Kidney Failure from Kidney Stones: An ANZDATA Registry Study.

INTRODUCTION: Kidney stones is common with an increasing trend over time and has been well studied in the general population. However, incidence and outcomes of kidney stones leading to kidney failure (KF) and receiving kidney replacement therapy (KRT) is poorly examined. We examined the incidence of KF due to kidney stones and compared outcomes to KRT patients due to other causes. METHODS: Adult patients who started KRT (January 1981-December 2020) and based in the Australia and New Zealand Dialysis and Transplant (ANZDATA) registry. Exposure was KRT patients due to kidney stones comparing them to those with other causes. We examined incidence, prevalence, patient survival (KRT and transplant) and graft survival (transplant). Cox regression models were fit to compare patient survival between kidney stones and non-kidney stones groups, overall KRT, dialysis and patient and graft survival after kidney transplant. RESULTS: A total of 834 (1.1%) patients commenced KRT due to kidney stones. Incidence was 1.17 per million population per year and remained stable during the study period (annual percentage change -0.3% [95%CI -1.5% to 0.9%]. Survival was higher in kidney stone patients receiving dialysis compared to the non-kidney stone group (hazard ratio [HR], 0.89, 95%CI 0.82- 0.96) with similar estimates in a matched cohort. In kidney transplant patients, time to transplant was longer for patients with kidney stone compared to non-kidney stone patients (2.5 vs 1.7 years, P=0.001). There was no mortality difference (HR 1.02, 95%CI 0.82- 1.28) or graft loss (HR 1.07, 95%CI 0.79- 1.45) between kidney stones vs non-kidney stones in the kidney transplant group. CONCLUSION: KF due to kidney stones incidence is unchanged over the study period. Survival of patients with kidney stones who require KRT was better compared to patients from other causes. For the kidney transplant group, survival and risk of graft failure were similar.

Genomics in the kidney transplant clinic: the future standard of care?

New evidence indicates potential benefit of genomics to illuminate nonkidney monogenic morbidity and mortality risk among kidney transplant recipients. This might be of direct relevance to an equivalent proportion of patients to those who harbor a monogenic kidney disease. Further evidence and replication are indicated, including a broadening potential range of monogenic and polygenic opportunities to improve clinical outcomes. Implementation will require such information, although it holds great promise.

It Is in Our DNA: Bringing Electronic Health Records and Genomic Data Together for Precision Medicine.

Health care is at a turning point. We are shifting from protocolized medicine to precision medicine, and digital health systems are facilitating this shift. By providing clinicians with detailed information for each patient and analytic support for decision-making at the point of care, digital health technologies are enabling a new era of precision medicine. Genomic data also provide clinicians with information that can improve the accuracy and timeliness of diagnosis, optimize prescribing, and target risk reduction strategies, all of which are key elements for precision medicine. However, genomic data are predominantly seen as diagnostic information and are not routinely integrated into the clinical workflows of electronic medical records. The use of genomic data holds significant potential for precision medicine; however, as genomic data are fundamentally different from the information collected during routine practice, special considerations are needed to use this information in a digital health setting. This paper outlines the potential of genomic data integration with electronic records, and how these data can enable precision medicine.

Metformin for preventing the progression of chronic kidney disease.

