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Machine Learning and Artificial Intelligence Algorithms to Optimize the Performance and Delivery of Acute Dialysis

Study acronym: SMART DIALYSIS
StatusNot Yet Recruiting
PhaseNot specified
Started
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Record History

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  1. Start date: Jun 1, 2026 to Oct 1, 2026
    • Trial sites expanded: 0 → 15 locations
    • Study description revised
  2. Study description expanded, about 3 wordsv2
  3. Study First Submitted
  • Stopped: an ongoing trial was terminated, suspended or withdrawn.
  • Notable: the trial’s plan changed such as a big move in its timeline or size, a new primary endpoint, sponsor or phase, or recruiting opened or resumed.
  • Minor: routine progress and housekeeping, such as sites added or enrollment closed.
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 Details
NCT Number NCT07312929
Lead Sponsor University of Alberta
Conditions
(from sponsor)		 Renal Dialysis, Renal Replacement Therapy, Renal Diseases, Quality Health Care
Related terms Kidney Diseases
Enrollment 7,500 participants
Start Date
Primary Completion (estimated)
Study Completion (estimated)
Updated on ClinicalTrials.gov