Evidence map›Paper›PMID 35321082›Full record

ArticleInfection and drug resistance2022

Derivation and Validation of a Predictive Scoring Model of Infections Due to

Kang Sun, Wangping Li, Yu Li, Guangyu Li, Lei Pan, Faguang Jin

Abstract read
In one paragraph

Article in Infection and drug resistance, 2022. 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
  2. Derivation and validation of a predictive mortality model of in-hospital patients with Acinetobacter baumannii nosocomial infection or colonization.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2024
    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

6 authors.

Kang SunDepartment of Respiratory and Critical Care Medicine, Tang Du Hospital, Air Force Military Medical University, Xi'an, Shaanxi Province, 710038, People's Republic of China.
Wangping LiDepartment of Respiratory and Critical Care Medicine, Tang Du Hospital, Air Force Military Medical University, Xi'an, Shaanxi Province, 710038, People's Republic of China.
Yu LiDepartment of Infectious Diseases, Shaanxi Provincial People's Hospital and The Affiliated Hospital of Xi'an Medical University, Xi'an, Shaanxi Province, 710068, People's Republic of China.
Guangyu LiDepartment of Pathology, University of Texas Medical Branch, Galveston, TX, 77555, USA.
Lei PanDepartment of Respiratory and Critical Care Medicine, Tang Du Hospital, Air Force Military Medical University, Xi'an, Shaanxi Province, 710038, People's Republic of China.
Faguang JinDepartment of Respiratory and Critical Care Medicine, Tang Du Hospital, Air Force Military Medical University, Xi'an, Shaanxi Province, 710038, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The prognosis of ABA-HAP patients is very poor. This study aimed to develop a scoring model to predict ABA-HAP in patients with GNB-HAP. Methods: A single center retrospective cohort study was performed among patients with HAP caused by GNB in our hospital during January 2019 to June 2019 (the derivation cohort, DC). The variables were assessed on the day when qualified respiratory specimens were obtained. A prediction score was formulated by using independent risk factors obtained from logistic regression analysis. It was prospectively validated with a subsequent cohort of GNB-HAP patients admitted to our hospital during July 2019 to Dec 2019 (the validation cohort, VC). Results: The final logistic regression model of DC included the following variables: transferred from other hospitals (3 points); blood purification (3 points); risk for aspiration (4 points); immunocompromised (3 points); pulmonary interstitial fibrosis (3 points); pleural effusion (1 points); heart failure (3 points); encephalitis (5 points); increased monocyte count (2 points); and increased neutrophils count (2 points). The AUROC of the scoring model was 0.845 (95% CI, 0.796 ~ 0.895) in DC and 0.807 (95% CI, 0.759 ~ 0.856) in VC. The scoring model clearly differentiated the low-risk patients (the score < 8 points), moderate-risk patients (8 ≤ the score < 12 points) and high-risk patients (the score ≥ 12 points), both in DC ( Conclusion: This simple scoring model could predict ABA-HAP with high predictive value and help clinicians to choose appropriate empirical antibiotic therapy.

Indexed as

Acinetobacter baumanniiempirical antibiotic therapyGram-negative bacillihospital acquired pneumoniapredictive scoring model

Identifiers

PMID35321082
PMCPMC8935085

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.