Evidence map›Paper›PMID 36760781›Full record

ArticleInfection and drug resistance2023

Development and Validation of a Diagnostic Nomogram for

Qin Lang, Lijuan Li, Yue Zhang, Xing He, Yafeng Liu, Zhen Liu, Haiying Yan

Open access · goldAbstract read
In one paragraph

Article in Infection and drug resistance, 2023. 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
2.8field-weighted citation impact, top 9% 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, 13 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Machine Learning Models for Prediction of SevereDiagnostics (Basel, Switzerland) · 2023
    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

7 authors at 4 institutions in 1 country.

Qin Lang *Department of Pulmonary and Critical Care Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610000, People's Republic of China.
Lijuan Li *National Clinical Research Centre for Respiratory Diseases, China-Japan Friendship Hospital, Capital Medical University, Beijing, 100000, People's Republic of China.ORCID 0000-0001-5984-1603
Yue Zhang *Department of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610000, People's Republic of China.
Xing HeSchool of Clinical Medicine, Chengdu Medical College, Chengdu, 610000, People's Republic of China.
Yafeng LiuSchool of Medicine, Anhui University of Science and Technology, Huainan, 232000, People's Republic of China.
Zhen LiuThe First Hospital of Liangshan Prefecture, Xichang, 615000, People's Republic of China.
Haiying Yan *Department of Pulmonary and Critical Care Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610000, People's Republic of China.
University of Electronic Science and Technology of China · CNAnhui University of Science and Technology · CNCapital Medical University · CNChengdu Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Patients and Methods: This study was a retrospective, cross-sectional research. The development group included 434 patients who were admitted with pneumonia from 6 hospitals. Demographics, symptomatic features, laboratory and computed tomography data were analyzed using the least absolute shrinkage and selection operator (LASSO) to select potential diagnostic indicators. Binary logistic regression was used to develop a diagnostic nomogram. Another 119 patients with pneumonia admitted at Sichuan Provincial People's Hospital was used as the validation group. The diagnostic performance of the nomogram was measured by area under the receiver-operating-characteristics curve (AUC), calibration curves, and the net benefit by decision curve. Results: PJP prevalence was 25.3% in the development group. LASSO regression revealed that age, lymphocyte count, fever, dry cough, respiratory failure, ground-glass opacity in lungs, glucocorticoid therapy duration, and immunosuppressive therapy were indicators of PJP. The nomogram showed robust discrimination, with an AUC of 0.82 (95% CI 0.77-0.86) in the development group and an AUC of 0.87 (95% CI 0.80-0.94) in the validation group, both showing acceptable calibration. In the decision curve analysis, our model consistently achieved a greater net benefit across almost all ranges of clinical thresholds. Conclusion: We developed a nomogram with good diagnostic power for PJP diagnosis in pneumonia patients receiving oral glucocorticoids. This nomogram may help promote timely treatment of PJP and thus reduce the mortality rate in these patients.

Indexed as

glucocorticoidnomogramPneumocystis jirovecii pneumoniapneumonia

Identifiers

PMID36760781
PMCPMC9907006
OpenAlexW4319792060

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.