Health Resource Utilization and Direct Costs Associated with Angina for Patients with Coronary Artery Disease in a US Managed Care Setting

Chest pain, or angina pectoris, is the primary symptom of coronary artery disease (CAD), or chronic heart disease, a leading cause of morbidity and mortality in the United States. An estimated 17.5 million Americans have CAD, 9 million have angina pectoris, and approximately 500,000 new cases of angina are diagnosed annually.^1,2 Current evidence-based treatment guidelines for patients with stable ischemic heart disease recommend lifestyle changes, drug therapy, and revascularization procedures.^3,4 Current treatment strategies are to identify and treat underlying conditions that may contribute to angina symptoms, modify risk factors, improve a patient’s health and survival through pharmacologic and nonpharmacologic means, and utilize revascularization procedures through evidence- based practice.⁵

To date, several studies have examined resource utilization in patients with angina pectoris. These studies have examined the direct costs of chronic angina using national health statistics, as well as information in 2 large clinical trials—the COURAGE and MERLIN-TIMI 36 trials.^1,6,7 However, no data exist comparing the manifestation of CAD with or without documented angina. Few US studies have examined treatment patterns, resource utilization, and the cost of care for patients with CAD plus angina.

Javitz and colleagues, who conducted the only database study to investigate the annual direct medical costs of chronic angina, reported annual costs ranging from $1.9 billion to $74.8 billion, depending on the way in which angina was defined.⁸ The study was conducted from a societal perspective, using the National Center for Health Statistics’ publicuse databases. Given the considerable burden of stable ischemic heart disease and the paucity of information about its economic impact, with particular emphasis in the public healthcare sector, the objective of the current study was to compare real-world treatment patterns and costs of CAD with angina versus CAD without angina in a large, commercially insured, managed care population.

Methods
Data Source

This large, retrospective database analysis used eligibility, medical, and pharmacy claims data from a nationwide commercial managed care plan affiliated with OptumInsight that covers more than 13 million individuals. Procedures for selecting study participants were in compliance with the Health Insurance Portability and Accountability Act of 1996.^9,10 This study was approved by the privacy board associated with the New England Institutional Review Board.

Study Sample

Patients in the study population had physician or hospital claims between July 1, 2004, and June 30, 2006, that included International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnostic, 11 or ICD-9-CM Current Procedural Terminology (CPT), or Healthcare Common Procedure Coding System (HCPCS) procedure codes (in any position) consistent with a diagnosis of CAD (ICD-9-CM codes 410.xx, 411.x, 412, 414.xx, 429.2, 429.5, 429.6, 429.7x, 996.03, V45.81, V45.82; ICD-9-CM procedure codes 36.0x, 36.1x, 36.2, 36.3x; CPT codes 33140, 33141, 33510-33523, 33533-33536, 33572, 92975, 92996, 93540; or HCPCS codes G0290, G0291, S0340-S0342, S2205-S2209).

Eligible patients were enrolled in the plan for 6 months before receiving their ICD-9-CM code (ie, the index date) and for 12 months after the index date (ie, postindex period).

A subgroup of these patients was identified as having CAD with angina. Patient identification was based on 6 algorithms used in the study by Javitz and colleagues.8 After those algorithms were modified to be more reflective of the patient population in the current study, a medical chart review was conducted. This review determined the positive predictive value of each algorithm for identifying the greatest number of patient charts with an angina diagnosis relative to the total number of charts reviewed.

Stratification of the algorithms by their positive predictive values (Table 1) showed that 3 had high values (algorithms 1, 3, and 5: 80%-95%), 1 had an intermediate value (algorithm 2: 67%), and 2 had low values (algorithms 4 and 6: 52% and 25%, respectively).

Table 1

The primary analyses for this study were based on a 3-algorithm patient-identification model (using algorithms 1, 3, and 5), which had a combined positive predictive value of 89% (95% confidence interval [CI], 0.79-0.95). Secondary analyses were based on a 4-algorithm model (using algorithms 1, 2, 3, and 5), which had a combined positive predictive value of 83% (95% CI, 0.73-0.90).

Patients were divided into 2 cohorts. The first cohort, consisting of patients with CAD with angina, included individuals who were identified by the 3-algorithm model (primary analyses) or by the 4-algorithm model (secondary analyses).

The second cohort, consisting of patients with stable ischemic heart disease without angina, included patients who were identified by algorithms with an intermediate or low positive predictive value and those who were not identified by any of the algorithms.

