An observational study in Renal Dialysis, Renal Replacement Therapy and Renal Diseases, sponsored by University of Alberta. Not yet recruiting at 15 sites in Canada. Per ClinicalTrials.gov, last updated 2026-09-24.
Sponsored by University of Alberta · Observational
Among 83 studies in Kidney Diseases with a site in Canada — 15 open to participants now, 25 under way, 9 of them phase 3. Kidney Diseases · Canada.
Among the 83 studies in Kidney Diseases with a site in Canada, this one is the 5th largest by planned enrollment of the 24 observational ones, is one of 3 that started in 2026, is one of 24 observational ones, reaches primary completion later than 18 of the 24 other live ones, and was the 80th registered.
969 studies on the registry are indexed under Kidney Diseases; 183 are open to participants now.
This study's planned enrollment of 7,500 is above the median of 245 across 277 observational studies indexed under Kidney Diseases.
Browse Kidney Diseases studies →15 of the 83 studies in Kidney Diseases with a site in Canada are open now, in 12 countries. Each count lists the studies.
Of the 128 Kidney Diseases studies recruiting now, 75 (59%) set no upper age limit and 33 (26%) stop at 75 or younger; 25 take children under 18; 22 take healthy volunteers.
This study: all sexes · no healthy volunteers.
The full table on the hub →83 studies; the count open now beside each. This study's row is marked.
First in 1997; this study’s year is marked. 3 started in 2026.
Median enrollment 155 across the 59 interventional studies, 473 across the 24 observational. Of the 53 that completed or were terminated, 17 (32%) have results posted.
37 of 83 have an industry lead sponsor. The 10 most frequent:
University of Alberta is the lead sponsor of 823 studies on the registry; 169 are open to participants now.
Of its 5 completed or terminated interventional studies of FDA-regulated products, 3 (60%) have results posted.
University of Alberta has 3 studies filed under Kidney Diseases — fewer than ten, so there is no comparison to draw.
9 of the studies in Kidney Diseases with a site in Canada that are under way reach primary completion within a year, 3 of them phase 3. Phase 3 first, then by date:
| Study | Primary completion |
|---|---|
| NCT06087835Phase 3AstraZenecaStudy to Investigate Efficacy, Safety, and Tolerability of Zibotentan/Dapagliflozin Compared to Dapagliflozin in Participants With Chronic Kidney Disease and High Proteinuria (ZENITH High Proteinuria) | 2027-02-18 |
| NCT05696613Phase 3Cerium Pharmaceuticals, Inc.A Study of SNP-ACTH (1-39) Gel in Patients With Primary Membranous Nephropathy | 2027-04-01 |
| NCT05852938Phase 3Novartis PharmaceuticalsA Study of Zigakibart in Adults With IgA Nephropathy | 2027-08-10 |
| NCT06124092St. Justine's HospitalOutcomes of Children After Hospitalization in Intensive Care Unit | 2027-03 |
| NCT02818192University of ManitobaImproving Renal Complications in Adolescents With Type 2 Diabetes Through REsearch Cohort Study (National iCARE Study) | 2027-03 |
Among the 83 studies in Kidney Diseases with a site in Canada, from their registry updates:
SMART DIALYSIS - Scaling Machine Learning and Artificial Intelligence AlgoRithms to OpTimize the Performance and Delivery of Acute DIALYSIS.
Hypothesis:
Can the investigators develop and implement Machine Learning and Artificial Intelligence Algorithms into Clinical Information Systems to Optimize the Prescription, Delivery, and Performance of Acute Dialysis?
Objective(s):
Study Background \& Rationale:
Acute dialysis is required in approximately 10-15% of all patients admitted to intensive care units (ICUs). Acute dialysis may take the form of continuous renal replacement therapy (CRRT), intermittent hemodialysis (IHD) or slow-low efficiency dialysis (SLED). Worldwide, CRRT remains the predominant form of acute dialysis, with over 75% of acute dialysis being CRRT.
The application of acute dialysis in the ICU is associated with poor patient outcomes. Despite advances in medical technology and care, mortality remains between 40-60%, which is similar to outcomes observed with severe acute respiratory distress syndrome (ARDS). Additionally, even when patients survive their critical illness, up to 10% of patients require ongoing long-term chronic dialysis therapy. This has a significant effect on the quality of life of survivors of critical illness, as well as important effects on their families, often requiring changes in work and housing status, as well as relocation to sites in closer proximity to dialysis centres. This results in not only significant healthcare and social costs (approximately $100,000/patient/year in Alberta, Canada), but also very important reductions in the health-related quality of lives for these patients. Currently, while evidence exist regarding the optimal initiation of acute dialysis, there is a paucity of evidence to predict timing of modality transition or liberation from this therapy. Using a completely integrated electronic Clinical Information System (eCIS) such as Connect Care (EPIC, Wisconsin, USA) in Alberta, the investigators can develop predictive algorithms that may anticipate patient and acute dialysis needs.
