Work

Development and validation of a clinical severity prediction model for hospitalized children with pertussis

Year: 2026

Type: article

Abstract: BACKGROUND: To explore the risk factors for severe pertussis and to develop a classification model for illness severity in hospitalized children with pertussis. METHODS: A regression analysis was carried out on the cases diagnosed with pertussis in Wuhan Children's Hospital from January 2022 to October 2024. With April 2024 as the cut-off point, the patients were divided into a training group and a verification group. SPSS and R language were used to analyze the risk factors of severe pertussis in the training group and build a classification model for illness severity in hospitalized children with pertussis. RESULTS: Among the relevant predictors, HR, RR, Moist rales, Hb, Alb and IgG are the independent risk factors for severe pertussis. In this study, a nomogram model for illness severity in hospitalized children with pertussis was constructed. Firstly, through training sets, it was confirmed that the prediction model had good discrimination (AUC = 0.935) and calibration (Mean squared error = 0.00062). Finally, the predictive value of the model was further confirmed in the internal validation sets as well (AUC = 0.93, Mean squared error = 0.00089). CONCLUSION: The nomogram constructed in this study provides a clinical tool for assessing severity stratification in hospitalized children with pertussis, guiding the early identification and clinical management of severe cases. CLINICAL TRIAL NUMBER: Not applicable.

Source: BMC Infectious Diseases

DOI: 10.1186/s12879-026-13470-9

Authors: Dongmei Zhang, Yue Zhang, Xiaoxia Lu, Dan Sun

Institutions: Wuhan Children's Hospital, Huazhong University of Science and Technology

Language: English

Topic: Bacterial Infections and Vaccines

Subfield: Microbiology

Field: Immunology and Microbiology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,090