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Maskinlæring og kunstig intelligens algoritmer til at optimere ydeevnen og leveringen af akut dialyse (SMART DIALYSIS)

22. september 2026 opdateret af: University of Alberta

SMART DIALYSIS - Skalering af Machine Learning og Kunstig Intelligens-algoritmer for at optimere præstationen og leveringen af akut dialyse.

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.

Studieoversigt

Status

Ikke rekrutterer endnu

Betingelser

Intervention / Behandling

Detaljeret beskrivelse

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.

Undersøgelsestype

Observationel

Tilmelding (Anslået)

7500

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiekontakt

  • Navn: Oleksa G Rewa, MD MSc FRCPC
  • Telefonnummer: 17802633280
  • E-mail: rewa@ualberta.ca

Undersøgelse Kontakt Backup

Studiesteder

  • Canada
    • Alberta
      • Calgary, Alberta, Canada, T2N 2T9
        • Foothills Medical Center
        • Kontakt:
      • Calgary, Alberta, Canada, T1Y 6J4
        • Peter Lougheed Center
        • Kontakt:
      • Calgary, Alberta, Canada, T2V 1P9
        • Rockyview General Hospital
        • Kontakt:
      • Calgary, Alberta, Canada, T3M 1M4
        • South Health Campus
        • Kontakt:
      • Edmonton, Alberta, Canada, T5H 3V9
        • Royal Alexandra Hospital
        • Kontakt:
      • Edmonton, Alberta, Canada, T6L 5X8
        • Grey Nuns Community Hospital
        • Kontakt:
      • Edmonton, Alberta, Canada, T6G-2B7
        • University of Alberta Hospital
        • Kontakt:
      • Edmonton, Alberta, Canada, T5R 4H5
        • Misericordia Community Hospital
        • Kontakt:
      • Edmonton, Alberta, Canada, T6G-2B7
        • Mazankowski Heart Institute
        • Kontakt:
      • Fort McMurray, Alberta, Canada, T9H 1P2
        • Northern Lights Regional Hospital.
        • Kontakt:
      • Grand Prairie, Alberta, Canada, T8V 4B1
        • Grand Prairie Regional Hospital
        • Kontakt:
      • Lethbridge, Alberta, Canada, T1J 1W5
        • Chinook Regional Hospital
        • Kontakt:
      • Medicine Hat, Alberta, Canada, T1A 4H6
        • Medicine Hat Regional Hospital
        • Kontakt:
      • Red Deer, Alberta, Canada, T4N 4E7
        • Red Deer Regional Hospital
        • Kontakt:
      • St. Albert, Alberta, Canada, T8N 6C4
        • Sturgeon Community Hospital
        • Kontakt:

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

  • Barn
  • Voksen
  • Ældre voksen

Tager imod sunde frivillige

Ingen

Køn, der er berettiget til at studere

Alle

Prøveudtagningsmetode

Ikke-sandsynlighedsprøve

Studiebefolkning

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

Beskrivelse

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.

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

  • Observationsmodeller: COHORT
  • Tidsperspektiver: PROSPECTIVE

Kohorter og interventioner

Gruppe / kohorte
Intervention / Behandling
Critically ill patients requiring acute dialysis

Admitted to an intensive care unit; requiring acute dialysis
Enhed: intermittent OR continuous renal replacement therapies

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

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Identify Key Performance Indicators that may be used by Machine Learning algorithms.
Tidsramme: 12 month

Key Performance Indicators
12 month
Develop Artificial Intelligence and Machine Learning algorithms
Tidsramme: 36 month

Artificial Intelligence and Machine Learning algorithms
36 month
Evaluate the performance of the developed Artificial Intelligence and Machine Learning algorithms.
Tidsramme: 60 month

ICU and hospital mortality; Renal Recovery at ICU and hospital discharge and 90 days; ICU and hospital lengths of stay; Hospital Costs
60 month

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

University of Alberta

Efterforskere

  • Ledende efterforsker: Oleksa G Rewa, MD MSc, University of Alberta

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Anslået)

1. oktober 2026

Primær færdiggørelse (Anslået)

30. juni 2030

Studieafslutning (Anslået)

30. juni 2031

Datoer for studieregistrering

Først indsendt

17. december 2025

Først indsendt, der opfyldte QC-kriterier

17. december 2025

Først opslået (Faktiske)

31. december 2025

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

24. september 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

22. september 2026

Sidst verificeret

1. september 2026

Mere information

Begreber relateret til denne undersøgelse

Nøgleord

Yderligere relevante MeSH-vilkår

Andre undersøgelses-id-numre

  • Pro00160780

Plan for individuelle deltagerdata (IPD)

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