Search

Objectives: Patients experiencing prolonged critical illness after cardiac surgery represent a resource- intensive group with a high risk of mortality. The authors sought to derive and validate a multivariate model that accurately predicts 1-year mortality in people who have been critically ill for at least 1 week after cardiac surgery., Design: This was a retrospective population-based cohort study using linked administrative data., Setting: Eleven hospitals providing cardiac surgical care in the Canadian province of Ontario., Participants: All adult patients aged ≥18 years undergoing 1 of the 5 most common major cardiac surgical procedures between April 1, 2009, and March 31, 2014., Interventions: None., Measurements and Main Results: The authors' primary exposure was presence in an intensive care unit on the seventh postoperative day (POD7) and the primary outcome was all-cause mortality occurring after POD7 and within 1 year from the date of surgery. Candidate predictors included patient demographics, surgical details, preoperative medical conditions, postoperative status, and life supportive therapies utilized on POD7. Descriptive statistics were used to compare predictor variables between participants who did or did not die in the year after surgery. The prediction model was derived in the full data set using logistic regression and the prespecified set of predictor variables. A total of 2,031 individuals remained in an intensive care unit on POD7 (4.8% of all cardiac surgery patients). Five hundred twenty-one people died (25.6%) in the year after surgery; 353 died before hospital discharge (17.3% of total cohort, 67.8% of deaths). Requirement for multiple vasoactive or inotropic medications was the strongest predictor of mortality, followed by need for invasive ventilation, 3 or more preoperative comorbidities, need for a single inotropic or vasoactive medication, and requirement for dialysis before surgery. The derivation area under the curve was 0.80, and the model was well- calibrated with a Hosmer-Lemeshow p value of 0.5 and good calibration across risk deciles., Conclusions: A prespecified multivariate model using clinically relevant, routinely available variables was able to accurately predict death among those with prolonged critical illness after cardiac surgery., Competing Interests: Conflict of Interest None of the authors declare any conflict of interest relevant to this work., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Objective: To externally validate the predictive performance of the logistic and additive Cardiac Surgery Score (CASUS), a postoperative severity of illness score designed specifically for prediction of mortality in the cardiac surgery intensive care unit., Design: A retrospective analysis of prospectively collected data between July 1, 2012, and September 30, 2015., Setting: Single university cardiac surgery intensive care unit in Canada., Participants: Consecutive adult patients (n = 4,519) admitted to the intensive care unit after cardiac surgery., Intervention: None., Measurements and Main Results: The mortality predicted by logistic CASUS was calculated for each patient on admission day 0 and postoperative days 2, 4, 7, and 10 using the original model equation. The mortality predicted by additive CASUS was determined on each day with separate logistic regression models, using the total score as a single variable. The observed mortality was 1.8%. Logistic CASUS overestimated mortality by 78%, 59%, 51%, 52%, and 29% on days 0, 2, 4, 7, and 10, respectively. After model updating with logistic calibration, logistic CASUS consistently provided estimates of death comparable with the observed mortality, as determined with the Hosmer-Lemeshow goodness-of-fit test. The stability of those estimates was confirmed by bootstrapping. Similar calibration results were obtained with additive CASUS. Logistic and additive CASUS had good discrimination with areas under the receiver operating characteristic curve greater than 0.7 on each study day., Conclusions: Recalibrated logistic CASUS reliably predicts mortality in the intensive care unit after cardiac surgery. Logistic regression models derived from additive CASUS perform as well as logistic CASUS., (Copyright © 2019 Elsevier Inc. All rights reserved.)