Development of a Prognosis Prediction Model for Pediatric Sepsis Based on the NLPR

Objective: Identifying high-risk children with poor prognoses during the early stages of sepsis and providing timely and appropriate interventions are imperative. The objective of this study was to develop a prognostic prediction model for pediatric sepsis utilizing the neutrophil to lymphocyte and platelet ratio (NLPR). Methods: A multivariable logistic regression analysis was conducted to investigate the association between the NLPR and in-hospital mortality among septic children upon admission. To minimize the potential confounding factors that could introduce bias, a propensity score matching analysis was employed. Subsequently, a nomogram prediction model was developed to assess the risk of in-hospital mortality in septic children, incorporating the NLPR as a key factor. The performance of this prediction model was then evaluated. Results: A total of 230 septic children were enrolled in the study. Multivariate logistic regression analysis revealed that the NLPR was an independent risk factor for in-hospital mortality, with an odds ratio of 8.31 (95% CI 3.69-18.68). The finding remained consistent after propensity score matching analysis. A nomogram prediction model was developed that incorporates the NLPR, arterial blood lactate level, and Pediatric Critical Illness Score (PCIS). Among the various models, this nomogram exhibited the highest area under the curve (AUC) value of 0.831. The calibration curve demonstrated good agreement between the predicted and observed outcomes. Decision curve analysis indicated that the prediction model outperformed the PCIS. Internal validation of the model yielded an AUC value of 0.824 and a kappa value of 0.420, indicating its reliability and accuracy. Conclusion: The NLPR serves as an independent risk factor for in-hospital mortality among septic children. The nomogram prognostic prediction model could effectively guide clinicians in accurately predicting the prognosis of septic children, thus enabling timely and effective treatment interventions.