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.

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Renal Replacement Therapy; Renal Dialysis; Quality Health Care; Renal Diseases
• Intervention / Treatment: Device: intermittent OR continuous renal replacement therapies

Detailed Description

Study Background & Rationale:

Acute dialysis is required in approximately 10-15% of all patients admitted to intensive care units (ICUs). Acute dialysis may take the form of continuous renal replacement therapy (CRRT), intermittent hemodialysis (IHD) or slow-low efficiency dialysis (SLED). Worldwide, CRRT remains the predominant form of acute dialysis, with over 75% of acute dialysis being CRRT.

The application of acute dialysis in the ICU is associated with poor patient outcomes. Despite advances in medical technology and care, mortality remains between 40-60%, which is similar to outcomes observed with severe acute respiratory distress syndrome (ARDS). Additionally, even when patients survive their critical illness, up to 10% of patients require ongoing long-term chronic dialysis therapy. This has a significant effect on the quality of life of survivors of critical illness, as well as important effects on their families, often requiring changes in work and housing status, as well as relocation to sites in closer proximity to dialysis centres. This results in not only significant healthcare and social costs (approximately $100,000/patient/year in Alberta, Canada), but also very important reductions in the health-related quality of lives for these patients. Currently, while evidence exist regarding the optimal initiation of acute dialysis, there is a paucity of evidence to predict timing of modality transition or liberation from this therapy. Using a completely integrated electronic Clinical Information System (eCIS) such as Connect Care (EPIC, Wisconsin, USA) in Alberta, the investigators can develop predictive algorithms that may anticipate patient and acute dialysis needs.

Once, acute dialysis is initiated, several factors may affect kidney recovery following acute dialysis. Intra-dialytic hypotension has been identified as a leading modifiable factor, but unfortunately, one where clinicians have limited capacity to accurately predict. This is an important knowledge gap that must be addressed. It has also been previously identified by our study team as one of the most important key performance indicators (KPI) for acute dialysis, especially in IHD and SLED. For CRRT, filter life has been identified as the most important and studied KPI. Both of these KPIs are currently being utilized by the ongoing QUALITY CRRT and DIALYZING WISELY programs to improve the performance and delivery of acute dialysis to critically ill patients. These two programs have been successfully implemented across Alberta and have established the infrastructure necessary to initiate the next steps in these Continuous Quality Initiatives for acute dialysis, the SMART DIALYSIS program.

Recently, advances in computer and machine processing have led to the 4th industrial revolution featuring the development of smart machines, devices, and learning algorithms that can aid humans in the management of patients and optimize the delivery of healthcare. Machine Learning (ML) and Artificial Intelligence (AI) algorithms have been previously used in medicine, but have only begun their implementation into critical care nephrology.

Current initiatives are primarily focused on pattern recognition and risk prediction. This program will contain 4 distinct phases.

In Phase 1, the investigators will continue work from our QUALITY CRRT and DIALYZING WISELY program and will aim to better understand the landscape surrounding decisions around transitions between acute dialysis modalities, termination, and attempts at liberation from acute dialysis, the incidence of intra-dialytic hypotension, and timing of filter clotting in the ICU.

In Phase 2, the investigators will develop models and subsequent Clinical Decision Support (CDS) tools and/or Best Practice Advisories (BPAs) for clinicians to better predict and manage 1) transitions between acute dialysis therapies, 2) management of intra-dialytic hypotension, 3) prediction of filter life and 4) liberation from acute dialysis. Concurrent with this work, we will work to develop an AI/ML Augmented Acute Dialysis Dashboard (i.e., SMART DIALYSIS Digital Dashboard) embedded within our electronic Clinical Information System (eCIS) to present these KPIs to clinicians.

Phase 3 will look into implementing and evaluating the performance and acceptability of these ML/AI algorithms in clinical practice.

Finally, Phase 4 will take our Alberta-derived results and look to implement and benchmark these across other large healthcare authorities to ensure that the algorithms have been developed and validated appropriately and ethically. These will include partners across Canada, the US, Europe and Australia and New Zealand.

• Study Type: Observational
• Enrollment (Estimated): 7500

Contacts and Locations

• Study Contact: Name: Oleksa G Rewa, MD MSc FRCPC; Phone Number: 17802633280; Email: rewa@ualberta.ca
• Study Contact Backup: Name: Fadi Hammal, MD MSc; Phone Number: 5879907454; Email: hammal@ualberta.ca

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

Inclusion Criteria

Patients admitted to an intensive care unit (ICU) who require acute renal replacement therapy, either intermittent or continuous.

Exclusion Criteria

Receipt of renal replacement therapy for less than 24 hours.

Pre-existing end-stage kidney disease.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Critically ill patients requiring acute dialysis; Admitted to an intensive care unit; requiring acute dialysis	Device: intermittent OR continuous renal replacement therapies; We will include any critically ill patient admitted to an intensive care unit requiring acute dialysis.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: University of Alberta
• Investigators: Principal Investigator: Oleksa G Rewa, MD MSc, University of Alberta

Study record dates

Study Major Dates

• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): June 30, 2030
• Study Completion (Estimated): June 30, 2031

Study Registration Dates

• First Submitted: December 17, 2025
• First Submitted That Met QC Criteria: December 17, 2025
• First Posted (Estimated): December 31, 2025

Study Record Updates

• Last Update Posted (Estimated): January 12, 2026
• Last Update Submitted That Met QC Criteria: January 8, 2026
• Last Verified: January 1, 2026

More Information

Terms related to this study

• Keywords: Dialysis; Machine Learning; Articfical Inteligence; Key performance indicators
• Additional Relevant MeSH Terms: Urogenital Diseases; Male Urogenital Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Kidney Diseases
• Other Study ID Numbers: Pro00160780

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No