Evidence map›Paper›PMID 42226999›Full record

ArticleInfection and drug resistance2026

Development of a Prediction Model for Severe Pediatric

Zeyu Zeng, Guang Li, Yanbing Xu, Na Dong, Danying Zhu, Rong Yin, Haiqin Zhong, Lang Yuan, Libing Shen, Guohui Ding and 1 more

Abstract read
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In one paragraph

Article in Infection and drug resistance, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Zeyu Zeng *Department of Pulmonology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Guang Li *Daozhi Precision Medicine Technology (Shanghai) Co., Ltd, Shanghai, People's Republic of China.ORCID 0000-0001-9268-0442
Yanbing XuDaozhi Precision Medicine Technology (Shanghai) Co., Ltd, Shanghai, People's Republic of China.
Na DongDepartment of Pulmonology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Danying ZhuDepartment of Pulmonology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Rong YinDepartment of Pulmonology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Haiqin ZhongDepartment of Pulmonology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Lang YuanDepartment of Pulmonology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Libing ShenInstitute for Digital Health, International Human Phenome Institutes (Shanghai), Shanghai, People's Republic of China.
Guohui DingInstitute for Digital Health, International Human Phenome Institutes (Shanghai), Shanghai, People's Republic of China.
Xiaoyan DongDepartment of Pulmonology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Methods: A total of 2381 children diagnosed with MPP at Shanghai Children's Hospital between November 2019 and December 2023 were retrospectively analyzed. Clinical characteristics, laboratory indices, and A2063/2064G mutation status were compared between SMPP and general MPP groups. A predictive model for SMPP was developed using multivariate logistic regression, and its performance was evaluated by receiver operating characteristic (ROC) curve analysis. Medication patterns and length of hospital stay in patients with A2063/2064G mutations were further assessed to formulate personalized treatment strategies. Results: Of 2381 patients, 71.3% developed SMPP; 46.9% of all cases carried the A2063/2064G mutation, and the mutation rate was significantly higher in the SMPP group (54.7% vs. 27.5%, Conclusion: The prediction model effectively identifies SMPP and guides interventions when combined with mutation status. Early TCs/FQs may benefit children with A2063/2064G-mutated MP when predicted as SMPP.

Indexed as

A2063/2064G mutationclinical prediction modelmacrolide resistanceMycoplasma pneumoniae pneumoniapersonalized treatment

Identifiers

PMID42226999
PMCPMC13222635

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