Construction and Verification of Urinary Tract Infection Prediction Model for Hospitalized Rehabilitation Patients with Spinal Cord Injury

To explore the influencing factors of urinary tract infection in hospitalized patients with spinal cord injury, and to construct and verify the nomogram prediction model. This study is a retrospective cohort study. From January 2017 to March 2022, 558 patients with spinal cord injury admitted to the Department of Rehabilitation Medicine of a tertiary hospital in Anhui Province, China were selected as the research objects, and they were randomly divided into training group (n=390) and verification group (n=168) according to the ratio of 7: 3, and clinical data including socio-demographic characteristics, disease-related data and laboratory examination data were collected. Univariate analysis and multivariate Logistic regression were used to analyze the influencing factors of urinary tract infection in hospitalized patients with spinal cord injury. Based on this, a nomogram prediction model was constructed with the use of R software, and the risk prediction efficiency of the nomogram model was verified by the receiver operating characteristic(ROC) curve and calibration curve. Logistic regression analysis showed that ASIA-E grade (compared with ASIA-A grade) was an independent protective factor for urinary tract infection in hospitalized patients with spinal cord injury (OR<1, P<0.05), while white blood cell count and indwelling catheter were independent risk factors for urinary tract infection in hospitalized patients with spinal cord injury (OR>1, P<0.05). Based on this, a nomogram risk predictive model for predicting urinary tract infection in hospitalized rehabilitation patients with spinal cord injury was constructed, which was proved to have good predictive efficiency. In the training group and the verification group, the area under the ROC curve(AUC) of the nomogram model is 0.808 and 0.767, and the 95%CI of the AUC of the nomogram in the training group and the verification group is 0.760∼0.856 and 0.688∼0.845, indicating the nomogram model has good discrimination. According to the calibration curve, the prediction probability of the nomogram model and the actual frequency of urinary tract infection in the training group and the verification group are in good consistency, and the results of the Hosmer-Lemeshow bias test also suggest that the nomogram model has good calibration degree in both the training group and the verification group (P=0.329, 0.067). ASIA classification level, white blood cell count and indwelling catheter are independent influencing factors of urinary tract infection in hospitalized patients with spinal cord injury. The nomogram prediction model based on the above factors can simply and effectively predict the risk of urinary tract infection in hospitalized patients with spinal cord injury, which is helpful for clinical medical staff to identify high-risk groups early and implement prevention, treatment and nursing strategies in time.