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Maskininlärning och artificiell intelligens-algoritmer för att optimera prestanda och leverans av akut dialys (SMART DIALYSIS)

22 september 2026 uppdaterad av: University of Alberta

SMART DIALYSIS – Skalning av maskininlärning och artificiell intelligensalgoritmer för att optimera prestandan och leveransen av akut 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.

Studieöversikt

Status

Har inte rekryterat ännu

Betingelser

Intervention / Behandling

Detaljerad beskrivning

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.

Studietyp

Observationell

Inskrivning (Beräknad)

7500

Kontakter och platser

Det här avsnittet innehåller kontaktuppgifter för dem som genomför studien och information om var denna studie genomförs.

Studiekontakt

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

Studera Kontakt Backup

Studieorter

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

Deltagandekriterier

Forskare letar efter personer som passar en viss beskrivning, så kallade behörighetskriterier. Några exempel på dessa kriterier är en persons allmänna hälsotillstånd eller tidigare behandlingar.

Urvalskriterier

Åldrar som är berättigade till studier

  • Barn
  • Vuxen
  • Äldre vuxen

Tar emot friska volontärer

Nej

Kön som är behöriga för studier

Allt

Testmetod

Icke-sannolikhetsprov

Studera befolkning

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

Beskrivning

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

Det här avsnittet ger detaljer om studieplanen, inklusive hur studien är utformad och vad studien mäter.

Hur är studien utformad?

Designdetaljer

  • Observationsmodeller: COHORT
  • Tidsperspektiv: PROSPECTIVE

Kohorter och interventioner

Grupp / Kohort
Intervention / Behandling
Critically ill patients requiring acute dialysis

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

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

Vad mäter studien?

Primära resultatmått

Resultatmått
Åtgärdsbeskrivning
Tidsram
Identify Key Performance Indicators that may be used by Machine Learning algorithms.
Tidsram: 12 month

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

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

Samarbetspartners och utredare

Det är här du hittar personer och organisationer som är involverade i denna studie.

Sponsor

University of Alberta

Utredare

  • Huvudutredare: Oleksa G Rewa, MD MSc, University of Alberta

Studieavstämningsdatum

Dessa datum spårar framstegen för inlämningar av studieposter och sammanfattande resultat till ClinicalTrials.gov. Studieposter och rapporterade resultat granskas av National Library of Medicine (NLM) för att säkerställa att de uppfyller specifika kvalitetskontrollstandarder innan de publiceras på den offentliga webbplatsen.

Studera stora datum

Studiestart (Beräknad)

1 oktober 2026

Primärt slutförande (Beräknad)

30 juni 2030

Avslutad studie (Beräknad)

30 juni 2031

Studieregistreringsdatum

Först inskickad

17 december 2025

Först inskickad som uppfyllde QC-kriterierna

17 december 2025

Första postat (Faktisk)

31 december 2025

Uppdateringar av studier

Senaste uppdatering publicerad (Faktisk)

24 september 2026

Senaste inskickade uppdateringen som uppfyllde QC-kriterierna

22 september 2026

Senast verifierad

1 september 2026

Mer information

Termer relaterade till denna studie

Nyckelord

Ytterligare relevanta MeSH-villkor

Andra studie-ID-nummer

  • Pro00160780

Plan för individuella deltagardata (IPD)

Planerar du att dela individuella deltagardata (IPD)?

OBESLUTSAM

Läkemedels- och apparatinformation, studiedokument

Studerar en amerikansk FDA-reglerad läkemedelsprodukt

Nej

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Nej

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