Predictors of mortality and cost among surgical patients requiring rapid response team activation
Bibliographic record
Abstract
Background: Prior 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. Methods: We 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. Results: We 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. Conclusion: RRT 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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".