Predictors of Symptomatic Kidney Stone Recurrence After the First and Subsequent Episodes
- PMID: 30527866
- PMCID: PMC6390834
- DOI: 10.1016/j.mayocp.2018.09.016
Predictors of Symptomatic Kidney Stone Recurrence After the First and Subsequent Episodes
Abstract
Objective: To predict symptomatic recurrence among community stone formers with one or more previous stone episodes.
Patients and methods: A random sample of incident symptomatic kidney stone formers in Olmsted County, Minnesota, was followed for all symptomatic stone episodes resulting in clinical care from January 1, 1984, through January 31, 2017. Clinical and radiographic characteristics at each stone episode predictive of subsequent episodes were identified.
Results: There were 3364 incident kidney stone formers with 4951 episodes. The stone recurrence rates per 100 person-years were 3.4 (95% CI, 3.2-3.7) after the first episode, 7.1 (95% CI, 6.4-7.9) after the second episode, 12.1 (95% CI, 10.3-13.9) after the third episode, and 17.6 (95% CI, 15.1-20.0) after the fourth or higher episode (P<.001 for trend). A parsimonious model identified the following independent risk factors for recurrence: younger age; male sex; higher body mass index; family history of stones; pregnancy; incident asymptomatic stone on imaging before the first episode; suspected stone episode before the first episode; history of a brushite, struvite, or uric acid stone; no history of calcium oxalate monohydrate stone; kidney pelvic or lower pole stone on imaging; no ureterovesical junction stone on imaging; number of kidney stones on imaging; and diameter of the largest kidney stone on imaging. The model had a C-index corrected for optimism of 0.681 and was used to develop a prediction tool. The risk of recurrence in 5 years ranged from 0.9% to 94%, depending on risk factors, number of past episodes, and years since the last episode.
Conclusion: The revised Recurrence Of Kidney Stone tool predicts the risk of symptomatic recurrence by using readily available clinical characteristics of stone formers.
Copyright © 2018 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.
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Comment in
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Prediction Tool to Predict Symptomatic Kidney Stone Episodes: A Step Toward Personalizing Kidney Stone Care.Mayo Clin Proc. 2019 Feb;94(2):179-181. doi: 10.1016/j.mayocp.2018.12.014. Mayo Clin Proc. 2019. PMID: 30711112 No abstract available.
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