Prediction of post-stroke urinary tract infection risk in immobile patients using machine learning: an observational cohort study.

BACKGROUND

Urinary tract infection (UTI) is one of major nosocomial infections significantly affecting the outcomes of immobile stroke patients. Previous studies have identified several risk factors, but it is still challenging to accurately estimate personal UTI risk.

AIM

To develop predictive models for UTI risk identification for immobile stroke patients.

METHODS

Research data were collected from our previous multicentre study. Derivation cohort included 3982 immobile stroke patients collected from November 1, 2015 to June 30, 2016; external validation cohort included 3837 patients collected from November 1, 2016 to July 30, 2017. Six machine learning models and an ensemble learning model were derived, based on 80% of derivation cohort, and effectiveness was evaluated with the remaining 20%. Shapley additive explanation values were used to determine feature importance and examine the clinical significance of prediction models.

FINDINGS

In all, 2.59% (103/3982) patients were diagnosed with UTI in derivation cohort, 1.38% (53/3837) in external cohort. The ensemble learning model performed the best in area under the receiver operating characteristic (ROC) curve in internal validation (82.2%); second best in external validation (80.8%). In addition, the ensemble learning model performed the best sensitivity in both internal and external validation sets (80.9% and 81.1%, respectively). Seven UTI risk factors (pneumonia, glucocorticoid use, female sex, mixed cerebrovascular disease, increased age, prolonged length of stay, and duration of catheterization) were also identified.

CONCLUSION

This ensemble learning model demonstrated promising performance. Future work should continue to develop a more concise scoring tool based on machine learning models and prospectively examining the model in practical use, thus improving clinical outcomes.