Patient-Level Medication Regimen Complexity Across Populations With Chronic Disease

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• 作者：Anne M. Libby ; PhD¹ ; ² ; ^{anne.libby@ucdenver.edu} ; Douglas N. Fish ; PharmD¹ ; Patrick W. Hosokawa ; MS² ; Sunny A. Linnebur ; PharmD ; FCCP¹ ; Kelli R. Metz ; PharmD¹ ; Kavita V. Nair ; PhD¹ ; Joseph J. Saseen ; PharmD ; FCCP¹ ; Joseph P. Vande Griend ; PharmD¹ ; Sara P. Vu ; BS¹ ; Jan D. Hirsch ; PhD ; RPh³
• 关键词：chronic disease ; comorbidities ; diabetes ; geriatric depression ; HIV ; hypertension ; medication regimen complexity ; MRCI
• 刊名：Clinical Therapeutics
• 出版年：2013
• 出版时间：April, 2013
• 年：2013
• 卷：35
• 期：4
• 页码：385-398.e1
• 全文大小：918 K

文摘

Background

Expected treatment effectiveness from medications can be diminished due to suboptimal adherence. Medication nonadherence has been linked to pill burden from the quantity of medications; however, medication regimens with similar quantities of medications vary in complexity due to multiple dosage forms, frequency of dosing, and additional usage directions. Thus, a simple medication count ignores medication regimen complexity, especially as it pertains to a patient-level perspective that includes prescription and over-the-counter medications. A gap exists in the study of a patient-level medication regimen complexity metric across disease-specific populations.

Objective

The goal of this study was to implement the quantitative Medication Regimen Complexity Index (MRCI) at the patient level in defined populations with chronic disease (geriatric depression, HIV, diabetes mellitus, and hypertension). Patient-level medication regimen complexity included all prescribed medications and over-the-counter medications documented in the electronic medication list.

Methods

Using electronic medical records at the University of Colorado Hospital ambulatory clinics, we sampled 4 retrospective cohorts of adult patients in active care in 2011 with a qualifying medical diagnosis and prescribed disease-specific medication. Samples were randomly selected from all qualifying patients; de-identified information was coded using the MRCI.

Results

Cohort-defining disease-specific prescription medications (eg, antidepressants for the depression-defined cohort) contributed <20 % to the total patient-level complexity MRCI score; the MRCI score was dominated by complexity associated with all other prescription medications. Within disease-specific cohorts, MRCI scores differentiated patients with the highest and lowest medication counts, comorbidity counts, and the Charlson comorbidity index scores. For example, geriatric depression patients had a highest quartile mean MRCI score of 41 and a lowest quartile mean MRCI score of 13. Between disease-specific cohorts, high and low MRCI scores differed because each cohort had its own MRCI ranges. For example, highest quartile MRCI scores varied from a mean MRCI score of 41 (geriatric depression) to 30 (hypertension); lowest quartile scores ranged from a mean MRCI score of 7 (hypertension and HIV) to 13 (geriatric depression).

Conclusions

MRCI components of dosing frequency and prescribed medications outside of the cohort-defining disease medications contributed the most to the patient-level scores. Thus, chronic disease management programs may want to consider all medications that patients are taking and examine ways to reduce complexity, such as reducing multiple dosing frequencies when possible. MRCI scores differentiated high and low patient-level complexity measures, representing possible utility as a prospective tool to identify target patients for intervention. Future work includes simplifying the MRCI and enhancing the scores with medication risk factors, as well as explicitly linking to adherence and health services.