Risk score models for urinary tract infection hospitalization.

Area of Science:

• Healthcare Analytics
• Predictive Modeling
• Public Health

Background:

• Urinary tract infections (UTIs) affect millions globally, causing significant patient and caregiver burden.
• Hospitalization for UTIs incurs substantial economic and social costs.
• Early identification of high-risk individuals is essential for preventative care.

Purpose of the Study:

• To develop and validate two novel risk score models for predicting UTI hospitalization.
• To assess the performance and interpretability of these predictive models.
• To identify key risk factors associated with UTI hospitalizations.

Main Methods:

• Utilized insurance claims data from the Centers for Medicare and Medicaid Services.
• Developed risk score models by scaling and rounding coefficients of a logistic regression model.
• Incorporated patient demographics, medical history, healthcare utilization, provider quality, and community metrics.

Main Results:

• The proposed risk score models demonstrated effectiveness in identifying patients at high risk for UTI hospitalization.
• Computational experiments validated the predictive performance of both models.
• Key risk factors identified include ICU admission, cognitive disorders, and low healthcare costs.

Conclusions:

• Risk score models are practical tools for identifying high-risk patients for UTI hospitalization.
• The study provides a quantitative assessment of significant risk factors.
• Findings can inform targeted interventions to prevent costly hospitalizations.