Evidence map›Paper›PMID 37377779›Full record

ArticleInternational journal of general medicine2023

Establishment and Validation of the Risk Nomogram of Poor Prognosis in Patients with Severe Pulmonary Infection Complicated with Respiratory Failure.

Beizhan Liu, Qiang Zhang

Open access · goldAbstract read
In one paragraph

Article in International journal of general medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.3field-weighted citation impact, top 18% 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

6 citing papers in PubMed, 5 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Beizhan LiuDepartment of Respiratory and Critical Care Medicine, The Third Xiangya Hospital, Changsha City, Hunan Province, People's Republic of China.
Qiang ZhangDepartment of Respiratory and Critical Care Medicine, The Third Xiangya Hospital, Changsha City, Hunan Province, People's Republic of China.
Central South University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To investigate the prognosis of patients with severe pulmonary infection combined with respiratory failure and analyze the influencing factors of prognosis. Methods: The clinical data of 218 patients with severe pneumonia complicated with respiratory failure were retrospectively analyzed. The risk factors were analyzed by univariate and multivariate logistic regression analyses. The risk nomogram and Bootstrap self-sampling method were used for internal inspection. Calibration curves and receiver operating characteristic (ROC) curve were drawn to assess the predictive ability of the model. Results: Among 218 patients, 118 (54.13%) cases had a good prognosis and 100 (45.87%) cases had a poor prognosis. Multivariate logistic regression analysis showed that the number of complicated basic diseases ≥5, APACHE II score >20, MODS score >10, PSI score >90, and multi-drug resistant bacterial infection were independent risk factors affecting the prognosis (P<0.05), and the level of Alb was an independent protective factor (P<0.05). The consistency index (C-index) was 0.775, and the Hosmer Lemeshow goodness-of-fit test showed that the model was not significant ( Conclusion: The risk nomograph model had good discrimination and accuracy in predicting the prognosis of patients with severe pulmonary infection combined with respiratory failure, which may provide a basis for early identification and intervention of patients at clinical risk and improve the prognosis.

Indexed as

influencing factorsprognosisrespiratory failuresevere pulmonary infection

Identifiers

PMID37377779
PMCPMC10291002
OpenAlexW4381663217

What OpenQuestion holds

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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.