Machine learning-based screening of characteristic factors for urinary tract infection following ureteral stone surgery and construction and validation of risk prediction models

Comprehensive Summary

Ureteroscopic lithotripsy is one of the most effective current treatments for ureteral stones due to its high success rates and minimal complications. However, postoperative urinary tract infections (UTIs) continue to be prevalent, indicating the need for specific analyses of post-ureteroscopic lithotripsy risk prediction tools. Specifically, by combining machine learning methodologies for screening key risk factors associated with UTI post-ureteroscopic lithotripsy with multivariable logistic regression, a nomogram model was developed to identify and predict UTI risk following ureteroscopic lithotripsy. Through a single-center cohort study, results revealed that machine learning identified 16 potential risk factors which were incorporated into the nomogram model to yield excellent discriminative ability, good fit between predicted probabilities and actual outcomes, and net clinical benefit across risk thresholds. This indicates that the nomogram model constructed based on multivariable logistic regression is a potential tool for clinical application in providing reference for perioperative management and individualized UTI prevention strategies.

Outcomes and Implications

The use of machine learning and the developed nomogram model offers an avenue to combat prolonged hospitalization, increased healthcare expenditure, and compromised patient outcomes resulting from post-operative UTIs from ureteroscopic lithotripsy. As a tool to identify and predict potential post-operative risks, this allows for more efficient and individualized patient care that can also be translated into other medical procedural practices.