Prevalence, Risk Factors, and Clinical Consequences of Recurrent Activation of a Rapid Response Team: A Multicenter Observational Study
Bibliographic record
Abstract
Introduction: Rapid response teams (RRTs) are groups of health-care providers, implemented by hospitals to respond to distressed hospitalized patients on the hospital wards. Patients assessed by the RRT for deterioration may be admitted to the intensive care unit (ICU) or may be triaged to remain on the wards, putting them at risk of recurrent deterioration and repeat RRT activation. Previous studies evaluating outcomes of patients with recurrent deterioration and multiple RRT activations have produced conflicting results. Methods: We used a prospectively collected multicenter registry from 2 hospitals within a single tertiary-level hospital system between 2012 and 2016. Comparisons were made between patients with a single RRT activation and those with multiple RRT activations over the course of their admission. Primary outcome was in-hospital mortality, which was analyzed using multivariable logistic regression. Results: A total of 5995 patients who had any RRT activation were analyzed. Of that, 1183 (19.7%) patients had recurrent deterioration and multiple RRT activations during their admission. Risk factors for recurrent deterioration included admission from a home setting (as opposed to a long-term care facility), RRT activation during nighttime hours, and delay (>1 hour) to RRT activation. Recurrent deterioration was associated with increased odds of mortality (adjusted odds ratio [OR]: 1.44 [1.28-1.64], P = <.001). Increasing number of RRT activations were associated with increasing risk of mortality. Patients with recurrent deterioration had prolonged median hospital length of stay (21.0 days vs 12.0 days, P < .001), while patients with only a single activation were more likely to be admitted to the ICU (adjusted OR: 2.30 [1.96-2.70], P < .001). Conclusions: Recurrent deteriorations leading to RRT activations among hospitalized patients are associated with increased odds of mortality and prolonged hospital length of stay. This work identifies a group of patients who warrant closer attention to help reduce adverse outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".