Analysis of the adverse events following the mRNA-1273 COVID-19 vaccine

OBJECTIVE: This study aims to characterize the adverse events (AEs) following the administration of the mRNA-1273 COVID-19 vaccine from the Vaccine Adverse Event Reporting System (VAERS) data.METHODS: In this case/non-case analysis, reports between 1 January 2021, and 27 October 2022, were extracted from VAERS. AEs were defined as preferred terms (PTs) by Medical Dictionary for Regulatory Activities (MedDRA) terminology. Disproportionality analyses were conducted to calculate the reporting odds and proportional reporting ratios. The Bayesian approach was used to calculate information component (IC) values and Empirical Bayesian Geometric Mean scores for all the AEs detected.RESULTS: 186 MedDRA PTs compromising 702,495 AEs associated with the mRNA-1273 vaccine were identified. Three statistically significant signals were identified for general and systemic AEs, administration site conditions, and product issues. Cardiac disorders were rarely reported, the most common being; 489 reports for 'myocarditis' (19.44%), 475 for 'acute myocardial infarction' (18.88%), 457 for 'myocardial infarction' (18.16%), 290 for 'bradycardia' (11.53%) and 281 for 'pericarditis' (11.17%).CONCLUSIONS: The most frequently identified AEs following mRNA-1273 vaccination agree with those listed within the Summary of Product Characteristics. In addition, disproportionality analysis did not find any statistically significant signals for myocarditis or pericarditis.</p

An overview of pharmacodynamic modelling, ligand-binding approach and its application in clinical practice

AbstractThe study of the magnitude and variation of drug response is defined as pharmacodynamics (PDs). PD models examine plasma concentration and effect relationship. It can predict the archetypal effect (E) of a drug as a function of the drug concentration (C) and estimate an unknown PD parameter (θpd). The PD models have been described as fixed, linear, log-linear, Emax, sigmoid Emax, and indirect PD response. Ligand binding model is an example of a PD model that works on the underpinning PD principle of a drug, eliciting its pharmacological effect at the receptor site. The pharmacological effect is produced by the drug binding to the receptor to either activate or antagonise the receptor. Ligand binding models describe a system of interacting components, i.e. the interaction of one or more ligands with one or more binding sites. The Emax model is the central method that provides an empirical justification for the concentration/dose-effect relationship. However, for ligand binding models justification is provided by theory of receptor occupancy. In essence, for ligand binding models, the term fractionaloccupancy is best used to describe the fraction of receptors occupied at a particular ligand concentration. It is stated that the fractionaloccupancy=occupiedbindingsites/totalbindingsites, which means the effect of a drug should depend on the fraction of receptors that are occupied. In the future, network-based systems pharmacology models using ligand binding principles could be an effective way of understanding drug-related adverse effects. This will facilitate and strengthen the development of rational drug therapy in clinical practice

An Updated Analysis of Psychotropic Medicine Utilisation in Older People in New Zealand from 2005 to 2019

Background: Psychotropic medicine utilisation in older adults continues to be of interest because of overuse and concerns surrounding its safety and efficacy. Objective: This study aimed to characterise the utilisation of psychotropic medicines in older people in New Zealand. Methods: We conducted a repeated cross-sectional analysis of national dispensing data from 1 January, 2005 to 31 December, 2019. We defined utilisation using the Anatomical Therapeutic Chemical classification defined daily dose system. Utilisation was measured in terms of the defined daily dose (DDD) per 1000 older people per day (TOPD). Results: Overall, the utilisation of psychotropic medicines increased marginally by 0.42% between 2005 and 2019. The utilisation increased for antidepressants (72.42 to 75.21 DDD/TOPD) and antipsychotics (6.06–19.04 DDD/TOPD). In contrast, the utilisation of hypnotics and sedatives (53.74–38.90 DDD/TOPD) and anxiolytics decreased (10.20–9.87 DDD/TOPD). The utilisation of atypical antipsychotics increased (4.06–18.72 DDD/TOPD), with the highest percentage change in DDD/TOPD contributed by olanzapine (520.6 %). In comparison, utilisation of typical antipsychotics was relatively stable (2.00–2.06 DDD/TOPD). The utilisation of venlafaxine increased remarkably by 5.7 times between 2005 and 2019. The utilisation of zopiclone was far greater than that of other hypnotics in 2019. Conclusions: There was only a marginal increase in psychotropic medicines utilisation from 2005 to 2019 in older adults in New Zealand. There was a five-fold increase in the utilisation of antipsychotic medicines. Continued monitoring of psychotropic medicine utilisation will be of interest to understand the utilisation of antidepressants and antipsychotic medicines during the coronavirus disease 2019 pandemic year.</p

