Dataset Information

A hybrid 4-item Krousel-Wood Medication Adherence Scale predicts cardiovascular events in older hypertensive adults.

ABSTRACT: BACKGROUND:There is a need for a brief, open access, self-report medication adherence scale that overcomes challenges of existing adherence tools, is associated with incident cardiovascular disease (CVD), and identifies low 'implementation' adherers to antihypertensive medications to facilitate blood pressure management. METHODS AND RESULTS:Antihypertensive medication adherence was assessed in a cohort of 1532 older hypertensive adults without prior CVD using the self-report 4-item Krousel-Wood Medication Adherence Scale (K-Wood-MAS-4), a hybrid tool developed to predict pharmacy refill and which captures four domains of adherence behavior: self-efficacy, physical function, intentional medication-taking, and forgetfulness. The 4-item scale categorized participants as low and high adherers using scores at least 1 and less than 1, respectively. Participants were followed after K-Wood-MAS-4 assessment to identify incident CVD events (stroke, myocardial infarction, congestive heart failure, or CVD death). The prevalence of low adherence was 38.7%. During a median follow-up of 2.8 years (maximum 3.8 years), 136 (8.9%) participants had an incident CVD event; 12.8 and 6.4% in low and high adherers, respectively. The adjusted hazard ratio (aHR) for incident CVD associated with low versus high adherence was 2.29 [95% confidence interval (CI): 1.61, 3.26]. Results were similar when stratified by age [<75 years - aHR 3.53 (95% CI: 1.65, 7.56); ?75 years - aHR 1.98 (95% CI: 1.32, 2.97)], sex [women - aHR 1.90 (95% CI: 1.16, 3.12); men - aHR 2.80 (95% CI: 1.68, 4.65)], and race [black - aHR 2.22 (95% CI: 0.93, 5.31); white - aHR 2.26 (95% CI: 1.54, 3.34)]. CONCLUSION:Low medication adherence using the 'hybrid' K-Wood-MAS-4 predicts incident CVD in a cohort of older adults with established hypertension.

SUBMITTER: Krousel-Wood M

PROVIDER: S-EPMC6485944 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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A hybrid 4-item Krousel-Wood Medication Adherence Scale predicts cardiovascular events in older hypertensive adults.

Krousel-Wood Marie M Peacock Erin E Joyce Cara C Li Shengxu S Frohlich Edward E Re Richard R Mills Katherine K Chen Jing J Stefanescu Andrei A Whelton Paul P Tajeu Gabriel G Kronish Ian I Muntner Paul P

Journal of hypertension 20190401 4

<h4>Background</h4>There is a need for a brief, open access, self-report medication adherence scale that overcomes challenges of existing adherence tools, is associated with incident cardiovascular disease (CVD), and identifies low 'implementation' adherers to antihypertensive medications to facilitate blood pressure management.<h4>Methods and results</h4>Antihypertensive medication adherence was assessed in a cohort of 1532 older hypertensive adults without prior CVD using the self-report 4-ite ...[more]

PMID: 30817468

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Project description:To improve and support medication adherence among patients with chronic diseases, especially for long-term medication, it is important to consider both their relationship with healthcare providers and their lifestyle. We tested the reliability and validity of a modified 12-item Medication Adherence Scale.We revised a 14-item measure of medication adherence, created in 2009, to a more concise and clear 12-item version, and we verified the reliability and validity of the 12-item scale. We included 328 patients with chronic diseases participating in the Chronic Disease Self-Management Program in Japan from 2011 to 2014. Confirmatory factor analysis was used to assess whether the four factors assessed were the same as the previous 14-item Medication Adherence Scale. Cronbach's coefficient alpha was used to assess internal consistency reliability, and the relationships between patient demographic characteristics and medication adherence were compared with previous studies.The 12 items were categorized into the four factors "medication compliance", "collaboration with healthcare providers", "willingness to access and use information about medication", and "acceptance to take medication and how taking medication fits patient's lifestyle". Confirmatory factor analysis showed ?2/df?=?2.6, CFI?=?0.94, and RMSEA?=?0.069. Cronbach's alpha for the 12-item scale was 0.78. Cronbach's alpha for the four subscales was 0.74, 0.81, 0.67, and 0.45. Higher medication adherence was significantly associated with being a female patient, living with someone else, and age 40-49 years versus age 20-29 years. These relationships were the same as in previous studies.We modified our original 14-item scale to a 12-item Medication Adherence Scale for patients with chronic diseases, which considers their relationship with healthcare providers and lifestyle. Refinement might be needed because of the relatively low reliability of subscales. However, the modified scale is expected to contribute to more effective self-management of medication and to improving medication adherence, particularly among patients with chronic diseases who require long-term medication not only in Japan but also in other countries.

Project description:BackgroundThis study assesses the feasibility of using machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Regression (SVR) and Self-Organizing Feature Maps (SOM) to identify and determine factors associated with hypertensive patients' adherence levels. Hypertension is the medical term for systolic and diastolic blood pressure higher than 140/90 mmHg. A conventional medication adherence scale was used to identify patients' adherence to their prescribed medication. Using machine learning applications to predict precise numeric adherence scores in hypertensive patients has not yet been reported in the literature.MethodsData from 160 hypertensive patients from a tertiary hospital in Kuala Lumpur, Malaysia, were used in this study. Variables were ranked based on their significance to adherence levels using the RF variable importance method. The backward elimination method was then performed using RF to obtain the variables significantly associated with the patients' adherence levels. RF, SVR and ANN models were developed to predict adherence using the identified significant variables. Visualizations of the relationships between hypertensive patients' adherence levels and variables were generated using SOM.ResultMachine learning models constructed using the selected variables reported RMSE values of 1.42 for ANN, 1.53 for RF, and 1.55 for SVR. The accuracy of the dichotomised scores, calculated based on a percentage of correctly identified adherence values, was used as an additional model performance measure, resulting in accuracies of 65% (ANN), 78% (RF) and 79% (SVR), respectively. The Wilcoxon signed ranked test reported that there was no significant difference between the predictions of the machine learning models and the actual scores. The significant variables identified from the RF variable importance method were educational level, marital status, General Overuse, monthly income, and Specific Concern.ConclusionThis study suggests an effective alternative to conventional methods in identifying the key variables to understand hypertensive patients' adherence levels. This can be used as a tool to educate patients on the importance of medication in managing hypertension.

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A hybrid 4-item Krousel-Wood Medication Adherence Scale predicts cardiovascular events in older hypertensive adults.

Publications

A hybrid 4-item Krousel-Wood Medication Adherence Scale predicts cardiovascular events in older hypertensive adults.

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