Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT05188001 |
| Other study ID # |
H20-03995 |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
January 1, 2022 |
| Est. completion date |
December 31, 2022 |
Study information
| Verified date |
January 2022 |
| Source |
University of British Columbia |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
The incidence of myocardial injury after non-cardiac surgery (MINS) is approximately 12-15%
and is associated with an increased risk of 30-day mortality, 1-year mortality, and 2-year
major vascular events. Using both traditional longitudinal analysis techniques and novel
methods in machine learning, investigators will explore whether intraoperative and
postoperative vital signs can enhance MINS surveillance by providing temporal prediction of
MINS events.
Description:
The incidence of myocardial injury after non-cardiac surgery (MINS) is approximately 12-15%
and is associated with an increased risk of 30-day mortality, 1-year mortality, and 2-year
major vascular events. Since more than 90% of patients with MINS are asymptomatic, routine
troponin monitoring is required for detection. The postoperative days 0, 1, and 2 accounts
for approximately 40%, 40%, and 10% of MINS, respectively. Presently, the Canadian
Cardiovascular Society (CCS) perioperative guidelines recommend patients identified to be at
risk according to the Revised Cardiac Risk Index (RCRI) and BNP/NT-pro B-type Natriuretic
Peptide (NT-pro BNP) receive daily postoperative troponin monitoring for three days to
identify MINS events (5). European and American societies have similar recommendations for
troponin monitoring to detect MINS.
Current risk stratification models have multiple limitations. Most importantly, they predict
an elevated risk over the postoperative period but cannot pinpoint when MINS may happen in
the postoperative course given the patients' changing condition. Moreover, prescription of
troponin monitoring is not universal, infrequent and inconsistent troponin monitoring may
lead to delayed detection and management, and the rise of troponin is delayed by 3-4 hours
from the time of injury.
Retrospective cohort studies show association amongst intraoperative and postoperative
derangements in vital signs and MINS. Vital signs are routinely available within the
electronic medical record, and may serve as objective predictors (i.e. as opposed to free
text and disease names that have higher risks of misclassification and errors).
Using both traditional longitudinal analysis techniques and novel methods in machine
learning, investigators will explore whether intraoperative and postoperative vital signs can
enhance MINS surveillance by providing temporal prediction of MINS events.
Objectives
1. To develop and internally validate a model that uses the duration and degree of
intraoperative and postoperative hypotension to predict the daily maximum troponin level
from postoperative days zero to two, in a high risk population where troponin monitoring
was ordered.
2. To develop and internally validate a model that uses the duration and degree of
intraoperative and postoperative hypotension to predict daily probability of MINS or
death (binary outcome, according to the 2021 American Heart Association (AHA)
definitions from postoperative days zero to two.
3. To evaluate how different definitions of hypotension affect the primary and secondary
models above, and analyze the intraoperative and postoperative hypotension separately to
determine whether intraoperative or postoperative hypotension alone are sufficient for
prediction.
4. To explore whether other intraoperative and postoperative vital sign information (heart
rate, oxygen saturation, and end-tidal carbon dioxide derangements at various
definitions) add predictive value to the primary and secondary models above.
5. To use machine learning methods to perform exploratory analysis to determine 1) optimal
methods for imputation for time series data; 2) visualization of time series data in the
setting of prediction; 3) development and internal validation of machine learning models
to use the time series data to predict troponin levels; and 4) to determine how many
hours earlier than a binary MINS diagnosis were vital signs able to predict a MINS
diagnosis (as determined by when in the time series prior to MINS diagnosis that the
model achieved various thresholds of predicted probability).
Preoperative laboratory values are collected within 30 days before surgery. Preoperative
vitals are collected within 24 hours before surgery. The total duration of subsequent data
collection will be from the time of surgery up to postoperative day 3 (since the MINS
protocol monitors for 3 days) or hospital discharge or death, whichever occurs first. Since
90% of MINS happen between postoperative days zero and two and the frequency of monitoring
would likely decrease by postoperative day 3, investigators will model troponin from
postoperative day 0 to postoperative day 2 for the primary analysis to balance utility of the
temporal model and prediction data quality.
