Utilising a 'Community of Practice' to support pharmacists to work in residential aged care: protocol for a longitudinal evaluation.

INTRODUCTION: A Community of Practice is briefly defined as a group of people with a shared interest in a given area of practice who work collaboratively to grow collective knowledge. Communities of Practice have been used to facilitate knowledge exchange and improve evidence-based practice. Knowledge translation within the residential aged care sector is lacking, with barriers such as inadequate staffing and knowledge gaps commonly cited. In Australia, a Federal inquiry into residential aged care practices led to a recommendation to embed pharmacists within residential aged care facilities. Onsite practice in aged care is a new role for pharmacists in Australia. Thus, support is needed to enable pharmacists to practice in this role.The primary aim is to evaluate the processes and outcomes of a Community of Practice designed to support pharmacists to work in aged care. METHODS AND ANALYSIS: A longitudinal, single-group, pretest-post-test design in which the intervention is a Community of Practice. The Community of Practice will be established and made available for 3 years to all Australian pharmacists interested in, new to or established in aged care roles. The Community of Practice will be hosted on online discussion platforms, with additional virtual meetings and annual symposia. The following data will be collected from all members of the Community of Practice: self-evaluation of the processes and outcomes of the Community of Practice (via the CoPeval scale) and confidence in evidence-based practice (EPIC scale), collected via online questionnaires annually; and discussion platform usage statistics and discussion transcripts. A subset of members will be invited to participate in annual semi-structured individual interviews.Data from the online questionnaire will be analysed descriptively. Discussion transcripts will be analysed using topic modelling and content analysis to identify the common topics discussed and their frequencies. Qualitative data from individual interviews will be thematically analysed to explore perceptions and experiences with the intervention for information/knowledge exchange, impact on practice, and sharing/promoting/implementing evidence-based practice. ETHICS AND DISSEMINATION: Human ethics approval has been granted by the University of Western Australia's Human Ethics Committee (2023/ET000000). No personal information will be included in any publications and reports to funding bodies.Findings will be disseminated to all members of the Community of Practice, professional organisations, social and mass media, peer-review journals, research and professional conferences and annual reports to the funding body.

Electronic health records and e-prescribing in Australia: An exploration of technological utilisation in Australian community pharmacies.

OBJECTIVE: This study aimed to assess the utilisation, benefits, and challenges associated with Electronic Health Records (EHR) and e-prescribing systems in Australian Community Pharmacies, focusing on their integration into daily practice and the impacts on operational efficiency, while also gathering qualitative insights from community pharmacists. METHODS: A mixed-methods online survey was carried out among community pharmacists throughout Australia to assess the utilisation of EHR and e-prescribing systems, including the benefits and challenges associated with their use. Data was analysed based on pharmacists' age, gender, and practice location (metropolitan vs. regional). The chi-square test was applied to examine the relationship between these demographic factors and the utilisation and operational challenges of EHR and e-prescribing systems. RESULTS: The survey engaged 120 Australian community pharmacists. Of the participants, 67 % reported usability and efficiency issues with EHR systems. Regarding e-prescribing, 58 % of pharmacists faced delays due to slow software performance, while 42 % encountered errors in data transmission. Despite these challenges, the benefits of e-prescribing were evident, with 79 % of respondents noting the elimination of illegible prescriptions and 40 % observing a reduction in their workload. Issues with prescription quantity discrepancies and the reprinting process were highlighted, indicating areas for improvement in workflow and system usability. The analysis revealed no significant statistical relationship between the utilisation and challenges of EHR and e-prescribing systems with the demographic variables of age, gender and location (p > 0.05), emphasising the necessity for healthcare solutions that address the needs of all pharmacists regardless of specific demographic segments. CONCLUSION: In Australian community pharmacies, EHR and e-prescribing may enhance patient care but come with challenges such as data completeness, technical issues, and usability concerns. Implementing successful integration relies on user-centric design, standardised practices, and robust infrastructure. While demanding for pharmacists, the digital transition improves efficiency and quality of care. Ensuring user-friendly tools is crucial for the smooth utilisation of digital health.

The influence of patients' beliefs about medicines and the relationship with suboptimal medicine use in community-dwelling older adults: a systematic review of quantitative studies.

