Impact of a Community Pharmacy Pharmacotherapy Follow-up (PTF) service in patients using opioid analgesic

The use of prescribed major opioid analgesics (fentanyl, tapentadol, morphine and oxycodone and combinations) for non-cancer chronic pain is fraught with risks that may generate Negative Medicine Outcomes (NMO). Among the factors associated with these risks, those related to the patient's characteristics and aberrant behavior, the treatment conditions, and the prescription health settings should be evaluated with the aim of minimizing unsafety during the health care process. The present study addresses, from a community pharmacy, the analysis of Drug Related Problems (DRP) and Negative Medicine Outcomes (NMO) in patients using these major opioid analgesics while it aims to demonstrate the role of pharmaceutical care interventions in promoting safety during the use of these molecules. A three step Pharmacotherapeutic Follow-up (PFT) protocol was designed to prevent, detect, and solve DRP and NMO associated with the use of opioid analgesics. 74.6% of the patients used opioid analgesics to treat musculoskeletal pain. Polypharmacy with benzodiazepines (61.9%); antidepressants (57.1%) and antiepileptics (30.2%) was detected in patients using these opioids. The Morisky-Green Adherence test revealed that 30.2% were nonadherent. It was observed, with statistical significance, that in all patients (63), the impact of the 14-week PFT supervised by the community pharmacist achieved an overall reduction in the prevalence of DRP and NMO. While the reduction in the number of DRPs reached 66.7%. Community pharmacies are a strategic point to promote and implement effective opioid stewardship due to both their central role in healthcare services and frequent interaction with patients.

A predictive model for medication adherence in older adults with heart failure

AIMS

Although many studies have examined the predictors of medication adherence (MA), further empirical research is required to clarify the best model for predicting MA for older adults with heart failure (HF). Thus, we hypothesized a model in which information (knowledge), motivation (social support and depressive symptoms), and behavioural skills (barriers to self-efficacy) would be associated with MA in patients with HF.

METHODS AND RESULTS

Using a cross-sectional survey, 153 adults aged ≥ 65 years taking medication for HF were recruited from a university hospital in Korea. Data were collected based on the information-motivation-behavioural skills (IMB) model constructs and MA. In the hypothesized path model, self-efficacy was directly related to MA (β = -0.335, P = 0.006), whereas social support was indirectly related to MA through self-efficacy (β = -0.078, P = 0.027). Depressive symptoms were directly related to MA (β = 0.359, P = 0.004) and indirectly related to MA through self-efficacy (β = 0.141, P = 0.004). The hypothesized MA model showed a good fit for the data. Knowledge, social support, and depressive symptoms accounted for 44.3% of the variance in self-efficacy (P = 0.004). Left ventricular ejection fraction, knowledge, social support, depressive symptoms, and self-efficacy explained 64.4% of the variance in MA (P = 0.004).

CONCLUSION

These results confirmed the IMB model's suitability for predicting MA in older adults with HF. These findings may guide and inform intervention programmes designed to alleviate depressive symptoms in older adults with HF and enhance their HF knowledge, social support, and self-efficacy, with the ultimate goal of improving their MA.

Relationship between People's Interest in Medication Adherence, Health Literacy, and Self-Care: An Infodemiological Analysis in the Pre- and Post-COVID-19 Era

The prevalence of non-communicable diseases has risen sharply in recent years, particularly among older individuals who require complex drug regimens. Patients are increasingly required to manage their health through medication adherence and self-care, but about 50% of patients struggle to adhere to prescribed treatments. This study explored the relationship between interest in medication adherence, health literacy, and self-care and how it changed during the COVID-19 pandemic. We used Google Trends to measure relative search volumes (RSVs) for these three topics from 2012 to 2022. We found that interest in self-care increased the most over time, followed by health literacy and medication adherence. Direct correlations emerged between RSVs for medication adherence and health literacy (r = 0.674, p < 0.0001), medication adherence and self-care (r = 0.466, p < 0.0001), and health literacy and self-care (r = 0.545, p < 0.0001). After the COVID-19 pandemic outbreak, interest in self-care significantly increased, and Latin countries showed a greater interest in self-care than other geographical areas. This study suggests that people are increasingly interested in managing their health, especially in the context of the recent pandemic, and that infodemiology may provide interesting information about the attitudes of the population toward chronic disease management.