Outcome Variables

Healthcare utilization and direct costs during the 12 months after the identification index date were determined for each patient in both cohorts. Utilization measures included the number of physician and other outpatient visits, emergency department visits, hospitalizations, and revascularization procedures, as well as the number of prescriptions filled. Annual CAD-related direct costs were measured for outpatient visits, hospitalizations, and pharmacy costs, and the total costs (medical plus pharmacy) were calculated. Total all-cause costs (medical plus pharmacy), including CAD-related and non–CAD-related costs, were also calculated. Costs were not consumer-price adjusted to the current year, and reflect the costs in the year in which the services occurred. Indirect costs were not evaluated, because such costs are not available in the database used.

The extent of clinically significant comorbidity was determined using the Charlson comorbidity index (CCI).¹² A patient’s CCI score reflects the cumulative increase in the likelihood of 1-year mortality as a result of severity of comorbidity, with higher scores indicating a greater risk for death. The CCI has been modified for use with administrative databases, including the ICD-9- CM database.^13,14 CCI scores before and after a patient’s index date were determined.

Statistical Analysis

All study variables, including baseline and follow-up measures, were analyzed descriptively using the SAS version 9.1.3 (SAS Institute, Cary, NC) data analysis system. Dichotomous variables were analyzed using chisquare tests, and continuous variables were compared using t-tests.

Healthcare costs of study participants were highly variable, with some accruing thousands and others accruing millions of dollars in costs. Therefore, a generalized linear model was used to compare CAD-related costs and overall costs between the cohort with CAD and angina and the group with CAD without angina. The model controlled for the effect of other confounding variables, including age, sex, health plan region, CCI score, and measures of baseline utilization. A generalized linear model assuming a gamma distribution and using a log link was employed to estimate adjusted costs for the 2 cohorts. Generalized linear modeling was conducted using Stata version 9 (Stata Corporation, College Station, TX).

Results

Table 2

Of 246,227 patients with CAD, the 3-algorithm model assigned 230,919 (93.8%) to the CAD-withoutangina cohort and 15,308 (6.2%) to the CAD-withangina cohort (Table 2). Patients identified as having angina were significantly older than, but not clinically differentiated from, those without angina (mean age, 61 vs 59 years, respectively; P <.001). Both cohorts had a preponderance of men, because women tend to have a lower incidence of CAD than men until more advanced age.¹⁵

In both cohorts, the mean CCI score was higher during the 12 months after the index date than in the 6 months before the index date, but baseline (preindex) and follow-up (postindex) CCI scores were significantly higher (P <.001) in patients with angina than in those without angina, suggesting an increased burden of comorbidity in patients with angina.

Resource Utilization

Figure 1

The CAD-with-angina cohort used considerably more resources than the cohort of patients with CAD without angina (Figure 1). Patients with CAD and angina were significantly more likely to be hospitalized (41% vs 11%, respectively; P <.001). Compared with 12% of patients with CAD without angina, 34% of those with angina visited the emergency department during the follow- up year (P <.001). The proportion of patients who visited a physician or other outpatient healthcare provider at least once during the follow-up period was 94% in the CAD-with-angina cohort and 79% in the CAD-without-angina cohort (P <.001), and the average number of outpatient visits was higher for patients with angina than for those without angina (6.7 vs 2.6 visits, respectively; P <.001).

Figure 2

Figure 2 shows the proportion of patients who received at least 1 prescription for cardiovascular medications during the follow-up period. Across all categories, patients who had CAD with angina were more likely to receive a prescription than those without angina.

Patients with CAD and angina had a significantly higher rate of revascularization procedures during the follow-up period than those without angina (35% [5288/15,308] vs 8% [19,466/230,919], respectively P <.001). Analysis of first revascularization interventions showed that percutaneous coronary intervention (PCI) or stent procedures were common in both cohorts, but that a significantly greater percentage of patients who had CAD with angina had such a procedure compared with those without angina (84% [4422/5288] vs 73% [14,300/19,466], respectively; P <.001). Among patients with CAD who had a revascularization procedure during the follow-up period, 57% (2994/5288) of the patients with angina compared with 53% (10,406/19,466) of those without angina had more than 1 procedure (data not shown in this article).

Direct Costs

The greater resource utilization by patients in the CAD-with-angina cohort is reflected in the direct costs of treating a patient with angina during the follow-up period. Comparison of 12-month CAD-related, perpatient costs showed that patients with angina had significantly higher costs than those without angina in all resource categories (Figure 3).

Figure 3

Average inpatient costs were higher for patients who had CAD with angina than for those without angina ($9536 vs $2169, respectively; P <.001). Emergency department and outpatient costs were also higher for patients with angina. Because patients who had CAD with angina were more likely to receive prescription medication, average annual pharmacy costs were higher for patients with angina than for those without angina ($1499 vs $891, respectively; P <.001).

