Abstract P465: Impact of Smoking Among Medicare Beneficiaries With HIV Admitted for Acute Myocardial Infarction in the United States; a Perspective Using the 2019 National Inpatient Sample
Bibliographic record
Abstract
Introduction: While smoking has shown to influence health negatively, there is a lack of data of its impact among HIV patients hospitalized for Acute Myocardial Infarction (AMI) under Medicare. Hypothesis: We assessed the hypothesis that the characteristics of smokers vs. non-smokers differ among Medicare beneficiaries with a diagnosis of HIV admitted for AMI in the US, and that worse hospitalization outcomes may be seen with smokers. Method: The 2019 National Inpatient Sample was used for our study. We first found patients with a principal admission for AMI, covered by Medicare and with a diagnosis of HIV. The presence of smoking(nicotine) was compared between various patient characteristics and an adjusted odds ratio (aOR) for the mortality risk among smokers with HIV admitted for AMI was also estimated. Results: Our study found 1315 cases of AMI among HIV patients covered by Medicare with 800 (60.8%) smokers. Various differences were seen in patient characteristics. Smokers were younger than non-smokers (mean age 61.13 years vs. 62.74 years) and a higher proportion were males (88.1%). Furthermore, smokers also had a higher prevalence of depression (16.9% vs. 10.7%), drug abuse (14.4% vs. 3.9%), chronic pulmonary disease (40.0% vs. 15.5%), and peripheral vascular disease (13.6% vs 10.0%). However, non-smokers had a higher prevalence of hypothyroidism (6.9% vs. 10.7%), cirrhosis (1.9 vs. 8.7%), diabetes mellitus (33.1% vs. 47.6%), hypertension (78.8% vs. 86.4%), anemia (25.0% vs 38.8%) and chronic kidney disease (35.0% vs 50.5%). After adjusting for variables, smokers in our sample had a higher inpatient mortality risk (aOR 2.203, 95% CI 1.027-4.728, p= 0.043). Conclusion: Inpatient mortality risk for HIV Medicare beneficiaries admitted with AMI was higher among smokers. Several differences in patient characteristics were also observed. More strict educational and sensitization campaigns among this population may be helpful.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".