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The American Journal of Managed Care January 2015
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Disease-Modifying Therapy and Hospitalization Risk in Heart Failure Patients
Fadia T. Shaya, PhD, MPH; Ian M. Breunig, PhD; and Mandeep R. Mehra, MD, FACC, FACP, FRCP
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Disease-Modifying Therapy and Hospitalization Risk in Heart Failure Patients

Fadia T. Shaya, PhD, MPH; Ian M. Breunig, PhD; and Mandeep R. Mehra, MD, FACC, FACP, FRCP
Substantive outcome improvement and savings to Medicaid may be achieved with small changes in prescribing rates or comorbidity prevalence among patients with heart failure.
Figures 1 to 3 report the adjusted risk of hospitalization over the follow-up period. The following results were estimated using the prevalence cohort of HF patients—note that estimates from the incidence cohort were similar. The presence of renal failure was the strongest predictor of hospitalization (average HR, 1.43; 95% CI, 1.36-1.51), followed by other cardiovascular disease (HR, 1.40; CI, 1.31-1.50), COPD (HR, 1.33; CI, 1.26-1.40), ischemic heart disease (HR, 1.28; CI, 1.22-1.35), stroke (HR, 1.27; CI, 1.20-1.34), diabetes (HR, 1.26; CI, 1.20-1.32), and hypertension (HR, 1.11; CI, 1.05-1.17). Patients with a psychological disorder (HR, 0.77; CI, 0.73-0.81) and/or hyperlipidemia (HR, 0.81; CI, 0.77-0.85) experienced significantly lower risk of hospitalization.

Hospitalization risk was significantly reduced through the use of ACEi/ARBs (HR, 0.77; CI, 0.73-0.81), betablockers (HR, 0.83; CI, 0.79-0.87), and/or other cardiovascular drugs (HR, 0.76; CI, 0.72-0.80) as first-line therapies for HF. However, initiation on AA and/or nitrates + hydralazine had no significant effect on hospitalization risk.

In the adjusted models, a diagnosis of HF coinciding with hospitalization significantly increased the risk for subsequent hospitalization (HR, 1.27; CI, 1.35-1.47), as did middle age (ages 45-54 years; HR, 1.13; CI, 1.06-1.19). When adjusted for other risk factors, Hispanics (HR, 0.81; CI, 0.68-0.98) or other race groups (HR, 0.83; CI, 0.75-0.93) were at lower risk for hospitalization than white or black patients.

The predicted HRs for significant risk factors were used to estimate the 6-month, 1-year, and 2-year risks of hospitalization among Maryland Medicaid patients diagnosed with HF (eAppendix). The model’s ability to correctly discriminate between individuals in the sample with high and low risk for hospitalization was good with a C statistic of 0.80 for any time horizon using both the prevalent and incident cohorts.

Cost and Savings to Maryland Medicaid

Table 2 presents the yielded annual numbers-needed-to-treat to prevent at least 1 hospitalization in the Maryland Medicaid prevalence-based HF cohort. As HF patients may experience multiple hospitalizations per year, our relative cost/savings estimates are conservative since they are based on the first hospitalization after HF diagnosis. While not all patients are candidates for certain cardiovascular therapies,30,31,36,37 estimates show that for every 12 patients started on ACEi/ARBs, at least 1 hospitalization can be prevented annually. A 20% increase in prescription rates in the sample resulted in savings of at least $85 (CI, $70-$101) per HF patient. Using our database of Medicaid claims to ascertain the number of non-dual-enrolled HF patients in Maryland Medicaid at any time in 2007 (12,054 patients), this estimate translated to a total annual savings of $1,024,590 (CI, $843,780-$1,217,454) for Medicaid.

For perspective, total medical expenditures by Maryland Medicaid on HF in 2007 was approximately $23.6 million.38 Thus, Medicaid spent approximately $2200 (in 2011 dollars) per HF patient. This may be a slightly high comparator since dual-enrolled individuals were not included in our data, but comprised roughly 11% of all Maryland Medicaid enrollees in 2007.39

For further comparison, Table 2 also presents the reduction in the number of cases of each comorbidity that was estimated to prevent 1 hospitalization per year. For instance, for every 8 HF patients presented with concomitant renal dysfunction, at least 1 hospitalization can be expected annually. Thus, a 20% decrease in the prevalence of renal dysfunction in the HF cohort resulted in an estimated saving of at least $111 (95% CI, $96-$127) per HF patient.