During the 12-month follow-up period, average medical and pharmacy costs were $14,851 for patients who had CAD with angina compared with $4449 for patients who had CAD without angina (P <.001). Comparison of all-cause total medical and pharmacy costs revealed a similar pattern. The average per-patient all-cause cost was $28,590 for patients who had CAD with angina compared with $14,334 for patients who had CAD without angina. This almost 2-fold increase in per-patient costs among patients with angina probably reflects the increased burden of comorbidity in this cohort.

The general linear model results in adjusted stable ischemic heart disease–related costs of $4418 for CAD without angina and $14,357 for CAD with angina. The adjusted all-cause costs were $14,415 for CAD without angina and $27,239 for CAD with angina (P <.001 for both).

Results Obtained with the 4-Algorithm Model

Although the patient cohorts identified by the 3- algorithm model were used for the primary study analyses, additional analysis based on the 4-algorithm model was conducted to assess the sensitivity of the results on cohort selection. Results based on the 4-algorithm model were similar to those obtained with the 3-algo-rithm model. For example, the proportion of patients with angina who had a revascularization procedure during the follow-up period was 35% using the 3-algorithm model and 34% using the 4-algorithm model.

Discussion

Whereas managed care pharmacy directors are often aware of the costs associated with managing CAD, they may not be as familiar with the additional incremental burden associated with angina. In evaluating therapies specifically for angina, this can be important information for making appropriate formulary decisions.

Our study shows that the CAD-with-angina cohort consumed more healthcare resources than the CADwithout- angina cohort, which is probably associated with an increase in the burden of comorbidities in this cohort. Compared with patients with CAD without angina, those with angina were more likely to be hospitalized, to visit the emergency department, to visit a physician or other outpatient healthcare provider, and to receive polypharmacy cardiovascular drug regimens. Similarly, patients who had CAD with angina were more likely than those without angina to have a first-time revascularization procedure or a first-time coronary artery bypass graft. Both CAD-related and non–CAD related medical and pharmacy costs were considerably higher in patients who had CAD with angina.

The implications of our study support that ischemic heart disease continues to account for the vast majority of cardiac disease, including hospitalizations for myocardial infarctions, unstable angina, and evaluation and treatment of stable chest pain syndromes. Of note, our study provides further evidence that patients with active symptoms remain at increased risk for morbidity, revascularization procedures, and polypharmacy, with a substantial increase in healthcare resource utilization compared with patients with CAD without angina.

Results similar to those obtained in this US-based study have previously been reported for studies conducted in the United Kingdom,^16,17 Sweden,^18,19 Switzerland,²⁰ Australia,²¹ and Italy.²² Although these studies showed that pharmacy costs and physician and emergency department visits contribute to the high and steadily growing annual healthcare costs of patients with chronic angina, the increasing use of revascularization procedures is the major contributor to these costs. This point is confirmed by the evaluation of the relative cost and cost-effectiveness of PCI compared with an optimal treatment strategy from the COURAGE trial.⁶ The addition of PCI to optimal medical therapy was not found to be a cost-effective initial management strategy for symptomatic chronic CAD.⁶

The findings of the present study are confirmed by a systematic review of 47 studies of the economic burden of illness for patients with CAD and angina, conducted by Reynolds and colleagues.²³ In addition to showing that revascularization procedures were the major determinant of healthcare costs for patients with chronic angina, that review suggested that these procedures had only a modest and transient beneficial effect on patients’ work status.²³

In many studies, the increase in the proportion of patients who were employed 6 months to 1 year after revascularization was less than 25%, and the results of studies with multiyear follow-up periods suggested that most patients who initially returned to work were no longer employed within several years of the initial procedure. Planned departure from the workforce upon reaching retirement age seems to have had only a minor impact on patients’ employment status.

Although our study did not directly address the indirect costs of CAD, those costs are important. Indirect costs of lost workdays, reduced productivity, and longterm medications may be as great as the direct costs measured by this present study, as suggested by Shaw and colleagues.²⁴

A more recent study—a substudy of the MERLINTIMI 36 trial investigating the economic impact of angina after acute coronary syndrome (ACS)—showed that patients with CAD plus angina had a >2-fold increase in resource utilization and an additional $4000 in incremental costs at 8 months of follow-up after an ACS event.⁷ The difference was mostly attributable to higher rates of hospitalization and revascularization procedures among patients admitted for ACS with more severe angina.

The magnitude of this problem is estimated to be in the tens of billions of dollars.

