Discontinuation of Heart Failure Therapy in patients Undergoing Non-Cardiac surgery: Data from a Real-world Cohort
Bibliographic record
Abstract
Abstract Background and Aims Patients with heart failure (HF) with reduced ejection fraction (HFrEF) are at high risk for cardiovascular events following non-cardiac surgery. The perioperative period represents many challenges to maintain guideline directed medical therapy (GDMT). We examined GDMT use in HFrEF patients following non-cardiac surgery, and the association of medication changes with cardiovascular outcomes. Methods Using linked administrative databases, a retrospective cohort of HFrEF patients undergoing major non-cardiac surgery between 2008 and 2020 was formed. Pre-operative use of GDMT was determined by outpatient prescriptions up to 90 days prior to surgery. Changes in GDMT was defined as discontinuation or a dose reduction (≥50%) of baseline therapies at 90 days after discharge. The primary composite outcome was HF hospitalization or all-cause mortality at one-year adjusted for age, sex, components of the Revised Cardiac Risk Index and the Charlson Comorbidity index. Results Of 397,829 index surgeries, there were 7667 (2%) patients with pre-existing HFrEF on at least one GDMT (50.6% female; mean age: 75 +/- 12 years). At 90 days post-operatively, 46% of patients had undergone major changes to GDMT. Compared to patients who continued GDMT, patients with any change to therapy had a higher incidence of the primary outcome (52% vs. 46%, aOR: 1.14, 95% CI: 1.03-1.25) and all-cause mortality at one year (8.5% vs. 4.9%, aOR: 1.57, 95% CI: 1.3-1.90). Conclusion Among patients with HFrEF undergoing major non-cardiac surgery, few are on optimal GDMT, and perioperative changes to GDMT is associated with higher odds for HF hospitalization or death.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".