Risk of delirium associated with antimuscarinics in older adults: a case‐time‐control study

BACKGROUND: Older adults are at an increased risk of delirium because of age, polypharmacy, multiple comorbidities and acute illness. Antimuscarinics are the backbone of the pharmacological management of overactive bladder. However, the safety profiles of antimuscarinics vary because of their dissimilarities to muscarinic receptor‐subtype affinities and are associated with differential central anticholinergic adverse effects. OBJECTIVE: This study aimed to examine delirium risk in new users of oxybutynin and solifenacin in older adults (≥ 65 years). In the secondary analyses, we examined the risk of delirium by type and dose of antimuscarinic. METHOD: We applied a case‐time‐control design to investigate delirium risk in older adults who started taking oxybutynin and solifenacin. We used a nationwide inpatient hospital data (2005–2016), National Minimum Data Set, maintained by the Ministry of Health, New Zealand (NZ), to identify older adults with a new‐onset diagnosis of delirium. Eligible patients were older adults aged 65 at entry into the cohort on 1/1/2006. We used dispensing claims data to determine antimuscarinic treatment exposure. The antimuscarinic included in the study were new users of oxybutynin and solifenacin. These two antimuscarinics are subsidised by the Pharmaceutical Management Agency and are the most frequently used antimuscarinic in NZ. A conditional logistic regression model was used to compute matched odds ratios (MORs) and 95% confidence intervals (CIs). In the case‐time‐control design, we made separate analyses to evaluate the dose–response risk of delirium. RESULTS: We identified 4818 individuals (mean age 82.14) from 2005 to 2015 with incident delirium and were exposed to at least one of the antimuscarinic of interest. The case‐time‐control matched odds ratio (MOR) for delirium with oxybutynin was (2.06, 95% confidence interval [CI] 1.07–3.96). Solifenacin was not associated with delirium (0.89 95%CI 0.64–1.23). In the sensitivity analyses, the case‐time‐control MOR for delirium using a shorter risk period (0–3 days) did not change the results. The dose–response risk of delirium was significant for oxybutynin (0.05, 95%CI 0.02–0.08) but not for solifenacin (−0.01, 95%CI −0.03 to 0.00). In addition, in the subgroup analyses, a statistically significant association of delirium was found for oxybutynin but not for solifenacin in the non‐dementia cohort (2.11,95% CI 1.08–4.13) and the dementia cohort (1.25, 95%CI 0.05–26.9). CONCLUSION: The study found that oxybutynin but not solifenacin is associated with a risk of new‐onset delirium in older adults. The higher blockade of M1 and M2 receptors by oxybutynin is likely to contribute to delirium than solifenacin, which is highly selective for the M3 receptor subtype. Therefore, the treatment choice with an M3 selective agent must be given due consideration, particularly in those with pre‐existing cognitive impairment

DEFEAT-polypharmacy:deprescribing anticholinergic and sedative medicines feasibility trial in residential aged care facilities

Background Prolonged use of anticholinergic and sedative medicines is correlated with worsening cognition and physical function decline. Deprescribing is a proposed intervention that can help to minimise polypharmacy whilst potentially improving several health outcomes in older people. Objective This study aimed to examine the feasibility of implementing a deprescribing intervention that utilises a patient-centred pharmacist-led intervention model; in order to address major deprescribing challenges such as general practitioner time constraints and lack of accessible deprescribing guidelines and processes. Setting Three residential care facilities. Methods The intervention involved a New Zealand registered pharmacist utilising peer-reviewed deprescribing guidelines to recommend targeted deprescribing of anticholinergic and sedative medicines to GPs. Main outcome measure The change in the participants' Drug Burden Index (DBI) total and DBI 'as required' (PRN) was assessed 3 and 6 months after implementing the deprescribing intervention. Results Seventy percent of potential participants were recruited for the study (n = 46), and 72% of deprescribing recommendations suggested by the pharmacist were implemented by General Pratitioners (p = 0.01; Fisher's exact test). Ninety-six percent of the residents agreed to the deprescribing recommendations, emphasising the importance of patient centred approach. Deprescribing resulted in a significant reduction in participants' DBI scores by 0.34, number of falls and adverse drug reactions, 6 months post deprescribing. Moreover, participants reported lower depression scores and scored lower frailty scores 6 months after deprescribing. However, cognition did not improve; nor did participants' reported quality of life. Conclusion This patient-centred deprescribing approach, demonstrated a high uptake of deprescribing recommendations and success rate. After 6 months, significant benefits were noted across a range of important health measures including mood, frailty, falls and reduced adverse reactions. This further supports deprescribing as a possible imperative to improve health outcomes in older adults.</p

