Predicting Lipid-Lowering Medication Persistence after the First Cardiovascular Disease Hospitalization.

OBJECTIVES: This study analyzed patient factors in medication persistence after discharge from the first hospitalization for cardiovascular disease (CVD) with the aim of predicting persistence to lipid-lowering therapy for 1 to 2 years. METHODS: A subcohort having a first CVD hospitalization was selected from 313,207 patients for proportional hazard model analysis. Logistic regression, support vector machine, artificial neural networks, and boosted regression tree (BRT) models were used to predict 1- and 2-year medication persistence. RESULTS: Proportional hazard modeling found significant association of persistence with age, diabetes history, complication and comorbidity level, days stayed in hospital, CVD diagnosis type, in-patient procedures, and being new to therapy. BRT had the best predictive performance with c-statistic of 0.811 (0.799-0.824) for 1-year and 0.793 (0.772-0.814) for 2-year prediction using variables potentially available shortly after discharge. CONCLUSION: The results suggest that development of a machine learning-based clinical decision support tool to focus improvements in secondary prevention of CVD is feasible.

Interrupted time series analysis on first cardiovascular disease hospitalization for adherence to lipid-lowering therapy.

PURPOSE: We analysed lipid-lowering medication adherence before and after the first hospitalization for cardiovascular disease (CVD) to explore the influence hospitalization has on patient medication adherence. METHODS: We extracted a sub-cohort for analysis from 313,207 patients who had primary CVD risk assessment. Adherence was assessed as proportion of days covered (PDC) ≥ 80% based on community dispensing records. Adherence in the 4 quarters (360 days) before the first CVD hospitalization and 8 quarters (720 days) after hospital discharge was assessed for each individual in the sub-cohort. An interrupted time series design using generalized estimating equations was applied to compare the differences of population-level medication adherence rates before and after the first CVD hospitalization. RESULTS: Overall, a significant improvement in medication adherence rate from before to after the hospitalization was observed (odds ratio (OR) 2.49 [1.74-3.57]) among the 946 patients included in the analysis. Patients having diabetes history had a higher OR of adherence before the hospitalization than patients without diabetes (1.50 [1.03-2.22]) but no significant difference after the hospitalization (OR 1.13 [0.89-1.43]). Before the first hospitalization, we observed that quarterly medication adherence rate was steady at around 55% (OR 0.97 [0.93-1.01), whereas the trend in adherence over the post-hospitalization period decreased significantly per quarter (OR 0.97 [0.94-0.99]). CONCLUSIONS: Patients were more likely to adhere to lipid-lowering therapy after experiencing a first CVD hospitalization. The change in medication adherence rate is consistent with patients having heightened perception of disease severity following the hospitalization.

Geography and patient history in long-term lipid lowering medication adherence for primary prevention of cardiovascular disease.

In order to determine the role of geographical and patient history factors in long-term medication adherence in cardiovascular disease (CVD), we analysed adherence to lipid-lowering therapy in a primary care cohort based on CVD decision support and linked health systems and census data from Auckland, New Zealand. Two-year adherence was examined for 10,410 patients aged between 30 and 74 with neither diabetes nor a history of CVD. Using logistic regression we found significant variation in adherence by age, ethnicity and being a new therapy user, and in 9 of 86 geographic zones. A large low-adherence 'cold-spot' of 13 contiguous geographic zones was detected through local Getis-Ord Gi* analysis. A set of 42 models to predict adherence was formulated on sets of demographic, geographic and refill history factors. We observed prediction ability to be improved by addition of refill history but not geographical variables, and boosted regression tree (BRT) models outperformed logistic regression.

A Temperature-Controlled, Conductive PANI@CNFs/MEO2 MA/PEGMA Hydrogel for Flexible Temperature Sensors.

Electrically conductive, yet stimuli-responsive hydrogels are highly desirable for many technological applications. However, the discontinuous conductivity of hydrogels during the response process has become a bottleneck that limits their application. To overcome this constraint, a linearly tunable, electrically conductive hydrogel is prepared using in-situ polymerized polyaniline (PANI) on a CNFs/MEO2 MA/PEGMA hydrogel (PANI@CMP hydrogel) substrate. The PANI@CMP hydrogel exhibits temperature-tunable electrical conductivity due to the liner relationship between thermosensitivity and temperature of the CMP hydrogel substrate. Furthermore, the stiffness and elasticity of the resultant hydrogel after PANI introduction is enhanced via physical interactions, and the compression load is improved by 42%. A highly sensitive temperature sensor is therefore fabricated with PANI@CMP hydrogel as the flexible induction element, and this sensor achieves temperature monitoring from 20 to 60 °C. This new temperature-controllable conductive hydrogel has excellent mechanical properties, showing great potential for applications in flexible smart sensors, conductive fillers, and medical devices.