Comparison of risk-adjusted cumulative quality control charts compared with standardized mortality ratios in critical care
Bibliographic record
Abstract
PURPOSE: The optimal method for monitoring intensive care unit (ICU) performance is unknown. We sought to compare process control charts using standardized mortality ratio (SMR), p-charts, and cumulative sum (CUSUM) charts for detecting increases in risk-adjusted mortality within ICUs. METHODS: Using data from 17 medical-surgical ICUs that included 29,592 patients in Ontario, Canada, we created risk-adjusted p-charts and SMRs on monthly intervals and CUSUM charts. We defined positive signals as any data point that was above the 3-sigma limit (approximating a 99% confidence interval [CI]) on a p-chart, any data point whose 95% CI did not include 1 for the SMR charts, and when a data point exceeded control limits for an odds ratio of 1.5 for CUSUM charts. We simulated increases in mortality of 10%, 30%, and 50% for each ICU to determine the sensitivity of each method. We calculated sensitivity as the number of positive signals divided by the number of ICUs (equal to number of simulated events). RESULTS: Cumulative sum charts generated 31 signals in 12 different ICUs, while p-charts and SMR agreed in 10 and 6 of these signals, respectively, followed by 21 signals from p-charts across 14 ICUs (agreement in 10 of these signals for both CUSUM and SMR) and 15 signals from SMR charts across eight ICUs (agreement from p-charts and CUSUM in 10 and six signals, respectively). The p-chart had a sensitivity of 88% (95% CI, 73 to 104) for a 50% simulated increase in ICU mortality followed by CUSUM at 71% (95% CI, 49 to 102) and SMR at 59% (95% CI, 35 to 82). Performance with lower simulated increases was poor for all three methods. CONCLUSIONS: P-charts created with risk-adjusted mortality at monthly intervals are potentially useful tools for monitoring ICU performance. Future studies should consider usability testing with ICU leaders and application of these methods to additional clinical domains.
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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 | 0.074 | 0.273 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".