BACKGROUND: Metformin has been used in the management of diabetes for decades. It is an effective, low-cost intervention with a well-established safety profile. Emerging evidence suggests that metformin targets a number of pathways that lead to chronic kidney damage, and long-term use may, therefore, slow the rate of kidney function decline and chronic kidney disease (CKD) progression. OBJECTIVES: To evaluate the effect of metformin therapy on kidney function decline in patients with CKD with or without diabetes mellitus and assess the safety and dose tolerability in this population. SEARCH METHODS: We searched the Cochrane Kidney and Transplant Register of Studies up to 19 July 2023 with assistance from an Information Specialist using search terms relevant to this review. Studies in the Register are identified through searches of CENTRAL, MEDLINE, and EMBASE, conference proceedings, the International Clinical Trials Registry Platform (ICTRP) Search Portal and ClinicalTrials.gov. SELECTION CRITERIA: We included randomised controlled trials (RCTs) that reported kidney-related outcomes with a minimum duration of 12 months delivery of the metformin intervention and whose eligibility criteria included adult participants with either i) a diagnosis of CKD of any aetiology and/or ii) those with a diagnosis of diabetes mellitus. Comparisons included placebo, no intervention, non-pharmacological interventions, other antidiabetic medications or any other active control. Studies that included patients on any modality of kidney replacement therapy were excluded. DATA COLLECTION AND ANALYSIS: Two authors independently carried out data extraction using a standard data extraction form. The methodological quality of the included studies was assessed using the Cochrane risk of bias tool. Summary estimates of effect were obtained using a random-effects model, and results were expressed as risk ratios (RR) and their 95% confidence intervals (CI) for dichotomous outcomes and mean difference (MD) and 95% CI for continuous outcomes. Confidence in the evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. MAIN RESULTS: This review included 11 studies reporting on 8449 randomised participants. Studies were conducted in patient populations with Autosomal Dominant Polycystic Kidney Disease (ADPKD) (four studies) or diabetes mellitus (seven studies). Six studies compared metformin with no active control, four studies compared metformin with active controls (rosiglitazone, glyburide, pioglitazone, or glipizide), and one study included treatment arms that randomised to either metformin, diet and lifestyle modifications, or other antidiabetic therapies. The risk of bias in included studies varied; two studies were abstract-only publications and were judged to have a high risk of bias in most domains. Other included publications were judged to have a low risk of bias in most domains. Across comparisons, GRADE evaluations for most outcomes were judged as low or very low certainty, except for those relating to side effects, tolerance, and withdrawals, which were judged as moderate certainty. The evidence suggests that compared to placebo, metformin may result in i) a slightly smaller decline in kidney function (3 studies, 505 participants: MD 1.92 mL/min, 95% CI 0.33 to 3.51; I2 = 0%; low certainty), ii) very uncertain effects on the incidence of kidney failure (1 study, 753 participants: RR 1.20, 95% CI 0.17 to 8.49), iii) little or no effect on death (3 studies, 865 participants: RR 1.00, 95% CI 0.76 to 1.32; I2 = 0%; moderate certainty), iv) little or no effect on the incidence of serious adverse events (3 studies, 576 participants: RR 1.15, 95% CI 0.76 to 1.72; I2 = 0%; moderate certainty), and v) likely higher incidence of intolerance leading to study withdrawal than placebo (4 studies, 646 participants: RR 2.19, 95% CI 1.46 to 3.27; I2 = 0%; moderate certainty). The certainty of the evidence for proteinuria was very uncertain. Compared to other active controls (rosiglitazone, glyburide, pioglitazone, or glipizide), metformin i) demonstrated very uncertain effects on kidney function decline, ii) may result in little or no difference in death (3 studies, 5608 participants: RR 0.95 95% CI 0.63 to 1.43; I2 = 0%; low certainty), iii) probably results in little or no difference in intolerance leading to study withdrawal (3 studies, 5593 participants: RR 0.92, 95% CI, 0.79 to 1.08; I2 = 0%; moderate certainty), iv) probably results in little or no difference in the incidence of serious adverse events (2 studies, 5545 participants: RR 1.16, 95% CI 0.79 to 1.71; I2 = 0%; moderate certainty), and v) may increase the urinary albumin-creatinine ratio (2 studies, 3836 participants: MD 14.61, 95% CI 8.17 to 21.05; I2 = 0%; low certainty). No studies reported the incidence of kidney failure. AUTHORS' CONCLUSIONS: This review highlights the lack of RCTs reporting on the effects of metformin on kidney function, particularly in patients with CKD. Future research in this field requires adequately powered RCTs comparing metformin to placebo or standard care in those with CKD. Seven ongoing studies were identified in this review, and future updates, including their findings, may further inform the results of this review.

Better kidney allograft survival despite higher-risk donor and recipient characteristics between 1995-2014.

BACKGROUND AND HYPOTHESIS: Advances in organ procurement, surgical techniques, immunosuppression regimens and prophylactic antibiotic therapies have dramatically improved short term kidney transplant graft failure. It is unclear how these interventions have affected longer term graft failure. It is hypothesised that graft failure has improved over the last 20 years. METHODS: Data on all first kidney transplants from 1995-2014 were extracted from the Australia and New Zealand Dialysis and Transplant Registry with follow-up as of 31 December, 2021. Primary exposure was transplant era, classified into 5-year intervals. Primary outcome was all-cause 5-year graft failure. Secondary outcomes included all-cause 10-year graft failure and cause-specific graft failure. Kaplan Meier curves and multivariable Cox Proportional Hazards Regression models were used to assess trends in all-cause graft failure. Fine-Gray subdistribution hazard models verified that changes in death rates were not biasing the Cox Proportional Hazards Regression models. Cumulative incidence functions were used to assess temporal trends in cause-specific graft failure. RESULTS: Across 10 871 kidney transplants, there was a shift towards transplanting more recipients aged over 45 years old, with more comorbidities, longer dialysis vintage, body mass index greater than 30 kg/m2 and greater human leukocyte antigen mismatches. Donor age has increased but no clear shift in donor source was observed. Compared to 1995-1999 (reference), the adjusted hazard ratio for 5-year graft failure was 0.78 (95% CI 0.67-0.91), 0.70 (95% CI 0.59-0.83) and 0.60 (95% CI 0.50-0.73) for 2000-2004, 2005-2009, and 2010-2014, respectively. Ten-year graft failure similarly reduced from 0.83 (95% CI 0.74-0.93) for 2000-04 to 0.78 (95% CI 0.68-0.89) for 2010-14, compared to 1995-99. CONCLUSION: Medium and long term all-cause graft failure has improved steadily since 1995-99. Significant reductions in graft failure due to rejection and vascular causes were observed at 5 years, and due to rejection, vascular causes, death and glomerular disease at 10 years.

