Methods

The Get With the Guidelines–Heart Failure (GWTG-HF) program is an ongoing, prospective, observational data collection and quality improvement initiative led by the AHA. Participating hospitals include institutions from all regions of the United States and represent community hospitals and tertiary referral centers. Trained personnel at each site submitted clinical information regarding medical history, hospital care, and outcomes for consecutive patients hospitalized for HF using an online, interactive Patient Management Tool (Outcome Sciences, Inc., Cambridge, Massachusetts). Variables entered included clinical characteristics and use of therapies during hospitalization and at hospital discharge. In addition, contraindications, intolerance, or other documented reasons for not prescribing evidence-based therapies were collected to evaluate appropriateness of prescription of 3 prespecified drug classes, namely ACE inhibitors/ARBs, β blockers, and aldosterone antagonists.

All participating institutions were required to comply with local regulatory and privacy guidelines and, if appropriate, to submit the GWTG-HF protocol for review and approval by their institutional review boards. Because data were used primarily at the local site for quality improvement, sites were granted a waiver of informed consent under the common rule. Outcome Sciences served as the clinical coordinating center for GWTG-HF. The Duke Clinical Research Institute (Durham, North Carolina) served as the analysis center.

The analysis cohort included hospitalized patients ≥18 years old from February 7, 2009 through March 26, 2010. Exclusion criteria were patients who died during the index event, were discharged to hospice/comfort care or transferred to another hospital, had missing values for left ventricular EF, and had preserved systolic function. Patients with HF and decreased systolic function (EF <40%) constituted the analysis cohort.

Descriptive statistics were performed for nominal variables using Pearson chi-square test and Wilcoxon rank-sum test (or Kruskal–Wallis test) for ordinal/continuous variables. For each evidence-based medication, usage rates at hospital admission, during hospitalization, and at hospital discharge were calculated after excluding subjects with contraindications, intolerance, or other patient/physician/system-documented reason for not prescribing the agent including presence of hyperkalemia or worsening renal function. Admission and discharge uses of calcium channel antagonists were assessed as a nonevidenced-based therapeutic comparator with the presumption that usage rates would not increase during the course of hospitalization. Evidence-based medication use patterns in patients on therapy at admission included continuation of therapy from admission to discharge and drug discontinuation during hospitalization or at discharge. Medication use patterns assessed in patients not on therapy at admission included initiation during hospitalization, initiation at hospital discharge, and medication not started. Multivariable logistic regression analysis was performed using generalized estimating equation models to account for within-hospital clustering to evaluate factors independently associated with discharge use.

The model included standard demographic (age, gender, body mass index), clinical (anemia, ischemic history, cerebrovascular disease, diabetes, hyperlipidemia, hypertension, chronic lung disease, peripheral vascular disease, renal insufficiency, systolic blood pressure, EF), and hospital (type, number of beds, interventional capabilities) characteristics. We tabulated odds ratios and associated 95% confidence intervals to quantify the magnitude of factors associated with guideline-appropriate treatment. We then calculated c-statistics to evaluate the predictive ability of our models. To assess multicollinearity, variance inflation factor values were also calculated for each predictor variable. Data management and analyses were performed centrally at the Duke Clinical Research Institute and the method was reviewed and approved by the AHA executive database steering committee. Data were screened for outliers and illogical cross comparisons before analysis. SAS 9.1 (SAS Institute, Inc., Cary, North Carolina) was used for all statistical analyses.

Results

Among hospitals participating in the GWTG-HF program that tracked inpatient process of care, patients were excluded because of in-hospital death (n = 650), discharge to hospice (n = 522) or comfort care (n = 563), transfer to another hospital (n = 452), discharge against medical advice (n = 200), or missing EF documentation (n = 471). From February 7, 2009 through March 26, 2010, 20,315 patients with HF were identified. The 10,841 patients with preserved systolic function were excluded, leaving 9,474 patients with decreased EF as the final analysis cohort from 113 hospitals.