The use of technology to improve medication adherence in heart failure patients: a systematic review of randomised controlled trials

BACKGROUND

Heart failure is an ever-growing contributor to morbidity and mortality in the ageing population. Medication adherence rates among the HF population vary widely in the literature, with a reported range of 10-98%. Technologies have been developed to improve adherence to therapies and other clinical outcomes.

AIMS

This systematic review aims to investigate the effect of different technologies on medication adherence in patients with heart failure. It also aims to determine their impact on other clinical outcomes and examine the potential of these technologies in clinical practice.

METHODS

This systematic review was conducted using the following databases: PubMed Central UK, Embase, MEDLINE, CINAHL Plus, PsycINFO and Cochrane Library until October 2022. Studies were included if they were randomised controlled trials that used technology to improve medication adherence as an outcome in heart failure patients. The Cochrane Collaboration's Risk of Bias tool was used to assess individual studies. This review was registered with PROSPERO (ID: CRD42022371865).

RESULTS

A total of nine studies met the inclusion criteria. Two studies showed statistically significant improvement in medication adherence following their respective interventions. Eight studies had at least one statistically significant result in the other clinical outcomes it measured, including self-care, quality of life and hospitalisations. All studies that evaluated self-care management showed statistically significant improvement. Improvements in other outcomes, such as quality of life and hospitalisations, were inconsistent.

CONCLUSION

It is observable that there is limited evidence for using technology to improve medication adherence in heart failure patients. Further studies with larger study populations and validated self-reporting methods for medication adherence are required.

Introduction of specialized heart failure nurses in primary care and its impact on readmissions

BACKGROUND

Heart failure (HF) has a 2% prevalence in the population and is a major cause of morbidity and mortality. Multiple efforts have been made worldwide to improve quality of care and decrease unplanned readmissions for HF patients, one of which has been the introduction of specialist HF nurses (HFN) in primary health care. The present evidence on the benefits of HFN is contradicting. This study aims to evaluate the impact of a quality improvement intervention, availability of a HFN in Swedish primary care, on hospital readmissions.

METHODS

All patients over the age of 65 with a HF diagnosis and with complete information on availability of a HFN were included in this retrospective register-based study. Using propensity score matching (PSM) techniques, two comparable groups of 128 patients each were created according to the exposure status, availability or no availability of a HFN. The rate of readmission was compared between the groups.

RESULTS

Using PSM, 256 patients were matched, 128 in the HFN group and 128 in the no-HFN group. A total of 50% and 46.09% of patients in the HFN and no-HFN groups were readmitted, respectively. Mean number of readmissions per patient was 1.19 (SD 0.61) in the HFN group and 1.10 (SD 0.44) in the no-HFN group. Patients in the HFN had 17.6% higher odds of being readmitted during the study period, OR: 1.176 (CI: 0.716-1.932), and 3.8% lower odds of being readmitted within 30 days, OR: 0.962 (CI: 0.528-1.750).

CONCLUSIONS

Availability of a HFN in primary care was not significantly associated with reduced readmissions for the patients included in this study. Further investigations are warranted looking at the impacts of availability and access to a HFN in primary care on readmissions and other patient outcomes.

Use of machine learning to predict medication adherence in individuals at risk for atherosclerotic cardiovascular disease

Background

Medication nonadherence is a critical problem with severe implications in individuals at risk for atherosclerotic cardiovascular disease. Many studies have attempted to predict medication adherence in this population, but few, if any, have been effective in prediction, sug-gesting that essential risk factors remain unidentified.