Once, acute dialysis is initiated, several factors may affect kidney recovery following acute dialysis. Intra-dialytic hypotension has been identified as a leading modifiable factor, but unfortunately, one where clinicians have limited capacity to accurately predict. This is an important knowledge gap that must be addressed. It has also been previously identified by our study team as one of the most important key performance indicators (KPI) for acute dialysis, especially in IHD and SLED. For CRRT, filter life has been identified as the most important and studied KPI. Both of these KPIs are currently being utilized by the ongoing QUALITY CRRT and DIALYZING WISELY programs to improve the performance and delivery of acute dialysis to critically ill patients. These two programs have been successfully implemented across Alberta and have established the infrastructure necessary to initiate the next steps in these Continuous Quality Initiatives for acute dialysis, the SMART DIALYSIS program.
Recently, advances in computer and machine processing have led to the 4th industrial revolution featuring the development of smart machines, devices, and learning algorithms that can aid humans in the management of patients and optimize the delivery of healthcare. Machine Learning (ML) and Artificial Intelligence (AI) algorithms have been previously used in medicine, but have only begun their implementation into critical care nephrology.
Current initiatives are primarily focused on pattern recognition and risk prediction. This program will contain 4 distinct phases.
In Phase 1, the investigators will continue work from our QUALITY CRRT and DIALYZING WISELY program and will aim to better understand the landscape surrounding decisions around transitions between acute dialysis modalities, termination, and attempts at liberation from acute dialysis, the incidence of intra-dialytic hypotension, and timing of filter clotting in the ICU.
In Phase 2, the investigators will develop models and subsequent Clinical Decision Support (CDS) tools and/or Best Practice Advisories (BPAs) for clinicians to better predict and manage 1) transitions between acute dialysis therapies, 2) management of intra-dialytic hypotension, 3) prediction of filter life and 4) liberation from acute dialysis. Concurrent with this work, we will work to develop an AI/ML Augmented Acute Dialysis Dashboard (i.e., SMART DIALYSIS Digital Dashboard) embedded within our electronic Clinical Information System (eCIS) to present these KPIs to clinicians.
Phase 3 will look into implementing and evaluating the performance and acceptability of these ML/AI algorithms in clinical practice.
Finally, Phase 4 will take our Alberta-derived results and look to implement and benchmark these across other large healthcare authorities to ensure that the algorithms have been developed and validated appropriately and ethically. These will include partners across Canada, the US, Europe and Australia and New Zealand.
The study population will comprise critically ill patients admitted to an intensive care unit who require acute renal replacement therapy.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Patients admitted to an intensive care unit (ICU) who require acute renal replacement therapy, either intermittent or continuous.
Exclusion Criteria
Receipt of renal replacement therapy for less than 24 hours.
Pre-existing end-stage kidney disease.
Admitted to an intensive care unit; requiring acute dialysis
Device: intermittent OR continuous renal replacement therapies
We will include any critically ill patient admitted to an intensive care unit requiring acute dialysis.
Identify Key Performance Indicators that may be used by Machine Learning algorithms.
Key Performance Indicators
Time frame: 12 month
Develop Artificial Intelligence and Machine Learning algorithms
Artificial Intelligence and Machine Learning algorithms
Time frame: 36 month
Evaluate the performance of the developed Artificial Intelligence and Machine Learning algorithms.
ICU and hospital mortality; Renal Recovery at ICU and hospital discharge and 90 days; ICU and hospital lengths of stay; Hospital Costs
Time frame: 60 month
Plan to share: Undecided
No publications or documents are linked to this record.
Start is estimated for Oct 2026; primary completion for Jun 2030, 44 months later.
Of 99 non-industry observational studies under Kidney Diseases, half reached primary completion within 68 months of starting (the median), counting those still running; a quarter within 33. This study's planned 44 months is below that.
Counting every sponsor, half of 116 studies reached it within 59 months; a quarter within 28 and three quarters within 118.
How these figures are worked out, and which studies are left out of them.
This study is not yet recruiting, as verified in Sep 2026. You cannot join it, but the record below documents what was studied.
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