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

NCT07312929 | Not Yet Recruiting

• 1 other identifier: Pro00160780
• Study Type: observational
• Target: 7,500
• Locations: 0 countries
• Sites: N/A

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. 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. 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. 3.Implement and evaluate the performance of the developed Machine Learning and Artificial Intelligence algorithms on patient-centered and health economic outcomes.
4. 4.Validate and benchmark the performance of the evaluated Machine Learning and Artificial Intelligence algorithms across multiple jurisdictions.

Conditions

Renal Replacement Therapy; Renal Diseases; Quality Health Care; Renal Dialysis

Keywords

Dialysis; Articfical Inteligence; Key performance indicators; Machine Learning

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

Device: intermittent OR continuous renal replacement therapies

Interventions

intermittent OR continuous renal replacement therapies DEVICE

We will include any critically ill patient admitted to an intensive care unit requiring acute dialysis.

Critically ill patients requiring acute dialysis

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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.

Sponsors & Collaborators

• University of Alberta: lead

MeSH Terms

Conditions

Kidney Diseases

Condition Hierarchy (Ancestors)

Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases

Study Officials

• Oleksa G Rewa, MD MSc

  University of Alberta

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Oleksa G Rewa, MD MSc FRCPC

CONTACT

17802633280; rewa@ualberta.ca

Fadi Hammal, MD MSc

CONTACT

5879907454; hammal@ualberta.ca

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 90 Days
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor, Director of Research & Innovation

Study Record Dates

• First Submitted: December 17, 2025
• First Posted: December 31, 2025
• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): June 30, 2030
• Study Completion (Estimated): June 30, 2031
• Last Updated: January 12, 2026

Record last verified: 2026-01