Objective

This study's objective was to (1) establish an accurate prediction model of medi-cation adherence in individuals at risk for atherosclerotic cardiovascular disease and (2) identify significant contributing factors to the predictive accuracy of medication adherence. In particular, we aimed to use only the baseline questionnaire data to assess medication adherence prediction feasibility.

Methods

A sample of 40 individuals at risk for atherosclerotic cardiovascular disease was recruited for an eight-week feasibility study. After collecting baseline data, we recorded data from a pillbox that sent events to a cloud-based server. Health measures and medication use events were analyzed using machine learning algorithms to identify variables that best predict medication adherence.

Results

Our adherence prediction model, based on only the ten most relevant variables, achieved an average error rate of 12.9%. Medication adherence was closely correlated with being encouraged to play an active role in their treatment, having confidence about what to do in an emergency, knowledge about their medications, and having a special person in their life.

Conclusions

Our results showed the significance of clinical and psychosocial factors for predicting medication adherence in people at risk for atherosclerotic cardiovascular diseases. Clini-cians and researchers can use these factors to stratify individuals to make evidence-based decisions to reduce the risks.

Development of Core Educational Content for Heart Failure Patients in Transition from Hospital to Home Care: A Delphi Study

Heart failure (HF) patients should be systematically educated before discharge on how to manage with standard written materials for patient self-management. However, because of the absence of readily available written materials to reinforce their learned knowledge, patients with HF feel inadequately informed in terms of the discharge information provided to them. This study aimed to develop core content to prepare patients with HF for transition from hospital to home care. The content was validated by expert panelists using Delphi methods. Nineteen draft items based on literature review were developed. We established a consensus on four core sections, including 47 categories and 128 subcategories through the Delphi survey: (1) understanding HF (five categories and 23 subcategories), (2) HF medication (19 categories and 45 subcategories), (3) HF management (20 categories and 47 subcategories), and (4) HF diary (three categories and 13 subcategories). Each section provided easy-to-understand educational contents using cartoon images and large or bold letters for older patients with HF. The developed core HF educational contents showed high consensus between the experts, along with clinical validity. The contents can be used as an educational booklet for both planning discharge education of patients with HF and for post-discharge management when transitioning from hospital to home. Based on this study, a booklet series for HF patients was first registered at the National Library of Korea. Future research should focus on delivering the core content to patients with HF in convenient and accessible format through various media.

2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines

AIM

The "2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure" replaces the "2013 ACCF/AHA Guideline for the Management of Heart Failure" and the "2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure." The 2022 guideline is intended to provide patient-centric recommendations for clinicians to prevent, diagnose, and manage patients with heart failure.

METHODS

A comprehensive literature search was conducted from May 2020 to December 2020, encompassing studies, reviews, and other evidence conducted on human subjects that were published in English from MEDLINE (PubMed), EMBASE, the Cochrane Collaboration, the Agency for Healthcare Research and Quality, and other relevant databases. Additional relevant clinical trials and research studies, published through September 2021, were also considered. This guideline was harmonized with other American Heart Association/American College of Cardiology guidelines published through December 2021. Structure: Heart failure remains a leading cause of morbidity and mortality globally. The 2022 heart failure guideline provides recommendations based on contemporary evidence for the treatment of these patients. The recommendations present an evidence-based approach to managing patients with heart failure, with the intent to improve quality of care and align with patients' interests. Many recommendations from the earlier heart failure guidelines have been updated with new evidence, and new recommendations have been created when supported by published data. Value statements are provided for certain treatments with high-quality published economic analyses.

2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure

AIM

The "2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure" replaces the "2013 ACCF/AHA Guideline for the Management of Heart Failure" and the "2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure." The 2022 guideline is intended to provide patient-centric recommendations for clinicians to prevent, diagnose, and manage patients with heart failure.

