Methodological Variations in 24-Hour Urine Collection for Nephrolithiasis: A Systematic Review of Reporting Practices and Clinical Implications

Abstract

Background:

A 24-hour urine collection is central to the metabolic evaluation and prevention of nephrolithiasis. Despite its widespread use, methodological inconsistencies in data reporting and analysis limit the reliability, reproducibility, and clinical utility of findings. We aim to review and evaluate how 24-hour urine parameters are reported, analyzed, and interpreted in nephrolithiasis research.

Materials and Methods:

We conducted a methodological review of 264 studies involving 450,624 patients with nephrolithiasis. Data extraction covered urine collection protocols, parameter reporting, units used, statistical methods, and missing data handling.

Results:

Retrospective cohort studies comprised 42.8% of included articles; cross-sectional and prospective cohort studies made up 25.0% and 19.3%, respectively. Only 6.8% (18/264) of studies reported power calculations, and 40.9% (108/264) provided reference ranges. Calcium was the most frequently reported parameter (93.9%), followed by citrate (87.5%), oxalate (86.7%), and uric acid (84.1%). Supersaturation indices were reported in 44.3% of studies. Reporting formats varied: continuous units were used in 94.7% for supersaturation, 91.7% for calcium/creatinine ratio, and 77.9% for calcium. Most common units were mg/day (e.g., calcium: 78.9%) and mmol/day (e.g., sodium: 56.9%). Regarding statistical analysis, 72.3% of studies used t-tests or Mann–Whitney U tests, 39.4% used chi-square tests, and only 21.6% used multivariate regression. Missing data were handled by complete case analysis in 71.2% of studies, while 27.7% did not report their approach.

Conclusions:

There is significant variability in how 24-hour urine data are reported and analyzed across nephrolithiasis studies. This inconsistency undermines evidence synthesis, limits external validation, and obstructs the integration of advanced tools like artificial intelligence. We recommend creating a standardized reporting checklist to improve the rigor, reproducibility, and clinical relevance of future research.