Early warning model construction and validation for urinary tract infection in patients with neurogenic lower urinary tract dysfunction (NLUTD): a retrospective study.

BACKGROUND

This study was performed to construct and validate an early risk warning model of urinary tract infection in patients with neurogenic lower urinary tract dysfunction (NLUTD).

METHODS

Eligible patients with NLUTD admitted to Shenzhen Longcheng hospital from January 2017 to June 2021 were recruited for model construction, internal validation and external validation. The first time point of data collection was within half a month of patients first diagnosed with NLUTD. The second time point was at the 6-month follow-up. The early warning model was constructed by logistic regression. The model prediction effects were validated using the area under the Receiver Operating Characteristic curve, the Boostrap experiment and the calibration plot of the combined data. The model was externally validated using sensitivity, specificity and accuracy.

RESULTS

Six predictors were identified in the model, namely patients ≥65 years old (OR = 2.478, 95%CI [1.215- 5.050]), female (OR = 2.552, 95%CI [1.286-5.065]), diabetes (OR = 2.364, 95%CI) [1.182-4.731]), combined with urinary calculi (OR = 2.948, 95%CI [1.387-6.265]), indwelling catheterization (OR = 1.988, 95%CI [1.003 -3.940]) and bladder behavior training intervention time ≥2 weeks (OR = 2.489, 95%CI [1.233-5.022]); and the early warning model formula was  = 0.907 ×  age+ 0.937 × sex + 0.860 × diabetes +1.081 × combined with urinary calculi+ 0.687 × indwelling catheterization+ 0.912 × bladder behavior training intervention time-2.570. The results show that the area under the ROC curve is 0.832, which is close to that of 1,000 Bootstrap internal validation (0.828). The calibration plot shows that the early warning model has good discrimination ability and consistency. The external validation shows the sensitivity is 62.5%, the specificity is 100%, and the accuracy is 90%.

CONCLUSION

The early warning model for urinary tract infection in patients with NLUTD is suitable for clinical practice, which can provide targeted guidance for the evaluation of urinary tract infection in patients with NLUTD.