METHODS

A comprehensive literature search was conducted from May 2020 to December 2020, encompassing studies, reviews, and other evidence conducted on human subjects that were published in English from MEDLINE (PubMed), EMBASE, the Cochrane Collaboration, the Agency for Healthcare Research and Quality, and other relevant databases. Additional relevant clinical trials and research studies, published through September 2021, were also considered. This guideline was harmonized with other American Heart Association/American College of Cardiology guidelines published through December 2021.

STRUCTURE

Heart failure remains a leading cause of morbidity and mortality globally. The 2022 heart failure guideline provides recommendations based on contemporary evidence for the treatment of these patients. The recommendations present an evidence-based approach to managing patients with heart failure, with the intent to improve quality of care and align with patients' interests. Many recommendations from the earlier heart failure guidelines have been updated with new evidence, and new recommendations have been created when supported by published data. Value statements are provided for certain treatments with high-quality published economic analyses.

Reducing the Heart Failure Burden in Romania by Predicting Congestive Heart Failure Using Artificial Intelligence: Proof of Concept

Due to population aging, we are currently confronted with an increased number of chronic heart failure patients. The primary purpose of this study was to implement a noncontact system that can predict heart failure exacerbation through vocal analysis. We designed the system to evaluate the voice characteristics of every patient, and we used the identified variations as an input for a machine-learning-based approach. We collected data from a total of 16 patients, 9 men and 7 women, aged 65–91 years old, who agreed to take part in the study, with a detailed signed informed consent. We included hospitalized patients admitted with cardiogenic acute pulmonary edema in the study, regardless of the precipitation cause or other known cardiovascular comorbidities. There were no specific exclusion criteria, except age (which had to be over 18 years old) and patients with speech inabilities. We then recorded each patient’s voice twice a day, using the same smartphone, Lenovo P780, from day one of hospitalization—when their general status was critical—until the day of discharge, when they were clinically stable. We used the New York Heart Association Functional Classification (NYHA) classification system for heart failure to include the patients in stages based on their clinical evolution. Each voice recording has been accordingly equated and subsequently introduced into the machine-learning algorithm. We used multiple machine-learning techniques for classification in order to detect which one turns out to be more appropriate for the given dataset and the one that can be the starting point for future developments. We used algorithms such as Artificial Neural Networks (ANN), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). After integrating the information from 15 patients, the algorithm correctly classified the 16th patient into the third NYHA stage at hospitalization and second NYHA stage at discharge, based only on his voice recording. The KNN algorithm proved to have the best classification accuracy, with a value of 0.945. Voice is a cheap and easy way to monitor a patient’s health status. The algorithm we have used for analyzing the voice provides highly accurate preliminary results. We aim to obtain larger datasets and compute more complex voice analyzer algorithms to certify the outcomes presented.

Prediction of Heart Failure Symptoms and Health-Related Quality of Life at 12 Months From Baseline Modifiable Factors in Patients With Heart Failure

BACKGROUND

In patients with heart failure (HF), good health-related quality of life (HRQOL) is as valuable as, or more valuable than, longer survival. However, HRQOL is remarkably poor, and HF symptoms are strongly associated with poor HRQOL. Yet, the multidimensional, modifiable predictors have been rarely examined.

OBJECTIVE

The aim of this study was to examine the baseline psychosocial, behavioral, and physical predictors of HF symptoms and HRQOL at 12 months and the mediator effect of HF symptoms in the relationship between depressive symptoms and HRQOL.

METHODS

We collected data from 94 patients with HF (mean ± SD age, 58 ± 14 years). Data included sample characteristics, depressive symptoms, perceived control, social support, New York Heart Association (NYHA) functional class, medication adherence, sodium intake, self-care management, and HF symptoms at baseline, as well as HF symptoms and HRQOL at 12 months. Multiple regression analyses were performed to address the purpose.

