Wang B, Zheng P, Zhang Y, Liu W, Liu L, Wang Y. A nomogram for predicting the hospital-acquired infections in children with spinal cord injuries: a retrospective, multicenter, observational study.
Spinal Cord 2024;
62:183-191. [PMID:
38409493 DOI:
10.1038/s41393-024-00966-x]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 02/04/2024] [Accepted: 02/08/2024] [Indexed: 02/28/2024]
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.
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