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Predictors of mortality and cost among surgical patients requiring rapid response team activation
Authors:Alexandre Tran  Shannon M. Fernando  Daniel I. McIsaac  Bram Rochwerg  Garrick Mok  Andrew J.E. Seely  Dalibor Kubelik  Kenji Inaba  Dennis Y. Kim  Peter M. Reardon  Jennifer Shen  Peter Tanuseputro  Kednapa Thavorn  Kwadwo Kyeremanteng
Abstract:BackgroundPrior studies of rapid response team (RRT) implementation for surgical patients have demonstrated mixed results with respect to reductions in poor outcomes. The aim of this study was to identify predictors of in-hospital mortality and hospital costs among surgical inpatients requiring RRT activation.MethodsWe analyzed data prospectively collected from May 2012 to May 2016 at The Ottawa Hospital. We included patients who were at least 18 years of age, who were admitted to hospital, who received either preoperative or postoperative care, and and who required RRT activation. We created a multivariable logistic regression model to describe mortality predictors and a multivariable generalized linear model to describe cost predictors.ResultsWe included 1507 patients. The in-hospital mortality rate was 15.9%. The patient-related factors most strongly associated with mortality included an Elixhauser Comorbidity Index score of 20 or higher (odds ratio [OR] 3.60, 95% confidence interval [CI] 1.96–6.60) and care designations excluding admission to the intensive care unit and cardiopulmonary resuscitation (OR 3.52, 95% CI 2.25–5.52). The strongest surgical predictors included neurosurgical admission (OR 2.09, 95% CI 1.17–3.75), emergent surgery (OR 2.04, 95% CI 1.37–3.03) and occurrence of 2 or more operations (OR 1.73, 95% CI 1.21–2.46). Among RRT factors, occurrence of 2 or more RRT assessments (OR 2.01, 95% CI 1.44–2.80) conferred the highest mortality. Increased cost was strongly associated with admitting service, multiple surgeries, multiple RRT assessments and medical comorbidity.ConclusionRRT activation among surgical inpatients identifies a population at high risk of death. We identified several predictors of mortality and cost, which represent opportunities for future quality improvement and patient safety initiatives.
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