Accuracy of the Canadian COVID-19 Mortality Score (CCMS) to predict in-hospital mortality among vaccinated and unvaccinated patients infected with Omicron: a cohort study
Bibliographic record
Abstract
Objective The objective is to externally validate and assess the opportunity to update the Canadian COVID-19 Mortality Score (CCMS) to predict in-hospital mortality among consecutive non-palliative COVID-19 patients infected with Omicron subvariants at a time when vaccinations were widespread. Design This observational study validated the CCMS in an external cohort at a time when Omicron variants were dominant. We assessed the potential to update the rule and improve its performance by recalibrating and adding vaccination status in a subset of patients from provinces with access to vaccination data and created the adjusted CCMS (CCMS adj ). We followed discharged patients for 30 days after their index emergency department visit or for their entire hospital stay if admitted. Setting External validation cohort for CCMS: 36 hospitals participating in the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN). Update cohort for CCMS adj : 14 hospitals in CCEDRRN in provinces with vaccination data. Participants Consecutive non-palliative COVID-19 patients presenting to emergency departments. Main outcome measures In-hospital mortality. Results Of 39 682 eligible patients, 1654 (4.2%) patients died. The CCMS included age, sex, residence type, arrival mode, chest pain, severe liver disease, respiratory rate and level of respiratory support and predicted in-hospital mortality with an area under the curve (AUC) of 0.88 (95% CI 0.87 to 0.88) in external validation. Updating the rule by recalibrating and adding vaccination status to create the CCMS adj changed the weights for age, respiratory status and homelessness, but only marginally improved its performance, while vaccination status did not. The CCMS adj had an AUC of 0.91 (95% CI 0.89 to 0.92) in validation. CCMS adj scores of <10 categorised patients as low risk with an in-hospital mortality of <1.6%. A score>15 had observed mortality of >56.8%. Conclusions The CCMS remained highly accurate in predicting mortality from Omicron and improved marginally through recalibration. Adding vaccination status did not improve the performance. The CCMS can be used to inform patient prognosis, goals of care conversations and guide clinical decision-making for emergency department patients with COVID-19.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".