Correction: Analysis of 33,616 urinary stone cases: novel findings on renal transplantation impact, comorbidity profiles, and composition patterns

Le résumé fourni par la source

Urinary stone disease represents a significant clinical challenge worldwide, affecting up to 14.8% of the global population with substantial healthcare burden and recurrence rates (1). Stone composition analysis provides crucial insights into underlying metabolic abnormalities and guides management strategies, with calcium oxalate being the most prevalent stone type in the general population (2,3). Recent large-scale studies have revealed complex patterns in stone composition related to demographics, comorbidities, and seasonal variations (4). However, renal transplant recipients represent a unique population with fundamentally altered physiology. The metabolic milieu of these patients-characterized by chronic immunosuppressive therapy, perturbed calcium-phosphate homeostasis, vitamin D dysregulation, and frequent urological complications-suggests distinct stone pathogenesis (5,6). Post-transplant metabolic bone disease, altered parathyroid function, and medications such as calcineurin inhibitors and corticosteroids further complicate the picture (7). Despite growing recognition of stone disease as a significant post-transplant complication affecting 1-4% of recipients, with contemporary registry data confirming a 3-year incidence of approximately 1.7% (5), large-scale studies systematically comparing stone composition patterns between transplant recipients and matched controls using rigorous propensity score matching methods remain scarce (8,9).Given the distinct physiological, pharmacological, and anatomical profiles of renal transplant recipients, we hypothesized that among patients who form urinary stones, the composition profile would differ substantially from that in non-transplant patients. The interplay between immunosuppressive medications, metabolic alterations including hypercalciuria and hyperuricosuria, structural changes such as ureteral strictures, and recurrent urinary tract infections may shift stone formation patterns in ways not yet fully elucidated (10). Previous small-scale studies have suggested increased infection stone prevalence in transplant recipients, but comprehensive analyses with adequate control groups and adjustment for confounding factors are lacking (11). Understanding these compositional differences is essential for developing targeted evaluation, treatment, and recurrence prevention strategies in this vulnerable population, where stone disease can threaten graft function and patient outcomes. Therefore, we conducted this large-scale retrospective observational study analyzing 33,616 urinary stone samples collected over a decade (2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022)(2023)(2024) to comprehensively characterize stone composition patterns in renal transplant recipients who developed nephrolithiasis with those in matched non-transplant stone formers. Using propensity score matching to minimize selection bias and rigorous statistical methods including progressive adjustment models and comprehensive subgroup analyses, we aimed to: (1) identify specific stone composition profiles associated with renal transplantation among stone-forming patients; (2) quantify the magnitude of these associations while controlling for potential confounders; (3) explore demographic and clinical factors that may modify these relationships; and (4) elucidate potential mechanistic pathways linking transplantation to these distinct stone composition patterns, thereby providing evidencebased guidance for the clinical evaluation, treatment, and prevention of stones in transplant patients (4,12).This retrospective observational study analyzed urinary stone composition data from 33,616 samples collected at the First Affiliated Hospital of Guangzhou Medical University and Yuebei People's Hospital of Shaoguan in Southern China between April 2014 and December 2024. The study population comprised exclusively patients who underwent surgical or spontaneous passage of urinary stones and subsequent compositional analysis. For each specimen, primary, secondary, and tertiary components were documented, along with patient demographics (age and sex) and detailed comorbidities. Patients were categorized into two groups: the renal transplantation group (patients with a history of kidney transplantation who subsequently developed urinary stones) and the control group (stone-forming patients without transplantation history) (Figure 1). Inclusion criteria were: (1) availability of complete stone composition analysis data, and (2) complete demographic and clinical information. Exclusion criteria included: (1) incomplete stone composition analysis, (2) missing critical baseline data, and (3) stones analyzed by methods other than infrared spectroscopy. Due to the retrospective nature of the study and the use of anonymized data, the requirement for informed consent was waived (ES-2025-K062). The data collection protocol adhered to the principles outlined in the Declaration of Helsinki.In our laboratory, stone composition was examined using Fourier Transform-Infrared Spectrometry (Thermo). As per the European Association of Urology guidelines, stones were first categorized into seven primary types, including calcium oxalate (CaOx), calcium phosphate (CaP), uric acid (UA), magnesium ammonium phosphate (MAP, struvite), carbonate apatite (CA), ammonium urate (AU), and cystine (CYS) (13). Stones with MAP, CA, or AU were classified as infection stones. To conduct a more detailed analysis, we reorganized these elements into five primary categories: calcium-containing stones (including various forms of calcium oxalate monohydrate [COM], calcium oxalate dihydrate [COD], calcium phosphate [CaP], and calcium carbonate [CC]), infection stones (comprising magnesium ammonium phosphate [MAP], carbonate apatite [CA], and ammonium urate [AU]), uric acid stones (including uric acid [UA] and its derivatives), cystine stones (CYS), and other rare compositions. Every stone sample was examined for its primary, secondary, and tertiary components. For the primary statistical analysis comparing composition prevalence between groups, stones were classified based on their dominant (primary) component.The documentation and classification of comorbidities included urological conditions (urinary tract infection [UTI], hydronephrosis and To reduce confounding by indication and selection bias, we performed 1:2 propensity score matching using nearest-neighbor matching with a caliper width of 0.2 standard deviations. The propensity score was calculated using a logistic regression model that included age and sex as covariates. Balance diagnostics were assessed using standardized mean differences (SMD), with SMD <0.1 considered indicative of adequate balance. After matching, we verified that baseline characteristics were well-balanced between the transplant and control groups.All statistical analyses were performed using R and IBM SPSS Statistics (Version 25.0). Continuous variables were presented as Study profile and baseline characteristics of kidney transplant recipients with urinary stones. The flowchart depicts the identification of kidney transplant recipients from a 10-year urinary stone disease database. Stone formers in this cohort were categorized into five groups based on their primary stone composition: calcium-containing stones, infectious stones, uric acid stones, cystine stones, and other stones.mean ± standard deviation or median (interquartile range) depending on distribution normality, and compared using Student's t-test or Mann-Whitney U test. Categorical variables were expressed as frequencies and percentages, and compared using chi-square test or Fisher's exact test when appropriate. Univariate logistic regression analysis was performed to identify associations between renal transplantation and specific stone compositions, with results presented as odds ratios (OR) and 95% confidence intervals (CI). To assess the robustness of associations, we conducted sensitivity

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