IntroductionCommunicating complex information about haemodialysis (HD) and ensuring it is well understood remains a challenge for clinicians. Informed consent is a high-impact checkpoint in ...augmenting patients’ decision awareness and engagement prior to HD. The aims of this study are to (1) develop a digital information interface to better equip patients in the decision-making process to undergo HD; (2) evaluate the effectiveness of the co-designed digital information interface to improve patient outcomes; and (3) evaluate an implementation strategy.Methods and analysisFirst, a co-design process involving consumers and clinicians to develop audio-visual content for an innovative digital platform. Next a two-armed, open-label, multicentre, randomised controlled trial will compare the digital interface to the current informed consent practice among adult HD patients (n=244). Participants will be randomly assigned to either the intervention or control group. Intervention group: Participants will be coached to an online platform that delivers a simple-to-understand animation and knowledge test questions prior to signing an electronic consent form. Control group: Participants will be consented conventionally by a clinician and sign a paper consent form. Primary outcome is decision regret, with secondary outcomes including patient-reported experience, comprehension, anxiety, satisfaction, adherence to renal care, dialysis withdrawal, consent time and qualitative feedback. Implementation of eConsent for HD will be evaluated concurrently using the Consolidation Framework for Implementation Research (CFIR) methodology. Analysis: For the randomised controlled trial, data will be analysed using intention-to-treat statistical methods. Descriptive statistics and CFIR-based analyses will inform implementation evaluation.Ethics and disseminationHuman Research Ethics approval has been secured (Metro North Health Human Research Ethics Committee B, HREC/2022/MNHB/86890), and Dissemination will occur through partnerships with stakeholder and consumer groups, scientific meetings, publications and social media releases.Trial registration numberAustralian and New Zealand Clinical Trials Registry (ACTRN12622001354774).
Acute kidney injury (AKI) is one of the most common and significant problems in patients with Coronavirus Disease 2019 (COVID-19). However, little is known about the incidence and impact of AKI ...occurring in the community or early in the hospital admission. The traditional Kidney Disease Improving Global Outcomes (KDIGO) definition can fail to identify patients for whom hospitalisation coincides with recovery of AKI as manifested by a decrease in serum creatinine (sCr). We hypothesised that an extended KDIGO (eKDIGO) definition, adapted from the International Society of Nephrology (ISN) 0by25 studies, would identify more cases of AKI in patients with COVID-19 and that these may correspond to community-acquired AKI (CA-AKI) with similarly poor outcomes as previously reported in this population.
All individuals recruited using the International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC)-World Health Organization (WHO) Clinical Characterisation Protocol (CCP) and admitted to 1,609 hospitals in 54 countries with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection from February 15, 2020 to February 1, 2021 were included in the study. Data were collected and analysed for the duration of a patient's admission. Incidence, staging, and timing of AKI were evaluated using a traditional and eKDIGO definition, which incorporated a commensurate decrease in sCr. Patients within eKDIGO diagnosed with AKI by a decrease in sCr were labelled as deKDIGO. Clinical characteristics and outcomes-intensive care unit (ICU) admission, invasive mechanical ventilation, and in-hospital death-were compared for all 3 groups of patients. The relationship between eKDIGO AKI and in-hospital death was assessed using survival curves and logistic regression, adjusting for disease severity and AKI susceptibility. A total of 75,670 patients were included in the final analysis cohort. Median length of admission was 12 days (interquartile range IQR 7, 20). There were twice as many patients with AKI identified by eKDIGO than KDIGO (31.7% versus 16.8%). Those in the eKDIGO group had a greater proportion of stage 1 AKI (58% versus 36% in KDIGO patients). Peak AKI occurred early in the admission more frequently among eKDIGO than KDIGO patients. Compared to those without AKI, patients in the eKDIGO group had worse renal function on admission, more in-hospital complications, higher rates of ICU admission (54% versus 23%) invasive ventilation (45% versus 15%), and increased mortality (38% versus 19%). Patients in the eKDIGO group had a higher risk of in-hospital death than those without AKI (adjusted odds ratio: 1.78, 95% confidence interval: 1.71 to 1.80, p-value < 0.001). Mortality and rate of ICU admission were lower among deKDIGO than KDIGO patients (25% versus 50% death and 35% versus 70% ICU admission) but significantly higher when compared to patients with no AKI (25% versus 19% death and 35% versus 23% ICU admission) (all p-values <5 × 10-5). Limitations include ad hoc sCr sampling, exclusion of patients with less than two sCr measurements, and limited availability of sCr measurements prior to initiation of acute dialysis.
