The Utility of ICU Readmission as a Quality Indicator and the Effect of Selection*
Bibliographic record
Abstract
OBJECTIVES: Intensive care readmission rates are used to signal quality, yet it is unclear whether they represent poor quality in the transition of care from the ICU to the ward, patient factors, or differences in survival of the initial admission. This study aims to measure the selection effect of surviving the initial ICU admission on readmission rates. DESIGN: Retrospective cohort study of adult patients admitted to ICUs participating in the Case Mix Program database from the Intensive Care National Audit Research Centre. SETTINGS: The study includes 262 ICUs in the United Kingdom. PATIENTS: The study includes 682,975 patients admitted to ICUs between 2010 and 2014. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The study includes 682,975 patients admitted to ICUs in the United Kingdom. There were 591,710 patients discharged alive, of which 9,093 (1.53%) were readmitted within the first 2 days of ICU discharge. Post-ICU admission hospital mortality and ICU readmission were poorly correlated (r = 0.130). The addition of a selection model resulted in a weaker correlation (r = 0.082). CONCLUSIONS: ICU readmission performed poorly as a performance metric. The selection process by which only patients who survive their index admission are eligible for readmission has a significant effect on ICU readmission rankings, particularly the higher ranked ICUs. Failure to consider this selection bias gives misleading signals about ICU performance and leads to faulty design of incentive schemes.
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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 | 0.062 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".