Inpatient Quality-of-Care Measures for Heart Failure: Treatment Gaps and Opportunities in the Contemporary Era
Bibliographic record
Abstract
BACKGROUND: Quality of care measures are vital tools to assess processes of care within and between health care systems. The 2020 American College of Cardiology/AHA performance measures for heart failure provide a new set of such measures. We evaluated the achievement of these and other performance measures within the Veterans Affairs hospital system in a contemporary cohort of patients hospitalized for heart failure. METHODS: Hospital discharges from January 2010 to February 2021 with a primary diagnosis of heart failure (n=289 810) were evaluated. Adherence to each measure was determined using the measure's stated definition and by site. RESULTS: Among patients with reduced ejection fraction (53.0%), beta blocker use was high (89.0%), ACE (angiotensin-converting enzyme) inhibitor, angiotensin receptor blocker, or angiotensin receptor-neprilysin inhibitor (ARNI) use decreased over time (75.3% in 2010, 55.8% in 2020), and hydralazine/nitrate use in eligible Black patients (19.3%) was low. While 68.1% were eligible for ARNI, only 6.0% received them, reaching 17.2% by 2020. Mineralocorticoid receptor antagonists were used in 49.3% of those eligible; laboratory testing 7 days after their initiation was 73.0%, detecting hyperkalemia in 2.2%, although it occurred in 13.7% by 90 days. Achievement of ≥50% target dose was low (beta blocker 45.9%, ACE inhibitor/angiotensin receptor blocker 31.6%, ARNI 19.0%) and for ACE inhibitor/angiotensin receptor blocker/ARNI, decreased over time. Discharge appointments were 56.2% at 7 days and 78.8% at 14 days. Cardiac rehabilitation referral was low (10.5%) but increased. There were significant site-level differences, particularly for hydralazine, ARNI, devices, and cardiac rehabilitation. CONCLUSIONS: Important inpatient quality of care measures can be readily measured across the Veterans Administration health care system from electronic health records. Treatment gaps and site-level differences persisted into the contemporary era and will likely be exacerbated as newer treatments are added to this complex baseline. These measures and methods also offer the opportunity to target global, local, and individual processes of care for innovative quality improvement initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".