Development and validation of a nomogram to predict intrapulmonary complications in pediatric lobar pneumonia, with auxiliary nonlinear risk profiling

Lobar pneumonia (LP) is a severe pediatric community-acquired pneumonia (CAP) subtype prone to intrapulmonary complications (IPCs). We developed and validated a nomogram to predict IPC risk and evaluate nonlinear associations of age and D-dimer. We retrospectively analyzed 662 children with LP admitted to the Children’s Hospital Affiliated to Zhengzhou University from 2020 to 2024. Patients were randomly divided (7:3) into training (n = 463) and validation (n = 199) cohorts. Predictors identified by LASSO and multivariable logistic regression were incorporated into a nomogram. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration, decision curve analysis (DCA), and clinical impact curves (CIC). Restricted cubic spline (RCS) analysis evaluated nonlinear associations. Six predictors were retained: AST ≥ 40 U/L, D-dimer ≥ 1 mg/L, past medical history (PMH), Oxy_ECG within 24 h, Mycoplasma pneumoniae positivity (MP⁺), and pulmonary imaging score (IS). The model showed good discrimination, calibration, and clinical utility. Nonlinear analysis revealed a U-shaped association for age (lowest risk 6–6.9 years) and a D-dimer threshold at 0.41 mg/L. This nomogram may enable early prediction of IPCs in children with LP and support risk stratification and individualized management, pending confirmation in multicenter prospective studies.