Methods

We linked Medicare inpatient claims data for January 1, 2003 through December 31, 2006 with data from the OPTIMIZE-HF and GWTG-HF registries. GWTG-HF is a continuation of the OPTIMIZE-HF under the sponsorship of the American Heart Association, and the registries had the same design, inclusion criteria, and data collection methods. Patients were eligible for inclusion in the registries if they were admitted to a hospital for an episode of worsening HF or developed significant HF symptoms during a hospitalization for which HF was the primary discharge diagnosis. Participating institutions submitted data on consecutive eligible patients in compliance with Joint Commission and Centers for Medicare and Medicaid Services standards. The registries included hospitals from all regions of the United States ranging in type from community hospitals to academic tertiary care referral centers. The validity and generalizability of the OPTIMIZE-HF registry have been described previously.

We obtained research-identifiable Medicare inpatient, carrier, and denominator files for each patient in the study for 2003 through 2007. Denominator files contain death date and Medicare eligibility dates. We used the inpatient files to identify readmissions and the carrier files to examine early physician follow-up after discharge from the index HF hospitalization. Carrier files contain claims from noninstitutional medical providers for services covered by Medicare Part B and include Healthcare Common Procedure Coding System codes, physician specialty codes, and service dates. We used data from 2007 for hospital discharges that occurred in December 2006.

This study was approved by the institutional review board of the Duke University Health System. All sites participating in OPTIMIZE-HF and GWTG-HF received local institutional review board approval, if applicable, and complied with all local regulatory and privacy guidelines. Outcome Sciences, Inc. (Cambridge, Massachusetts) served as the data collection and coordination center for GWTG-HF. The Duke Clinical Research Institute (Durham, North Carolina) served as the data analysis center and entered an agreement to analyze the aggregate deidentified data for research purposes ( http://www.Clinicaltrials.gov , identifier NCT00344513 ).

We used a method that links registry records for patients ≥65 years with inpatient Medicare claims files based on indirect identifiers. Using this method we were able to link 62,311 (78%) of the 79,837 eligible hospitalizations in the OPTIMIZE-HF and GWTG-HF registries with Medicare inpatient claims. Eligible patients were enrolled in fee-for-service Medicare and were discharged alive from a fully participating site in the OPTIMIZE-HF or GWTG-HF program. For patients with multiple admissions we selected the first as the index admission. We excluded patients who were discharged to a skilled nursing facility (n = 9,166) or to hospice care (n = 804). We also excluded 1,390 patients from 143 hospitals that had <25 eligible patients remaining in the cohort.

We defined early physician follow-up as an outpatient evaluation and management visit with a physician (Healthcare Common Procedure Coding System codes 992.xx through 994.xx) within 7 days after discharge from the index hospitalization. Although early follow-up can be defined from any number of time points, we chose 7 days to be consistent with the American College of Cardiology’s H2H national quality-improvement initiative. The H2H initiative involves >800 centers in an effort to decrease hospital readmissions in patients with HF and myocardial infarction by 20% by 2012. Postdischarge physician follow-up within 7 days is 1 of 3 core goals of the initiative. The importance of physician follow-up within 7 days is also supported by evidence from a recent study in which hospitals that arranged 7-day follow-up for a larger proportion of patients with HF had lower readmission rates than other hospitals. We excluded emergency department visits from calculations of early follow-up rates because they were unplanned visits not reflecting a system of care.

We obtained patient demographic characteristics, medical history, results of admission laboratory tests and examinations, discharge pharmacy records, and procedural information for the index hospitalization from the registry data. Patients were assigned to race categories using options available on the case-report form. We used the reported category “black” and combined all others as “other.” Variables in the analysis had low rates of “missingness” (i.e., <5% of records) with the exception of left ventricular function (12.1%). For continuous variables and the variable for left ventricular function, we created categorical variables that included a category for missing values. For other dichotomous variables (i.e., smoker within previous year, discharge processes and performance measurements, and index hospitalization procedures) we imputed missing values to “no.” We used the enrollment code in the month of the index hospitalization to ascertain Medicaid eligibility, a marker of socioeconomic status. A dichotomous indicator variable for rural status was derived from rural-urban commuting area scores based on zip code of residence using the University of Washington classification C algorithm. We assigned each patient to a hospital referral region according to the zip code of residence. We assigned hospital referral regions to quartiles based on total number of physicians per 100,000 residents and linked rankings to patients by hospital referral region.

