Pharmacotherapy Timing in Heart Failure and Impact on Healthcare Costs

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The American Journal of Pharmacy Benefits, November/December 2017, Volume 9, Issue 6

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.

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.

RESULTS

A total of 117,911 individuals meeting sample selection criteria were included in the final sample (Figure 1). Of these, 81,853 had complete 1-year follow-up while 61,035 had 2-year follow-up (eAppendix Figure). All results are presented for M1-2 and M3-12 or by treatment pattern during Year 1 of follow-up: Treated, Interrupted Treatment, Delayed Treatment, and Not Treated.

Demographic and Clinical Characteristics of Overall Sample and Treatment Groups

The study sample had a mean (SD) patient age of 71.3 (13.2) years, and was 51.6% female, 72.8% white, and 75.50% MAPD-enrolled (Table 1). Approximately one-third (32.8%) of individuals had HF diagnosis in the 12-month pre-index period, while 26.4% and 41.7% had evidence of AF and diabetes, respectively.

The distribution of individuals across the 4 treatment groups were as follows: 51.2% Treated, 1.2% Interrupted Treatment, 7.1% Delayed Treatment, and 9.9% Not Treated. Of the 4 treatment groups, the Treated group had the highest proportion of females (53.1%), African Americans (13.8%), patients with pre-index HF (34.7%), and patients with comorbid diabetes (43.3%). The Not Treated group had the highest proportion of MAPD enrollees (77.8%) and patients residing in the West region (10.9%). The Interrupted group had the lowest proportion of patients with AF (14.3%).

Treatment Patterns for Overall Sample

Nearly three-fourths (71.3%) of patients in the overall sample received HFRx during M1-2 (Table 2). Dual-therapy ACE-I/ARB plus BB (24.0%) was most common, followed by monotherapy BB (18.0%) and monotherapy ACE-I/ARB (12.7%). There was no evidence of prescription fills for any HFRx during M1-2 in 28.7% of patients.

Among patients followed through Year 1 (n = 81,853) who received any HFRx during M1-2 (71.3% of total), nearly all of them (97.6%) continued treatment the rest of the year, with 43.6% receiving dual therapy ACE-I/ARB plus BB, and 19.2% and 14.3% receiving monotherapy BB and monotherapy ACE-I/ARB, respectively. In contrast, of those who had no evidence of treatment in M1-2 (28.7% of total), about three-fifths (58.0%) of them had no evidence of prescription fills the remainder of Year 1.

Similarly, for patients whose post index period was 2 years (n = 61,035), nearly all (94.9%) who had evidence of Year 1 HFRx continued on HFRx in Year 2. Approximately two-fifths (40.9%) received dual therapy ACE-I/ARB plus BB, and 19.5% and 14.5% received monotherapy BB and monotherapy ACE-I/ARB, respectively. Of every 100 patients with follow-up for 1 year and not receiving treatment in M1-2, 58 did not receive treatment through M3-12 of Year 1, and of every 100 patients with follow-up for 2 years and not receiving treatment Year 1, 86 still did not receive treatment through the second year of follow-up.

Impact of Heart Failure-Related Pharmacotherapy Within the First 60 Days (M1-2) on PPPM Costs

The multivariable models examined the impact of the presence of HFRx fills within M1-2 of Year 1 of follow-up. Results are presented in Table 3. As compared with the presence of M1-2 monotherapy ACE-I/ARB (reference treatment), no evidence of M1-2 HFRx was associated with significantly higher all-cause (2.44; P <.001) and HF-related (3.66; P <.001) PPPM costs. Male gender, Quan-Charlson scores (≥3), and pre-index diabetes were each associated with higher all-cause and HF-related costs (all P <.001). By contrast, pre-index HF was associated with lower costs (Table 3).

Treatment Timing and Year 1 and Year 2 Annual Healthcare Costs

The multivariable findings point to the influence of HFRx within the period immediately following the index HF event; however, characterization of the specific treatment timing patterns remained of interest. Further descriptive analyses were conducted examining annual healthcare costs in Years 1 and 2, based upon timing of treatment in Year 1 (Table 4).

The Year 1 all-cause cost (mean [SD]) was $41,294 ($70,184), half of which was contributed by hospitalizations ($20,706 [$51,395]). Pharmacy costs accounted for 10.3% of total cost ($4270 [$7503]). Patients in the Delayed Treatment group had the highest costs ($58,772 [$107,638]), while those in the Interrupted Treatment group had slightly higher all-cause costs ($43,646 [$70,830]) than their Treated group counterparts ($40,289 [$62,323]). The Not Treated group had the lowest annual ($33,563 [$72,167]) and pharmacy ($1600 [$5891]) costs.

