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Construction and efficacy evaluation of a model for early diagnosis of pediatric sepsis based on LASSO-logistic
Yan Jiang1, Weikai Wang1, Ruifeng Xu1
1Pediatric Emergency Center, Gansu Provincial Maternity and Child-Care Hospital (Gansu Provincial Central Hospital), Lanzhou, China.
Insights
A new predictive model using seven key indicators can help doctors quickly assess pediatric sepsis risk. This tool aids in early detection and management of sepsis in children.
Area of Science:
- Pediatric critical care medicine
- Clinical informatics
- Biostatistics
Background:
- Pediatric sepsis is a life-threatening condition requiring timely diagnosis.
- Accurate risk assessment is crucial for effective management in primary care settings.
- Existing predictive tools may lack efficiency or broad applicability.
Purpose of the Study:
- To identify clinical and laboratory risk factors for pediatric sepsis.
- To develop and validate a predictive column-line diagram model for pediatric sepsis.
- To provide a quantitative tool for primary care physicians to assess sepsis risk.
Main Methods:
- Retrospective analysis of 834 pediatric patients with severe infections.
- Utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression for feature selection.
- Developed a predictive column-line diagram model and validated it using ROC curves, calibration curves, and decision curve analysis.
Main Results:
- Identified seven independent risk factors: respiratory rate, temperature, immature granulocyte percentage, platelets, procalcitonin, fibrinogen, and lactic acid.
- A column-line diagram model was constructed using these predictors.
- The model demonstrated good discriminatory ability, calibration, and clinical applicability upon internal validation.
Conclusions:
- A validated column-line diagram effectively predicts pediatric sepsis incidence.
- The model incorporates readily available clinical and laboratory data.
- This tool can enhance early sepsis detection and management in pediatric care.
Objective:
The aim of this study was to analyse the clinical characteristics and related risk factors of Pediatric Sepsis, construct a column-line diagram model to predict the likelihood of Pediatric Sepsis, and validate the model to facilitate primary care paediatricians to quickly and quantitatively assess the risk of Pediatric Sepsis.
Methods:
This single-center retrospective study included children hospitalized for infections at Gansu Provincial Maternity and Child-Care Hospital from January 2018 to June 2024. Data on 39 variables covering baseline characteristics, vital signs, and laboratory indicators were collected. The samples were randomized into training and validation groups in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for initial data screening and dimensionality reduction, followed by Logistic regression to identify independent risk factors for sepsis. Predictive modeling was then performed. The performance of the column-line plots was internally validated using ROC curves, calibration curves, and decision curve analysis (DCA).
Results:
The development dataset included 834 patients with severe infections, of whom 212 (25.4%) developed sepsis. Seven predictors were identified as independent risk factors: respiratory rate, temperature, immature granulocyte percentage, platelets, procalcitonin, fibrinogen, and lactic acid. A predictive column-line diagram was created using these predictors. Internal validation showed that the column-line diagrams had good discriminatory ability, calibration, and clinical applicability.
Conclusion:
A column-line diagram was successfully developed to predict the incidence of sepsis in children using seven commonly used clinical and laboratory indicators. The model demonstrated good performance and clinical validity through internal validation.
