Evidence map›Paper›PMID 39060316›Full record

ArticleScientific reports2024

Development and validation of a diagnostic prediction model for children with pertussis.

Qiang Gao, Die Xu, XiaoYan Guan, Peng Jia, XiaoPing Lei

Abstract readValidation Study
In one paragraph

Article in Scientific reports, 2024. 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. Observational
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

5 authors.

Qiang Gao *Division of Neonatology, Department of Pediatrics, The Affiliated Hospital of Southwest Medical University, No. 8, Section 2, Kangcheng Road, Luzhou, 646000, Sichuan, China.
Die Xu *Division of Neonatology, Department of Pediatrics, The Affiliated Hospital of Southwest Medical University, No. 8, Section 2, Kangcheng Road, Luzhou, 646000, Sichuan, China.
XiaoYan Guan *Department of Pediatrics, Zigong First People's Hospital, Zigong, China.
Peng JiaDepartment of Pediatrics, Zigong First People's Hospital, Zigong, China. 11039132@qq.com.
XiaoPing LeiDivision of Neonatology, Department of Pediatrics, The Affiliated Hospital of Southwest Medical University, No. 8, Section 2, Kangcheng Road, Luzhou, 646000, Sichuan, China. leixiaoping2020@swmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To develop and validate a diagnostic prediction model based on blood parameters for predicting the pertussis in children. A retrospective study of 477 children with suspected pertussis at Zigong First People's Hospital was performed between January 2020 and December 2021. The patients were randomly divided into training cohort and validation cohort. Stepwise regression and R software was performed to develop and validate the model. Stepwise regression analysis showed that white blood cell (WBC), hematocrit (HCT), lymphocyte (LYMPH), C-reactive protein (CRP) and platelet distribution width to mean platelet volume ratio (PDW-MPV-R) were found to be independent factors associated with pertussis. The model containing WBC, CRP and PDW-MPV-R had the best performance. The area under curve (ROC, 0.77 for the training cohort and 0.80 for the validation cohort) of the model indicated satisfactory discriminative ability. The sensitivity and specificity of the model were 72.1% and 72.6% in training cohort and 74% and 72.1%, respectively, in validation cohort. Based on the ROC analysis, calibration plots, and decision curve analysis, we concluded that the model exhibited excellent performance. A model based on blood parameters is sufficiently accurate to predict the probability of pertussis in children, and may provide some reference for clinical decisions.

Indexed as

C-Reactive ProteinWhooping CoughChildChild, PreschoolFemaleHematocritHumansInfantLeukocyte CountMaleRetrospective StudiesROC CurveSensitivity and SpecificityC-Reactive Protein

Identifiers

PMID39060316
PMCPMC11282082

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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.