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.

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.

Inclusion Criteria:

• Experienced a perioperative stroke
• Underwent cardiac surgery

Exclusion Criteria:

- none

All patients who received a diagnosis of stroke in the NSQIP database after a cardiac surgical procedure between 2005 and 2020.

The NSQIP data is available upon request to investigators at participating sites. We will not directly share our dataset as per the NSQIP Participant Data Use agreement.