Construction of a prediction model for pneumonia in children undergoing neurosurgery based on regular clinical laboratory tests and baseline information.

Objectives: Pneumonia is a common complication in children undergoing neurosurgery, leading to prolonged length of stay as well as increased hospital expenses. A prediction model for pneumonia in children undergoing neurosurgery based on common laboratory indicators is an effective clinical measure for early intervention in high-risk patients. In this study, we proposed to construct a pneumonia prediction model for children undergoing neurosurgery by selecting routine baseline characteristics and laboratory indicators. Methods: This study retrospectively collected children admitted from January 2021 to April 2025. The data collected included common clinical baseline data and regular laboratory test results. Variables were filtered by multivariate regression and constructed a prediction model. Results: Screening revealed that whether emergency admission, whether surgical treatment, type of disease, serum creatinine level and neutrophil count were statistically different indicators. A prediction model was constructed based on the above indicators, and the C-statistic values of the model were 0.835 (test set, 95% CI: 0.7692-0.9006) and 0.716 (validation set, 95% CI: 0.5803-0.8509), which were satisfactory. And a clinically usable nomogram based on the above model was constructed. Conclusions: Hospital-acquired pneumonia is a common complication in children undergoing neurosurgery and may be related to a variety of factors. Using basic clinical baseline data and laboratory data to monitor and detect high-risk patients in the early stages of the disease is a useful clinical attempt that deserves further exploration.