On 1 July 2023, the Therapeutic Goods Administration approved 3,4-methylenedioxymethamphetamine (MDMA)-assisted psychotherapy for the treatment of post-traumatic stress disorder (PTSD). Accordingly, the purpose of this review is to provide an overview of MDMA and what pharmacists should know as this treatment emerges.
EMBASE, MEDLINE, and CINAHL databases were searched to provide an overview narrative synthesis of the literature on MDMA, relevant to pharmacists.
Cytochrome P450 2D6 is involved in the metabolic pathway of MDMA, an enzyme inhibited by a number of drugs used in the pharmacological management of PTSD (e.g. selective serotonin reuptake inhibitors). Co-administration of these drugs with MDMA can lead to increases in plasma MDMA concentrations. Additionally, inhibition of the serotonin transporter can inhibit the uptake of MDMA into the presynaptic terminal, dampening the effects of MDMA and potentially, limiting the effectiveness of MDMA-assisted psychotherapy. Accordingly, these drugs are typically withdrawn prior to treatment with MDMA. While selective serotonin reuptake inhibitor/serotonin noradrenaline reuptake inhibitor drugs with MDMA are unlikely to cause serotonin toxicity, monoamine oxidase inhibitors can. Other drugs, such as caffeine, alcohol, over-the-counter medicines, and complimentary/alternative medicines, can also interact with MDMA.
With a detailed knowledge of the pharmacology of MDMA, pharmacists may play a role in MDMA-assisted psychotherapy by collecting a detailed medication history and assisting clinicians in the withdrawal of interacting medicines.
Venous thromboembolism (VTE) is a leading cause of preventable morbidity and mortality, with total hip arthroplasty (THA) and total knee arthroplasty (TKA) at the highest risk. Safe and appropriate thromboprophylaxis is essential. However, investigations into prescribing practices have been limited.
To describe current VTE prophylaxis regimens in Australian patients following an elective THA/TKA and compare these regimens to an international standard.
A retrospective multisite case series of patients admitted for a THA/TKA in six tertiary hospitals in Queensland, Australia, was conducted over 12 months (1 October 2017–30 September 2018). Patient and medication data were collected following surgery and for 60 days after discharge to determine changes to the patients' thromboprophylaxis regimen. Results were summarised and compared to National Institute for Health and Care Excellence (NICE) guidelines. Ethical approval was granted by the Metro South Human Research Ethics Committee (Reference no: HREC/2018/QMD/46757) and the study conforms to the National Statement on Ethical Conduct in Human Research.
The study included 1011 patients (43.1% THA, 56.9% TKA), and thromboprophylaxis was used in 98.1% of inpatients and in 94.3% of discharge patients for 5.2 (±5.2) and 29.2 (±15.9) days (±standard deviation) respectively. Low-molecular-weight heparins (LMWHs) were the primary drugs for inpatients (71.2%) and aspirin 150 mg for discharge (42.0%), most commonly for 6 weeks (31.8%). Aspirin was used for significantly longer duration than rivaroxaban and LMWH (p < 0.001). A two-staged prophylaxis regimen was implemented, most commonly any anticoagulant as an inpatient; followed by rivaroxaban on discharge (32.7%) or an anticoagulant as an inpatient with aspirin on discharge (26.4%). Overall, adherence to NICE guidelines was low; THA: 8.7%, TKA: 5.9%.
VTE prophylaxis regimens varied considerably, and consequently, adherence to international guidelines was low. There is a need for local, peer-led guidelines to ensure consistent, safe, and effective prophylaxis.
Imagine hospitals where clinicians can quickly and accurately identify patients at risk of medication harm and why. This is what artificial intelligence (AI) promises, and it’s closer than we think.
While the past decade brought electronic health records (EHRs) and decision support systems, AI-enabled machine learning (ML) prediction models and large language models have emerged, with the potential to greatly assist clinical decision-making and improve patient outcomes. For example, AI can predict optimal doses of pharmacokinetically complex medications1 and identify adverse drug reactions among coded discharge data. These new tools can support busy pharmacists by automating tedious tasks and discerning clinical scenarios warranting pharmacist intervention.
This editorial highlights considerations relating to AI/ML technologies applied to medicine management in Australian hospitals, drawing insights from local experience in designing and evaluating a ML dosing algorithm for unfractionated heparin (UFH).
