Supply of nirmatrelvir/ritonavir and molnupiravir for patients with COVID-19 in the first eight months since listing on the Australian Pharmaceutical Benefits Scheme: A retrospective observational study.

OBJECTIVES: To compare the supply of molnupiravir and nirmatrelvir/ritonavir in relation to patient characteristics and other co-prescribed medicines and to estimate the number of patients without contraindications to nirmatrelvir/ritonavir who were treated with molnupiravir. STUDY DESIGN, SETTING: Retrospective observational study of patients identified in the Pharmaceutical Benefits Scheme (PBS) 10 % sample dataset who were supplied with either molnupiravir or nirmatrelvir/ritonavir between May and December 2022. We supplemented the PBS dataset with aggregated counts from published literature to determine prevalence of clinical contraindications to nirmatrelvir/ritonavir. MAIN OUTCOME MEASURES: We used multivariable Poisson regression to estimate risk ratios (RR) of receiving nirmatrelvir/ritonavir over molnupiravir. RESULTS: We identified 54,550 patients who received either nirmatrelvir/ritonavir (26.8 %) or molnupiravir (73.2 %). Their average age was 71.6 (SD = 13.4) years and 57.1 % were female. Patients were less likely to receive nirmatrelvir/ritonavir with increasing age (RR = 0.50; 95 % CI: 0.48-0.53; for ages 85 + compared to < 65 years) or who had received medicines contraindicated for use with nirmatrelvir/ritonavir (RR = 0.66; 95 % CI: 0.64-0.68). During the study period, we estimated that between 28.4 % and 45.4 % of patients aged ≥ 65 years had received molnupiravir in the absence of pharmacological and clinical contraindications to nirmatrelvir/ritonavir. CONCLUSION: Many prescriptions were written for molnupiravir where there were no contraindications to nirmatrelvir/ritonavir. The benefits that followed from prompt government action in approving and obtaining nirmatrelvir/ritonavir were therefore likely to be less than they could potentially have been. Governments should consider investing in quality improvement systems to ensure the best outcomes in terms of efficacy and safety.

Health in a Virtual Environment (HIVE): A Novel Continuous Remote Monitoring Service for Inpatient Management.

The aim of this study was to describe the implementation of a novel 50-bed continuous remote monitoring service for high-risk acute inpatients treated in non-critical wards, known as Health in a Virtual Environment (HIVE). We report the initial results, presenting the number and type of patients connected to the service, and assess key outcomes from this cohort. This was a prospective, observational study of characteristics and outcomes of patients connected to the HIVE continuous monitoring service at a major tertiary hospital and a smaller public hospital in Western Australia between January 2021 and June 2023. In the first two and a half years following implementation, 7541 patients were connected to HIVE for a total of 331,118 h. Overall, these patients had a median length of stay of 5 days (IQR 2, 10), 11.0% (n = 833) had an intensive care unit admission, 22.4% (n = 1691) had an all-cause emergency readmission within 28 days from hospital discharge, and 2.2% (n = 167) died in hospital. Conclusions: Our initial results show promise, demonstrating that this innovative approach to inpatient care can be successfully implemented to monitor high-risk patients in medical and surgical wards. Future studies will investigate the effectiveness of the program by comparing patients receiving HIVE supported care to comparable patients receiving routine care.

Predicting multifaceted risks using machine learning in atrial fibrillation: insights from GLORIA-AF study.

Aims: Patients with atrial fibrillation (AF) have a higher risk of ischaemic stroke and death. While anticoagulants are effective at reducing these risks, they increase the risk of bleeding. Current clinical risk scores only perform modestly in predicting adverse outcomes, especially for the outcome of death. We aimed to test the multi-label gradient boosting decision tree (ML-GBDT) model in predicting risks for adverse outcomes in a prospective global AF registry. Methods and results: We studied patients from phase II/III of the Global Registry on Long-Term Oral Anti-Thrombotic Treatment in Patients with Atrial Fibrillation registry between 2011 and 2020. The outcomes were all-cause death, ischaemic stroke, and major bleeding within 1 year following the AF. We trained the ML-GBDT model and compared its discrimination with the clinical scores in predicting patient outcomes. A total of 25 656 patients were included [mean age 70.3 years (SD 10.3); 44.8% female]. Within 1 year after AF, ischaemic stroke occurred in 215 (0.8%), major bleeding in 405 (1.6%), and death in 897 (3.5%) patients. Our model achieved an optimized area under the curve in predicting death (0.785, 95% CI: 0.757-0.813) compared with the Charlson Comorbidity Index (0.747, P = 0.007), ischaemic stroke (0.691, 0.626-0.756) compared with CHA2DS2-VASc (0.613, P = 0.028), and major bleeding (0.698, 0.651-0.745) as opposed to HAS-BLED (0.607, P = 0.002), with improvement in net reclassification index (10.0, 12.5, and 23.6%, respectively). Conclusion: The ML-GBDT model outperformed clinical risk scores in predicting the risks in patients with AF. This approach could be used as a single multifaceted holistic tool to optimize patient risk assessment and mitigate adverse outcomes when managing AF.

