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Pharmacotherapy Timing in Heart Failure and Impact on Healthcare Costs

The findings of a retrospective study suggest that timing of heart failure (HF)-related pharmacotherapy following a HF-related encounter has potential implications on subsequent healthcare costs.
Published Online: Nov 16,2017
Stuart J. Turner, MPH; BPharm; Jason P. Swindle, PhD, MPH; Engels N. Obi, BPharm, PhD; Patricia A. Russo, PhD, MSW, RN; and Aylin Altan, PhD
Abstract
Objectives: Heart failure (HF) is a common chronic condition associated with substantial healthcare cost burden. This study aimed to characterize patients with HF, compare costs by receipt and timing of HF-related outpatient pharmacotherapy (HFRx), and determine associations between patient and clinical characteristics and costs.
Study Design: This was a retrospective administrative database study of adults in commercial and Medicare Advantage with Part D (MAPD) plans.
Methods: Included patients had ≥2 non-inpatient or ≥1 inpatient claim(s) containing HF diagnosis codes during 2010-2011; the date of the earliest claim was the index date. Costs (medical [inpatient hospitalizations, ambulatory, emergency, other] and pharmacy) were calculated up to 24 months post index. Patient characteristics and HFRx timing were examined descriptively and assessed by multivariable analysis for associations with costs.
Results: A total of 117,911 patients were identified (mean age 71.3 years, 48.4% male, 75.0% MAPD enrollees); 28.7% had no evidence of HFRx within 60 days post index. Patients receiving HFRx only after 60 days had the highest Year 1 costs ($58,771 [SD $107,638]) with inpatient hospitalization contributing more than half of that total (55.0%). This trend continued into Year 2, although costs were lower. Having no HFRx within 60 days post index, higher comorbidity burden, and male gender were associated with higher costs in multivariable analysis.
Conclusions: Patients who did not receive HFRx within 60 days of a HF-related encounter had higher healthcare costs. This trend remained after adjustment in multivariable analysis, suggesting an opportunity to reduce costs by better optimizing HF disease management.

                                                                                          Am J Pharm Benefits. 2017;9(6):-e15

Heart failure (HF) is a common disease with a debilitating course, poor prognosis, and high mortality. In the United States, HF affects approximately 5.7 million adults and its prevalence is expected to exceed 8 million by 2030.1-3 It is also the leading cause of hospitalization among Medicare enrollees.4 Approximately 20% of patients with HF die within 12 months of onset and 50% die within 5 years.5,6

As prevalence of HF rises in the United States, annual direct costs are expected to exceed $69 billion by 2030.3 The main drivers of high costs are inpatient hospitalizations and re-hospitalizations,7-9 thus preventive care efforts to mitigate such expensive resource use hold importance.

Optimizing HF treatment with guideline-directed HF-related pharmacotherapy (HFRx),10 such as angiotensin-converting-enzyme inhibitors (ACE-Is), angiotensin-receptor blockers (ARBs), aldosterone receptor antagonists (AAs), and beta blockers (BBs), has been found to reduce HF disease burden by slowing the progression of disease, thus decreasing hospitalizations, costs, and mortality.11-15 Nevertheless, such HFRx remains relatively underutilized,16,17 suggesting many patients may not be optimally managed following a HF event.

Less well understood in HF patients is the relationship between HF treatment delay and other patient characteristics on their healthcare cost burden. This information would be useful to healthcare providers and payers and may have disease management and cost implications. This study aimed to a) describe a real-world sample of patients with HF by receipt of HFRx within 60 days of an index encounter; b) compare healthcare costs based on receipt of HFRx within varying post index time periods; and c) examine associations between HFRx timing and healthcare costs.

METHODS

Data Source and Sample
This is a retrospective cohort study of administrative claims data from the Optum™ Research Database (ORD) from January 1, 2009, to December 31, 2013. The ORD includes approximately 14 million enrollees in commercial plans and 500,000 enrollees in Medicare Advantage with Part D (MAPD) plans, and it provides a geographically diverse sample representative of the US population. Medical and pharmacy claims (including diagnosis, procedure, and facility codes and costs) and enrollment data were obtained from this de-identified database and merged with race/ethnicity information. Social Security Administration dates of death were obtained to censor data collection for patients who died prior to the end of the study period. The study was conducted in compliance with the Health Insurance Portability and Accountability Act.

