Abstract 13004: Continuous Quality Review of ICU CPR Events: Performance, Outcomes and Effect of Systems-Level Changes
Bibliographic record
Abstract
Introduction: Outcomes of in-hospital cardiac arrest remain disappointing. Continuous quality review (CQR) has been used to improve provider, team and systems performance during CPR. Little data exists on the incidence of individual, team and systems errors during in-hospital CPR and the influence of errors and systems-level changes on outcomes are equally unexplored. Hypothesis: CQR will identify consistent errors in three performance domains (technical, team, systems). Environmental factors and systems changes will affect code quality and outcomes. Methods: Analysis of prospectively collected data describing all ICU CPR events from September 2009-December 2014 in a tertiary care children’s hospital. Monthly quality review by a multidisciplinary team focused on three quality metrics (noise, leadership and equipment failure) and was later expanded to six metrics (communication, technical or systems errors). Results: There were 243 arrests in 193 patients, most of whom had primary cardiac (39%) or pulmonary disease (23%). Most events occurred during the day (7am-7pm); 27% occurred on a weekend or holiday. Median compression time was 5 minutes (IQR 2-15min). Event survival was 80% (70% ROSC, 10% ECMO) and 48% of hospitalizations ended in death. Excess noise (19%), leadership confusion (11%) and equipment failure (6%) were common. Quality CPR (all 3 elements) occurred 74% of the time and was not associated with event survival. Excess noise (p<0.01) and leadership confusion (p=0.02) were more common during the day. A more recent cohort (n=34) demonstrated frequent communication, systems and technical errors (15%, 21% and 21% respectively). Only 38% of arrests had all 6 quality elements. In multivariable analysis, leadership confusion was associated with ICU survival (OR 2.2; 0.66-1). Statistical process control (SPC) only showed special cause variation in single quality metrics despite multiple systems level changes. Code incidence trended down after opening a new hospital and creation of a separate cardiac ICU service (p=0.07). Conclusion: Individual, team and systems errors are common during CPR in critically ill children. CQR incorporating SPC should be used to track performance and monitor the impact of QI initiatives and systems changes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".