BACKGROUND: Medication use in older adults is increasing, therefore, reducing the risk of suboptimal medicine use is imperative in achieving optimal therapeutic outcomes. Research suggests that factors such as personal beliefs and beliefs about medicines may be associated with non-adherence and inappropriate medicine use. AIM: To systematically review and identify quantitative research on the influence of beliefs about medicines and the relationship with suboptimal medicine use in older adults. METHOD: Searches were conducted on PubMed, EMBASE, CINAHL, and PsycINFO for quantitative studies (inception to March 2023). INCLUSION CRITERIA: (1) exposure: participants' beliefs (personal, cultural, and medication-related), (2) outcomes: polypharmacy, potentially inappropriate medicines use, or non-adherence, and (3) participants: community-dwelling adults 65 years or above. Study selection, data extraction and quality appraisal (Joanna Briggs Institute critical appraisal checklist) were completed independently by two investigators. Data were combined in a narrative synthesis and presented in a summary of findings table. RESULTS: Nineteen articles were included: 15 cross-sectional and four cohort studies. Outcomes of included papers were as follows; adherence (n = 18) and potentially inappropriate medicine use (n = 1). Ten studies found stronger beliefs in the necessity of medicines and/or fewer concerns led to better adherence, with one paper contradicting these findings. Three studies did not find associations between adherence and beliefs. One study confirmed an association between unnecessary drug use and a lack of belief in a "powerful other" (e.g. doctor). CONCLUSION: Further investigation is necessary to (1) ascertain the importance of necessity or concern beliefs in fostering adherence and, (2) examine the influence of beliefs on polypharmacy and inappropriate medicine use.

Medicines Regimens Charted for Older People Living in Residential Aged Care: A Repeated Cross-Sectional Study Describing the Number of Medicines, Regimen Complexity, High-Risk Prescribing, and Potential Underprescribing.

OBJECTIVE: We aimed to explore medicines regimens charted for older people living in residential aged care facilities (RACFs). DESIGN: Repeated cross-sectional study using routinely collected data sampled in a cross-sectional manner at 11 time points (day of admission, then at 1, 3, 7, 14, and 30 days, and 3, 6, 12, 18, and 24 months post admission). SETTING AND PARTICIPANTS: The cohort is set in 34 RACFs managed by a single Australasian provider. People aged ≥65 years admitted to permanent care between January 1, 2017, and October 1, 2021, with medicines charted on the date of admission. METHODS: Medicines charted were evaluated for potentially suboptimal prescribing including number of medicines, high-risk prescribing (eg, potentially inappropriate medicines, anticholinergic burden), and potential underprescribing. RESULTS: The 3802 residents in the final cohort had a mean age of 84.9 ± 7.2 years at admission. At least 1 example of suboptimal prescribing was identified in 3479 (92%) residents at admission increasing to 1410 (97%) at 24 months. The number of medicines charted for each resident increased over time from 6.0 ± 3.8 regular and 2.8 ± 2.7 as required medicines at admission to 8.9 ± 4.1 regular and 8.1 ± 3.7 as required medicines at 24 months. Anticholinergic drug burden increased from 1.6 ± 2.4 at admission to 3.0 ± 2.8 at 24 months. Half the residents (2173; 57%) used at least 1 potentially inappropriate medicine at admission, which rose to nearly three-quarters (1060; 73%) at 24 months admission. CONCLUSION AND IMPLICATIONS: The total number of medicines charted for older adults living in RACFs increases with length of stay, with charted as required medicines nearly tripling. Effective interventions to optimize medicines use in this vulnerable population are required.

Development of a list of Australian potentially inappropriate medicines using the Delphi technique.

BACKGROUND: Older people are at high risk of medicines-related harms. otentially inappropriate medicines (PIMs) list has been developed to assist clinicians and researchers to identify medicines with risks that may potentially outweigh their benefits in order to improve medication management and safety. AIM: To develop a list of PIMs for older people specific to Australia. METHODS: The study obtained expert consensus through the utilisation of the Delphi technique in Australia. A total of 33 experts partook in the initial round, while 32 experts engaged in the subsequent round. The primary outcomes encompass medicines assessed as potentially inappropriate, the specific contexts in which their inappropriateness arises and potentially safer alternatives. RESULTS: A total of 16 medicines or medicine classes had one or more medicines deemed as potentially inappropriate in older people. Up to 19 medicines or medicine classes had specific conditions that make them more potentially inappropriate, while alternatives were suggested for 16 medicines or classes. CONCLUSION: An explicit PIMs list for older people living in Australia has been developed containing 19 drugs/drug classes. The PIMs list is intended to be used as a guide for clinicians when assessing medication appropriateness in older people in Australian clinical settings and does not substitute individualised treatment advice from clinicians.

