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

ConditionsRenal DialysisRenal Replacement TherapyRenal DiseasesQuality Health Care
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
AgeNot listed
SponsorUniversity of Alberta

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

None

Disqualifiers

None

Trial population

The study population will comprise critically ill patients admitted to an intensive care unit who require acute renal replacement therapy.

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

  • intermittent OR continuous renal replacement therapies

    Device

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

Treatment groups

7,500 Participants
are divided into 1 treatment group
Group A: Critically ill patients requiring acute dialysis1 intervention

Trial outcomes

Primary outcomes

1

Identify Key Performance Indicators that may be used by Machine Learning algorithms.

Key Performance Indicators

Time frame
12 month
2

Develop Artificial Intelligence and Machine Learning algorithms

Artificial Intelligence and Machine Learning algorithms

Time frame
36 month
3

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

Secondary outcomes

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

University of Alberta

Lead sponsor

Source ClinicalTrials.gov

This trial is not recruiting at the moment. You can still explore other options: