PREDICTING TIME TO DEATH IN DCD & THE IMPACT OF WLSM ... and Dying T… · Happened within 30...

DEATH….

THE GREAT UNKNOWN

PREDICTING TIME TO DEATH IN DCD & THE IMPACT OF WLSM PROCEDURES

Dr Jason Shahin

MDCM FRCPC MSC

McGill University

Dept of Critical Care

TWO QUESTIONS TODAY?

1) When do we die

2) How do we die

QUESTION 1- WHEN DO WE DIEDCD-BARRIERS AND CONTROVERSIES

• Time period required to confirm “irreversible death”

• Autoresuscitation

• Ethical considerations “violation of the dead donor rule”

• Time to death prediction

- warm ischemia

DCD OVERVIEW60-120 minutes

DCD OVERVIEW60-120 minutes

Withdrawal of life support

therapy

Systematic review

DEPPART SUB-STUDY OVERVIEW

Research Question:

Can time to death be predicted ?

Primary Objective:

To develop a model that can predict the time from WLSM to death in

potential DCD donors

7

STUDY DESIGN & GOALS

Prospective observational study

306 patients from 19 ICUs in Canada (12 adult, 2

pediatric), Czech republic and The Netherlands

Inclusion criteria: all DCD eligible patients where

imminent death anticipated and a decision for WLSM

had been made

8

`

WLSM

•Vitals/Neuro exam

•First act of WLSM

•Time of extubation

•Death Prediction (Physician)

Circulatory, Respiratory, pharmacological 1h before WLSM until declaration of death

Declaration of death

Death

•Labs, CT scan

•APACHE IIDecision to WLSM

+/-Consent to DCD

•Demographic data

•Diagnosis

•Comorbidities

•Length of ICU stay

Consent to study

DATA COLLECTION– TIMELINE

WHAT WE DID

Developed a risk prediction tool

using 2 philosophically different

approaches

1. Classical regression- multivariable

analysis logistic regression

2. Machine learning- Purely

statistical using a Random Forrest

ensemble method

1. Predictors chosen either a priori or based on statistical

significance in sample population

2. Logistic regression used with death as the binary

outcome

3. Predictors may be eliminated based on statistical

association with the outcome to produce a parsimonious

model

CLASSICAL APPROACH

OVERFITTING

1. Predictors chosen either a priori or based on statistical

significance in sample population

2. Logistic regression used with death as the binary

outcome

3. Predictors may be eliminated based on statistical

association with the outcome to produce a parsimonious

model

CLASSICAL APPROACH

WHAT WE DID

Developed a risk prediction tool

using 2 philosophically different

approaches

1. Classical regression- multivariable

analysis

2. Machine learning- Purely

statistical using a Random Forrest

ensemble method

WHAT IS RANDOM FORREST?

HOW DOES A RANDOM FORREST WORK

ANALYSIS AND RESULTS

Expert Opinion

Classical model

13

Machine learning

model

22

Pool of 22

predictors

Predictors from

literature and clinical

judgement

Whole cohort of WLST

639

DCD eligible

306 (47.9%)

Proceeded to DCD

87 (67.9%)

No Organ procurement

21 (24.2%)

Organ procurement

67 (75.8%)

-Time to death deemed too long

-Family uninterested

-SDM refused

Other

Prediction Model

Survival curves for whole cohort

60% of patients died

within 2-hrs

2

TIME TO DEATH-BY 30 MIN INTERVALS

Classical model Random Forest

Full data Accuracy 0.77 0.90

False Negative Rate 0.21 0.10

False Positive Rate 0.27 0.11

Misclassification Rate 0.23 0.10

Area Under the Curve 0.86 0.96

Performance measures for DCD eligible group

Classical

model

Random

Forest


Performance measures for DCD eligible group

10-fold cross validationClassical

model

Random

Forest


Performance measures for DCD eligible group-validation

Performance measures of prediction models for entire cohort (639)

Classical

model

Random

Forest


Performance measures for entire cohort

10-fold cross validation Classical

model

Random

Forest


Potential predictors Odds Ratio (95% CI)

Admission Diagnosis (ref: Traumatic brain injury)

Non-traumatic brain injury neurological 0.9 (0.38, 2.14)

Surgery (non-traumatic brain injury) 1.09 (0.26, 4.58)

Medical 1.01 (0.42, 2.46)

Comorbidities (ref: Cardio-respiratory)

Other 1.49 (0.74, 3.00)

None 2.11 (1.01, 4.39) *

BMI (kg/m2) [≥30 vs <30] 1.78 (0.95, 3.33)

APACHE II Score [ref: Score <15]

Score 15-24 0.53 (0.15, 1.81)

Score ≤25 0.06 (0.17, 2.08)

At one-hour pre-WLST

Cardiac arrest with resuscitation in 24 hours pre-WLST [Yes vs No] 0.98 (0.40, 2.38)

Vasopressor use 1-hour pre-WLST [Yes vs No] 0.98 (0.52 1.86)

Systolic blood pressure (mm Hg) [>100 vs ≤ 100] 0.64 (0.32, 0.28)

Opioid analgesic use 1-hour pre-WLST [Yes vs No] 1.65 (0.80, 3.43)

GCS score [3 vs >3] 2.37 (1.26, 4.45) *

Pupillary reflex [Yes vs No] 0.63 (0.2, 1.27)

PaO2 to FiO2 ratio [ref: ≤100]

PaO2 to FiO2 ratio 101-200 0.98 (0.28, 3.40)

PaO2 to FiO2 ratio >200 0.43 (0.13, 1.38)

Respiratory rate (breaths/ minute) [ref: <12]

Respiratory rate 12-25 0.63 (0.23, 1.71)

Respiratory rate >25 0.77 (0.24, 2.47)

Spontaneous respiration [Yes vs No] 0.93 (0.32, 2.71)

Physician’s Prediction [Yes vs No] 7.21 (3.89, 13.38) *

Potential predictors Odds Ratio (95%

CI)

GCS score [3 vs >3] 2.37 (1.26, 4.45) *

Physician’s Prediction [Yes v No] 7.21 (3.89, 13.38) *

• Access to real time data

• Patient trajectory

Predicted outcome

Physician prediction

Physiology

Demographics

&

Co-morbidities

WLSM

method

HOW DO PEOPLE WITHDRAW LIFE SUPPORT?