Patient characteristics are listed in Table 1 . Mean age of the population was 69 years, 62% were men, and 30% were of African-American or Hispanic ethnicity. Over 1/2 of patients had an underlying ischemic cardiomyopathy and nearly 75% had persistent hypertension. Mean left ventricular EF was 25%. Characteristics of the 113 hospitals participating in this study are listed in Table 2 and spanned a broad range of hospital capabilities. Within the study population, 76.8% of patients were eligible for therapy with an ACE inhibitor or ARB, 93.4% were eligible for a β blocker, and 76.7% were eligible to receive an aldosterone antagonist ( Table 3 ). At admission, 65.3% of patients were receiving an ACE inhibitor or ARB, 72.6% were being treated with a β blocker, and 15.6% were concurrently treated with an aldosterone antagonist. Use of these medications in eligible patients increased consistently during hospitalization and increased further at hospital discharge ( Figure 1 and Table 3 ). At discharge 92.9%, 94.6%, and 32.2% of eligible patients were receiving ACE inhibitors/ARBs, β blockers, or aldosterone antagonists, respectively. In contrast, calcium channel antagonist use rates did not change from admission to discharge (10.6% to 9.7%).

Proportion of patients receiving the 3 medication categories at admission (medium gray bars) , during hospitalization (dark gray bars) , and at discharge (light gray bars) with associated upper and lower boundaries of 95% confidence intervals.

Of patients already prescribed an ACE inhibitor/ARB, β blocker, or aldosterone antagonist at admission, the overwhelming majority had these medications continued during hospitalization and at discharge ( Table 4 ). Continuation of medical therapy from hospital admission to discharge was 88.5% for ACE inhibitors/ARBs, 91.6% for β blockers, and 71.9% for aldosterone antagonists. The portion of patients in whom these medications were discontinued or held during hospitalization was 2.6% for ACE inhibitors/ARBs and β blockers and 13.2% for aldosterone antagonists. Despite no documented contraindication, 74.9% of patients with acute decompensated HF and decreased EF were never started on an aldosterone antagonist.

In multivariable analysis, several patient-level factors were associated with evidence-based medication use at hospital discharge ( Table 5 ). The strongest associations with discharge use of ACE inhibitors/ARBs were ACE inhibitor/ARB therapy at admission and absence of concomitant renal insufficiency. Similarly, the strongest associations with β-blocker use at discharge were β-blocker therapy on admission and absence of renal insufficiency. Aldosterone antagonist use at admission was the strongest predictor of discharge therapy. C-statistics for the multivariable model were 0.79 for ACE inhibitors/ARBs, 0.75 for β blockers, and 0.79 for aldosterone antagonists. Variance inflation factors for all individual variables in the 3 models were <3.0.

Table 5

Multivariate predictors of evidence-based medication usage at discharge ^⁎

Variable	ACE Inhibitor/ARB Use		Beta-Blocker Use		Aldosterone Antagonist Use
	Adjusted OR (95% CI)	p Value	Adjusted OR (95% CI)	p Value	Adjusted OR (95% CI)	p Value
Receiving medication at admission	7.4 (4.6–11.8)	<0.0001	6.0 (3.9–9.4)	<0.0001	20.9 (15.1–29.0)	<0.0001
Younger age (per 10-year decrease)	1.23 (1.08–1.40)	0.0019	1.18 (1.06–1.31)	0.0026	1.21 (1.16–1.26)	<0.0001
Male vs female gender	1.30 (1.03–1.64)	0.0270	1.17 (0.95–1.45)	0.1471	1.04 (0.90–1.20)	0.5824
Higher systolic blood pressure	1.01 (1.00–1.02)	0.0016	1.00 (0.99–1.01)	0.3337	0.99 (0.99–1.00)	0.0038
No concomitant hypertension	1.34 (1.02–1.77)	0.0343	0.92 (0.70–1.21)	0.5551	1.16 (0.99–1.35)	0.0611
No concomitant chronic lung disease	1.23 (0.99–1.51)	0.0517	1.35 (1.10–1.66)	0.0045	1.01 (0.88–1.15)	1.021
No concomitant peripheral vascular disease	1.55 (1.17–2.05)	0.0021	1.12 (0.80–1.58)	0.5037	1.04 (0.81–1.34)	0.7629
No concomitant renal insufficiency	2.7 (2.1–3.4)	<0.0001	1.43 (1.15–1.78)	0.0013	1.21 (0.97–1.52)	0.0963
West vs northeast hospital region	1.49 (0.66–3.4)	0.3350	2.4 (1.14–5.2)	0.0213	2.3 (1.16–4.4)	0.0167
West vs midwest hospital region	1.18 (0.51–2.7)	0.6940	1.95 (0.90–4.2)	0.0905	2.5 (1.22–5.2)	0.0121
Academic vs nonacademic hospital	2.4 (1.2–4.7)	0.0116	2.1 (1.01–4.5)	0.0474	1.58 (0.93–2.7)	0.0915

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