Clinical Trial Details
— Status: Recruiting
Administrative data
NCT number |
NCT05333146 |
Other study ID # |
H21-03915 |
Secondary ID |
|
Status |
Recruiting |
Phase |
|
First received |
|
Last updated |
|
Start date |
April 18, 2022 |
Est. completion date |
July 1, 2023 |
Study information
Verified date |
June 2022 |
Source |
University of British Columbia |
Contact |
Alana Flexman, MD |
Phone |
(604) 806-8337 |
Email |
aflexman[@]providencehealth.bc.ca |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
Clinical Trial Summary
Perioperative stroke is a devastating complication of cardiac surgery that is currently
poorly characterized but occurs in 1-5% of patients and is associated with poor outcomes
including increased mortality. Given the uncommon nature of this complication, relatively
little is known about which factors predict these outcomes among those who experience a
perioperative stroke. The study objectives are to identify predictors of mortality, length of
stay and discharge disposition after perioperative stroke in cardiac surgery using the
prospectively-collected American College of Surgeons National Surgical Quality Improvement
Program database between 2005 and 2020.
Description:
BACKGROUND Perioperative stroke is a devastating complication of cardiac surgery that is
currently poorly characterized. Perioperative stroke is a cerebrovascular event that occurs
after cardiac surgery, and affects between1-5% of patients. The current literature has
identified that patients who experience a stroke after surgery have a higher rate of
mortality, length of stay and discharge to a facility, but given the rare nature of this
complication less is known about which factors predict these outcomes among those who
experience a perioperative stroke.
OBJECTIVES
1. Derive and externally validate risk prediction models for mortality (primary outcome),
adverse discharge, and length of stay after perioperative stroke.
2. Describe temporal trends in mortality after perioperative stroke between 2005 and 2020.
METHODS This study is a retrospective analysis of the prospectively-collected American
College of Surgeons National Surgical Quality Improvement Program database between 2004 and
2020. The study cohort will be extracted from the NSQIP database and include all patients who
experienced a stroke within 30 days of surgery and who underwent a cardiac surgical
procedure.
STUDY POPULATION Patients who underwent any cardiac surgical procedure and who experienced a
perioperative stroke in the NSQIP database between 2005 and 2020 will be included.
OUTCOMES Primary outcome is 30-day mortality; secondary outcomes are length of hospital stay
and adverse discharge (non-home facility or death).
Candidate predictor variables: Outcome after perioperative stroke is potentially related to
patient, surgical, and anesthetic factors, as well as characteristics of the stroke.
Candidate predictor variables will include patient characteristics (age, sex, comorbidities),
surgical characteristics (complexity, type, emergency status, aortic surgery), postoperative
complications (cardiac arrest, myocardial ischemia, transfusion) and stroke characteristics
(severity as determined by associated tracheostomy or craniectomy), timing relative to
operation, readmission for stroke vs inpatient stroke). Continuous variables will be
considered for transformation using fractional polynomials to allow a continuous non-linear
association.
ANALYSIS Multivariable models to predict 30-day mortality (primary outcome), adverse
discharge and length of stay will be created. To avoid over-fitting, we will undertake a data
reduction strategy and exclude variables with greater than 10% missing data or less than 20
observations, where >1% but <10% data are missing, we will consider multiple or mean
imputation.
Pre-specified predictor variables will be used to construct a logistic regression model using
a principle component analysis. We will a priori examine the following interactions:
age*gender, surgical complexity (operation time)*age. Given the potential differential
mechanisms of early (<48h) and late (>48h and <30 days) perioperative stroke, we will include
days from surgery to event as both a continuous and categorical variable.
Model discrimination will be evaluated using the area under the receiver operating
characteristic curve (c-statistic). Model calibration will be assessed with a loess smoothed
plot of observed vs predicted risks over the risk spectrum. A similar analysis will be used
to create a prediction model for length of stay. As death is a competing outcome for
discharge disposition, adverse discharge will be modelled as an ordinal outcome (home,
non-home discharge, or death). Following derivation, 5,000 bootstrap samples will be used for
internal validation.
Temporal trends in mortality will be analyzed first using an exploratory unadjusted ordinary
least squares regression model with annual mortality rate after perioperative stroke as the
dependent variable and year as the predictor to estimate the yearly change in mortality rate
over time. A multivariable linear regression model will be specified, adjusting for important
predictors.