An extended KDIGO definition of AKI resulted in a significantly higher detection rate in this population. These additional cases of AKI occurred early in the hospital admission and were associated with worse outcomes compared to patients without AKI.
Acute kidney injury (AKI) is one of the most common and consequential complications among hospitalized patients. Timely AKI risk prediction may allow simple interventions that can minimize or avoid ...the harm associated with its development. Given the multifactorial and complex etiology of AKI, machine learning (ML) models may be best placed to process the available health data to generate accurate and timely predictions. Accordingly, we searched the literature for externally validated ML models developed from general hospital populations using the current definition of AKI. Of 889 studies screened, only three were retrieved that fit these criteria. While most models performed well and had a sound methodological approach, the main concerns relate to their development and validation in populations with limited diversity, comparable digital ecosystems, use of a vast number of predictor variables and over-reliance on an easily accessible biomarker of kidney injury. These are potentially critical limitations to their applicability in diverse socioeconomic and cultural settings, prompting a need for simpler, more transportable prediction models which can offer a competitive advantage over the current tools used to predict and diagnose AKI.
A doctor has a legal duty to secure the informed consent of a patient prior to performing a medical or surgical procedure. The elements of the legal doctrine of informed consent include capacity, ...voluntariness and the provision and understanding of relevant information. This article examines the doctrine in the context of renal dialysis. Dialysis is a complex therapy that impacts upon quality of life and has limited survival advantage in some patients. It is likely that informed consent is often not fully integrated into the care of patients commencing dialysis. The article analyses the common law doctrine of informed consent as it relates to dialysis and presents the findings of a retrospective study of the adequacy of the consent process based on interviews with dialysis patients who commenced dialysis in the previous 12 months. It concludes with recommendations for improvement in practice.
Peritoneal dialysis (PD) is an important home-based treatment for kidney failure and accounts for 11% of all dialysis and 9% of all kidney replacement therapy globally. Although PD is available in ...81% of countries, this provision ranges from 96% in high-income countries to 32% in low-income countries. Compared with haemodialysis, PD has numerous potential advantages, including a simpler technique, greater feasibility of use in remote communities, generally lower cost, lesser need for trained staff, fewer management challenges during natural disasters, possibly better survival in the first few years, greater ability to travel, fewer dietary restrictions, better preservation of residual kidney function, greater treatment satisfaction, better quality of life, better outcomes following subsequent kidney transplantation, delayed need for vascular access (especially in small children), reduced need for erythropoiesis-stimulating agents, and lower risk of blood-borne virus infections and of SARS-CoV-2 infection. PD outcomes have been improving over time but with great variability, driven by individual and system-level inequities and by centre effects; this variation is exacerbated by a lack of standardized outcome definitions. Potential strategies for outcome improvement include enhanced standardization, monitoring and reporting of PD outcomes, and the implementation of continuous quality improvement programmes and of PD-specific interventions, such as incremental PD, the use of biocompatible PD solutions and remote PD monitoring.
Background
With rising costs and burden of chronic kidney disease (CKD), timely referral of patients to a kidney specialist is crucial. Currently, Kidney Health Australia (KHA) uses a ‘heat map’ ...based on severity and not future risk of kidney failure, whereas the kidney failure risk equation (KFRE) score predicts future risk of progression.
Aims
Evaluate whether a KFRE score assists with timing of CKD referrals.
Methods
Retrospective cohort of 2137 adult patients, referred to tertiary hospital outpatient nephrologist between 2012 and 2020, were analysed. Referrals were analysed for concordance with the KHA referral guidelines and, with the KFRE score, a recommended practice.
Results
Of 2137 patients, 626 (29%) did not have urine albumin‐to‐creatinine ratio (UACR) measurement at referral. For those who had a UACR, the number who met KFRE preferred referral criteria was 36% less than KHA criteria. If the recommended KFRE score was used, then fewer older patients (≥40 years) needed referral. Positively, many diabetes patients were referred, even if their risk of kidney failure was low, and 29% had a KFRE over 3%. For patients evaluated meeting KFRE criteria, a larger proportion (76%) remained in follow‐up, with only 8% being discharged.
Conclusions
KFRE could reduce referrals and be a useful tool to assist timely referrals. Using KFRE for triage may allow those patients with very low risk of future kidney failure not be referred, remaining longer in primary care, saving health resources and reducing patients' stress and wait times. Using KFRE encourages albuminuria measurement.
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