For baseline patient characteristics and index hospitalization measurements we present categorical variables as frequencies with percentages and continuous variables as medians with interquartile ranges. To test for differences in early physician follow-up, we used chi-square tests for categorical variables and Kruskal–Wallis tests for continuous variables. We used univariate logistic regression models and multivariable logistic regression models with site-level random effects to examine predictors of early physician follow-up. In multivariable analysis we modeled early follow-up as a function of age, gender, race, medical history, results of admission laboratory tests and examinations, completion of discharge instructions, referral to an HF disease management program, length of stay >7 days for the index hospitalization, rural location, state Medicaid buy-in, quartile of physicians per 100,000 residents in the hospital referral region, and year of index hospitalization. We used a similar approach to examine unadjusted relations between covariates and early physician follow-up. In the primary analysis we included all patients in the study population. In a sensitivity analysis we excluded patients who died or were readmitted within 7 days after discharge from the index hospitalization. In a second sensitivity analysis we determined unadjusted and adjusted relations between covariates and early physician follow-up defined as 14 days including and excluding patients who died or were readmitted at 14 days. We used SAS 9.2 (SAS Institute, Cary, North Carolina) for all analyses.

Results

The study population included 30,136 patients from 225 hospitals. Within 7 days of discharge from the index HF hospitalization, 11,420 patients (37.9%) had a follow-up visit with a physician. Table 1 lists baseline demographic characteristics of the study population stratified by early follow-up status. Median age in the 2 groups was 79 years. Compared to the early follow-up cohort, the cohort of patients without early follow-up had a larger proportion of black patients and women. The early follow-up cohort had higher rates of preserved systolic function and history of atrial arrhythmias. The early follow-up cohort also had lower rates of chronic obstructive pulmonary disease and chronic renal insufficiency. A larger proportion of patients with HF in the early follow-up cohort resided in a hospital referral region with a higher concentration of physicians. More patients in the cohort without early follow-up lived in a rural location and had lower socioeconomic status.

Table 2 presents inpatient procedures, discharge process measurements, and discharge medications stratified by early follow-up status. In general the 2 cohorts were similar with respect to findings at discharge and procedures performed. However, larger proportions of patients in the early follow-up group were referred to an outpatient HF disease management program and had discharge instructions completed. The early follow-up cohort also had higher rates of discharge with evidence-based HF medications including angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, β blockers, and aldosterone antagonists. A larger proportion of the early follow-up cohort was discharged on diuretics and lipid-lowering agents. The early follow-up group had a smaller proportion of patients discharged with antiplatelet medications.

Table 3 lists unadjusted and adjusted associations with early physician follow-up.

