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Construction of a prediction model for pneumonia in children undergoing neurosurgery based on regular clinical
Shumei Zhang1,2,3, Hongyao Wang1,2,3, Shuting Lin1
1Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Insights
A new prediction model accurately identifies pneumonia risk in pediatric neurosurgery patients using routine lab tests and clinical data. This tool aids early intervention, potentially reducing hospital stays and costs for children undergoing neurosurgery.
Area of Science:
- Pediatric Neurosurgery
- Infectious Disease Epidemiology
- Clinical Prediction Modeling
Background:
- Pneumonia is a frequent complication in pediatric neurosurgery patients.
- It leads to extended hospital stays and increased healthcare costs.
- Early detection and intervention are crucial for managing this complication.
Purpose of the Study:
- To develop a predictive model for pneumonia in pediatric neurosurgery patients.
- To utilize routine clinical characteristics and laboratory indicators for prediction.
- To facilitate early identification of high-risk individuals.
Main Methods:
- Retrospective collection of data from pediatric patients (January 2021 - April 2025).
- Inclusion of clinical baseline data and laboratory test results.
- Multivariate regression analysis for variable selection and model construction.
Main Results:
- Key predictors identified: emergency admission, surgical treatment status, disease type, serum creatinine, and neutrophil count.
- The prediction model achieved satisfactory performance with C-statistics of 0.835 (test set) and 0.716 (validation set).
- A clinically applicable nomogram was developed based on the model.
Conclusions:
- Hospital-acquired pneumonia is a significant concern in pediatric neurosurgery.
- Clinical and laboratory data can effectively monitor and detect high-risk patients.
- This predictive approach warrants further clinical investigation and application.
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.
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