Abstract 060: Association of Body Mass Index With Risk Factor Optimization and Guideline Directed Medical Therapy in Veterans With Cardiovascular Disease
Bibliographic record
Abstract
Background: Obesity is an epidemic in the United States and has been linked to the development of cardiovascular diseases (CVD) including atherosclerosis, heart failure and hypertension. However, obesity has been associated with better survival once CVD is established and has been referred to as the “obesity paradox”. Medical management and risk factor optimization are recommended for patients with all forms of CVD. The association of body mass index (BMI) with performance measure compliance is not known. Methods: In a large national cohort, we identified 1,242,015 patients with CVD receiving care in 130 Veterans Affairs facilities between 10/1/2013 and 9/30/2014. CVD was defined as the presence of ischemic heart disease, peripheral artery disease, or ischemic cerebrovascular disease. We assessed the frequency of compliance with performance measures in patients divided into 5 groups: underweight (BMI <18.5 Kg/m 2 ), normal BMI (18.5-24.9 Kg/m 2 ), overweight (25-29.9 Kg/m 2 ), obese (30-39.9 Kg/m 2 ), and extremely obese (>=40 Kg/m 2 ). We compared compliance with hypertension control (BP <140/90 mmHg), diabetes control (HbA1C <=9% among diabetics), use of statin, and use of antiplatelet therapy among the 5 groups. A composite of all 4 measures (BP control, statin use, antiplatelet use, HbA1C <=9% among diabetics) termed optimal medical therapy (OMT) was also compared among the groups. Multivariable logistic regression was performed with normal BMI as the referent category. Results (Table): Underweight comprised 12,623 (1.1%), normal BMI 230,471 (20.5%), overweight 413,590 (36.8%), obese 404,105 (36%), and extremely obese 61,778 (5.5%). Compliance with risk factor control (hypertension and diabetes control) was higher in the underweight and normal BMI group and lowest in the extremely obese group. However, statin and antiplatelet use was lowest in the underweight group and highest in the obese and extremely obese groups. Overall, only 32.7%-45.5% received OMT (i.e. met the composite measure) and was highest in the overweight group. Conclusions: Compliance with OMT was low in all patients. Patients that were underweight and extremely obese were least likely to receive OMT. Our results suggest potential for improvement in OMT for all CVD patients especially those that are underweight and extremely obese.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".