Purpose: To ensure that research on kidney stones provides meaningful impact for the kidney stone community, patients and caregivers should be engaged as stakeholders in clinical trial design, starting at study inception. This project aimed to elicit, refine, and prioritize research ideas from kidney stone stakeholders to develop a patient-centered research agenda for clinical trials.
Materials And Methods: The Kidney Stone Engagement Core, a group of patients, caregivers, advocates, clinicians, and researchers, executed an iterative process of surveys and focus groups to elicit and refine research themes, which were then translated into research questions. A separate group of patients, caregivers, and clinicians prioritized these questions through parallel modified Delphi and crowdsourced digital platforms. A research agenda was developed by the Kidney Stone Engagement Core based on the highest rated questions during a hybrid virtual/in-person capstone session.
Results: A total of 70 individuals (57 patients and caregivers, 13 researchers and clinicians) participated in the elicitation, 20 individuals (15 patients and caregivers, 5 researchers and clinicians) participated in refinement, and an additional 80 individuals (81 patients and caregivers, 9 researchers and clinicians) participated in prioritization. Key novel themes emerged from elicitation and refinement: ureteral stents, genetic evaluation, shared surgical decision-making, key subgroups, cumulative disease burden, genetic evaluation, and psychosocial support. Stakeholders generated 6 proposed trials from these themes focused on surveillance, surgical intervention, and medical prevention.
Conclusions: Patients and caregivers valued comparative effectiveness kidney stone research that focused on individualized care, shared decision-making, and improvement of patient-reported experiences. This process provided actionable recommendations for future patient-centered clinical trials within kidney stone disease.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1097/JU.0000000000004242 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Do Emergency Department Observation Units Help Prevent Revisits for Patients with Renal Colic?
Am J Emerg Med
December 2024
Department of Emergency Medicine, Good Samaritan University Hospital, 1000 Montauk Highway, West Islip, New York, United States of America. Electronic address:
Introduction: Renal colic is generally considered a diagnosis appropriate for discharge home once pain is adequately controlled and no other admission criteria are met. The increasing prevalence of ED observation units (EDOU) represent another disposition option for patients with renal colic. In this study, we sought to describe the rates of 14-day revisits for renal colic among patients placed in an EDOU as compared to those discharged from the ED.
View Article and Find Full Text PDFRisk factors of systemic inflammatory response syndrome after minimally invasive percutaneous nephrolithotomy with a controlled irrigation pressure.
BMC Urol
December 2024
Department of Urology, Dongguan Tungwah Hospital, Dongguan, Guang dong, 523110, China.
Objective: This study aims to identify the risk factors for systemic inflammatory response syndrome (SIRS) after minimally invasive percutaneous nephrolithotomy (PCNL) with a controlled irrigation pressure and to find which patients undergoing PCNL are likely to develop SIRS under the pressure-controlled condition.
Methods: A total of 303 consecutive patients who underwent first-stage PCNL in our institute between July 2016 and June 2018 were retrospectively reviewed. All the procedures were performed with an 18 F tract using an irrigation pump setting the irrigation fluid pressure at 110 mmHg and the flow rate of irrigation at 0.
Diabetes mediates the relationship between cardiometabolic index and kidney stones: a cross-sectional study.
Sci Rep
December 2024
Department of Urology, The Affiliated Hospital of Jiangsu University, Jiangsu University, Zhengjiang, 212000, Jiangsu Province, China.
Previous studies have shown that diabetes is one of the risk factors for kidney stone formation. The Cardiometabolic Index (CMI) is a composite index used to assess an individual's cardiovascular health and metabolic status. CMI has been associated with several metabolic diseases.
View Article and Find Full Text PDFComparison of three different scoring systems in predicting success of retrograde intrarenal surgery in kidney stones larger than 20 millimeters.
World J Urol
December 2024
Department of Urology, Health Sciences University, Adana City Training and Research Hospital, Adana, Turkey.
Purpose: To evaluate stone free rate (SFR) predictivity of three different scoring systems in patients with kidney stones larger than 20 millimeters undergoing retrograde intrarenal surgery(RİRS).
Methods: Digital records of a total of 166 patients were reviewed retrospectively. Epidemiological characteristics (age, gender, medical history) of the patients, stone and affected kidney characteristics (size, volume, location, density, opaque, presence of urinary system anomaly, presence of stones in different calyx, number of stones, lower pole stone, renal infundibulopelvic angle (IPA), renal infundibulopelvic length (RIL), hydronephrosis), and operative characteristics (preoperative ureteral stent, operation duration, postoperative residual fragments, hospitalization time and complications were recorded.
Machine Learning Models Decoding the Association Between Urinary Stone Diseases and Metabolic Urinary Profiles.
Metabolites
December 2024
Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, NE 68198, USA.
Employing advanced machine learning models, we aim to identify biomarkers for urolithiasis from 24-h metabolic urinary abnormalities and study their associations with urinary stone diseases. We retrospectively recruited 468 patients at Peking Union Medical College Hospital who were diagnosed with urinary stone disease, including renal, ureteral, and multiple location stones, and had undergone a 24-h urine metabolic evaluation. We applied machine learning methods to identify biomarkers of urolithiasis from the urinary metabolite profiles.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!