Benefits and barriers associated with e-prescribing in community pharmacy - A systematic review.

Background: The utilization of electronic prescribing is growing, prompted by lockdown measures during the COVID-19 pandemic. However, despite this increasing adoption, there is a notable dearth of consolidated evidence regarding the challenges and opportunities associated with the integration of electronic prescribing systems within the daily clinical practices of community pharmacists. Objective: This paper aims to systematically review the community pharmacists' perspectives on barriers and facilitators to electronic prescribing, addressing the significant need for understanding how electronic prescribing impacts the workflow and decision-making processes of pharmacists, ultimately influencing the quality of patient care. Methods: PubMed, Embase, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases were searched from January 1, 2000, to October 25, 2022, using search terms related to electronic prescribing, computerised physician order entry, community/retail pharmacies, and pharmacists. Results: A total of 28 studies were included in the systematic review. In these studies, community pharmacists perceived that design, interoperability, attitude towards e-prescribing technology, information quality, workflow, productivity, and accessible resources facilitated e-prescribing. In addition, the included studies emphasized the importance of technological support for the successful implementation of electronic prescribing systems. The system's design characteristics significantly improve e-prescribing technology's favourable effects. According to our review, it has been proposed that a poorly designed e-prescribing system can have a negative impact on the quality of care, implementation, and user satisfaction. In contrast, a well-designed system can significantly contribute to improvements. Conclusions: The review highlighted that e-prescribing has both barriers and facilitators, with the quality of the system and its implementation influencing these factors. Technical issues and user acceptance (patient/prescribers/pharmacists) can act as barriers or enablers, highlighting the need for comprehensive consideration and monitoring of e-prescribing to identify and address potential issues.

Transaminases Provide Key Chiral Building Blocks for the Synthesis of Selective M1/M4 Agonists.

We have developed a chiral route toward the synthesis of muscarinic M4 agonists that was enabled by the biocatalytic synthesis of the key spirocyclic diamine building blocks 10 and 12. Using these bifunctional compounds we were able to optimize a synthetic sequence toward a collection of advanced intermediates for further elaboration. These advanced intermediates were then used as starting points for early medicinal chemistry and the identification of selective M1/M4 agonists.

Malignant and non-malignant oral lesions classification and diagnosis with deep neural networks.

OBJECTIVES: Given the increasing incidence of oral cancer, it is essential to provide high-risk communities, especially in remote regions, with an affordable, user-friendly tool for visual lesion diagnosis. This proof-of-concept study explored the utility and feasibility of a smartphone application that can photograph and diagnose oral lesions. METHODS: The images of oral lesions with confirmed diagnoses were sourced from oral and maxillofacial textbooks. In total, 342 images were extracted, encompassing lesions from various regions of the oral cavity such as the gingiva, palate, and labial mucosa. The lesions were segregated into three categories: Class 1 represented non-neoplastic lesions, Class 2 included benign neoplasms, and Class 3 contained premalignant/malignant lesions. The images were analysed using MobileNetV3 and EfficientNetV2 models, with the process producing an accuracy curve, confusion matrix, and receiver operating characteristic (ROC) curve. RESULTS: The EfficientNetV2 model showed a steep increase in validation accuracy early in the iterations, plateauing at a score of 0.71. According to the confusion matrix, this model's testing accuracy for diagnosing non-neoplastic and premalignant/malignant lesions was 64% and 80% respectively. Conversely, the MobileNetV3 model exhibited a more gradual increase, reaching a plateau at a validation accuracy of 0.70. The MobileNetV3 model's testing accuracy for diagnosing non-neoplastic and premalignant/malignant lesions, according to the confusion matrix, was 64% and 82% respectively. CONCLUSIONS: Our proof-of-concept study effectively demonstrated the potential accuracy of AI software in distinguishing malignant lesions. This could play a vital role in remote screenings for populations with limited access to dental practitioners. However, the discrepancies between the classification of images and the results of "non-malignant lesions" calls for further refinement of the models and the classification system used. CLINICAL SIGNIFICANCE: The findings of this study indicate that AI software has the potential to aid in the identification or screening of malignant oral lesions. Further improvements are required to enhance accuracy in classifying non-malignant lesions.

