Well-validated risk prediction models help to stratify individuals on the basis of their disease risks and to guide health care professionals in decision-making. The incidence of nephrolithiasis has been increasing in Korea. Racial differences in the distribution of and risk for nephrolithiasis have been reported in Asia but no population-specific nephrolithiasis models have been developed. We aimed to develop a simplified nephrolithiasis prediction model for the Korean population by using data from general medical practice.
This was a prospective, population-based cohort study in Korea. A total of 497,701 participants from the National Health Insurance Service-National Sample Cohort (NHIS-NSC) were enrolled from 2002 to 2010. A Cox proportional hazards model was used.
During a median follow-up time of 8.5 years (range, 2.0-8.9 years) and among 497,701 participants, there were 15,783 cases (3.2%) of nephrolithiasis. The parsimonious model included age, sex, income grade, alcohol consumption, body mass index, total cholesterol, fasting blood glucose, and medical history of diseases. The Harrell's C-statistic was 0.806 (95% confidence interval [CI], 0.790-0.821) and 0.805 (95% CI, 0.782-0.827) in the derivation and validation cohorts, respectively.
The results of the present study imply that nephrolithiasis risk can be predicted by use of data from general medical practice and based on predictors that clinicians and individuals from the general population are likely to know. This model comprises modifiable risk factors and can be used to identify those at higher risk who can modify their lifestyle to lower their risk for nephrolithiasis. This study also offers an opportunity for external validation or updating of the model through the incorporation of other risk predictors in other settings.
Investigative and clinical urology. 2020 Feb 12 [Epub]
David Mukasa, Joohon Sung
Complex Disease and Genome Epidemiology Branch, Department of Public Health, Graduate School of Public Health, Seoul National University, Seoul, Korea.