!--page-->The mean annual HF-related medical costs were $18,748 ($46,277), accounting for 45.4% of the all-cause total, with HF-related hospitalizations contributing the largest portion to those costs (83.8%) (Table 4). Similar to the pattern for all-cause costs, the Delayed Treatment patients had the highest HF-related medical costs ($28,048 [$77,007]), the Interrupted Treatment group had higher medical costs ($19,959 [$39,595] than the Treated group ($18,107 [39,944]), and those in the Not Treated group had the lowest costs ($15,185 [$47,161]).

Costs (all-cause) in Year 2 were markedly lower than in Year 1 ($24,397 [$47,951]) (Table 4). Although the hospitalization costs decreased substantially, they still contributed one-third of the total all-cause costs in Year 2. All-cause pharmacy costs were comparable in both years. Cost patterns in the second year tracked similarly to the pattern observed in Year 1: Delayed Treatment patients had the highest costs ($27,906 [$51,940]), followed by those in the Treated ($25,231 [$48,210]) and Interrupted Treatment ($19,812 [$40,910]) groups. Those in the Not Treated group had the lowest costs ($18,175 [$43,750]).

The HF-related costs also decreased considerably in Year 2 ($6554 [$26,958]); the bulk of the costs (73.8%) remained attributable to inpatient hospitalizations (Table 4). The cost patterns differed slightly in Year 2 with the Treated group exhibiting costs similar to Delayed Treatment group (Treated: $6986 [$28,127]; Delayed: $6803[$22,958]). The Not Treated group experienced lower costs than their Treated and Delayed Treatment counterparts, but had higher costs than those in the Interrupted Treatment group (Not Treated: $4523 [$24,273]; Interrupted: $3509 [$14,452]).

DISCUSSION

HF is a progressively debilitating disease that carries a high risk of morbidity4 and mortality.5,6 Optimizing HF-related outpatient pharmacotherapy has been associated with better outcomes11-15; however, the impact of timing of such treatment on disease burden is less understood. The current study adds to the literature by describing patients with HF, including type and timing of HF-related outpatient pharmacotherapy, and associations of these with healthcare costs.

Consistent with prior literature,8,9,24,25 total medical costs for HF patients were high (Year 1 all-cause costs: $41,295; Year 2: $24,397) with a large proportion attributable to hospitalizations. The HF-related medical costs were impacted to an even larger degree due to hospitalizations. Comparison of HF-related costs with other cardiovascular encounters/events was hampered because the specific nature of the encounter could not be directly aligned with event definitions used in other research studies. Nevertheless, 1 recent study examining patients hospitalized for transient ischemic attack or ischemic stroke found costs of $26,201-$30,349 through 1 year, varying by pharmacotherapies prescribed at discharge.25 Punekar et al24 reported mean 2-year costs of $51,058 among patients who had a myocardial infarction and were taking hyperlipidemia medications for primary prevention. Specific examination of events and comorbid conditions may elucidate what exactly is driving high costs.

The apparent influence of not having HFRx within the first 60 days (M1-2) on PPPM cost was notable (>2-fold increased risk of higher costs). The association of male gender and higher comorbidity burden to higher costs contrasted with older age and pre-index HF diagnoses, which were associated with lower costs. It is possible that longer history of disease may be accompanied by a more established treatment course resulting in lower costs. These findings suggest timing of guideline-informed treatment plays an important role in costs and could inform disease management approaches, particularly regarding acute events.

Further characterization of the timing of treatment patterns allowed a more detailed view of cost differences within the study sample. All-cause and HF-related costs in Year 1 were highest among those not having HFRx within the first 60 days but who did have subsequent HFRx (Delayed Treatment group). This may point to a critical window during which timely treatment may exert an impact on disease progression and resulting costs. During Year 2, the pattern of all-cause costs remained similar to Year 1. However, for HF-related medical costs in Year 2, the pattern of higher costs among those with delayed treatment reflected a closer symmetry to their treated counterparts. Those treated had slightly higher costs (<$200) than those with delayed treatment. This similarity may be attributable to the amount of time in treatment through Year 1 (at least 9 months for each group), possibly indicating a longer-term effect of total time in treatment in addition to timing of treatment initiation. Furthermore, patients who were not treated at all in Year 1 experienced lower costs overall but demonstrated higher HF-related medical costs in Year 2 than those with interrupted treatment. This may indicate that HFRx treatment timing may have been predicated on clinical severity or ejection fraction (EF) not measured in this study. The Year 2 findings seem to suggest that patients who were treated or had delayed treatment in Year 1 may be more similar to each other and may be distinct from those who experienced interrupted or no treatment in Year 1. Notably, the subset of study patients with no evidence of HF-related pharmacotherapy within 60 days post index (M1-2) but who did receive HFRx during the remainder of the Year 1 (Delayed Treatment) exhibited the worst cost outcomes across the 4 treatment timing groups.