RESULTS

Baseline depressive symptoms (P < .001), medication adherence (P = .010), sodium intake (P = .032), and NYHA functional class (P = .040) significantly predicted 12-month HF symptoms, controlling for covariates (F = 7.363, R = 47%, P < .001). Baseline medication adherence (P = .001), NYHA functional class (P < .001), and HF symptoms (P = .013) significantly predicted 12-month HRQOL (F = 10.701, R = 59%, P < .001). Baseline HF symptoms fully mediated the relationship between baseline depressive symptoms and 12-month HRQOL.

CONCLUSION

Symptoms of HF and HRQOL could be improved by targeting multidimensional, modifiable predictors, such as self-care, depressive symptoms, and NYHA functional class.

Cognitive Stabilization Intervention during the Era of COVID-19

As COVID-19 halted traditional neuropsychological assessment due to infection risk, neuropsychologists considered alternative practice models. Cognitive stabilization intervention (CSI) via telehealth, was developed to stabilize cognition in advance of neuropsychological assessment. It incorporates elements of evidence-based treatments, including cognitive training, sleep training, and medication adherence training within a motivational interview framework. Two case vignettes are described. One vignette describes an elder man who received CSI to manage sleep difficulties, forgetfulness, and mood symptoms. Another vignette describes a woman who completed CSI following an autoimmune disorder episode to improve sleep, organization, and attention. The benefits and limitations of CSI are discussed.

Medication Nonadherence or Self-care? Understanding the Medication Decision-Making Process and Experiences of Older Adults With Heart Failure

BACKGROUND

More than half of all patients with heart failure (HF) do not take medications as prescribed, resulting in negative health outcomes. Research has shown that medication adherence may be intentional rather than the ability to follow prescribed regimens, yet very little is known about medication-taking decisions in older patients with HF.

OBJECTIVE

The purpose of this qualitative study was to gain insight into the decision-making processes and experiences of older patients with HF by exploring the different aspects in choosing to take or not take medications as prescribed in the community setting.

METHODS

Using a narrative inquiry approach, the personal narratives of 11 adults 65 years or older who took at least 2 daily medications for HF were gathered using in-depth, semistructured interviews. The data in this study were organized and analyzed using Riessman's framework for narrative analysis.

RESULTS

Participants made intentional decisions to take particular medications differently than prescribed. A worrisome symptom prompted a naturalistic decision-making process. When a medication interfered with attaining a personal goal, participants coped by individualizing their medication regimen. Participants did not consider taking a medication differently than prescribed as nonadherence but a necessary aspect of maintaining a personal level of health, which could be seen as self-care.

CONCLUSIONS

The older patient with HF should be carefully assessed for nonadherence. The development of interventions that are patient specific, target medications with the greatest potential for nonadherence, and use easy-to-access resources may promote decisions for medication adherence. More research is needed to develop interventions that promote decisions for medication adherence.

Factors Associated With Medication Adherence In Elderly Retired Outpatients In São Paulo, Brazil

OBJECTIVE

To evaluate medication adherence and associated socioeconomic factors in elderly Brazilians.

METHODOLOGY

This observational study was conducted with 159 elderly retired in an outpatient clinic in the city of São Paulo. Treatment adherence was assessed with the questions from the Morisky Green Levine Medication Adherence Questionnaire, and medications were classified using the Anatomical Therapeutic Chemical system. Statistical tests and adjusted Poisson regression models were used to analyze variables.

RESULTS

The study population was mostly female (67.5%), had an average age of, and took an average of 6.5 medications per day. The most commonly used drugs were agents acting on the renin-angiotensin system (67.9%), statins (62.3%), antithrombotic agents (48.4%), and biguanides (37.1%) for the treatment of hypertension (76.7%), dyslipidemia (54.1%), and diabetes (47.8%). The rate of adherence was below 60% in the groups of participants that were analyzed except for the high household income category, which had a rate of 75.8%.

CONCLUSION

Medication adherence among the elderly was low in all categories except for the high household income category, a relevant finding that will help to understand medication adherence patterns in elderly Brazilians.