Construction and verification of urinary tract infection prediction model for hospitalized rehabilitation patients with spinal cord injury
Abstract
OBJECTIVE
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.
METHODS
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.
RESULTS
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 (OR1, 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,…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 390 participants or records.
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