Disease-Modifying Therapy and Hospitalization Risk in Heart Failure Patients

Substantive outcome improvement and savings to Medicaid may be achieved with small changes in prescribing rates or comorbidity prevalence among patients with heart failure.
Published Online: January 20, 2015
Fadia T. Shaya, PhD, MPH; Ian M. Breunig, PhD; and Mandeep R. Mehra, MD, FACC, FACP, FRCP
To examine comorbidity and therapy use rates in a Medicaid population with heart failure (HF), evaluate hospitalization risk as a function of comorbidity and therapy use, and assess the impact of modification on costs to the Medicaid program.

Study Design
Historical prospective cohort study. Claims were from adult enrollees in Maryland Medicaid (managed care organization or fee-for-service) diagnosed with HF between 2005 and 2009.

The end point was first hospitalization after index HF. Average hazard ratios (HRs) were estimated by multivariate weighted Cox regression. Budget impact of modifications was assessed using annual number-needed-to-treat calculations and external estimate of average cost of HF hospitalization.

Most patients were >45 years (71%), women (56%), and black (60%). Medication use: beta-blockers (26%), angiotensin-converting enzyme inhibitors and/or angiotensin II receptor antagonists (ACEi/ARBs) (29%), aldosterone antagonists (5%), and others including nitrates-hydralazine (37%). Nearly all (98%) were diagnosed with 1 or more comorbidities. Relative risk of hospitalization was higher with most, but not all, comorbidities investigated. ACEi/ARBs (HR, 0.77; CI, 0.73-0.81), beta-blockers (HR, 0.83; CI, 0.79-0.87), and other cardiovascular drugs (HR, 0.76; CI, 0.72-0.80) had beneficial effects. A 20% increase in the use prevalence of ACEi/ARBs and beta-blockers translated to annual Medicaid savings of at least $85 and $57 per HF patient, respectively.

Findings call attention to comorbidities and optimization of disease-modifying therapy in Medicaid patients with HF. Certain disease-modifying medications mitigated risk, but were used infrequently. Substantive outcome improvement and savings to Medicaid may be achieved with small changes in prescribing rates or comorbidity prevalence.

Am J Manag Care. 2015;21(1):39-47
The projected increase in heart failure prevalence coupled with greater numbers of patients presenting with comorbidities are likely to place greater strain on state Medicaid budgets and disease management programs.
  • This study elucidates the impact of comorbidities on the risk of hospitalization among patients with heart failure.
  • Certain disease-modifying medications mitigated the risk of hospitalization, but were used infrequently.
  • Substantive outcome improvement and savings to Medicaid may be achieved with small changes in prescribing rates or comorbidity prevalence.
Heart failure (HF) was estimated to affect nearly 5.7 million Americans in 2008,1 and its prevalence has been projected to rise by 25% through 2030.2 This rise in prevalence is accompanied by a projected 215% increase in direct medical costs over the next 20 years in the United States, from approximately $24.7 to $95.6 billion (2008 US dollars).2 It was estimated that most (77%) medical costs following diagnosis of HF accrue during hospitalization. 3 While hospitalizations are frequent among HF patients, occurring about once per year, only one-third had HF as the primary admitting diagnosis.4

The clinical and corresponding economic burden of HF is often compounded by concurrent morbidities (eg, chronic obstructive pulmonary disease [COPD], renal dysfunction, psychological disorders, and stroke, among others).5-9 Little is known on how comorbidities may affect the risk of hospitalization among patients with HF—specifically patients in expanding Medicaid plans, which have typically shown high prevalence of HF.1,10,11 The literature has not cited many population-based studies with real-world data, which may stand to inform disease management. Much of the evidence based on clinical trial populations is invaluable in clinical practice; however, it is often difficult to interpret and apply in populations whose demographics and risk-factor profiles vary from those cited.12,13 A case in point is the Medicaid population.10,14-20

Recent healthcare policy changes in the American Recovery and Reinvestment Act and the Affordable Care Act have relegated more responsibilities and expanded funding to states themselves, resulting in increased enrollment in Medicaid plans.21,22 For instance, Medicaid enrollment grew by 9 million over the past year, increasing total enrollment to 66.5 million Americans as of October 2014, or 21.2% of the total population.21 The projected increase in HF prevalence coupled with greater numbers of patients presenting with comorbidities are likely to place greater strain on state budgets and disease management programs.5,23,24 Given the high risk profile among Medicaid patients,

increasing enrollment in state Medicaid programs, sparse literature on population-based HF studies, and the burden of hospitalization among HF patients, we investigated risk factors for HF in a contemporary Medicaid population and developed a risk score for hospitalization associated with specific medication use and comorbidities. Finally, we assessed the impact of a potential modification of risk factors on hospitalization and cost.


This study was a historical-prospective cohort study sampling from the population of Maryland Medicaid recipients enrolled in 1 of 7 prepaid state-contracted managed care organizations (MCOs) or fee-for-service (FFS) programs. Patients between the ages of 18 and 64 years diagnosed with HF between July 1, 2005, and December 31, 2009, were enrolled in the study. We obtained all of their encounter and prescription data, and followed them from their date of enrollment through first hospitalization to end of study (June 20, 2010), or the date of disenrollment as recorded in their last Medicaid MCO or FFS encounter claim. The study design allowed for at least 6 months and for up to 5 years of follow-up post diagnosis.

