined by the Receiver Operating Characteristic curves and the area under the curves. Combination models were established using several logistic regressions to save the predicted probabilities. Four factors with independently prognostic significance were shared among all the groups, namely Albumin, BUN, NLR and Pulse, using univariate analysis and multiple logistic regression analysis. Then we built a model with these 4 variables (as ABNP model) to predict the in-hospital mortality in all three groups. The AUC value of the ABNP model were 0.888 (95% CI 0.854-0.917, p < 0.000), 0.912 (95% CI 0.880-0.938, p < 0.000) and 0.872 (95% CI 0.833-0.905, p < 0.000) in group 1, 2 and 3, respectively. We established a predictive model for mortality based on an age variable -specific study of elderly patients with CAP, with higher AUC value than PSI, CURB-65 and qSOFA in predicting mortality in different age groups (66-75/ 76-85/ over 85 years).

Indexed as

Community-Acquired InfectionsPneumoniaAgedAged, 80 and overBiomarkersHumansPrognosisRetrospective StudiesROC CurveSeverity of Illness IndexBiomarkers

Identifiers

PMID37528213
PMCPMC10393957
OpenAlexW4385449482

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.