Abstract 16309: Impact of the Pandemic on AMI Hospitalizations and Outcomes: A Comprehensive Medicare Fee-for-Service Analysis
Bibliographic record
Abstract
Introduction: Studies suggest hospitalizations for acute myocardial infarction (AMI) declined early in the COVID-19 pandemic, but national patterns during the entire pandemic are not well characterized. This study aims to characterize the changes in AMI hospitalizations and 30-day mortality rates among Medicare Fee-For-Service (FFS) beneficiaries during the pandemic. Methods: We used Medicare FFS Part A data from March 2014 to November 2021 consisting of people aged ≥65 years. The dataset was divided by quarters each representing the four seasons, March 2014 to February 2020 defined as the prepandemic phase, March 2020 to May 2020 defined as the early pandemic phase, and June 2020 to August 2021 as the late pandemic phase. Patients with a principal discharge diagnosis of AMI were identified using ICD-9 and 10 codes. The number of AMI hospitalizations and crude 30-day mortality was calculated for each quarter. Results: Before the pandemic, number of AMI hospitalizations in the US were gradually declining from 48,258 per quarter in Q2 2017 to 42,639 in Q2 2019 (12 % decrease compared with 2017) with regular seasonal increases during the winter months (Q1). Thereafter, a sharp decline to 29,446 in Q2 2020 (31% decrease compared with 2019) was observed followed by a return to 34,105 in Q2 2021 (20% decrease compared with 2019). The crude 30-day mortality rate was 12.3% in the 12 months before the pandemic but increased to 13.3% in Q2 2020 during the early pandemic and remained the same at 13.3% after Q3 2020. Conclusions: In the US Medicare fee-for-service population, the number of AMI hospitalizations was in a steady decline before the pandemic, but dramatically declined during the first 3 months of the pandemic. In contrast, the crude 30-day mortality increased during the pandemic. Why these changes occurred remains a source of continuing investigation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".