Table 3

Univariate and multivariable predictors of seven-day physician follow-up

Variable	Unadjusted OR (95% CI)	p Value	Adjusted OR ⁎ (95% CI)	p Value
Age group (years)
65–69	1.00 (reference)		1.00 (reference)
70–74	1.04 (0.96–1.13)	0.35	1.01 (0.93–1.11)	0.74
75–79	1.18 (1.08–1.28)	<0.001	1.12 (1.03–1.22)	0.007
≥80	1.11 (1.03–1.20)	0.005	1.04 (0.96–1.13)	0.29
Female gender	0.88 (0.84–0.92)	<0.001	0.87 (0.83–0.91)	<0.001
Black race	0.76 (0.70–0.83)	<0.001	0.84 (0.77–0.92)	<0.001
Medical history
Atrial arrhythmia	1.20 (1.14–1.26)	<0.001	1.17 (1.11–1.23)	<0.001
Chronic obstructive pulmonary disease	0.94 (0.89–0.99)	0.02	0.94 (0.89–1.00)	0.04
Coronary artery disease or ischemic heart disease	1.05 (1.00–1.10)	0.07	1.03 (0.98–1.09)	0.21
Depression	0.98 (0.90–1.07)	0.68	0.99 (0.91–1.08)	0.87
Diabetes mellitus	0.99 (0.94–1.04)	0.62	1.01 (0.96–1.06)	0.68
Hyperlipidemia	1.10 (1.05–1.16)	<0.001	1.09 (1.04–1.15)	0.001
Peripheral vascular disease	0.94 (0.88–1.01)	0.12	0.94 (0.87–1.00)	0.06
Previous cerebrovascular accident or transient ischemic attack	0.98 (0.92–1.05)	0.53	0.99 (0.92–1.05)	0.68
Smoker within previous year	0.87 (0.80–0.94)	0.001	0.92 (0.84–1.01)	0.07
Systolic function
Preserved systolic function	1.00 (reference)		1.00 (reference)
Left ventricular systolic dysfunction	0.91 (0.87–0.96)	0.001	0.89 (0.85–0.94)	<0.001
Findings on admission
Serum creatinine (mg/dl)
<1.5	1.00 (reference)		1.00 (reference)
1.5–<2.0	1.00 (0.94–1.06)	0.92	0.97 (0.91–1.03)	0.35
≥2.0	0.89 (0.84–0.95)	<0.001	0.88 (0.82–0.94)	<0.001
Systolic blood pressure (mm Hg)
<120	1.00 (reference)		1.00 (reference)
120–<140	1.02 (0.95–1.09)	0.62	1.02 (0.95–1.09)	0.66
140–<160	1.01 (0.95–1.09)	0.72	1.02 (0.95–1.10)	0.54
≥160	1.02 (0.95–1.09)	0.59	1.05 (0.98–1.13)	0.19
Missing	1.11 (0.75–1.65)	0.59	1.00 (0.67–1.52)	0.98
Serum sodium (mEq/L)
<135	1.00 (reference)		1.00 (reference)
135–<145	0.90 (0.85–0.96)	0.001	0.90 (0.85–0.96)	0.001
≥145	0.88 (0.76–1.01)	0.06	0.89 (0.77–1.02)	0.09
Hemoglobin (g/dl)
<9	1.00 (reference)		1.00 (reference)
9–<12	1.07 (0.96–1.20)	0.23	1.05 (0.94–1.17)	0.40
≥12	1.02 (0.91–1.13)	0.77	0.97 (0.87–1.08)	0.59
Findings at discharge
Referral to outpatient heart failure management program	1.16 (1.07–1.25)	<0.001	1.14 (1.05–1.24)	0.002
Discharge instructions completed	1.07 (1.01–1.13)	0.01	1.04 (0.98–1.10)	0.20
Length of index hospitalization >7 days	0.96 (0.90–1.02)	0.19	0.97 (0.91–1.04)	0.38
Rural location	0.84 (0.78–0.90)	<0.001	0.84 (0.78–0.91)	<0.001
State Medicaid buy-in	0.74 (0.69–0.79)	<0.001	0.79 (0.74–0.85)	<0.001
Quartile of physicians per 100,000 residents in hospital referral region
1 (116–175)	1.00 (reference)		1.00 (reference)
2 (176–189)	1.23 (1.08–1.41)	0.002	1.21 (1.06–1.38)	0.005
3 (190–210)	1.29 (1.14–1.47)	<0.001	1.29 (1.14–1.46)	<0.001
4 (211–320)	1.33 (1.16–1.52)	<0.001	1.29 (1.12–1.48)	<0.001
Year of index hospitalization
2003	1.00 (reference)		1.00 (reference)
2004	1.04 (0.97–1.12)	0.22	1.03 (0.96–1.10)	0.46
2005	1.00 (0.91–1.10)	0.99	0.99 (0.89–1.09)	0.77
2006	1.12 (1.02–1.23)	0.02	1.09 (0.99–1.20)	0.08

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