A nomogram for predicting the hospital-acquired infections in children with spinal cord injuries: a retrospective, multicenter, observational study.

ABSTRACT

STUDY DESIGN: Retrospective cohort study. OBJECTIVES: Hospital-acquired infections (HAIs) pose a significant risk for pediatric patients with spinal cord injuries (SCIs), potentially leading to extended hospital stays and increased morbidity. This study aims to identify patients at higher risk for HAIs. SETTING: Hospitals from multiple areas in China. METHODS: This retrospective study included 220 pediatric SCI patients from Jan 2005 to Dec 2023, divided into a training set (n = 154) and a validation set (n = 66). We conducted a multivariate logistic regression analysis to identify risk factors associated with HAIs and constructed a predictive nomogram. The model's performance was assessed using receiver operating characteristic (ROC) curves, area under the ROC curve (AUC) and calibration plots, while decision curve analysis was utilized to determine clinical utility. RESULTS: The nomogram incorporated age, time from injury to the hospital, history of urinary and pulmonary infections, urobilinogen positivity, damaged segments, and admission American Spinal Injury Association (ASIA) scores. The model demonstrated excellent discrimination in the training set (AUC = 0.957) and good discrimination in the validation set (AUC = 0.919). Calibration plots indicated an acceptable fit between predicted probabilities and observed outcomes. Decision curve analysis confirmed the model's net benefit over clinical decision thresholds in both sets. CONCLUSION: We developed and validated a predictive nomogram for HAIs in pediatric SCI patients that shows promise for clinical application. The model can assist healthcare providers in identifying patients at higher risk for HAIs, potentially facilitating early interventions and improved patient care strategies.