Clinical and immunological predictors of severe pertussis in children: a nomogram-based prediction model - Shiying Zhang; Na Shan; Junfang Qin; Ying Li; Chang Liu; Yuejie Yang (BMC Infectious Diseases)
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.