Patient assignment optimization in cloud healthcare systems: a distributed genetic algorithm.

Integrating Internet technologies with traditional healthcare systems has enabled the emergence of cloud healthcare systems. These systems aim to optimize the balance between online diagnosis and offline treatment to effectively reduce patients' waiting times and improve the utilization of idle medical resources. In this paper, a distributed genetic algorithm (DGA) is proposed as a means to optimize the balance of patient assignment (PA) in cloud healthcare systems. The proposed DGA utilizes individuals as solutions for the PA optimization problem and generates better solutions through the execution of crossover, mutation, and selection operators. Besides, the distributed framework in the DGA is proposed to improve its population diversity and scalability. Experimental results demonstrate the effectiveness of the proposed DGA in optimizing the PA problem within the cloud healthcare systems.

A deep reinforcement learning-based wireless body area network offloading optimization strategy for healthcare services.

Wireless body area network (WBAN) is widely adopted in healthcare services, providing remote real-time and continuous healthcare monitoring. With the massive increase of detective sensor data, WBAN is largely restricted by limited storage and computation capacity, resulting in severely decreased efficiency and reliability. Mobile edge computing (MEC) technique can be combined with WBAN to resolve this issue. This paper studies the joint optimization problem of computational offloading and resource allocation (JCORA) in MEC for healthcare service scenarios. We formulate JCORA as a Markov decision process and propose a deep deterministic policy gradient-based WBAN offloading strategy (DDPG-WOS) to optimize time delay and energy consumption in interfered transmission channels. This scheme employs MEC to mitigate the computation pressure on a single WBAN and increase the transmission ability. Further, DDPG-WOS optimizes the offloading strategy-making process by considering the channel condition, transmission quality, computation ability and energy consumption. Simulation results verify the effectiveness of the proposed optimization schema in reducing energy consumption and computation latency and increasing the utility of WBAN compared to two competitive solutions.

Australian dental students' knowledge on antibiotics prophylaxis for dental procedures.

BACKGROUND: Prescribing medicine is integral to clinical dentistry. Infective endocarditis may be rare but fatal if left untreated. As a result, judicious prescribing of antibiotics should be implemented due to potential. To our knowledge, no Australian study has examined dental students' knowledge and perceptions about antibiotic prophylaxis for dental procedures. METHODS: Australian dental students were invited to undertake the survey comprising case vignettes to investigate their medication knowledge. A total of 117 responses were received. The questions were 12 clinically relevant questions and three perception-based questions. Results were analysed using descriptive statistics as well as the chi-squared test. RESULTS: The 117 respondents had a mean correct response of 7.34 ± 2.64 (range 3-12 out of 12). Out of 117 students, 89 (76%) answered more than half of the questions correctly. Only three students (3%) answered all the questions correctly. Nearly two-thirds felt that they knew about antibiotic prophylaxis used for dental procedures. CONCLUSION: Most respondents answered more than half, but not all, of the clinical questions correctly. It is crucial to highlight that dental student may never receive any more training on antimicrobial stewardship (AMS) at any point in their future careers. It may be ideal that this issue is addressed at the dental school. One way to target this is to potentially nationalised teaching delivery of dental AMS across Australia.

Textural feature based intelligent approach for neurological abnormality detection from brain signal data.

The diagnosis of neurological diseases is one of the biggest challenges in modern medicine, which is a major issue at the moment. Electroencephalography (EEG) recordings is usually used to identify various neurological diseases. EEG produces a large volume of multi-channel time-series data that neurologists visually analyze to identify and understand abnormalities within the brain and how they propagate. This is a time-consuming, error-prone, subjective, and exhausting process. Moreover, recent advances in EEG classification have mostly focused on classifying patients of a specific disease from healthy subjects using EEG data, which is not cost effective as it requires multiple systems for checking a subject's EEG data for different neurological disorders. This forces researchers to advance their work and create a single, unified classification framework for identifying various neurological diseases from EEG signal data. Hence, this study aims to meet this requirement by developing a machine learning (ML) based data mining technique for categorizing multiple abnormalities from EEG data. Textural feature extractors and ML-based classifiers are used on time-frequency spectrogram images to develop the classification system. Initially, noises and artifacts are removed from the signal using filtering techniques and then normalized to reduce computational complexity. Afterwards, normalized signals are segmented into small time segments and spectrogram images are generated from those segments using short-time Fourier transform. Then two histogram based textural feature extractors are used to calculate features separately and principal component analysis is used to select significant features from the extracted features. Finally, four different ML based classifiers are used to categorize those selected features into different disease classes. The developed method is tested on four real-time EEG datasets. The obtained result has shown potential in classifying various abnormality types, indicating that it can be utilized to identify various neurological abnormalities from brain signal data.

