The role of the prescriber in supporting patients to discontinue benzodiazepines: A qualitative study

Background: Given the prevalence of long-term benzodiazepine (BZDs) prescribing, increased monitoring through the implementation of prescription monitoring programs (PMPs) may be the necessary impetus to promote BZD deprescribing. Despite evidence promoting the importance of patient-centred care, GPs have not been sufficiently supported to implement these principles through current deprescribing practice. Aim: To investigate patients’ perception of their prescriber’s influence on ceasing BZD use, including their willingness to take on their advice, and to understand how a patients’ stage of change influences the barriers and facilitators they perceive to discontinuing BZDs. Design and Setting: An online survey and qualitative interviews with 22 long-term BZD users (≥6 months), aged 18-69 years, recruited from the general population in Victoria, Australia. Method: Two groups of BZD users participated, one in the process of reducing their BZD and one not reducing, and were categorised according to their stage of change. Data underwent thematic analysis to identify barriers and facilitators to reducing BZDs both at the patient-level and prescriber-level. Results: BZD patients’ perceptions of the prescriber influence were characterised by prescribing behaviours, treatment approach, and attitude. Barriers and facilitators to reducing their BZD were mapped against their stage of change. Irrespective of their stage of change, participants reported they would be willing to try reducing their BZD if they trusted their prescriber. Conclusion: This study illustrates that with a few key strategies at each step of the deprescribing conversation, GPs are well-positioned to tackle the issue of long-term BZD use in a manner that is patient-centred.

Safe opioid prescribing: a prognostic machine learning approach to predicting 30-day risk after an opioid dispensation in Alberta, Canada

ObjectiveTo develop machine learning models employing administrative health data that can estimate risk of adverse outcomes within 30 days of an opioid dispensation for use by health departments or prescription monitoring programmes.Design, setting and participantsThis prognostic study was conducted in Alberta, Canada between 2017 and 2018. Participants included all patients 18 years of age and older who received at least one opioid dispensation. Pregnant and cancer patients were excluded.ExposureEach opioid dispensation served as an exposure.Main outcomes/measuresOpioid-related adverse outcomes were identified from linked administrative health data. Machine learning algorithms were trained using 2017 data to predict risk of hospitalisation, emergency department visit and mortality within 30 days of an opioid dispensation. Two validation sets, using 2017 and 2018 data, were used to evaluate model performance. Model discrimination and calibration performance were assessed for all patients and those at higher risk. Machine learning discrimination was compared with current opioid guidelines.ResultsParticipants in the 2017 training set (n=275 150) and validation set (n=117 829) had similar baseline characteristics. In the 2017 validation set, c-statistics for the XGBoost, logistic regression and neural network classifiers were 0.87, 0.87 and 0.80, respectively. In the 2018 validation set (n=393 023), the corresponding c-statistics were 0.88, 0.88 and 0.82. C-statistics from the Canadian guidelines ranged from 0.54 to 0.69 while the US guidelines ranged from 0.50 to 0.62. The top five percentile of predicted risk for the XGBoost and logistic regression classifiers captured 42% of all events and translated into post-test probabilities of 13.38% and 13.45%, respectively, up from the pretest probability of 1.6%.ConclusionMachine learning classifiers, especially incorporating hospitalisation/physician claims data, have better predictive performance compared with guideline or prescription history only approaches when predicting 30-day risk of adverse outcomes. Prescription monitoring programmes and health departments with access to administrative data can use machine learning classifiers to effectively identify those at higher risk compared with current guideline-based approaches.

Respiratory concerns of gabapentin and pregabalin: What does it mean to the pharmacovigilance systems in developing countries?

Gabapentin and pregabalin, commonly known as gabapentinoids, have been widely used globally. This paper highlights the serious breathing problems due to using gabapentin and pregabalin which was warned by the United States Food and Drug Administration on December, 2019. In this article, we tried to recommend suggestions for controlling these adverse drug reactions (ADRs). Safety reports of gabapentin and pregabalin should be obtained from concerned manufacturers and reviewed for respiratory depression effects. There should be strict prescription monitoring and drug use evaluation studies. Concurrent use of gabapentin and pregabalin with other respiratory depressants such as opioids should be strictly monitored. Educating patients can help in the early detection of ADRs due to gabapentin and pregabalin. Anecdotal reports on these medications should be encouraged.