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Predicting 1-Year Mortality After Cardiac Surgery Complicated by Prolonged Critical Illness: Derivation and Validation of a Population-Based Risk Model.
Metadata
Journaljournal of cardiothoracic and vascular anesthesia2.258Date
2020 May 15
4 months ago
Type
Journal Article
Volume
2020-Oct / 34 : 2628-2637
Author
McDonald B 1, van Walraven C 2, McIsaac DI 3
Affiliation
  • 2. Ottawa Hospital Research Institute, Ottawa, Canada; Institute for Clinical Evaluative Sciences, Ottawa, Canada; Department of Medicine, University of Ottawa and The Ottawa Hospital, Ottawa, Canada; School of Epidemiology,Public Health and Preventative Medicine, University of Ottawa, Ottawa, Canada.
  • 3. Department of Anesthesiology and Pain Medicine, University of Ottawa, Ottawa, Canada; Ottawa Hospital Research Institute, Ottawa, Canada; Institute for Clinical Evaluative Sciences, Ottawa, Canada; School of Epidemiology,Public Health and Preventative Medicine, University of Ottawa, Ottawa, Canada.
Doi
PMIDMESH
Abstract
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.
Keywords: cardiac surgery critical illness mortality outcomes prediction
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J Cardiothorac Vasc Anesthjournal of cardiothoracic and vascular anesthesia
Metadata
LocationUnited States
FromW B SAUNDERS CO-ELSEVIER INC

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