Trends and Outcomes Associated With Bariatric Surgery and Pharmacotherapies With Weight Loss Effects Among Patients With Heart Failure and Obesity
Bibliographic record
Abstract
BACKGROUND: Utilization patterns of bariatric surgery among older patients with heart failure (HF), and the associations with cardiovascular outcomes, are not well known. METHODS: Medicare beneficiaries with HF and at least class II obesity from 2013 to 2020 were identified with Medicare Provider Analysis and Review 100% inpatient files and Medicare 5% outpatient files. Patients who underwent bariatric surgery were matched to controls in a 1:2 ratio (matched on exact age, sex, race, body mass index, HF encounter year, and HF hospitalization rate pre-surgery/matched period). In an exploratory analysis, patients prescribed pharmacotherapies with weight loss effects (semaglutide, liraglutide, naltrexone-bupropion, or orlistat) were identified and matched to controls with a similar strategy in addition to HF medical therapy data. Cox models evaluated associations between weight loss therapies (as a time-varying covariate) and mortality risk and HF hospitalization rate (calculated as the rate of HF hospitalizations following index HF encounter per 100 person-months) during follow-up. RESULTS: Of 298 101 patients with HF and body mass index ≥35 kg/m 2 , 2594 (0.9%) underwent bariatric surgery (45% men; mean age, 56.2 years; mean body mass index, 51.5 kg/m 2 ). In propensity-matched analyses over a median follow-up of 4.7 years, bariatric surgery was associated with lower risk of all-cause mortality (HR, 0.55 [95% CI, 0.49–0.63]; P <0.001), greater reduction in HF hospitalization rate (rate ratio, 0.72 [95% CI, 0.67–0.77]; P <0.001), and lower atrial fibrillation risk (HR, 0.78 [95% CI, 0.65–0.93]; P =0.006). Use of pharmacotherapies with weight loss effects was low (4.8%), with 96.3% prescribed GLP-1 (glucagon-like peptide-1) agonists (semaglutide, 23.6%; liraglutide, 72.7%). In propensity-matched analysis over a median follow-up of 2.8 years, patients receiving pharmacotherapies with weight loss effects (versus matched controls) had a lower risk of all-cause mortality (HR, 0.82 [95% CI, 0.71–0.95]; P =0.007) and HF hospitalization rate (rate ratio, 0.87 [95% CI, 0.77–0.99]; P =0.04). CONCLUSIONS: Bariatric surgery and pharmacotherapies with weight loss effects are associated with a lower risk of adverse outcomes among older patients with HF and obesity; however, overall utilization remains low.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".