ArticleInvestigative and clinical urology2020
A prediction model of Nephrolithiasis Risk: A population-based cohort study in Korea.
Article in Investigative and clinical urology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Clinical characteristics of surgically managed patients with asymptomatic renal stones: Comparison of patients with symptomatic renal stones.Investigative and clinical urology · 2023Article
- Nutritional status assessed by the Controlling Nutritional Status (CONUT) score as a predictor of recurrence of urolithiasis.Investigative and clinical urology · 2021Article
- A high basal metabolic rate is an independent predictor of stone recurrence in obese patients.Investigative and clinical urology · 2021Article
- Urinary stone management during the COVID-19 pandemic: a suggested approach and review of literature.Therapeutic advances in urologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: 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. Materials and Methods: 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. Results: 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. Conclusions: 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.