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. 2013 Dec 5:8:1-13.
doi: 10.2147/PPA.S54520. eCollection 2013.

Electronically monitored medication adherence predicts hospitalization in heart failure patients

Barbara Riegel  1 , George J Knafl  2
Affiliations

Electronically monitored medication adherence predicts hospitalization in heart failure patients

Barbara Riegel et al. Patient Prefer Adherence. .

Abstract

Background: Hospitalization contributes enormously to health care costs associated with heart failure. Many investigators have attempted to predict hospitalization in these patients. None of these models has been highly effective in prediction, suggesting that important risk factors remain unidentified.

Purpose: To assess prospectively collected medication adherence, objectively measured by the Medication Event Monitoring System, as a predictor of hospitalization in heart failure patients.

Materials and methods: We used recently developed adaptive modeling methods to describe patterns of medication adherence in a sample of heart failure patients, and tested the hypothesis that poor medication adherence as determined by adaptive methods was a significant predictor of hospitalization within 6 months.

Results: Medication adherence was the best predictor of hospitalization. Besides two dimensions of poor adherence (adherence pattern type and low percentage of prescribed doses taken), four other single factors predicted hospitalization: low hemoglobin, depressed ejection fraction, New York Heart Association class IV, and 12 or more medications taken daily. Seven interactions increased the predictive capability of the model: 1) pattern of poor adherence type and lower score on the Letter-Number Sequencing test, a measure of short-term memory; 2) higher number of comorbid conditions and higher number of daily medications; 3) higher blood urea nitrogen and lower percentage of prescribed doses taken; 4) lower hemoglobin and much worse perceived health compared to last year; 5) older age and lower score on the Telephone Interview of Cognitive Status; 6) higher body mass index and lower hemoglobin; and 7) lower ejection fraction and higher fatigue. Patients with none of these seven interactions had a hospitalization rate of 9.7%. For those with five of these interaction risk factors, 100% were hospitalized. The C-index (the area under the receiver-operating characteristics [ROC] curve) for the model based on the seven interactions was 0.83, indicating excellent discrimination.

Conclusion: Medication adherence adds important new information to the list of variables previously shown to predict hospitalization in adults with heart failure.

Keywords: heart failure; hospitalization; medication adherence; outcomes; patient compliance; self-care.

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Figures

Figure 1
Figure 1
Sample adherence patterns. Notes: The raw data are plotted as diamonds. The middle curve is the estimated mean adherence over time while the other curves are unit error bands, ±1 estimated standard deviation around mean adherence.
Figure 2
Figure 2
Plots of average mean adherence and of average adherence variability for patients in adherence types.
See this image and copyright information in PMC

References

    1. Heidenreich PA, Albert NM, Allen LA, et al. Forecasting the Impact of Heart Failure in the United States: a policy statement From the American Heart Association. Circ Heart Failure. 2013;6(3):606–619. - PMC - PubMed
    1. Giamouzis G, Kalogeropoulos A, Georgiopoulou V, et al. Hospitalization epidemic in patients with heart failure: risk factors, risk prediction, knowledge gaps, and future directions. J Card Fail. 2011;17(1):54–75. - PubMed
    1. Philbin EF, DiSalvo TG. Prediction of hospital readmission for heart failure: development of a simple risk score based on administrative data. J Am Coll Cardiol. 1999;33(6):1560–1566. - PubMed
    1. Krumholz HM, Chen YT, Wang Y, Vaccarino V, Radford MJ, Horwitz RI. Predictors of readmission among elderly survivors of admission with heart failure. Am Heart J. 2000;139(1 Pt 1):72–77. - PubMed
    1. Felker GM, Leimberger JD, Califf RM, et al. Risk stratification after hospitalization for decompensated heart failure. J Card Fail. 2004;10(6):460–466. - PubMed

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