Evidence map›Paper›PMID 35360112›Full record

Observational studyFrontiers in cellular and infection microbiology2022

A Clinically Applicable Nomogram for Predicting the Risk of Invasive Mechanical Ventilation in

Rongjun Wan, Lu Bai, Yusheng Yan, Jianmin Li, Qingkai Luo, Hua Huang, Lingmei Huang, Zhi Xiang, Qing Luo, Zi Gu and 7 more

Open access · goldAbstract readMulticenter StudyObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Frontiers in cellular and infection microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.4field-weighted citation impact, top 20% 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

8 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Epidemiology and Clinical Relevance ofJournal of clinical medicine · 2025
    Article
  4. Article
  5. Machine Learning Models for Prediction of SevereDiagnostics (Basel, Switzerland) · 2023
    Article
  6. Development and Validation of a Diagnostic Nomogram forInfection and drug resistance · 2023
    Article
  7. Article
  8. 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

17 authors at 11 institutions in 1 country.

Rongjun WanDepartment of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China.
Lu BaiDepartment of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China.
Yusheng YanDepartment of Pulmonary and Critical Care Medicine, First Hospital of Changsha, Changsha, China.
Jianmin LiDepartment of Pulmonary and Critical Care Medicine, Hunan Provincial People's Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, China.
Qingkai LuoDepartment of Pulmonary and Critical Care Medicine, First People's Hospital of Chenzhou, Chenzhou, China.
Hua HuangMedical Center of Tuberculosis, Second People's Hospital of Chenzhou, Chenzhou, China.
Lingmei HuangDepartment of Pulmonary and Critical Care Medicine, Yueyang Central Hospital, Yueyang, China.
Zhi XiangDepartment of Respiratory Medicine, First People's Hospital of Huaihua, Huaihua, China.
Qing LuoDepartment of Pulmonary and Critical Care Medicine, Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China.
Zi GuDepartment of Pulmonary and Critical Care Medicine, Xiangtan Central Hospital, Xiangtan, China.
Qing GuoDepartment of Pulmonary and Critical Care Medicine, Yiyang Central Hospital, Yiyang, China.
Pinhua PanDepartment of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China.
Rongli LuDepartment of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China.
Yimin FangDepartment of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China.
Chengping HuDepartment of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China.
Juan JiangDepartment of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China.
Yuanyuan LiDepartment of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China.
Hunan Provincial Center for Disease Control and Prevention · CNCentral South University · CNChenzhou First People's Hospital · CNNational Clinical Research Center for Digestive Diseases · CNHuaian First People’s Hospital · CNHunan Normal University · CNShanghai University of Traditional Chinese Medicine · CNThe First Hospital of Changsha · CNUniversity of South China · CNXiangyang Central Hospital · CNYichang Central People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Methods: A multicenter, observational cohort study was conducted in 10 hospitals in China. Patients diagnosed with PCP were included, and their baseline clinical characteristics were collected. A Boruta analysis was performed to identify potentially important clinical features associated with the use of IMV during hospitalization. Selected variables were further analyzed using univariate and multivariable logistic regression. A logistic regression model was established based on independent risk factors for IMV and visualized using a nomogram. Results: In total, 103 patients comprised the training cohort for model development, and 45 comprised the validation cohort to confirm the model's performance. No significant differences were observed in baseline clinical characteristics between the training and validation cohorts. Boruta analysis identified eight clinical features associated with IMV, three of which were further confirmed to be independent risk factors for IMV, including age (odds ratio [OR] 2.615 [95% confidence interval (CI) 1.110-6.159]; Conclusions: The nomogram demonstrated accurate prediction of IMV in patients with PCP. Clinical application of this model enables early identification of patients with PCP who require IMV, which, in turn, may lead to rational therapeutic choices and improved clinical outcomes.

Indexed as

Pneumonia, PneumocystisRespiration, ArtificialHospitalizationHumansNomogramsRisk Factorsinvasive mechanical ventilation (IMV)machine learningnomogramPneumocystis jirovecii pneumonia (PCP)predictive model

Identifiers

PMID35360112
PMCPMC8961324
OpenAlexW4220893453

What OpenQuestion holds

Textmetadata
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.