Observational evidence linking psychotropic medicines to the dispensing of opioid agents in later life.

BACKGROUND: The use of opioid medicines is common in developed countries, particularly among older adults and those with mental health disorders. It is unclear if the association between mental disorders and opioid medicines is causal, or is due to reverse causality or confounding. METHODS: We used a 10% random sample of the Australian Pharmaceutical Benefits Scheme (years 2012-2022) to examine the cross-sectional, case-control and longitudinal association between the dispensing of antidepressants, anxiolytics, hypnotics, antipsychotics and lithium, and opioid medicines. We used logistic regression, structural equation models (SEM), and Cox regression to analyze the data. Analyses were adjusted for age (years), sex, and number of non-psychotropic medicines dispensed during the year. RESULTS: The 2022 file contained 804 334 individuals aged 50 years or over (53.1% women), of whom 181 690 (22.6%) received an opioid medicine. The adjusted odds ratio of being dispensed opioid medicines was 1.44 (99% CI = 1.42-1.46) for antidepressants, 1.97 (99% CI = 1.92-2.03) for anxiolytics, 1.55 (99% CI = 1.51-1.60) for hypnotics, 1.32 (99% CI = 1.27-1.38) for antipsychotics, and 0.60 (99% CI = 0.53-0.69) for lithium. Similar associations were noticed when we compared participants who were or not dispensed opioid medicines in 2022 for exposure to psychotropic agents between 2012 and 2021. SEM confirmed that this association was not due to reverse causality. The dispensing of antidepressants was associated with increased adjusted hazard (HR) of subsequent dispensing of opioid medicines (HR = 1.29, 99% CI = 1.27-1.30). Similar associations were observed for anxiolytics, hypnotics and antipsychotics, but not lithium. CONCLUSIONS: The dispensing of opioid medicines is higher among older individuals exposed to antidepressants, anxiolytics, hypnotics and antipsychotics than those who are not. These associations are not due to reverse causality or study design. Preventive strategies seeking to minimise the risk of inappropriate use of opioid medicines in later life should consider targeting this high-risk population.

Cross-sectional, case-control and longitudinal associations between exposure to glucagon-like peptide-1 receptor agonists and the dispensing of antidepressants.

AIM: To determine if the dispensing of glucagon-like peptide (GLP)-1 receptor agonists is associated with increased dispensing of antidepressants. MATERIALS AND METHODS: We used cross-sectional, case-control and retrospective cohort study designs to examine the association between dispensed GLP-1 receptor agonists and antidepressants between 2012 and 2022 in the 10% random sample of the Australian Pharmaceutical Benefits Scheme (PBS) data. PBS-listed GLP-1 receptor agonists, exenatide, dulaglutide and semaglutide were the exposures. Outcomes were the odds ratio [ORs; 99% confidence interval (CI)] and hazard ratio (99% CI) of being dispensed any antidepressant. Analyses were adjusted for demographic measures and the dispensing of medicines to manage cardiovascular diseases or anxiety/insomnia. Statistical tests were two-sided at the 1% level of significance. RESULTS: In total, 358 075 of 1 746 391 individuals were dispensed antidepressants, and 8495 of the 24 783 dispensed a GLP-1 receptor agonist were also dispensed an antidepressant in 2022 (OR 1.44; 99% CI 1.38-1.50); 24 103 of the 1 746 391 participants had been dispensed a GLP-1 receptor agonist between 2012 and 2021, and of these 8083 were dispensed antidepressants in 2022 (OR 1.52; 99% CI 1.46-1.59). The 2012 cohort included 1 213 316 individuals who had not been dispensed antidepressants that year. The hazard ratio of being dispensed an antidepressant between 2013 and 2022 following the dispensing of a GLP-1 receptor agonist was 1.19 (99% CI 1.12-1.27). Additional analyses restricting the time of exposure confirmed these associations for all PBS-listed GLP-1 receptor agonists. CONCLUSIONS: Individuals exposed to GLP-1 receptor agonists are at greater risk of being dispensed antidepressants. The possible impact of GLP-1 receptor agonists on the mood of consumers requires ongoing vigilance and further research.

Analysis and evaluation of explainable artificial intelligence on suicide risk assessment.

This study explores the effectiveness of Explainable Artificial Intelligence (XAI) for predicting suicide risk from medical tabular data. Given the common challenge of limited datasets in health-related Machine Learning (ML) applications, we use data augmentation in tandem with ML to enhance the identification of individuals at high risk of suicide. We use SHapley Additive exPlanations (SHAP) for XAI and traditional correlation analysis to rank feature importance, pinpointing primary factors influencing suicide risk and preventive measures. Experimental results show the Random Forest (RF) model is excelling in accuracy, F1 score, and AUC (>97% across metrics). According to SHAP, anger issues, depression, and social isolation emerge as top predictors of suicide risk, while individuals with high incomes, esteemed professions, and higher education present the lowest risk. Our findings underscore the effectiveness of ML and XAI in suicide risk assessment, offering valuable insights for psychiatrists and facilitating informed clinical decisions.