Algorytmy uczenia maszynowego i sztucznej inteligencji do optymalizacji wydajności i dostarczania ostrej dializy (SMART DIALYSIS)
SMART DIALYSIS - Skalowanie algorytmów uczenia maszynowego i sztucznej inteligencji w celu optymalizacji wydajności i dostarczania ostrej dializy.
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):
- 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.
- 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.
- Implement and evaluate the performance of the developed Machine Learning and Artificial Intelligence algorithms on patient-centered and health economic outcomes.
- Validate and benchmark the performance of the evaluated Machine Learning and Artificial Intelligence algorithms across multiple jurisdictions.
Przegląd badań
Status
Interwencja / Leczenie
Szczegółowy opis
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.
Typ studiów
Zapisy (Szacowany)
Kontakty i lokalizacje
Kontakt w sprawie studiów
- Nazwa: Oleksa G Rewa, MD MSc FRCPC
- Numer telefonu: 17802633280
- E-mail: rewa@ualberta.ca
Kopia zapasowa kontaktu do badania
- Nazwa: Fadi Hammal, MD MSc
- Numer telefonu: 5879907454
- E-mail: hammal@ualberta.ca
Lokalizacje studiów
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Alberta
-
Calgary, Alberta, Kanada, T2N 2T9
- Foothills Medical Center
-
Kontakt:
- Oleksa Rewa, MD
- Numer telefonu: 7802633280
- E-mail: rewa@ualberta.ca
-
Calgary, Alberta, Kanada, T1Y 6J4
- Peter Lougheed Center
-
Kontakt:
- Oleksa Rewa, MD
- Numer telefonu: 7802633280
- E-mail: rewa@ualberta.ca
-
Calgary, Alberta, Kanada, T2V 1P9
- Rockyview General Hospital
-
Kontakt:
- Oleksa Rewa, MD
- Numer telefonu: 7802633280
- E-mail: rewa@ualberta.ca
-
Calgary, Alberta, Kanada, T3M 1M4
- South Health Campus
-
Kontakt:
- Oleksa Rewa, MD
- Numer telefonu: 7802633280
- E-mail: rewa@ualberta.ca
-
Edmonton, Alberta, Kanada, T5H 3V9
- Royal Alexandra Hospital
-
Kontakt:
- Oleksa Rewa, MD
- Numer telefonu: 7802633280
- E-mail: rewa@ualberta.ca
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Edmonton, Alberta, Kanada, T6L 5X8
- Grey Nuns Community Hospital
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Kontakt:
- Oleksa Rewa, MD
- Numer telefonu: 7802633280
- E-mail: rewa@ualberta.ca
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Edmonton, Alberta, Kanada, T6G-2B7
- University of Alberta Hospital
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Kontakt:
- Oleksa Rewa, MD
- Numer telefonu: 7802633280
- E-mail: rewa@ualberta.ca
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Edmonton, Alberta, Kanada, T5R 4H5
- Misericordia Community Hospital
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Kontakt:
- Oleksa Rewa, MD
- Numer telefonu: 7802633280
- E-mail: rewa@ualberta.ca
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Edmonton, Alberta, Kanada, T6G-2B7
- Mazankowski Heart Institute
-
Kontakt:
- Oleksa Rewa, MD
- Numer telefonu: 7802633280
- E-mail: rewa@ualberta.ca
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Fort McMurray, Alberta, Kanada, T9H 1P2
- Northern Lights Regional Hospital.
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Kontakt:
- Oleksa Rewa, MD
- Numer telefonu: 7802633280
- E-mail: rewa@ualberta.ca
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Grand Prairie, Alberta, Kanada, T8V 4B1
- Grand Prairie Regional Hospital
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Kontakt:
- Oleksa Rewa, MD
- Numer telefonu: 7802633280
- E-mail: rewa@ualberta.ca
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Lethbridge, Alberta, Kanada, T1J 1W5
- Chinook Regional Hospital
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Kontakt:
- Oleksa Rewa, MD
- Numer telefonu: 7802633280
- E-mail: rewa@ualberta.ca
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Medicine Hat, Alberta, Kanada, T1A 4H6
- Medicine Hat Regional Hospital
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Kontakt:
- Oleksa Rewa, MD
- Numer telefonu: 7802633280
- E-mail: rewa@ualberta.ca
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Red Deer, Alberta, Kanada, T4N 4E7
- Red Deer Regional Hospital
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Kontakt:
- Oleksa Rewa, MD
- Numer telefonu: 7802633280
- E-mail: rewa@ualberta.ca
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St. Albert, Alberta, Kanada, T8N 6C4
- Sturgeon Community Hospital
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Kontakt:
- Oleksa Rewa, MD
- Numer telefonu: 7802633280
- E-mail: rewa@ualberta.ca
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Kryteria uczestnictwa
Kryteria kwalifikacji
Wiek uprawniający do nauki
- Dziecko
- Dorosły
- Starszy dorosły
Akceptuje zdrowych ochotników
Płeć kwalifikująca się do nauki
Metoda próbkowania
Badana populacja
The study population will comprise critically ill patients admitted to an intensive care unit who require acute renal replacement therapy.
Opis
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.
Plan studiów
Jak projektuje się badanie?
Szczegóły projektu
- Modele obserwacyjne: COHORT
- Perspektywy czasowe: PROSPECTIVE
Kohorty i interwencje
Grupa / Kohorta |
Interwencja / Leczenie |
|---|---|
|
Critically ill patients requiring acute dialysis
Admitted to an intensive care unit; requiring acute dialysis |
We will include any critically ill patient admitted to an intensive care unit requiring acute dialysis. |
Co mierzy badanie?
Podstawowe miary wyniku
Miara wyniku |
Opis środka |
Ramy czasowe |
|---|---|---|
|
Identify Key Performance Indicators that may be used by Machine Learning algorithms.
Ramy czasowe: 12 month
|
Key Performance Indicators |
12 month
|
|
Develop Artificial Intelligence and Machine Learning algorithms
Ramy czasowe: 36 month
|
Artificial Intelligence and Machine Learning algorithms |
36 month
|
|
Evaluate the performance of the developed Artificial Intelligence and Machine Learning algorithms.
Ramy czasowe: 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
|
Współpracownicy i badacze
Sponsor
Śledczy
- Główny śledczy: Oleksa G Rewa, MD MSc, University of Alberta
Daty zapisu na studia
Główne daty studiów
Rozpoczęcie studiów (Szacowany)
Zakończenie podstawowe (Szacowany)
Ukończenie studiów (Szacowany)
Daty rejestracji na studia
Pierwszy przesłany
Pierwszy przesłany, który spełnia kryteria kontroli jakości
Pierwszy wysłany (Rzeczywisty)
Aktualizacje rekordów badań
Ostatnia wysłana aktualizacja (Rzeczywisty)
Ostatnia przesłana aktualizacja, która spełniała kryteria kontroli jakości
Ostatnia weryfikacja
Więcej informacji
Terminy związane z tym badaniem
Dodatkowe istotne warunki MeSH
Inne numery identyfikacyjne badania
- Pro00160780
Plan dla danych uczestnika indywidualnego (IPD)
Planujesz udostępniać dane poszczególnych uczestników (IPD)?
Informacje o lekach i urządzeniach, dokumenty badawcze
Bada produkt leczniczy regulowany przez amerykańską FDA
Bada produkt urządzenia regulowany przez amerykańską FDA
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