DEFEAT-polypharmacy:deprescribing anticholinergic and sedative medicines feasibility trial in residential aged care facilities

Background Prolonged use of anticholinergic and sedative medicines is correlated with worsening cognition and physical function decline. Deprescribing is a proposed intervention that can help to minimise polypharmacy whilst potentially improving several health outcomes in older people. Objective This study aimed to examine the feasibility of implementing a deprescribing intervention that utilises a patient-centred pharmacist-led intervention model; in order to address major deprescribing challenges such as general practitioner time constraints and lack of accessible deprescribing guidelines and processes. Setting Three residential care facilities. Methods The intervention involved a New Zealand registered pharmacist utilising peer-reviewed deprescribing guidelines to recommend targeted deprescribing of anticholinergic and sedative medicines to GPs. Main outcome measure The change in the participants' Drug Burden Index (DBI) total and DBI 'as required' (PRN) was assessed 3 and 6 months after implementing the deprescribing intervention. Results Seventy percent of potential participants were recruited for the study (n = 46), and 72% of deprescribing recommendations suggested by the pharmacist were implemented by General Pratitioners (p = 0.01; Fisher's exact test). Ninety-six percent of the residents agreed to the deprescribing recommendations, emphasising the importance of patient centred approach. Deprescribing resulted in a significant reduction in participants' DBI scores by 0.34, number of falls and adverse drug reactions, 6 months post deprescribing. Moreover, participants reported lower depression scores and scored lower frailty scores 6 months after deprescribing. However, cognition did not improve; nor did participants' reported quality of life. Conclusion This patient-centred deprescribing approach, demonstrated a high uptake of deprescribing recommendations and success rate. After 6 months, significant benefits were noted across a range of important health measures including mood, frailty, falls and reduced adverse reactions. This further supports deprescribing as a possible imperative to improve health outcomes in older adults.</p

Factors associated with inappropriate prescribing among older adults with complex care needs who have undergone the interRAI assessment

Aim To identify factors associated with prescribing potentially inappropriate medications (PIMs) in older adults (≥ 65 years) with complex care needs, who have undertaken a comprehensive geriatric risk assessment.METHODS: A nationwide cross-sectional (retrospective, observational) study was performed. The national interRAI Home Care assessments conducted in New Zealand in 2015 for older adults were linked to the national pharmaceutical prescribing data (PHARMS). The 2015 Beers criteria were applied to the cross-matched data to identify the prevalence of PIMs. The factors influencing PIMs were analysed using a multinomial logistic regression model.RESULTS: 16,568 older adults were included in this study. Individuals diagnosed with cancer, dementia, insomnia, depression, anxiety, and who were hospitalized in the last 90 days, were more likely to be prescribed PIMs than those who were not diagnosed with the above disorders, and who were not hospitalized in the last 90 days. Individuals over 75 years of age, the Māori ethnic group among other ethnicities, individuals who were diagnosed with certain clinical conditions (diabetes, chronic obstructive pulmonary disease, stroke, or congestive cardiac failure), individuals requiring assistance with activities of daily living and better self-reported health, were associated with a lesser likelihood of being prescribed PIMs.CONCLUSION: The study emphasizes the identification of factors associated with the prescription of PIMs during the first completed comprehensive geriatric assessment. Targeted strategies to reduce modifiable factors associated with the prescription of PIMs in subsequent assessments has the potential to improve medication management in older adults.</p

Application of machine learning approaches in predicting clinical outcomes in older adults – a systematic review and meta-analysis

BACKGROUND: Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care.DESIGN: Systematic review and meta-analyses.PARTICIPANTS: Older adults (≥ 65 years) in any setting.INTERVENTION: Machine learning models for predicting clinical outcomes in older adults were evaluated. A random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months.OUTCOME MEASURES: Studies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric.RESULTS: Thirty-seven studies that satisfied the systematic review criteria were appraised, and eight studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 - 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 - 0.86) for mortality over 6 months, signifying good discriminatory power.CONCLUSION: The meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes.</p

Application of machine learning approaches in predicting clinical outcomes in older adults – a systematic review and meta-analysis

BACKGROUND: Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care.DESIGN: Systematic review and meta-analyses.PARTICIPANTS: Older adults (≥ 65 years) in any setting.INTERVENTION: Machine learning models for predicting clinical outcomes in older adults were evaluated. A random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months.OUTCOME MEASURES: Studies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric.RESULTS: Thirty-seven studies that satisfied the systematic review criteria were appraised, and eight studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 - 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 - 0.86) for mortality over 6 months, signifying good discriminatory power.CONCLUSION: The meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes.</p