The contribution of sub-optimal prescription of preoperative antiplatelets and statins to race and ethnicity-related disparities in major limb amputation
Bibliographic record
Abstract
ABSTRACT Background People undergoing revascularization for symptomatic peripheral artery disease (PAD) have a high incidence of major limb amputation in the year following their surgical procedure. The incidence of limb amputation is particularly high in patients from racial and ethnic minority groups. The purpose of our study was to investigate the role of sub-optimal prescription of preoperative antiplatelets and statins in producing disparities in risk of major amputation following revascularization for symptomatic PAD. Methods We used data from adult (≥18 years old) patients in the Vascular Quality Initiative (VQI) registry who underwent a revascularization procedure from 2011-2018. Patients were categorized as non-Hispanic Black, non-Hispanic White, and Hispanic. We estimated the crude probability of a patient being prescribed a preoperative antiplatelet and preoperative statin. We calculated one year risk incidence of amputation by prescription groups and by race/ethnicity. We estimated the amputation risk difference between race/ethnicity groups (the proportion of disparity) that could be eliminated under a hypothetical intervention where a pre-operative antiplatelet and statin was provided to all patients. Results Across 100,579 revascularizations recorded in the Vascular Quality Initiative, a vascular procedure-based registry in the United States and Canada, 1-year risk of amputation was 2.5% (95% CI: 2.4%,2.6%) in White patients, 5.3% (4.9%,5.6%) in Black patients and 5.3% (4.7%,5.9%) in Hispanic patients. Black (57.5%) and Hispanic patients (58.7%) were only slightly less likely than White patients (60.9%) to receive recommended antiplatelet and statin therapy prior to their procedures. However, the effect of antiplatelets and statins was greater in Black and Hispanic patients such that, had all patients received the appropriate guideline recommended medications, the estimated risk difference comparing Black to White patients would have reduced by 8.9% (−2.9%,21.9%) and the risk difference comparing Hispanic to White patients would have been reduced by 17.6% (−0.7%,38.6%). Conclusions Even though guideline-based care appeared evenly distributed by race/ethnicity, increasing access to such care may still decrease health care disparities in major limb amputation.
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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 | 0.006 |
| 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 | 0.000 |
| 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".