MedTrace Logo

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

NCT07312929 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 7500

Last updated 2026-01-12

No results posted yet for this study

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.

Conditions

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

Interventions

DEVICE

intermittent OR continuous renal replacement therapies

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

Sponsors & Collaborators

  • University of Alberta

    lead OTHER

Principal Investigators

  • Oleksa G Rewa, MD MSc · University of Alberta

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-06-01
Primary Completion
2030-06-30
Completion
2031-06-30

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07312929 on ClinicalTrials.gov