jason shahin assistant professor department of critical care · pulse oximeter plethysmography...
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Jason Shahin MD Msc FRCPC Assistant Professor McGill University Department of Critical Care
DCD overview
Withdrawal of care in the ICU
Time to death- models and important variables
Future research
•1st cadaveric transplants •Poor outcomes
•Concept of brain death emerges •Legislation adopted approving neurological definition of death •Field of transplantation is opened up
•Due to success organ need outstrips supply
•DCD re-examined
Criteria used to determine death
Time period required to confirm “irreversible death”
Autoresuscitation
Ethical considerations “violation of the dead donor rule”
Time to death prediction- warm ischemia
Resource use
60-120 minutes
“Orchestration of technology”
“Socially negotiated”
“Nuanced for each patient”
“Some people walk in and yank the endotracheal tube and
others will say “let’s stop the drugs, let’s stop the oxygen.” I
have trouble yanking out the endotracheal tube probably
because I think that it increases the chances that the patient
is going to die actively trying to breathe against an
obstructed airway. I don’t think that’s a nice way to die. I find
it a little tougher to do that than to say, “I think if we turn off
the drug he’s not going to last very long.” For me, personally,
it’s a lot easier to turn off the drug. I guess it relates to how I
see the patient’s comfort. “[interview with intensivist]
Are there variables and or models/clinical
decision rules that exist that can accurately
predict the time it will take a patient to die
after withdrawal of life sustaining therapy ?
Systematic review of the literature
1. Jason Shahin 2. Laveena Munshi
In patients who have a withdrawal of life sustaining therapy what are the risk factors/ risk prediction models/clinical decision tools that are associated with and/or predict time to death.
In the potential organ donor who has a
withdrawal of life sustaining therapy what are the risk factors/ risk prediction models/clinical decision tools that are associated with and/or predict time to death? (Class III DCD)
Participants Study type Outcome
Pediatric and adults RCT Time to death from WLST
Withdrawal of life sustaining therapy (mechanical ventilation and or hemodynamic support)
Observational studies
WLST occuring in a critical care unit
Single centre or multicentre
No case series
Collaborated with a University librarian
trained in systematic review searches
MEDLINE, EMBASE and Central
No year of publication limit
Limits: English, humans
Most variables focus on pre withdrawal physiology and clinical signs (neuro, cardiac, resp, treatment plan)
Most widely used models (University of Wisconsin) developed using small sample sizes and require a ventilator cessation trial
Other models exist but have not been externally validated yet
The Brevia model may not be generalisable
Physician assessment may be as good as any model??
www.ddepict.com
Sonny Dhanani Principal investigator- Critical Care, Children’s Hospital of Eastern Ontario, University of Ottawa-Principal investigator
Laura Hornby Clinical Research Project Manager, Montreal Children’s Hospital Research Institute
Katherine Smith Central coordinator, Loeb Research Chair in organ and tissue donation, University of Ottawa
Sam Shemie Senior investigator-Critical Care, Montreal Children's Hospital, Chair Loeb Research Consortium, Faculty of Arts, Univ. of Ottawa
Jason Shahin Lead investigator for complimentary study-time to death prediction tool-Critical Care, McGill University Health Centre
www.ddepict.com
Primary Objective To determine the incidence of
autoresuscitation (as reported by the healthcare team) in critically ill adults and children who die in the ICU, following WLST.
Criteria used to determine death
Time period required to confirm “irreversible death”
Autoresuscitation
Ethical considerations “violation of the dead donor rule”
Time to death prediction- warm ischemia
Resource use
www.ddepict.com
DePPaRT study
Complimentary study 1
Time to death prediction study
Complimentary study 2
Surrogate decision making in DCD
DePPaRT Collaborators/Co-investigators
CANADA
Andrew Baker
Stephen Beed
Jane Chamber-Evans
Jennifer Chandler
Chip Doig
Peter Dodek
Rob Fowler
Jan Friedrich
Teneille Gofton
Vanessa Gruben
AnneMarie Guerguerian
Christophe Herry
George Isac
Greg Knoll
Jim Kutsogiannis
Lauralyn McIntyre
Maureen Meade
Laveena Munshi
Tim Ramsay
Steven Reynolds
Damon Scales
Jason Shahin
Andrew Seely
Janet Squires
Alexis Turgeon
Bryan Young
US
Tom Nakagawa
Paul Shore
UK
Christian Brailsford
Dale Gardiner OTHER Frantisek Duska TRAINEES Alvin Li Loretta Norton Amanda van Beinum TEAM Laura Hornby Katherine Smith Nathan Scales
Primary Objective To develop a new reliable tool to predict time
to death following WLST in critically ill adults eligible for DCD.
Inclusion criteria
Patients who have a WLST in an ICU
Age > 1 month
Subjects will have a minimum of the following bedside monitors in place:
▪ Pulse oximeter plethysmography
▪ Continuous 3-lead electrocardiogram
▪ Invasive arterial blood pressure monitoring
▪ EEG- (a few centres)
Exclusion criteria Declared dead by NDD criteria Functioning pacemaker
GROUP 1: DCD patients had consented to be DCD donors and whose organs were
recovered
GROUP 2: “DCD Eligible” patients consented to DCD but did not proceed to donation or
GROUP 3: “DCD non-Eligible” General ICU patients Any other patients who fulfill study inclusion and
exclusion criteria but who would did not meet criteria to be considered eligible to be DCD donors.
At least 300
Up to 200
DATA COLLECTION
Demographic Comorbidities Physiological Imaging (Ct head) Analgesia and sedatives Withdrawal process Intensivist opinion Time to death
ANALYSIS
Multivariable logistic regression analysis
Parsimonious model building
Step 1-Systematic review, expert opinion
Step 2-Prospective data collection
Step 3
Model development
Step 4 Model
validation
1. More accurate prediction tool will allow health care practitioners to focus efforts on candidates with a high probability of dying within 2 hours
2. Increased confidence in and better implementation of DCD practice
Time to death is one of the major barriers to Further implementation of DCD
Existing data has contributed to understanding of important variables-but may be insufficient
Future research on death determination being carried out at a hospital near you
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