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. 2025 Jul 29;25(1):959.
doi: 10.1186/s12879-025-11366-8.

Clinical and immunological predictors of severe pertussis in children: a nomogram-based prediction model

Shiying Zhang #  1 , Na Shan #  1 , Junfang Qin  2 , Ying Li  3 , Chang Liu  4 , Yuejie Yang  5
Affiliations

Clinical and immunological predictors of severe pertussis in children: a nomogram-based prediction model

Shiying Zhang et al. BMC Infect Dis. .

Abstract

Background: Despite widespread vaccination, pertussis remains a significant health concern, especially for infants and young children. Severe pertussis can lead to severe complications, but the specific risk factors, particularly immunological markers, are not fully understood.

Methods: This retrospective case analysis was conducted from January to December 2023 at the Department of Infection, Tianjin Second People's Hospital. Data were collected from 249 children with pertussis (209 common and 40 severe cases) who met the inclusion criteria. Clinical and immunological parameters were compared between severe and common pertussis groups. Lasso regression and multivariate logistic regression were used to identify independent risk factors, and a nomogram prediction model was constructed and validated.

Results: Key findings included demographic and clinical differences between severe and common pertussis, such as higher rates of pneumonia, longer hospital stays, and delayed vaccination in the severe group. Immunological differences showed that children with severe pertussis had altered levels of humoral and cellular immune markers. Risk factors for severe pertussis included premature birth, incomplete vaccination, high white blood cell count, and altered lymphocyte profiles. The nomogram prediction model showed excellent performance with a C-index of 0.899 and strong discriminatory ability (AUC = 0.899). Decision curve analysis demonstrated substantial clinical utility.

Conclusions: This study highlights the clinical and immunological markers that contribute to severe pertussis in children. The nomogram prediction model developed provides a reliable tool for early identification of high-risk children, improving clinical decision-making and potential outcomes for pertussis management.

Keywords: Immunological markers; Nomogram; Predictive model; Risk factors; Severe pertussis.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This study was approved by the Ethics Committee of Tianjin Second People’s Hospital (No.2019-46), and all methods were carried out in accordance with the relevant guidelines and regulations. Written informed consent was obtained from parents of each under aged participant. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. Clinical trial: Clinical trial number: not applicable.

Figures

Fig. 1
Fig. 1
Flowchart of study design and participant selection
Fig. 2
Fig. 2
Lasso regression for screening risk factors (A) The binomial deviance plotted against log(λ), showing the optimal λ value that minimizes cross-validated error (B) Coefficient profiles of variables against log(λ), illustrating the variable selection process
Fig. 3
Fig. 3
Construction, validation, and clinical utility of the nomogram prediction model for severe pertussis. (A) Correlation heatmap visualizing relationships among independent risk factors (B) Nomogram prediction model for severe pertussis, constructed using weighted regression coefficients of independent risk factors (C) Receiver Operating Characteristic (ROC) curve evaluating the nomogram model’s predictive performance (D) The optimal cutoff point for the prediction index (PI), determined by the highest Youden index (0.1109), yielded a specificity of 69.38% and sensitivity of 97.5% (E) Decision curve analysis (DCA) illustrating the clinical utility of the nomogram prediction model for severe pertussis
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