Australian community pharmacists' ability to identify the indications for dental prescriptions-a case vignette study.

OBJECTIVES: Pharmacists are known as medicine experts. Dentists can independently prescribe and administer medications related to dental conditions such as antimicrobials, anti-inflammatories and analgesics. However, little is known about pharmacists' knowledge and perceptions of medicines prescribed for dentistry. Therefore, this study aimed to assess community pharmacists' ability to identify the indications for dental prescriptions using hypothetical vignettes. METHODS: Australian community pharmacists were invited through email and social media to undertake a web-based questionnaire consisting of nine case vignettes of dental prescriptions and their indicated uses in dental settings and two perception-based questions. The results were provided as a percentage of the correct answers to the case vignettes. In addition, Pearson chi-square tests were performed to examine associations between categorical variables. KEY FINDINGS: Of the 202 pharmacists who completed the questionnaire, the mean number of correct responses was 5 ± 2 (out of 9). More than three-quarters (78.5%) of pharmacists believed that thorough knowledge of prescriptions for dental ailments was necessary for safe and effective community pharmacy practice. In addition, nearly two-thirds (64.1%) felt confident that they could dispense medicines indicated for dental conditions safely and effectively. CONCLUSIONS: The knowledge demonstrated by participants through correct identification of the indications for dental prescription was less than optimal. Professional development courses for pharmacists in dental ailments could prove beneficial.

Automatic breast lesion segmentation in phase preserved DCE-MRIs.

We offer a framework for automatically and accurately segmenting breast lesions from Dynamic Contrast Enhanced (DCE) MRI in this paper. The framework is built using max flow and min cut problems in the continuous domain over phase preserved denoised images. Three stages are required to complete the proposed approach. First, post-contrast and pre-contrast images are subtracted, followed by image registrations that benefit to enhancing lesion areas. Second, a phase preserved denoising and pixel-wise adaptive Wiener filtering technique is used, followed by max flow and min cut problems in a continuous domain. A denoising mechanism clears the noise in the images by preserving useful and detailed features such as edges. Then, lesion detection is performed using continuous max flow. Finally, a morphological operation is used as a post-processing step to further delineate the obtained results. A series of qualitative and quantitative trials employing nine performance metrics on 21 cases with two different MR image resolutions were used to verify the effectiveness of the proposed method. Performance results demonstrate the quality of segmentation obtained from the proposed method.

Automated detection of COVID-19 through convolutional neural network using chest x-ray images.

The COVID-19 epidemic has a catastrophic impact on global well-being and public health. More than 27 million confirmed cases have been reported worldwide until now. Due to the growing number of confirmed cases, and challenges to the variations of the COVID-19, timely and accurate classification of healthy and infected patients is essential to control and treat COVID-19. We aim to develop a deep learning-based system for the persuasive classification and reliable detection of COVID-19 using chest radiography. Firstly, we evaluate the performance of various state-of-the-art convolutional neural networks (CNNs) proposed over recent years for medical image classification. Secondly, we develop and train CNN from scratch. In both cases, we use a public X-Ray dataset for training and validation purposes. For transfer learning, we obtain 100% accuracy for binary classification (i.e., Normal/COVID-19) and 87.50% accuracy for tertiary classification (Normal/COVID-19/Pneumonia). With the CNN trained from scratch, we achieve 93.75% accuracy for tertiary classification. In the case of transfer learning, the classification accuracy drops with the increased number of classes. The results are demonstrated by comprehensive receiver operating characteristics (ROC) and confusion metric analysis with 10-fold cross-validation.

Applicability of explicit potentially inappropriate medication lists to the Australian context: A systematic review.