Exploring the impact and utility of genomic sequencing in established CKD.

Clinical genetics is increasingly recognized as an important area within nephrology care. Clinicians require awareness of genetic kidney disease to recognize clinical phenotypes, consider use of genomics to aid diagnosis, and inform treatment decisions. Understanding the broad spectrum of clinical phenotypes and principles of genomic sequencing is becoming increasingly required in clinical nephrology, with nephrologists requiring education and support to achieve meaningful patient outcomes. Establishment of effective clinical resources, multi-disciplinary teams and education is important to increase application of genomics in clinical care, for the benefit of patients and their families. Novel applications of genomics in chronic kidney disease include pharmacogenomics and clinical translation of polygenic risk scores. This review explores established and emerging impacts and utility of genomics in kidney disease.

Characteristics and clinical outcomes of patients with kidney failure of unknown aetiology from ANZDATA registry.

INTRODUCTION: Kidney failure of unknown aetiology (uESKD) is also heavily location dependent varying between 27% in Egypt to 54% in Aguacalientes, Mexico. There is limited information about the characteristics of people with uESKD in Australia and New Zealand, as well as their clinical outcomes on kidney replacement therapy. METHODS: Data on people commencing kidney replacement therapy 1989-2021 were received from the Australia and New Zealand Dialysis and Transplant (ANZDATA) registry. Primary exposure was cause of kidney failure-uESKD or non-uESKD (known-ESKD). Primary outcome was mortality. Secondary outcome was kidney transplantation. Dialysis and transplant cohorts were analysed separately. Cox Proportional Hazards Regression models were used to evaluate correlations between cause of kidney failure and mortality risk. Subgroup analyses were completed to compare mortality risk in people with uESKD to those with diabetic nephropathy, autosomal dominant polycystic kidney disease (ADPKD), glomerular disease and other kidney diseases. RESULTS: This study included 60,448 people on dialysis and 20,859 transplant recipients. 1-year, 3-year and 5-year mortality rates in people with uESKD on dialysis were 31.6%, 58.7% and 77.2%, respectively. 1-year, 3-year and 5-year mortality rates in transplant recipients with uESKD were 2.8%, 13.8% and 24.0%, respectively. People with uESKD on dialysis had a higher mortality risk compared to those without uESKD on univariable and multivariable analyses (adjusted hazard ratio [AHR] 1.10, 95% CI 1.06-1.16, p<0.001). Transplant recipients with uESKD have a higher mortality risk compared to those without uESKD on univariable and multivariable analyses (AHR 1.17, 95% CI 1.01-1.35, p<0.05). People with uESKD had similar likelihood of kidney transplantation compared to people with known-ESKD. CONCLUSION: People with uESKD on kidney replacement therapy have higher mortality risk compared to people with other kidney diseases. Further studies are required to identify contributing factors to these findings.

People with genetic kidney diseases on kidney replacement therapy have different clinical outcomes compared to people with other kidney diseases.

Despite increasing awareness of genetic kidney disease prevalence, there is limited population-level information about long term outcomes of people with genetic kidney disease receiving kidney replacement therapy. This analysis included people who commenced kidney replacement therapy between 1989 and 2020 as recorded in the Australian and New Zealand Dialysis and Transplant registry. Genetic kidney diseases were subclassified as majority and minority monogenic. Non-genetic kidney diseases were included as the comparator group. Primary outcome measures were 10-year mortality and 10-year graft failure. Cox proportional hazard regression were used to calculate unadjusted and adjusted hazard ratios (AHRs) for primary outcomes. There were 59,231 people in the dialysis subgroup and 21,860 people in the transplant subgroup. People on dialysis with genetic kidney diseases had reduced 10-year mortality risk (majority monogenic AHR: 0.70, 95% CI 0.66-0.76; minority monogenic AHR 0.86, 95% CI 0.80-0.92). This reduced 10-year mortality risk continued after kidney transplantation (majority monogenic AHR: 0.82, 95% CI 0.71-0.93; minority monogenic AHR 0.80, 95% CI 0.68-0.95). Majority monogenic genetic kidney diseases were associated with reduced 10-year graft failure compared to minority monogenic genetic kidney diseases and other kidney diseases (majority monogenic AHR 0.69, 95% CI 0.59-0.79). This binational registry analysis identified that people with genetic kidney disease have different mortality and graft failure risks compared to people with other kidney diseases.