DISCUSSION

Given the high cost burden of hospitalizations and the potential ensuing complications and course of HF, our findings, in a unique Medicaid population, call for attention to strategies to attend to comorbidities and optimize use of disease-modifying therapy in such patients with HF.10,18,19 We found that a diagnosis of COPD, stroke, renal dysfunction, diabetes, ischemic heart disease, hypertension, or other cardiovascular diseases in HF significantly increased the risk of hospitalization: by about 30% for COPD or stroke, 25% for diabetes and ischemic heart disease, 10% for hypertension, and 40% for renal dysfunction and other cardiovascular diseases. Conversely, disease-modifying therapies reduced that risk by about 20%. Medicaid patients between 45 and 54 years of age were at the highest risk of hospitalization. Interestingly, the risk was 30% greater for individuals who were diagnosed with HF upon their first recorded hospitalization, perhaps suggesting a need for more proactive differential diagnosis approaches or faster referral patterns in the first point-of-care setting.18-20

Medications that have been shown in clinical trials to improve systolic HF have not been shown to improve HFpEF, and prior data suggest that patients with HFpEF tend to have a history of hypertension and be older and female.25,26,28 Thus, our results likely overestimate the impact of disease-modifying therapies on hospitalization risk in all HF patients given that the broader population of HF patients might be more heavily represented by an older population with a higher prevalence of HFpEF. Moreover, our database consisted of only non-dual-enrolled Medicaid beneficiaries; therefore, caution should be used when generalizing our findings on relative risk to a dual-enrolled population. The objective of this study was to quantify the hospitalization risk in a high-risk Medicaid population with HF and should not be generalized to the Medicare population since it is likely represented by distinctly different risk groups.

We used the WCR method to estimate the average HRs on the comorbidities and other variables to reduce the possibility of biased estimates from a proportional hazards model. Given the nature of the claims-based data, we did not have access to lab values or medication logs per se, and thus could not assess disease severity nor ascertain that prescriptions filled were indeed taken by the patients. However, assuming this lack of information is systemic in the data and thus affects all comparison groups, it should not affect the risk comparisons.

To the extent that the study population was selected from among Medicaid enrollees, all study patients were, by definition, from a relatively homogeneous socioeconomic group and had the same insurance coverage, with presumably equal chance at access to medical care, medication therapy, or hospital services. Some cultural norms, beliefs and values, factors of cultural competency, literacy, self-efficacy, and other related measures may directly or indirectly affect the risk of hospitalization. We tried to adjust for these confounders indirectly, and at least partly, by adjusting for race/ethnicity. Further, we relied on the literature on the association between socioeconomic status, race/ethnicity, and socio-cultural-behavioral determinants of access to care to confirm that although worth pursuing in further research, the residual confounding due to unobservable variables may not present a notable bias in this study. We further matched samples by comorbidity in order to account for the possible imbalance of covariates across those comorbidities. Theoretically, this approach would have eliminated confounding of our estimates, to the extent possible, due to lifestyle factors or other unobservable variables that may contribute to the risk of hospitalization. The results were similar to those reported.

Perhaps physician practice styles, type of training, time in practice, or cultural competency may be concerns for bias.40-43 However, by design, in the general Medicaid setting, particularly that of managed care and related practice, reimbursement and incentive structures somewhat limit variations in practice style. Thus, to the extent that patients are likely to see various healthcare professionals during their course of care, we do not think that the lack of information on individual physicians in our study population poses a serious validity threat.

This study is the first to address the epidemiology of comorbidities in a high-risk Medicaid population, reflecting a demographic largely under-represented in large-scale studies or clinical trials.13-15 We show the unmet needs of this population and the clinical and hospitalization issues associated with prevalent disease and therapies. The burden of comorbidity was much higher than that observed in national statistics on HF patients, and the prescribing prevalence was lower than expected given the high-risk profile of the population. These findings may point to a high-priority area for Medicaid plans. Most notably, in the context of the study’s population demographic and clinical profile, we found that even small increments in disease-modifying therapies would result in significant reduction in costs to state plans. For instance, in the Maryland Medicaid program, a 20% increase in prescribing rates of ACEi/ARBs or beta-blockers would have led to an approximate annual savings of at least $85 or $57 per HF patient, respectively, or over $1 million total.

Limitations

A substantial fraction of patients had multiple comorbidities of various assortments. Thus, we modeled each comorbidity independent of the others, adjusting for concomitant conditions. While the model’s ability to discriminate was good, it is still limited in its specificity; comorbidity reflects a spectrum of disease. Consider, for instance, that patients with hyperlipidemia and renal dysfunction may represent a different risk group than renal dysfunction alone or that chronic kidney disease stage 4 is different from stage 3. An obvious implication of our findings is that more work should be done to examine the management of comorbid conditions and the impact it has on HF control, hospitalizations, and costs.

First-line therapies were also modeled independently. This facilitated examining how an increase in the use prevalence of a single medication would have impacted the Medicaid budget for HF patients. However, this approach was not amenable to looking specifically at the potential budget impact of increasing the rate of 2 or more disease-modifying agents being prescribed concurrently. Certainly, not all HF patients are eligible to receive every therapy or require concomitant treatment, but the exercise may have been interesting nonetheless.

CONCLUSIONS

Healthcare reform and ongoing healthcare discussions have stimulated an interest in needs and risk assessment for target high-risk populations. In particular, the growing ranks of Medicaid plans and the rise of national health and other entitlement programs call for more deliberate, proactive, and cost-effective disease and risk management of plan enrollees.12,13 Our study elicits the specific risk attributable to lead risk factors in HF patients enrolled in Medicaid plans and shows how disease-modifying therapies can quantifiably mitigate the risk for hospitalization in those patients. We further show the economic implications to the state by using a budget-impact approach to demonstrate the potential cost savings from a move to more optimal therapy.

 
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