Journal article
Predicting urinary stone recurrence: a joint model analysis of repeated 24-hour urine collections from the MSTONE database
Urolithiasis, Vol.52(1), 156
11/01/2024
DOI: 10.1007/s00240-024-01653-5
PMCID: PMC11530469
PMID: 39485566
Abstract
To address the limitations in existing urinary stone recurrence (USR) models, including failure to account for changes in 24-hour urine (24U) parameters over time and ignoring multiplicity of stone recurrences, we presented a novel statistical method to jointly model temporal trends in 24U parameters and multiple recurrent stone events. The MSTONE database spanning May 2001 to April 2015 was analyzed. A joint recurrent model was employed, combining a linear mixed-effects model for longitudinal 24U parameters and a recurrent event model with a dynamic first-order Autoregressive (AR(1)) structure. A mixture cure component was included to handle patient heterogeneity. Comparisons were made with existing methods, multivariable Cox regression and conditional Prentice-Williams-Peterson regression, both applied to established nomograms. Among 396 patients (median follow-up of 2.93 years; IQR, 1.53–4.36 years), 34.6% remained free of stone recurrence throughout the study period, 30.0% experienced a single recurrence, and 35.4% had multiple recurrences. The joint recurrent model with a mixture cure component identified significant associations between 24U parameters - including urine pH (adjusted HR = 1.991; 95% CI 1.490–2.660;
p
< 0.001), total volume (adjusted HR = 0.700; 95% CI 0.501–0.977;
p
= 0.036), potassium (adjusted HR = 0.983; 95% CI 0.974–0.991;
p
< 0.001), uric acid (adjusted HR = 1.528; 95% CI 1.105–2.113,
p
= 0.010), calcium (adjusted HR = 1.164; 95% CI 1.052–1.289;
p
= 0.003), and citrate (adjusted HR = 0.796; 95% CI 0.706–0.897;
p
< 0.001), and USR, achieving better predictive performance compared to existing methods. 24U parameters play an important role in prevention of USR, and therefore, patients with a history of stones are recommended to closely monitor for future recurrence by regularly conducting 24U tests.
Details
- Title: Subtitle
- Predicting urinary stone recurrence: a joint model analysis of repeated 24-hour urine collections from the MSTONE database
- Creators
- Zifang Kong - Southern Methodist UniversityBrett A. Johnson - The University of Texas Southwestern Medical CenterNaim M. Maalouf - The University of Texas Southwestern Medical CenterStephen Y. Nakada - University of Wisconsin–MadisonChad R. Tracy - University of IowaRyan L. Steinberg - University of IowaNicole Miller - Vanderbilt UniversityJodi A. Antonelli - Duke University HospitalYair Lotan - The University of Texas Southwestern Medical CenterMargaret S. Pearle - The University of Texas Southwestern Medical CenterYu-Lun Liu - The University of Texas Southwestern Medical Center
- Resource Type
- Journal article
- Publication Details
- Urolithiasis, Vol.52(1), 156
- DOI
- 10.1007/s00240-024-01653-5
- PMID
- 39485566
- PMCID
- PMC11530469
- NLM abbreviation
- Urolithiasis
- ISSN
- 2194-7228
- eISSN
- 2194-7236
- Publisher
- Springer Berlin Heidelberg
- Grant note
- R01DK128237; R01DK128237; R01DK128237 / National Institute of Diabetes and Digestive and Kidney Diseases (http://dx.doi.org/10.13039/100000062)
- Language
- English
- Date published
- 11/01/2024
- Academic Unit
- Radiology; Urology
- Record Identifier
- 9984740954302771
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