Risk Stratification for Patients with Chest Pain Discharged Home from the Emergency Department
Bibliographic record
Abstract
For patients with chest pain who are deemed clinically to be low risk and discharged home from the emergency department (ED), it is unclear whether further laboratory tests can improve risk stratification. Here, we investigated the utility of a clinical chemistry score (CCS), which comprises plasma glucose, the estimated glomerular filtration rate, and high-sensitivity cardiac troponin (I or T) to generate a common score for risk stratification. In a cohort of 14,676 chest pain patients in the province of Ontario, Canada and who were discharged home from the ED (November 2012–February 2013 and April 2013–September 2015) we evaluated the CCS as a risk stratification tool for all-cause mortality, plus hospitalization for myocardial infarction or unstable angina (primary outcome) at 30, 90, and 365 days post-discharge using Cox proportional hazard models. At 30 days the primary outcome occurred in 0.3% of patients with a CCS < 2 (n = 6404), 0.9% of patients with a CCS = 2 (n = 4336), and 2.3% of patients with a CCS > 2 (n = 3936) (p < 0.001). At 90 days, patients with CCS < 2 (median age = 52y (IQR = 46–60), 59.4% female) had an adjusted HR = 0.51 (95% confidence interval (CI) = 0.32–0.82) for the composite outcome and patients with a CCS > 2 (median age = 74y (IQR = 64–82), 48.0% female) had an adjusted HR = 2.80 (95%CI = 1.98–3.97). At 365 days, 1.3%, 3.4%, and 11.1% of patients with a CCS < 2, 2, or >2 respectively, had the composite outcome (p < 0.001). In conclusion, the CCS can risk stratify chest pain patients discharged home from the ED and identifies both low- and high-risk patients who may warrant different medical care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".