Interestingly, more than a quarter (28.7%) of patients had no claims for HFRx within 60 days post index. Similarly, Deschaseaux et al found more than one-third of patients did not receive HFRx within 30 days of the initial diagnosis.26 These percentages appear little improved since 2005, when Lee et al demonstrated a ACE-I or ARB prescription rate of only 67% within 90 days of discharge among patients with HF at high risk for death; low prescribing rates continued up to 1 year.27 Similar claims-based studies found that 30% of patients did not receive ACE-I/ARB during 1 year following an index HF encounter14 and at least 46% of patients received no form of HFRx post discharge.16 The current study showed about half of patients without treatment for HF within 60 days after index HF diagnosis remained untreated over 2 years; this may highlight an opportunity to address an unmet need among patients with HF.

!--page-->Limitations

Because this study utilized administrative claims data, measures were subject to possible inaccuracy; however, error rates are not expected to vary by comparison groups. The evidence of a condition was established by ICD-9-CM codes, which reflect the claims submitted for reimbursement, rather than a confirmed diagnosis. Also, this study did not capture New York Heart Association Functional Classification, EF, laboratory results, blood pressure, smoking status, or body mass index, all of which might influence a physician’s decisions regarding when and what to prescribe. In particular, the lack of EF prevented the distinction of HF with reduced EF (HFrEF) versus HF with preserved EF (HFpEF). Recommended therapy for patients with HFpEF focuses on controlling systolic and diastolic blood pressure, and expert opinion is diverging regarding treatment with BBs, ACE-Is, and ARBs. Given also that HFpEF represents approximately half of patients with HF,28 it is entirely possible that the patients in this study who had no claims for HFRx within 2 months post index were largely patients with HFpEF. As such, these patients would not be considered undertreated according to guidelines, and treatment with an ACE-I, ARB, or BB may not have resulted in lower costs. Separately, specific indicators of severity are not available among claims data; the absence of this information potentially confounds the analysis. Furthermore, the claims data do not capture medication fills not processed through the primary insurer. In addition, it was not possible to account for patient barriers to filling prescriptions, or for nonadherence. Claims data may underestimate prescription fills and associated costs for generic medications commonly used to treat HF (eg, carvedilol, metoprolol, lisinopril). Finally, the data came from a managed care population; therefore, results may not be applicable to patients in non-managed care settings. However, the population largely affected by HF is >65 years of age, and the majority of patients in this study were in a MAPD healthcare plan, thus generalization to a large proportion of Medicare-insured individuals in the US is reasonable.

CONCLUSIONS

These analyses revealed that receipt of guideline-directed HFRx soon after an HF-related encounter influenced healthcare costs through 2 post index years. Costs were primarily driven by hospitalizations, and were higher for patients without evidence of HFRx within 60 days, which points to the importance of early intervention following an HF event/encounter. This study also revealed evidence of suboptimal prescribing of HFRx within the 60 days following the HF event. These findings together suggest an opportunity for providers to address an unmet need in terms of pharmacotherapy and for payers to influence costs for patients with HF.

Acknowledgments

The authors thank Chun-Lan Chang, PhD, for contributions to data interpretation and manuscript development. Medical writing support was provided by Caroline Jennermann, MS, of Optum, funded by Novartis Pharmaceuticals Corporation (NPC).

Authorship Information: Concept and design (SJT, JPS, ENO, PAR, AA); acquisition of data (SJT, JPS); analysis and interpretation of data (SJT, JPS, ENO, PAR, AA); drafting of the manuscript (SJT, JPS, ENO, PAR); critical revision of the manuscript for important intellectual content (SJT, JPS, ENO, PAR, AA); statistical analysis (JPS, PAR).

Author affiliations: Health Economics & Outcomes Research, Novartis Pharmaceuticals Corporation (SJT, PAR), East Hanover, NJ; Health Economics & Outcomes Research, Optum, Inc (JPS, AA), Eden Prairie, MN; Institute for Health Outcomes, Policy & Economics, Rutgers University (ENO), Piscataway, NJ.

Funding source: This study was funded by NPC.

Address correspondence to: Jason P. Swindle, PhD, MPH, Optum, Inc, 11000 Optum Circle, Eden Prairie, MN 55344; Tel: 312-348-6337; E-mail: jason.swindle@optum.com.

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