OBJECTIVES: To determine i) the similarity of potentially inappropriate medications specified in and between existing explicit lists and ii) the availability in Australia of medications included on existing lists to determine their applicability to the Australian context. METHODS: This systematic review identified explicit potentially inappropriate medication lists that were published on EMBASE (1974 - April 2021), MEDLINE (1946 - April 2021) and Elsevier Scopus (2004 - April 2021). The reference lists of seven previously published systematic reviews were also manually reviewed. Lists were included if they were explicit, and the most recent version and the complete list were published in English. Lists based on existing lists were excluded if no new items were added. Potentially inappropriate medications identified on each list were extracted and compared to the medications available on the Australian Register of Therapeutic Goods and Australian Pharmaceutical Benefits Schemes. RESULTS: Thirty-five explicit published lists were identified. A total of 645 unique potentially inappropriate medications were extracted, of which 416 (64%) were available in Australia and 262 (41%) were subsided by the general Pharmaceutical Benefits Scheme. Applicability of each explicit list ranged from 50-96% according to medications available in Australia and 25-83% according to medications available under subsidy. CONCLUSIONS: Pooling data from different lists may help to identify potentially inappropriate medications that may be applicable to local settings. However, if selecting a list for use in the Australian context, consideration should also be given to the intended purpose and setting for application.

Multicenter, Prospective, Randomized Study of Dexamethasone Intravitreal Implant in Patients with Center-Involved Diabetic Macular Edema in the Asia-Pacific Region.

PURPOSE: To evaluate the safety and efficacy of dexamethasone intravitreal implant 0.7 mg (DEX) compared with laser photocoagulation in patients with diabetic macular edema (DME). PATIENTS AND METHODS: This Phase 3, multicenter, randomized, efficacy evaluator-masked, parallel-group, 12-month clinical study enrolled adults in China and the Philippines with reduced visual acuity secondary to fovea-involved DME in the study eye. Participants were randomized 1:1 to study eye treatment with laser photocoagulation every 3 months as needed (n = 139) or DEX every 5 months (n = 145). The main efficacy measures were best-corrected visual acuity (BCVA), central retinal thickness (CRT), and leakage area. The primary endpoint was the average change in BCVA from baseline over 12 months (area-under-the-curve method). Preplanned subgroup analyses evaluated outcomes in Chinese patients. RESULTS: Mean average change in BCVA from baseline during the study (letters) was 4.3 with DEX (n = 145) versus 1.4 with laser (n = 127) overall (P = 0.001) and 4.6 with DEX (n = 129) versus 0.6 with laser (n = 113) in Chinese patients (P < 0.001). At Month 12, mean change in CRT from baseline was -209.5 µm with DEX versus -120.3 µm with laser (P < 0.001) and mean change in total leakage area from baseline was -8.367 mm2 with DEX versus -0.637 mm2 with laser (P < 0.001). The most common treatment-emergent adverse events in the DEX group were increased intraocular pressure and cataract. CONCLUSION: DEX administered every 5 months provided significantly greater improvement in BCVA, CRT, and total leakage area compared with laser treatment. DEX demonstrated an acceptable safety profile, consistent with an intraocular corticosteroid, and similar to that reported in completed global registration studies.

Image Preprocessing in Classification and Identification of Diabetic Eye Diseases.

Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model's development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.

Anticholinergics and clinical outcomes amongst people with pre-existing dementia: A systematic review.

Many medicines have anticholinergic properties, which have previously been correlated with a range of adverse effects, including cognitive impairment, hallucinations and delirium. These effects are potentially of concern for people with dementia. This systematic review investigated the effect of anticholinergic medicines on the health outcomes of people with pre-existing dementia. Embase, Medline and the Cochrane Library were searched from January 2000 to January 2021. Studies were included if they matched the following criteria: (1) the intervention involved anticholinergic medications; (2) the study was conducted in people with pre-existing dementia; (3) there was at least one comparator group; and (4) the outcome of interest was clinically measurable. A total of 14 studies met the inclusion criteria. Most studies used an anticholinergic burden scale to measure anticholinergic exposure. Five high-quality studies consistently identified a strong association between anticholinergic medications and all-cause mortality. Anticholinergics were also found to be associated with longer hospital length of stay in three studies. Inconsistent findings were reported for cognitive function (in 4 studies) and neuropsychiatric functions (in 2 studies). In single studies, anticholinergic medications were associated with the composite outcome of stroke and mortality, pneumonia, delirium, poor physical performance, reduced health-related quality of life and treatment modifications due to reduced treatment response or symptom exacerbation. While the evidence suggests that anticholinergic medication use for people with dementia has a strong association with all-cause mortality, the association with cognitive and other clinical outcomes remains uncertain. Hence, further studies are needed to substantiate the evidence for other outcomes.