Association between critical care occupancy and code status decisions during resource scarcity: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Code status determination typically relies on the expected benefits and harms of treatment intensification and patient values and preferences. Resource availability may also influence code status decisions. During the COVID-19 pandemic, the demand for critical care often exceeded the available resources. This study investigated the association between critical care occupancy and code status decisions during the COVID-19 pandemic. METHODS: We conducted a retrospective cohort study of adult patients hospitalized at Geneva University Hospital for acute COVID-19-related illness during two successive pandemic waves, in spring and autumn 2020. Multivariable logistic regression was used to analyze the association between critical care occupancy at admission and code status attribution while accounting for clinical and demographic characteristics, including age, sex, ROX index (pulse oximetry/fraction of inspired oxygen/respiratory rate), comorbidities, malignancy, nationality, insurance, and socioeconomic status. RESULTS: A total of 2,122 patients were included in the analysis. Higher critical care occupancy was associated with an increased likelihood of being assigned an intensive care unit (ICU)-ineligible code status. The odds ratios (ORs) were 1.61 (95% CI 1.11-2.32), 1.59 (1.11-2.28) and 1.71 (1.06-2.76) for critical care occupancy levels of 100-119%, 120-139% and ≥ 140%, respectively, compared with the prepandemic baseline capacity. Other factors significantly associated with the assignment of an ICU-ineligible code status included age 70-79 years (OR 8.56; 95% CI 4.12-17.77), 80-89 years (OR 32.78; 95% CI 16.16-66.50) and ≥90 years (OR 49.04; 95% CI 23.05-104.31) and a higher comorbidity index (OR 1.22; 95% CI 1.07-1.39). Conversely, complementary hospitalization insurance was associated with lower odds of being assigned an ICU-ineligible code status (OR 0.52; 95% CI 0.29-0.92). CONCLUSIONS: Our study revealed a positive association between critical care occupancy and ICU-ineligible code status, suggesting the presence of implicit triaging during periods of high resource strain. This raises several ethical concerns, including the use of non-consensual triage criteria, lack of transparency and the risk of moral distress for healthcare professionals.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".