Determining priority indicators of utility for genomic testing in rare disease: A Delphi study.

PURPOSE: Determining the value of genomic tests in rare disease necessitates a broader conceptualization of genomic utility beyond diagnostic yield. Despite widespread discussion, consensus toward which aspects of value to consider is lacking. This study aimed to use expert opinion to identify and refine priority indicators of utility in rare disease genomic testing. METHODS: We used 2 survey rounds following Delphi methodology to obtain consensus on indicators of utility among experts involved in policy, clinical, research, and consumer advocacy leadership in Australia. We analyzed quantitative and qualitative data to identify, define, and determine priority indicators. RESULTS: Twenty-five experts completed round 1 and 18 completed both rounds. Twenty indicators reached consensus as a priority in value assessment, including those relating to prognostic information, timeliness of results, practical and health care outcomes, clinical accreditation, and diagnostic yield. Whereas indicators pertaining to discovery research, disutility, and factors secondary to primary reason for testing were considered less of a priority and were removed. CONCLUSION: This study obtained expert consensus on different utility indicators that are considered a priority in determining the value of genomic testing in rare disease in Australia. Indicators may inform a standardized approach to evidence generation and assessment to guide future research, decision making, and implementation efforts.

Attitudes and barriers towards deprescribing in older patients experiencing polypharmacy: a narrative review.

Polypharmacy, commonly defined as ≥5 medications, is a rising public health concern due to its many risks of harm. One commonly recommended strategy to address polypharmacy is medication reviews, with subsequent deprescription of inappropriate medications. In this review, we explore the intersection of older age, polypharmacy, and deprescribing in a contemporary context by appraising the published literature (2012-2022) to identify articles that included new primary data on deprescribing medications in patients aged ≥65 years currently taking ≥5 medications. We found 31 articles were found which describe the current perceptions of clinicians towards deprescribing, the identified barriers, key enabling factors, and future directions in approaching deprescribing. Currently, clinicians believe that deprescribing is a complex process, and despite the majority of clinicians reporting feeling comfortable in deprescribing, fewer engage with this process regularly. Common barriers cited include a lack of knowledge and training around the deprescribing process, a lack of time, a breakdown in communication, perceived 'abandonment of care', fear of adverse consequences, and resistance from patients and/or their carers. Common enabling factors of deprescribing include recognition of key opportunities to instigate this process, regular medication reviews, improving lines of communication, education of both patients and clinicians and a multidisciplinary approach towards patient care. Addressing polypharmacy requires a nuanced approach in a generally complex group of patients. Key strategies to reducing the risks of polypharmacy include education of patients and clinicians, in addition to improving communication between healthcare providers in a multidisciplinary approach.

Real world evaluation of kidney failure risk equations in predicting progression from chronic kidney disease to kidney failure in an Australian cohort.

BACKGROUND: Chronic kidney disease progression to kidney failure is diverse, and progression may be different according to genetic aspects and settings of care. We aimed to describe kidney failure risk equation prognostic accuracy in an Australian population. METHODS: A retrospective cohort study was undertaken in a public hospital community-based chronic kidney disease service in Brisbane, Australia, which included a cohort of 406 adult patients with chronic kidney disease Stages 3-4 followed up over 5 years (1/1/13-1/1/18). Risk of progression to kidney failure at baseline using Kidney Failure Risk Equation models with three (eGFR/age/sex), four (add urinary-ACR) and eight variables (add serum-albumin/phosphate/bicarbonate/calcium) at 5 and 2 years were compared to actual patient outcomes. RESULTS: Of 406 patients followed up over 5 years, 71 (17.5%) developed kidney failure, while 112 died before reaching kidney failure. The overall mean difference between observed and predicted risk was 0.51% (p = 0.659), 0.93% (p = 0.602), and - 0.03% (p = 0.967) for the three-, four- and eight-variable models, respectively. There was small improvement in the receiver operating characteristic-area under the curve from three-variable to four-variable models: 0.888 (95%CI = 0.819-0.957) versus 0.916 (95%CI = 0.847-0.985). The eight-variable model showed marginal receiver operating characteristic-area under the curve improvement: 0.916 (95%CI = 0.847-0.985) versus 0.922 (95%CI = 0.853-0.991). The results were similar in predicting 2 year risk of kidney failure. CONCLUSIONS: The kidney failure risk equation accurately predicted progression to kidney failure in an Australian chronic kidney disease population. Younger age, male sex, lower estimated glomerular filtration rate, higher albuminuria, diabetes mellitus, tobacco smoking and non-Caucasian ethnicity were associated with increased risk of kidney failure. Cause-specific cumulative incidence function for progression to kidney failure or death, stratified by chronic kidney disease stage, demonstrated differences within different chronic kidney disease stages, highlighting the interaction between comorbidity and outcome.