Patients aged ≥18 years were included if a diagnosis code for HF (International Classification of Diseases, Ninth Edition, Clinical Modification [ICD-9-CM] codes 402.x1, 404.x1, 404.x3, 428.xx)18,19 was observed in any position for ≥1 inpatient stay or ≥2 non-inpatient encounters within the study identification period of January 1, 2010, through December 31, 2011 (Figure 1). The index date was assigned as date of the first qualifying claim. Continuous plan enrollment was required for 12 months prior to the index date and at least 1 month (30 days) from index until the earliest of end of study period, health plan disenrollment, date of death if before end of the study period, or 729 days from index date. Patients were excluded if: gender, geographic region, or health plan type were missing; age at index was ≥65 years among commercial enrollees; or any claim was dated >45 days following date of death. Outcomes were assessed for 4 non-mutually exclusive post index time periods including Months 1-2 (M1-2, ie days 1-60), Months 3-12 (M3-12, ie days 61-365), Year 1 (days 1-365), and Year 2 (days 366-729).

Patient Characteristics and Outcomes
Patient characteristics examined for the pre-index period included age, gender, health plan type (commercial or MAPD), US geographic Census region,20 and race/ethnicity. Length of the post index period was also recorded. Clinical characteristics included pre-index HF, Quan-Charlson21 comorbidity score (grouped as 0, 1-2, 3-4, ≥5), comorbid atrial fibrillation (AF), comorbid diabetes, and receipt of HFRx.

Receipt of HFRx was identified from outpatient pharmacy claims within the 4 post index time periods: M1-2, M3-12, Year 1, and Year 2. The HFRx identified included ACE-Is, ARBs, AAs, BBs, and HFRx classified as Other (eg, diuretics, hydralazine + isosorbide dinitrate, digoxin) (eAppendix Table; eAppendices available at www.ajpb.com). Treatment patterns for receipt of HFRx were described as mono-, dual, or triple therapy, or no HFRx (eAppendix Table). Annual and per-patient-per-month (PPPM) costs were calculated by combining plan-paid and patient-paid amounts and categorized as total (medical plus pharmacy), medical (ambulatory [office and outpatient], emergency department, inpatient, and other), and pharmacy. All costs were adjusted to 2013 US dollars using the Medical Care Component of the Consumer Price Index.22

Treatment Group Definitions
Individuals in the study sample were stratified into 1 of 4 groups based on timing of treatment in the first year (Year 1) post index (eAppendix Figure). These were the “Treated” group, defined as patients receiving HFRx in both M1-2 and M3-12; the “Interrupted Treatment” group, defined as patients receiving HFRx in M1-2 but not in M3-12; the “Delayed Treatment” group, defined as patients not receiving HFRx in M1-2 but receiving HFRx in M3-12; and the “Not Treated” group, defined as patients not receiving HFRx in either M1-2 or M3-12. Annual costs for Year 2 were anchored on presence and timing of HFRx treatment in Year 1.

Data Analyses
All study variables were described as means (SDs) or percentages. Pearson’s χ2 test was used for dichotomous and polychotomous variables. Independent samples t test and 1-way analysis of variance were used for continuous variables with 2 cohorts and >2 cohorts, respectively. A significance level of α = .05 was applied.

Multivariable generalized linear models (GLMs) with gamma distribution and log link were used to examine associations among select patient, clinical, and insurance characteristics and post index healthcare costs.23 PPPM cost was selected as the dependent variable in the regression models because of varying follow-up lengths of individuals in the study sample. Independent variables included M1-2 HFRx, age, gender, health plan type, race/ethnicity, geographic region, pre-index HF, Quan-Charlson comorbidity score, AF, and diabetes.




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