Machine Learning and Artificial Intelligence Algorithms to Optimize the Performance and Delivery of Acute Dialysis
SMART DIALYSIS
SMART DIALYSIS - Scaling Machine Learning and Artificial Intelligence AlgoRithms to OpTimize the Performance and Delivery of Acute DIALYSIS.
1 other identifier
observational
7,500
1 country
15
Brief Summary
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):
- 1.Identify variables surrounding identified Key Performance Indicators that may be used by Machine Learning and Artificial Intelligence algorithms to optimize the prescription and performance of acute dialysis.
- 2.Develop Machine Learning and Artificial Intelligence algorithms to help guide the prescription and delivery of acute dialysis in the development of Clinical Decision Support tools and Best Practice Advisories and create a ML/AI Augmented SMART DIALYSIS Digital Dashboard.
- 3.Implement and evaluate the performance of the developed Machine Learning and Artificial Intelligence algorithms on patient-centered and health economic outcomes.
- 4.Validate and benchmark the performance of the evaluated Machine Learning and Artificial Intelligence algorithms across multiple jurisdictions.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Oct 2026
Longer than P75 for all trials
15 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
December 17, 2025
CompletedFirst Posted
Study publicly available on registry
December 31, 2025
CompletedStudy Start
First participant enrolled
October 1, 2026
CompletedPrimary Completion
Last participant's last visit for primary outcome
June 30, 2030
ExpectedStudy Completion
Last participant's last visit for all outcomes
June 30, 2031
September 24, 2026
September 1, 2026
3.7 years
December 17, 2025
September 22, 2026
Conditions
Keywords
Outcome Measures
Primary Outcomes (3)
Identify Key Performance Indicators that may be used by Machine Learning algorithms.
Key Performance Indicators
12 month
Develop Artificial Intelligence and Machine Learning algorithms
Artificial Intelligence and Machine Learning algorithms
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
60 month
Study Arms (1)
Critically ill patients requiring acute dialysis
Admitted to an intensive care unit; requiring acute dialysis
Interventions
We will include any critically ill patient admitted to an intensive care unit requiring acute dialysis.
Eligibility Criteria
The study population will comprise critically ill patients admitted to an intensive care unit who require acute renal replacement therapy.
You may qualify if:
- Patients admitted to an intensive care unit (ICU) who require acute renal replacement therapy, either intermittent or continuous.
You may not qualify if:
- Receipt of renal replacement therapy for less than 24 hours.
- Pre-existing end-stage kidney disease.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (15)
Peter Lougheed Center
Calgary, Alberta, T1Y 6J4, Canada
Foothills Medical Center
Calgary, Alberta, T2N 2T9, Canada
Rockyview General Hospital
Calgary, Alberta, T2V 1P9, Canada
South Health Campus
Calgary, Alberta, T3M 1M4, Canada
Royal Alexandra Hospital
Edmonton, Alberta, T5H 3V9, Canada
Misericordia Community Hospital
Edmonton, Alberta, T5R 4H5, Canada
Mazankowski Heart Institute
Edmonton, Alberta, T6G-2B7, Canada
University of Alberta Hospital
Edmonton, Alberta, T6G-2B7, Canada
Grey Nuns Community Hospital
Edmonton, Alberta, T6L 5X8, Canada
Northern Lights Regional Hospital.
Fort McMurray, Alberta, T9H 1P2, Canada
Grand Prairie Regional Hospital
Grand Prairie, Alberta, T8V 4B1, Canada
Chinook Regional Hospital
Lethbridge, Alberta, T1J 1W5, Canada
Medicine Hat Regional Hospital
Medicine Hat, Alberta, T1A 4H6, Canada
Red Deer Regional Hospital
Red Deer, Alberta, T4N 4E7, Canada
Sturgeon Community Hospital
St. Albert, Alberta, T8N 6C4, Canada
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Oleksa G Rewa, MD MSc
University of Alberta
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Target Duration
- 90 Days
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
December 17, 2025
First Posted
December 31, 2025
Study Start
October 1, 2026
Primary Completion (Estimated)
June 30, 2030
Study Completion (Estimated)
June 30, 2031
Last Updated
September 24, 2026
Record last verified: 2026-09