IS WLSM DIFFERENT FOR DCD CANDIDATES?

QUESTION 2: HOW DO PEOPLE DIE

“The management of the dying

process...should proceed according to

existing ICU practice...not be influenced

by donation potential”

“Thou shalt not do anything different”

WLSM APPROACH

WLSM APPROACH

Reduce ventilator supportReduce ventilator and vasopressors

Extubation only Stopped vasopressors only

Stopped vasopressors and extubated

Multi-step ventilator reduction

WLSM APPROACH

Extubation Stopped vasopressors

Everything else is considered weaning

Vasopressor used

Stopped within 30

minutes of WLSM

Extubated

Happened within 30

minutes of WLSM

Description of Predictor: Vasopressor stopped & Extubated within 30 minutes of WLSM

Considered

vasopressor:

dobutamine

dopamine

ECMO epinephrine

intra-aortic balloon

milrinone

norepinephrine

phenylephrine

terlipressin

vasopressin

Whole cohort of WLSM

639

DCD eligible

205 (32.1%)

Approached for DCD 138 (67.3%)

Proceeded to attempted DC

89 (64.5%)

No Organ procurement

23 (25.9%)

Organ procurement

66 (74.1%)

SDM refused

Other

-DCD not attempted

116 (56.6%)

Relationship between methods of WLSM and time to death [DCD eligible: 205 patients]

Time to death Analysis Crude Analysis Odds ratio lower CI upper CI P-value

30 minAdjusted Vasopressor method[stopped] 1.37 0.87 2.16 0.18

Extubation [Yes] 2.51 0.97 6.50 0.06

1 hourAdjusted Vasopressor method[stopped] 1.44 0.83 2.50 0.20

Extubation [Yes] 2.54 1.15 5.60 0.02

2 hourAdjusted Vasopressor method [stopped] 1.45 0.64 3.27 0.37

Extubation [Yes] 2.16 1.00 4.64 0.05

• Adjusted for age, APACHE II , admission diagnosis and center

clustering

DCD Attempted DCD not-attempted p-value

Variables Categories count % count %

89 116

Vasopressor method of WLSM stopped 24 92.3 49 98.0 0.4

Kept on 1 1.14 1 0.86

wean 0 0 0 0

not on-

vasopressor63 71.59 66 56.9

Respiratory method of WLSM Extubated 74 83.15 75 64.66<0.001

not-extubated 5 5.62 38 32.76

wean 10 11.24 3 2.59

Table 1: Methods of WLSM in DCD attempted versus not attempted

DCD Attempted DCD not-attemptedp-value

Variables Categories count % count %

Respiratory

method of WLSMExtubated 74 83.15 75 64.66

<0.001

not-extubated 5 5.62 38 32.76

wean 10 11.24 3 2.59

Table 1: Methods of WLSM in DCD attempted versus not attempted

Table 3: Opioid dose type and total doses [DCD eligible 204 patients]

Opioid delivery of morphine equivalency

Dose type Total dose

count % Mean SD

Bolus only-(mg) 17 9.50 16.65 24.89

Drip only-(mg/hr) 122 68.16 5.97 7.77

Both-(mg) 40 22.35 38 37.38

Table 4: Opioid dose type and total doses by DCD attempt

DCD

attempted

DCD not

attempted

Mean SD Mean SD

Bolus only-mg 21.56 34.55 12.28 12.19

Drip only-mg/hr 4.48 4.86 6.97 9.11

Both-mg 40.64 39.89 33.59 33.64

Table 4: Opioid dose type and total doses by DCD attempt

DCD

attempted

DCD not

attempted

Mean SD Mean SD

P-

value

Total dose 17.2 29.2 11.6 18.2 0.13

WE GOT TO THINKING….

• Time has to play a role…

• Are there differences in patients who

die quickly than those that don’t

Morphine doses between DCD attempted vs. not attempted who died within 2 hrs of WLSM

Comparing total doses between DCD attempted vs. not attempted who died and did not died

within 2 hours of WLST

Did not die=

Died=

STUDY CONCLUSIONS

Results

• Small differences in in

extubation rates

• Potential Small

differences in opioid

dosing

Keep in mind…

• Small sample size-hypothesis

generating

• other sedatives!!

• protocolized WLSM

• Didn’t account for Drug kinetics

THE TAKE HOME SLIDE

• Time to death can be predicted using a combination of demographic,

physiological, physician prediction and WLSM approach predictors

• Approach to WLSM may be different between DCD and non DCD

patients- more research needed!

• This work further demonstrates the utmost importance of a

standardized and systematic approach to WLSM in all patients.

THANK YOU

[email protected]

PREDICTING TIME TO DEATH IN DCD & THE IMPACT OF WLSM ... and Dying T… · Happened within 30...

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