Risk prediction algorithms are common, such as the CHADS-VASc and HAS-BLED scores, developed using conventional statistical (regression) methods. But with the availability of ‘big data’ from EHRs within multiple hospitals, clinician researchers, data scientists, and informaticians can now collaborate to develop more accurate real-time predictive algorithms using AI/ML. Some examples include predicting an individual’s likelihood of a medication-related hospital readmission, suffering a bleed with anticoagulant therapy, or rapid deterioration due to undertreated illness. Detecting and treating these conditions can optimise patient outcomes.
The ultimate question is whether AI tools enable clinicians to work smarter and more efficiently, save healthcare costs, and render patient care more effective and safe. Machines don’t tire and are not influenced by emotions, and they can learn and process vast amounts of information faster and more accurately than humans. But human oversight and judgement remains crucial in ensuring the appropriate design and use of algorithms and monitoring their performance. Machines exist to augment, not usurp, clinician decision-making, empowering pharmacists to focus more on empathic patient interactions, education, and counselling and fostering interprofessional healthcare delivery; integral care components for which no machine can substitute.
The future of hospital pharmacy is undeniably intertwined with the evolution of AI, and we should embrace and lead the agenda in using them as supportive tools to enhance our clinical practice.
The authors declare that they have no conflicts of interest.
Conceptualisation: NF, IS, MB. Investigation: NF. Writing — original draft: NF, IS, MB. Writing — review and editing: NF, IS, MB.
Ethical approval was not required for this editorial as it did not contain any human data or participants.
Not commissioned,
Antimicrobial resistance is emerging as one of the most potentially disastrous threats of the 21st century. Pseudomonas aeruginosa (P. aeruginosa) is a leading resistant pathogen and is clinically significant due to its limited available treatment using antibiotics. Rising resistance of P. aeruginosa is of increasing concern and it is currently listed as one of the top three critically resistant pathogens by the World Health Organization. It is currently known that resistance in P. aeruginosa is significantly linked with the consumption of all antibiotics, making usage surveillance of particular concern.
The aim of the current study was to model current and future antibiotic usage using available prescription data for antipseudomonal antibiotics.
A time-series analysis was performed on Pharmaceutical Benefits Scheme/Repatriation Pharmaceutical Benefits Scheme data from January 2000–June 2020 using Facebook Prophet time-series methods in Python 3.9.14 to analyse and forecast trends to 2025. Ethical approval was not required for this research article as it used publicly available data and did not involve human subjects.
The usage of antipseudomonal antibiotics decreased by 14.3% from 2014 to 2020 (95% confidence interval [CI] −30.4% to 2.3%) and is projected to further decrease by 11.7% by 2025 (95% CI −30.6% to 7.3%).
The current study showed a decline in the use of certain antipseudomonal antibiotics in Australia since 2015 and projects that their use will continue to decline. This is likely due to an increased judicious use of these antibiotics.
The increasing incidence of opioid-related harm associated with inappropriate opioid prescription underscores the need for effective interventions directed at enhancing analgesic prescribing practices. Analgesic stewardship (AGS) programs have emerged to facilitate appropriate analgesic use, reducing analgesic-related harm and optimising patient outcomes.
This study aimed to assess the impact of an AGS program on the appropriateness of opioid analgesia prescribing for adult inpatients within a metropolitan health network during the initial two months following program implementation.
This retrospective audit examined de-identified aggregate data of 100 adult inpatients of a major Australian metropolitan hospital network between August–September 2022. The multidisciplinary AGS program involving pharmacy, nursing, and medical staff provided clinical interventions and recommendations for patients, supported by organisational interventions, including education and guideline reinforcement. Data collection included patient demographics, clinical data, and AGS program recommendations documented in patient medical records. Ethical approval was granted by the Eastern Health Office of Research and Ethics (Reference no: QA23-015-91886) and the study confirms to the Australian Statement on Ethical Conduct in Human Research.
It was found that 51% of opioid-naïve patients were prescribed modified-release opioids for acute non-cancer pain, contrary to local guidelines. The AGS program recommendations resulted in an 89.7% cessation of these prescriptions. For non-opioid-naïve patients, there was a statistically significant reduction in mean Oral Morphine Equivalent Daily Dose of 19.85 mg (95% confidence interval [CI] 10.3–29.4 mg, p < 0.05). AGS recommendations were predominantly adhered to, with 82% showing full or partial compliance.
This study highlights the positive impact of an AGS program on the appropriateness of opioid analgesic prescribing for adult inpatients. These findings support the implementation of AGS programs into healthcare systems to mitigate opioid-related harm and improve patient outcomes.
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