Presentation, care, and outcomes of patients with NSTEMI according to World Bank country income classification: the ACVC-EAPCI EORP NSTEMI Registry of the European Society of Cardiology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The majority of NSTEMI burden resides outside high-income countries (HICs). We describe presentation, care, and outcomes of NSTEMI by country income classification. METHODS AND RESULTS: Prospective cohort study including 2947 patients with NSTEMI from 287 centres in 59 countries, stratified by World Bank country income classification. Quality of care was evaluated based on 12 guideline-recommended care interventions. The all-or-none scoring composite performance measure was used to define receipt of optimal care. Outcomes included in-hospital acute heart failure, stroke/transient ischaemic attack, and death, and 30-day mortality. Patients admitted with NSTEMI in low to lower-middle-income countries (LLMICs), compared with patients in HICs, were younger, more commonly diabetic, and current smokers, but with a lower burden of other comorbidities, and 76.7% met very high risk criteria for an immediate invasive strategy. Invasive coronary angiography use increased with ascending income classification (LLMICs, 79.2%; upper middle income countries [UMICs], 83.7%; HICs, 91.0%), but overall care quality did not (≥80% of eligible interventions achieved: LLMICS, 64.8%; UMICs 69.6%; HICs 55.1%). Rates of acute heart failure (LLMICS, 21.3%; UMICs, 12.1%; HICs, 6.8%; P < 0.001), stroke/transient ischaemic attack (LLMICS: 2.5%; UMICs: 1.5%; HICs: 0.9%; P = 0.04), in-hospital mortality (LLMICS, 3.6%; UMICs: 2.8%; HICs: 1.0%; P < 0.001) and 30-day mortality (LLMICs, 4.9%; UMICs, 3.9%; HICs, 1.5%; P < 0.001) exhibited an inverse economic gradient. CONCLUSION: Patients with NSTEMI in LLMICs present with fewer comorbidities but a more advanced stage of acute disease, and have worse outcomes compared with HICs. A cardiovascular health narrative is needed to address this inequity across economic boundaries.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it