Patterns of analgesic adherence predict health care utilization among outpatients with cancer pain

Salimah H Meghani,1 George J Knafl2 1Department of Biobehavioral Health Sciences, NewCourtland Center of Transitions and Health, School of Nursing, University of Pennsylvania, Philadelphia, PA, 2School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Background: Studies in chronic noncancer pain settings have found that opioid use increases health care utilization. Despite the key role of analgesics, specifically opioids, in the setting of cancer pain, there is no literature to our knowledge about the relationship between adherence to prescribed around-the-clock (ATC) analgesics and acute health care utilization (hospitalization) among patients with cancer pain. Purpose: To identify adherence patterns over time for cancer patients taking ATC analgesics for pain, cluster these patterns into adherence types, combine the types into an adherence risk factor for hospitalization, identify other risk factors for hospitalization, and identify risk factors for inconsistent analgesic adherence. Materials and methods: Data from a 3-month prospective observational study of patients diagnosed with solid tumors or multiple myeloma, having cancer-related pain, and having at least one prescription of oral ATC analgesics were collected. Adherence data were collected electronically using the medication event-monitoring system. Analyses were conducted using adaptive modeling methods based on heuristic search through alternative models controlled by likelihood cross-validation scores. Results: Six adherence types were identified and combined into the risk factor for hospitalization of inconsistent versus consistent adherence over time. Twenty other individually significant risk factors for hospitalization were identified, but inconsistent analgesic adherence was the strongest of these predictors (ie, generating the largest likelihood cross-validation score). These risk factors were adaptively combined into a model for hospitalization based on six pairwise interaction risk factors with exceptional discrimination (ie, area under the receiver-operating-characteristic curve of 0.91). Patients had from zero to five of these risk factors, with an odds ratio of 5.44 (95% confidence interval 3.09–9.58) for hospitalization, with a unit increase in the number of such risk factors. Conclusion: Inconsistent adherence to prescribed ATC analgesics, specifically the interaction of strong opioids and inconsistent adherence, is a strong risk factor for hospitalization among cancer outpatients with pain. Keywords: cancer pain, opioids, analgesics, medication adherence, MEMS, hospitalizatio

Patterns of analgesic adherence predict health care utilization among outpatients with cancer pain

Salimah H Meghani,1 George J Knafl2 1Department of Biobehavioral Health Sciences, NewCourtland Center of Transitions and Health, School of Nursing, University of Pennsylvania, Philadelphia, PA, 2School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Background: Studies in chronic noncancer pain settings have found that opioid use increases health care utilization. Despite the key role of analgesics, specifically opioids, in the setting of cancer pain, there is no literature to our knowledge about the relationship between adherence to prescribed around-the-clock (ATC) analgesics and acute health care utilization (hospitalization) among patients with cancer pain. Purpose: To identify adherence patterns over time for cancer patients taking ATC analgesics for pain, cluster these patterns into adherence types, combine the types into an adherence risk factor for hospitalization, identify other risk factors for hospitalization, and identify risk factors for inconsistent analgesic adherence. Materials and methods: Data from a 3-month prospective observational study of patients diagnosed with solid tumors or multiple myeloma, having cancer-related pain, and having at least one prescription of oral ATC analgesics were collected. Adherence data were collected electronically using the medication event-monitoring system. Analyses were conducted using adaptive modeling methods based on heuristic search through alternative models controlled by likelihood cross-validation scores. Results: Six adherence types were identified and combined into the risk factor for hospitalization of inconsistent versus consistent adherence over time. Twenty other individually significant risk factors for hospitalization were identified, but inconsistent analgesic adherence was the strongest of these predictors (ie, generating the largest likelihood cross-validation score). These risk factors were adaptively combined into a model for hospitalization based on six pairwise interaction risk factors with exceptional discrimination (ie, area under the receiver-operating-characteristic curve of 0.91). Patients had from zero to five of these risk factors, with an odds ratio of 5.44 (95% confidence interval 3.09–9.58) for hospitalization, with a unit increase in the number of such risk factors. Conclusion: Inconsistent adherence to prescribed ATC analgesics, specifically the interaction of strong opioids and inconsistent adherence, is a strong risk factor for hospitalization among cancer outpatients with pain. Keywords: cancer pain, opioids, analgesics, medication adherence, MEMS, hospitalizatio

What puts heart failure patients at risk for poor medication adherence?