In our database study, we used a rigorous methodology to identify patients with angina. We hypothesized that identifying patients in a claims database would be challenging for several reasons. For example, physicians may use angina codes during the screening of patients eventually found to have other causes of chest discomfort, and various ICD-9-CM diagnostic and medication codes are routinely used for patients with angina. Although the use of managed care claims data is attractive, because it shows real-world practice patterns, it does not explain why clinicians selected a particular code for a diagnosis or medication, nor does it permit an assessment of the accuracy of these codes for identifying a specific disease.

A typical method for identifying a patient with a particular disease in a claims database is to find 2 separate events with a primary ICD-9-CM disease diagnostic code combined with at least 2 claims with the medication code for the drug most often used to treat that disease. When we considered the best approach for identifying patients with angina, we realized that this symptom could be described as either “angina” or “chest pain.” Although nitrates are the primary drug treatments for angina, many other medications may also be prescribed. We therefore assumed that we would fail to identify a large proportion of patients with angina if we used only the diagnostic code for angina and the medication codes for nitrates.

To increase the likelihood of obtaining a representative sample of patients with angina, we examined the algorithms used in the study by Javitz and colleagues,8 modified them to reflect the composition of our database, and based on the results of a medical chart review, stratified the 6 algorithms according to their positive predictive value. We then defined our 2 study cohorts using a model that incorporated the 3 algorithms with the highest positive predictive value (a combined value of 89%), although we performed some secondary analyses using a 4-algorithm model with a combined positive predictive value of 83%.

When we compared the outcomes of patients identified as having angina by the 3- and 4-algorithm models, we observed only minor differences. Both models revealed significantly higher rates of resource utilization and significantly higher CAD-related and non–CADrelated direct costs for patients with angina than for those without angina. The 3-algorithm patient-identification model was chosen because of its higher positive predictive value, indicating that the 3-algorithm model selects patients with true angina with a higher probability than other algorithms.

We found that the outcome measures we used were essentially unrelated to the slightly different definitions of angina in the 2 models, adding to the robustness of our findings. Therefore, in the 3-algorithm model used in this article, 35% of angina patients underwent revascularization compared with 34% in the 4-algorithm model.

Limitations

This study shares the limitations inherent to all administrative claims studies. Claims data are collected for the purpose of determining reimbursement rather than of facilitating research, and the extent to which these data can accurately capture a diagnosis is limited. ^25-34 In contrast, claims data provide a snapshot of a real-world treatment environment based on the actual use of healthcare resources and their costs. Admin -istrative data have demonstrated reasonably good concordance with medical record or patient survey data.^25-35 The cause of the incremental cost differences is difficult to assess; for example, some of the increased burden associated with patients who have CAD with angina may be because of increased physician contact and services resulting from the additional diagnosis, and not specifically from angina.

The results of this study can be generalized only to a limited extent. The managed care database on which the study was based represents a population of commercially insured patients. Therefore, these patients are relatively younger (ie, non-Medicare population), with a lower burden of comorbidity than would typically be found in databases of Medicare recipients.

The results of this study are relevant to the treatment of cardiovascular disease in a managed care setting.

Conclusion

This study demonstrates that among commercially insured US patients diagnosed with and treated for CAD in a managed care setting, patients with angina make significantly greater use of healthcare resources than patients without angina. In the year after their CAD diagnosis, patients with angina are considerably more likely to have a revascularization procedure than those without angina. This greater resource utilization, if not based on evidence-based practice, may result in significantly higher annual direct costs for patients with angina. Additional analyses are warranted to further evaluate the impact of these findings as they relate to medical practice and pharmacy services, perhaps via prospective trial designs and/or observational research.

The pattern of healthcare utilization observed in this study suggests that patients with angina may seek care, obtain incomplete relief of their symptoms, and then continue to seek care. For patients treated in a managed care environment, therapeutic approaches that are consistent with current treatment guidelines and reduce the frequency and severity of angina attacks could result in substantial savings in terms of resource usage and direct economic costs in managed care organizations.

Acknowledgment

This study was supported by funding from Gilead Pharmaceuticals (formerly CV Therapeutics).

Author Disclosure Statement

Dr Kempf was employed by CV Therapeutics (Gilead) when this article was written; Ms Buysman is an employee of OptumInsight, which was contracted by CV Therapeutics (Gilead) to conduct this research; Dr Brixner has received consultant support for this research.

Dr Kempf was Executive Director, Outcomes Research, CV Therapeutics (Gilead), Palo Alto, CA, at the time of the study and is currently an employee of Genzyme; Ms Buysman is Associate Director, Observational Research, OptumInsight, Eden Prairie, MN; Dr Brixner is Professor and Chair, Department of Pharmacotherapy, and Executive Director, Outcomes Research Center, University of Utah, Salt Lake City.

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