A machine learning-based nomogram model for predicting the recurrence of cystitis glandularis.

Abstract

Background: Cystitis glandularis is a chronic inflammatory disease of the urinary system characterized by high recurrence rates, the reasons for which are still unknown.

Objectives: This study aims to identify potential factors contributing to recurrence and propose a simple and feasible prognostic model through nomogram construction.

Design: Patients with confirmed recurrence based on outpatient visits or readmissions were included in this study, which was subsequently divided into training and validation cohorts.

Methods: Machine learning techniques were utilized to screen for the most important predictors, and these were then employed to construct the nomogram. The reliability of the nomogram was assessed through receiver operating characteristic curve analysis, decision curve analysis, and calibration curves.

Results: A total of 252 patients met the screening criteria and were enrolled in this study. Over the 12-month follow-up period, the relapse rate was found to be 57.14% (n = 144). The five final predictors identified through machine learning were urinary infections, urinary calculi, eosinophil count, lymphocyte count, and serum magnesium. The area under curve values for all three time points assessing recurrence exceeded 0.75. Furthermore, both calibration curves and decision curve analyses indicated good performance of the nomogram.

Conclusion: We have developed a reliable machine learning-based nomogram for predicting recurrence in cystitis glandularis.