Preventing Adverse Drug Reactions After Hospital Discharge (PADR-AD): Protocol for a randomised-controlled trial in older people

BackgroundAdverse drug reactions (ADRs) and adverse drug events (ADEs) in older people contribute to a significant proportion of hospital admissions and are common following discharge. Effective interventions are therefore required to combat the growing burden of preventable ADRs. The Prediction of Hospitalisation due to Adverse Drug Reactions in Elderly Community Dwelling Patients (PADR-EC) score is a validated risk score developed to assess the risk of ADRs in people aged 65 years and older and has the potential to be utilised as part of an intervention to reduce ADRs.ObjectivesThis trial was designed to investigate the effectiveness of an intervention to reduce ADR incidence in older people and to obtain further information about ADRs and ADEs in the 12–24 months following hospital discharge.MethodsThe study is an open-label randomised-controlled trial to be conducted at the Royal Hobart Hospital, a 500-bed public hospital in Tasmania, Australia. Community-dwelling patients aged 65 years and older with an unplanned overnight admission to a general medical ward will be recruited. Following admission, the PADR-EC ADR score will be calculated by a research pharmacist, with the risk communicated to clinicians and discussed with participants. Following discharge, nominated general practitioners and community pharmacists will receive the risk score and related medication management advice to guide their ongoing care of the patient. Follow-up with participants will occur at 3 and 12 and 18 and 24 months to identify ADRs and ADEs. The primary outcome is moderate-severe ADRs at 12 months post-discharge, and will be analysed using the cumulative incidence proportion, survival analysis and Poisson regression.SummaryIt is hypothesised that the trial will reduce ADRs and ADEs in the intervention population. The study will also provide valuable data on post-discharge ADRs and ADEs up to 24 months post-discharge.     

Dose optimization with population pharmacokinetics of ritonavir-boosted lopinavir for Thai people living with HIV with and without active tuberculosis

BackgroundPrior to dolutegravir availability, ritonavir-boosted lopinavir (LPV/r) was an alternative recommendation when first-line drugs could not be used. A high concentration of protease inhibitors was observed in the Thai people living with HIV (PLWH). Thus, dose reduction of LPV/r may be possible. However, the pharmacokinetics and dose optimization of LPV/r have never been investigated. This study aimed to develop a population pharmacokinetic model of LPV/r and provide dosage optimization in Thai PLWH.MethodsLPV and RTV trough concentrations from Thai PLWH were combined with intensive data. The data were analyzed by the nonlinear mixed-effects modeling approach. The influence of RTV concentration on LPV oral clearance (CL/F) was investigated.ResultsRifampicin (RIF) use increased LPV and RTV CL/F by 2.16-fold and 1.99-fold, respectively. The reduced dose of 300/75 and 200/150 mg twice daily provided a comparable percentage of patients achieving LPV target trough concentration to the standard dose for PI-naïve patients. For HIV/TB co-infected patients receiving RIF who could not tolerate the recommended dose, the reduced dose of 600/150 mg twice daily was recommended.ConclusionThe population pharmacokinetic model was developed by integrating the interaction between LPV and RTV. The reduced LPV/r dosage offers sufficient LPV exposure for Thai PLWH.     

Evidence-based strategies to reduce the incidence of postoperative delirium: a narrative review

Delirium is one of the most commonly occurring postoperative complications in older adults. It occurs due to the vulnerability of cerebral functioning to pathophysiological stressors. Identification of those at increased risk of developing delirium early in the surgical pathway provides an opportunity for modification of predisposing and precipitating risk factors and effective shared decision-making. No single delirium prediction tool is used widely in surgical settings. Multi-component interventions to prevent delirium involve structured risk factor modification supported by geriatrician input; these are clinically efficacious and cost effective. Barriers to the widespread implementation of such complex interventions exist, resulting in an ‘implementation gap’. There is a lack of evidence for pharmacological prophylaxis for the prevention of delirium. Current evidence suggests that avoidance of peri-operative benzodiazepines, careful titration of anaesthetic depth guided by processed electroencephalogram monitoring and treatment of pain are the most effective strategies to minimise the risk of delirium. Addressing postoperative delirium requires a collaborative, whole pathway approach, beginning with the early identification of those patients who are at risk. The research agenda should continue to examine the potential for pharmacological prophylaxis to prevent delirium while also addressing how successful models of delirium prevention can be translated from one setting to another, underpinned by implementation science methodology.     

青年人群近距离工作后眼部参数的变化及恢复时间

目的：观察青年人群近距离用眼后眼部生理及功能性参数的变化及恢复时间。

方法：前瞻性研究。随机选取2019-12/2020-06在我院进行医学验光的患者69例138眼，根据主觉验光结果分为正视组(+0.75D≤等效球镜度≤-0.50D，18例36眼)、低度近视组(-0.75D≤等效球镜度≤-3.00D，25例50眼)和中度近视组(-3.25D≤等效球镜度≤-6.00D，26例52眼)。所有受试者近距离阅读20min后远眺放松20min，分别于近距离用眼前、近距离用眼20min后、远眺5、10、15、20min时测量受试者眼部生理性参数\〖前房深度(ACD)、眼轴长度(AL)\〗和功能性参数\〖正相对调节(PRA)、调节反应(BCC)\〗，分析各参数的达极时间和恢复时间。

结果：近距离用眼后眼轴变长，前房变浅，PRA绝对值变大，BCC无明显变化，75%(52/69)的受试者AL在近距离用眼20min后达极，87%(60/69)的受试者ACD在远眺5min时达极，96%(66/69)的受试者PRA在近距离用眼20min后达极，且以上参数均在远眺10min后逐渐恢复至初始状态。

结论：近距离用眼后眼部参数发生改变，眼轴变长，前房变浅，PRA绝对值增大，但均在远眺放松过程中逐渐回退，且均需要10min以上才能恢复至初始状态。     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

Stakeholder collaboration in long-term care of older people in Lithuania

Demographic situation, changes in the role of women in society and growing demand for long-term care (LTC) of older people have challenged the ability to meet the growing LTC needs in most developed countries. In countries where responsibility for LTC is still largely laid on families, it is, however, even more critical and calls for improvements in formal LTC systems. More intensive stakeholder collaboration in LTC policy development, organising and delivery are of primary importance in improving LTC systems. Such collaboration, however, is not always successful; thus, it is critical to understand what makes it effective and efficient. In this paper, we specifically look into multistakeholder collaboration in LTC in Lithuania, one of the fastest ageing countries in the EU, with the demand for LTC services growing fast and exceeding the supply despite rising business and NGO engagement. To determine facilitators of such collaboration, we build on the data obtained through eight focus group discussions with all key stakeholder representatives (LTC policymakers, organisers and service providers [public, private and NGOs], 54 participants in total). Our findings indicate that in addition to national and organisational level facilitators studied in prior research, there are important individual level factors, such as meaningfulness at work, concern and care for others, possibility for personal growth and development, satisfaction with supervision, a sense of belonging and role clarity. On the other hand, our results show that collaboration is constrained by a shortage of human resources, increased workload caused by growing LTC demand, bureaucratic requirements, legal restrictions, lack of awareness of LTC service availability among elder persons, and prevailing social norms and attitudes to institutionalised care. Interestingly, a lack of financial resources is not perceived as a major constraint.     

The Effect of Residential Aged Care Size,Ownership Model,and Multichain Affiliation on Resident Comfort and Symptom Management at the End of Life

Context

In most resource-rich countries, a large and growing proportion of older adults with complex needs will die while in a residential aged care (RAC) facility.