We recorded demographics (age, sex, race), dates of service (physician visits, hospitalizations, medication dispensing), and primary through tertiary International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes. Pharmacy claims also included American Society of Health-System Pharmacists Pharmacologic-Therapeutic Classification (AHFS) numbers.

The date of diagnosis for HF was defined as the earliest physician visit associated with ICD-9-CM code 428.xx. In addition to this prevalence cohort, we built an incidence cohort to include all patients whose first HF claim was at least 6 months after their first Medicaid claim. Dates of hospitalization were derived from the first date of service on records of inpatient hospital encounters. Less than half (47%) of HF diagnoses coincided with patients' first hospitalization, in which case a second hospitalization was used as the end of follow-up. Of 24,635 HF patients identified, the final prevalence cohort included 14,149 observations and the incidence cohort included 7470 patients.

Systolic HF versus HF with preserved ejection fraction (HFpEF) was denoted if specified by secondary coding (428.2x vs 428.3x, or 428.40-428.43 combined). Although systolic HF and HFpEF share the same clinical phenotype, they are thought to differ in pathophysiology and thus tend to respond differently to treatment.18,19,25-30 Medications that have been shown in clinical trials to produce unequivocal improvements in systolic HF have not produced similar effects in HFpEF, and the optimal treatment strategy for HFpEF has yet to be defined.25,26,31 The type of HF was specified in only 5% of our sample; consequently, we could not distinguish which patients were considered most susceptible to disease-modifying therapy. Since it is known that about half of all patients with HF display HFpEF and the remainder systolic HF,25 there is little reason to suspect that HF was unspecified on claims for 95% of our sample in any systematic fashion. All patients were retained, regardless of specified or unspecified HF.

Covariates included first-line disease-modifying therapies (guideline-recommended), comorbidities, age, race/ethnicity, gender, and an indicator for HF diagnosis during hospitalization. First-line therapies were ascertained using pharmacy claims with drugs dispensed between HF diagnosis and the end of follow-up. Therapies examined include ACEi/ARBs (AHFS 24:32.04 or 24:32.08); betablockers (AHFS 24:24); aldosterone receptor antagonists (AAs) (AHFS 24:32.20); nitrates + hydralazine combination (AHFS 24:08.20 or 24:08.20 with 24:12.08); “other” cardiovascular therapies, including nitrates or hydralazine alone; and all medications with AHFS 24:xx but not described above. Drugs do not include medications that are not reimbursed by Medicaid.

Diagnoses of comorbidities were determined from 3-digit ICD-9-CM codes recorded in the primary through tertiary fields of claims for medical encounters between the earliest claim and within 3 months after HF diagnosis. Comorbidities include COPD, stroke, renal dysfunction, diabetes, psychological disorder(s), hyperlipidemia, hypertension, and chronic ischemic heart disease (ICD-9-CM: 410.xx-414.xx, which includes past myocardial infarction, angina pectoris, and other forms of ischemic heart disease). An indicator for “other” cardiovascular disease captures all ICD-9-CM diagnosis codes between 390.xx and 459.xx, but not hypertension, ischemic heart disease, HF, or stroke. Classification of therapies and comorbidities is described in eAppendix Table 1 (available at www.ajmc.com). Age (18-44 years, 45-54 years, and 55-64 years), gender, and race/ethnicity (white, black, Hispanic, and other) were identified at HF diagnosis.

Statistical Analysis

We report the prevalence of comorbidities in the prevalence cohort as well as cross-tabulations of these comorbidities (eAppendix Figure 1, available at www.ajmc.com). Demographic distributions are examined in the prevalence cohort and across comorbidities. Kaplan-Meier estimates were used to estimate median days until hospitalization after HF diagnosis.

We built survival analysis models to assess the impact of first-line use of disease-modifying therapies with HF patients on their risk of hospitalization. We estimated the risk of hospitalization, adjusting for demographic risk factors and the presence of comorbidities. We also adjusted for the diagnosis of 1 or more comorbidities after 3 months of HF diagnosis, since new or undiagnosed morbidity might have confounded estimates for the risk of hospitalization over follow-up.

Weighted Cox regression (WCR) was used to model the average hazard ratios (HRs) of factors for hospitalization after HF diagnosis. WCR provided a simplified method for estimating HRs adjusted for covariates and averaged over the follow-up period, regardless of whether risk varies over time (ie, nonproportional hazards).32 WCR is described in the eAppendix and was implemented using SAS version 9.2 (SAS Institute, Cary, North Carolina) macro program, WCM, provided on the Web by Heinze.33

Using the WCR estimates, we computed the budget impact to the Medicaid plan, given a 20% increase in the prevalence of comorbidity or therapy use in the HF cohort. The mean cost of a primary HF hospitalization among nondually enrolled Maryland Medicaid patients in 2011 was $16,341.34 Of note, average hospitalization costs have been estimated to be up to 43% higher when HF was a secondary, rather than the primary, diagnosis for hospitalization.35

The numbers-needed-to-treat approach was adopted to determine the number of patients with comorbidity or using a first-line therapy associated with at least 1 hospitalization per year. We calculated the savings per patient, as attributed to the expected impact on hospitalization rates.


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