George J Knafl,1 Barbara Riegel2,31School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; 2School of Nursing, University of Pennsylvania, Philadelphia, PA, USA; 3Leonard Davis Institute, University of Pennsylvania, Philadelphia, PA, USABackground: Medication nonadherence is a major cause of hospitalization in patients with heart failure (HF), which contributes enormously to health care costs. We previously found, using the World Health Organization adherence dimensions, that condition and patient level factors predicted nonadherence in HF. In this study, we assessed a wider variety of condition and patient factors and interactions to improve our ability to identify those at risk for hospitalization. Materials and methods: Medication adherence was measured electronically over the course of 6 months, using the Medication Event Monitoring System (MEMS). A total of 242 HF patients completed the study, and usable MEMS data were available for 218 (90.1%). Participants were primarily white (68.3%), male (64.2%), and retired (44.5%). Education ranged from 8–29 years (mean, 14.0 years; standard deviation, 2.9 years). Ages ranged from 30–89 years (mean, 62.8 years; standard deviation, 11.6 years). Analyses used adaptive methods based on heuristic searches controlled by cross-validation scores. First, individual patient adherence patterns over time were used to categorize patients in poor versus better adherence types. Then, risk factors for poor adherence were identified. Finally, an effective model for predicting poor adherence was identified based on identified risk factors and possible pairwise interactions between them. Results: A total of 63 (28.9%) patients had poor adherence. Three interaction risk factors for poor adherence were identified: a higher number of comorbid conditions with a higher total number of daily medicines, older age with poorer global sleep quality, and fewer months since diagnosis of HF with poorer global sleep quality. Patients had between zero and three risk factors. The odds for poor adherence increased by 2.6 times with a unit increase in the number of risk factors (odds ratio, 2.62; 95% confidence interval, 1.78–3.86; P<0.001).Conclusion: Newly diagnosed, older HF patients with comorbid conditions, polypharmacy, and poor sleep are at risk for poor medication adherence. Interventions addressing these specific barriers are needed.Keywords: heart failure, medication adherence, multiple chronic conditions, risk factors, self-care, sleep qualit

Electronically monitored medication adherence predicts hospitalization in heart failure patients

Barbara Riegel,1,2 George J Knafl31University of Pennsylvania School of Nursing, 2University of Pennsylvania Leonard Davis Institute, Philadelphia, PA, USA; 3University of North Carolina School of Nursing, Chapel Hill, NC, USABackground: Hospitalization contributes enormously to health care costs associated with heart failure. Many investigators have attempted to predict hospitalization in these patients. None of these models has been highly effective in prediction, suggesting that important risk factors remain unidentified.Purpose: To assess prospectively collected medication adherence, objectively measured by the Medication Event Monitoring System, as a predictor of hospitalization in heart failure patients.Materials and methods: We used recently developed adaptive modeling methods to describe patterns of medication adherence in a sample of heart failure patients, and tested the hypothesis that poor medication adherence as determined by adaptive methods was a significant predictor of hospitalization within 6 months.Results: Medication adherence was the best predictor of hospitalization. Besides two dimensions of poor adherence (adherence pattern type and low percentage of prescribed doses taken), four other single factors predicted hospitalization: low hemoglobin, depressed ejection fraction, New York Heart Association class IV, and 12 or more medications taken daily. Seven interactions increased the predictive capability of the model: 1) pattern of poor adherence type and lower score on the Letter–Number Sequencing test, a measure of short-term memory; 2) higher number of comorbid conditions and higher number of daily medications; 3) higher blood urea nitrogen and lower percentage of prescribed doses taken; 4) lower hemoglobin and much worse perceived health compared to last year; 5) older age and lower score on the Telephone Interview of Cognitive Status; 6) higher body mass index and lower hemoglobin; and 7) lower ejection fraction and higher fatigue. Patients with none of these seven interactions had a hospitalization rate of 9.7%. For those with five of these interaction risk factors, 100% were hospitalized. The C-index (the area under the receiver-operating characteristics [ROC] curve) for the model based on the seven interactions was 0.83, indicating excellent discrimination.Conclusion: Medication adherence adds important new information to the list of variables previously shown to predict hospitalization in adults with heart failure.Keywords: heart failure, outcomes, hospitalization, patient compliance, medication adherence, self-car

Discontinuation of angiotensin-converting enzyme inhibitors or beta-blockers and the impact on heart failure hospitalization rates

© The European Society of Cardiology 2019. Background: Adherence to evidence-based therapy is essential for optimal management of heart failure. Yet, medication adherence is poor in heart failure patients. The Ascertaining Barriers to Compliance Project decomposed the medication adherence process into initiation, implementation, and discontinuation stages, but electronic monitoring-based adherence analyses usually do not consider this process. Aims: The aim of this study was to describe individual-patient patterns of medication adherence from electronic monitoring data among adults with chronic heart failure, adherence types, and risk factors for increased all-cause hospitalization including measures of poor adherence such as discontinuation. Methods: Data from two prospective studies of adherence measured with electronic monitoring for heart failure patients were combined and restricted to monitoring of angiotensin-converting enzyme inhibitors and beta-blockers over an initial three-month period. Hospitalizations were recorded for this period as well as for a three-month follow-up period. Analyses were conducted using adaptive modeling methods to identify individual-patient adherence patterns, adherence types, and risk factors for an increased hospitalization rate. Results: Using electronic monitoring data for 254 heart failure patients, four adherence types were identified: highly consistent, consistent but variable, moderately consistent, and poorly consistent. Sixteen individually significant risk factors for increased hospitalization rates were identified and used to generate a multiple risk factors model. Medication discontinuation was the most important individual risk factor and most important in the multiple risk factors model. Conclusion: Discontinuation of angiotensin-converting enzyme inhibitors or beta-blockers increases hospitalization rates for heart failure patients. Interventions that effectively address this problem are urgently needed