Trials in children with chronic kidney disease do not consistently report outcomes that are critically important to patients and caregivers. This can diminish the relevance and reliability of evidence for decision making, limiting the implementation of results into practice and policy. As part of the Standardized Outcomes in Nephrology-Children and Adolescents (SONG-Kids) initiative, we convened 2 consensus workshops in San Diego, California (7 patients, 24 caregivers, 43 health professionals) and Melbourne, Australia (7 patients, 23 caregivers, 49 health professionals). This report summarizes the discussions on the identification and implementation of the SONG-Kids core outcomes set. Four themes were identified; survival and life participation are common high priority goals, capturing the whole child and family, ensuring broad relevance across the patient journey, and requiring feasible and valid measures. Stakeholders supported the inclusion of mortality, infection, life participation, and kidney function as the core outcomes domains for children with chronic kidney disease.

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IntroductionNephCure Accelerating Cures Institute (NACI) is a collaborative organization sponsored by NephCure Kidney International and the University of Michigan. The Institute is composed of 7 cores designed to improve treatment options and outcomes for patients with glomerular disease: Clinical Trials Network, Data Warehouse, Patient-Reported Outcomes (PRO) and Endpoints Consortium, Clinical Trials Consulting Team, Quality Initiatives, Education and Engagement, and Data Coordinating Center.MethodsThe Trials Network includes 22 community- and hospital-based nephrology practices, 14 of which are trial-only sites. Eight sites participate in the NACI Registry, and as of October 2017, 1054 patients are enrolled with diagnoses including but not limited to focal segmental glomerulosclerosis, minimal change disease, membranous nephropathy, IgA nephropathy, and childhood-onset nephrotic syndrome. By using electronic health record data extraction, robust and efficient clinical data are captured while minimizing the burden to site-based network staff.ResultsThe Data Warehouse includes her-extracted data from registry patients, PRO development data, and data from completed observational studies and clinical trials. The Clinical Trial Consulting Team provides support for trial design in rare diseases leveraging these data. The PRO and Endpoints Consortium develops shorter-term endpoints while capturing the patient-reported significance of interventions under study. The Quality Initiatives and Education/Engagement cores elevate the level of care for patients. The Data Coordinating Center manages the analysis and operations of the Institute.ConclusionBy engaging with patients, academia, industry, and patient advocate community representatives, including our Patient Advisory Board, NACI strives for better outcomes and treatments using evidence-based support for clinical trial design.

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IntroductionThe goal of this study was to assess the occurrence of steroid-associated adverse events (SAAE) in patients with primary proteinuric kidney disease.MethodsThe Kidney Research Network Registry consists of children and adults with primary proteinuric kidney disease. SAAEs of interest were hypertension, hyperglycemia and diabetes, overweight and obesity, short stature, ophthalmologic complications, bone disorders, infections, and psychosis. Events were identified using International Classification of Diseases, Ninth Revision/Tenth Revision codes, blood pressures, growth parameters, laboratory values, and medications. Poisson generalized estimating equations tested the association between steroid onset and dose on SAAE risk.ResultsA total of 884 participants were included in the analysis; 534 (60%) were treated with steroids. Of these, 62% had at least one SAAE. The frequency of any SAAE after initiation of steroids was 293 per 1000 person-years. The most common SAAEs were hypertension (173.7 per 1000 person-years), diabetes (78.7 per 1000 person-years), obesity (66.8 per 1000 person-years), and infections (46.1 per 1000 person-years). After adjustment for demographics, duration of kidney disease, estimated glomerular filtration rate (eGFR), proteinuria, and other therapies, steroid exposure was associated with a 40% increase in risk of any SAAE (Relative risk [RR]: 1.4; 95% confidence interval [CI]: 1.3–1.6). A 1-mg/kg per day increase in steroid dose was associated with a 2.5-fold increase in risk of any SAAE.ConclusionMost patients with primary proteinuric kidney disease treated with steroids experienced at least one SAAE. Steroid therapy increased risk of hypertension, diabetes, weight gain, short stature, fractures, and infections after adjusting for disease-related factors. This study highlights the importance of surveillance and management of SAAE and provides rationale for the development of steroid minimization protocols.

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Background: Primary nephrotic syndromes are rare diseases which impedes adequate sample size for observational patient-oriented research and clinical trial enrollment. A computable phenotype may be powerful in identifying patients with these diseases for research across multiple institutions. Methods: A comprehensive algorithm of inclusion and exclusion ICD-9 and ICD-10 codes to identify patients with primary nephrotic syndrome was developed. The algorithm was executed against the PCORnet® CDM at 3 institutions from Jan 1, 2009 to Jan 1, 2018, where a random selection of 50 cases and 50 non-cases (individuals not meeting case criteria seen within the same calendar year and within five years of age of a case) were reviewed by a nephrologist, for a total of 150 cases and 150 non-cases reviewed. The classification accuracy (sensitivity, specificity, positive and negative predictive value, F1 score) of the computable phenotype was determined. Results: The algorithm identified a total of 2,708 patients with nephrotic syndrome from 4,305,092 distinct patients in the CDM at all sites from 2009-2018. For all sites, the sensitivity, specificity, and area under the curve of the algorithm were 99% (95% CI: 97-99%), 79% (95% CI: 74-85%), and 0.9 (0.84-0.97), respectively. The most common causes of false positive classification were secondary FSGS (9/39) and lupus nephritis (9/39). Conclusion: This computable phenotype had good classification in identifying both children and adults with primary nephrotic syndrome utilizing only ICD-9 and ICD-10 codes, which are available across institutions in the United States. This may facilitate future screening and enrollment for research studies and enable comparative effectiveness research. Further refinements to the algorithm including use of laboratory data or addition of natural language processing may help better distinguish primary and secondary causes of nephrotic syndrome.

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