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Clinical impact of medication adherence on 10-year cardio-cerebrovascular mortality in newly diagnosed hypertensive patients.

ABSTRACT: The purpose of this study is to evaluate the impact of medication adherence on cardio-cerebrovascular (CCV) mortality in newly diagnosed hypertensive patients. The authors retrospectively reviewed data from 20,836 patients who newly diagnosed hypertension from January 1, 2003 to December 31, 2005. Medication adherence was calculated from the compliance ratio (CR) during the first year after the diagnosis of hypertension. CCV mortality for 10 years was assessed according to the presence or absence of complications of hypertension. The risk of CCV death was significantly reduced in the CR ≥ 70% group than in the CR < 70% group (hazard ratio, 0.70; p = .004) for 10 years. In the patients without complications, the risk of CCV death was significantly lower in the CR ≥ 70% group than in the CR < 70% group (hazard ratio, 0.56; p = .014). However, in patients with complications, there was no significant difference in risk of CCV death between the CR ≥ 70% group and the CR < 70% group (hazard ratio, 0.79; p = .100). Only the CR ≥ 90% group had a significantly lower risk of CCV death (hazard ratio, 0.56; p < .001) for those with complications. Medication adherence is significantly associated with CCV mortality during 10 years in newly diagnosed hypertensives patients. Patients with complications of hypertension have to continue a high adherence rate (CR ≥ 90) for better long-term clinical outcomes.

SUBMITTER: Kim CL

PROVIDER: S-EPMC8678795 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesProphylactic drugs currently used for migraine treatment are not specific. Furthermore, few studies in existing literature describe drugs utilisation patterns and adherence to migraine prophylactic treatment. This study is aimed to describe utilisation patterns of migraine drugs, evaluate adherence to prophylactic medications and investigate drug-related costs.DesignRetrospective population-based study using an administrative health-related database.SettingPrimary care setting in the Campania region, Southern Italy.ParticipantsThis study was carried out between 1 January 2016 and 31 December 2018, involving 12 894 subjects with any primary or secondary hospital discharge with migraine diagnosis, or at least two medical dispensations of migraine-specific acute or prophylactic medications (triptans or pizotifen). Subjects were classified into four treatment cohorts: no treatment, acute, prophylactic and both acute and prophylactic. Subjects were followed-up for 1 year.Outcome measuresUtilisation patterns of migraine drugs at treatment initiation; adherence to prophylactic treatment; discontinuation, restart and switching rates; annual migraine drug costs per patient.ResultsOverall, 81.1% of subjects received acute treatment as their initial migraine treatment regimen, 10.7% prophylactic treatment, 8.2% both acute and prophylactic treatment. 599 patients were treated prophylactically; of these, 26.2% adhered to their initial treatment while 73.8% reported interruptions in treatment. Among the latter, 46.4% of patients discontinued the treatment completely within 103 days (IQR 89.0), 31% restarted treatment 46 days after interruption (IQR 60.0) and 22.6% switched to another treatment within 98 days (IQR 57.5) (p<0.001). The median annual cost of drugs per patient was €103 for those treated acutely, €75 for those treated prophylactically, €163 for those treated both.ConclusionsMigraine treatment with acute medications is still prevalent in Italy; only few patients received prophylactic treatment with poor adherence to treatment. These findings reflect an unmet need for improved prophylactic therapies in order to provide a better disease management.

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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Clinical impact of medication adherence on 10-year cardio-cerebrovascular mortality in newly diagnosed hypertensive patients.

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