DESIGN: Cross-sectional study.
SETTING: Teaching Hospital.
PARTICIPANTS: 1085 individuals aged 55 years or over.
MEASUREMENTS: Phase Angle was obtained using bioimpedance analysis with the Bodystat QuadScan® 4000. Diabetes mellitus was considered present with fasting hyperglycaemia (serum fasting glucose >6.66 mmol/l), HbA1c > 42 mmol/mol (6.1%), or self-reported Diabetes or the consumption of glucose-lowering agents.
RESULTS: The mean age of the (standard deviation) of the 1,085 participants was 68.11 (7.12) years and 60.7% were women. Among male participants, individuals with PhA within the lowest quartile (PhA ≤4.9) were significantly more likely to have diabetes mellitus [odds Ratio (95% confidence interval, CI), 2.02 (1.17-3.47)] following adjustments for age, body mass index and other comorbidities. The above relationship was attenuated following further adjustment hypoglycaemic medications. Men on oral hypoglycaemic agents had significantly reduced PhA [mean difference (95% CI), -0.44 (-0.67 to -0.22)]. No significant relationship between PhA and diabetes existed among women.
CONCLUSION: The association between lower PhA (≤4.9) in men aged 55 and over and diabetes which is accounted for by oral hypoglycaemic agents. The mechanisms underlying this relationship remain unclear. This relationship should also be evaluated further to determine the potential of PhA as a prognostic tool for diabetes.
Objective: This trial aimed to evaluate the programme effectiveness of home medication review by community pharmacists (HMR-CP) in optimising diabetes care and reducing medication wastage.
Methods: A randomised controlled trial was conducted on 166 patients with Type 2 Diabetes Mellitus (T2DM) who were randomly assigned to the intervention or control groups. The intervention group received HMR-CP at 0-month, 3-month, and 6-month. The primary outcome was haemoglobin A1c (HbA1c) while clinical outcomes, anthropometric data, and humanistic outcomes were the secondary outcomes. For the intervention group, drug-related problems (DRP) were classified according to the Pharmaceutical Care Network Europe Foundation (PCNE). Medication adherence was determined based on the Pill Counting Adherence Ratio (PCAR). The cost of medication wastage was calculated based on the total missed dose by the T2DM patients multiplied by the cost of medication. General linear model and generalised estimating equations were used to compare data across the different time-points within and between the groups, respectively.
Results: No significant difference was observed in the demographic and anthropometric data at baseline between the two groups except for fasting blood glucose (FBG). There was a significant reduction in the HbA1c (-0.91%) and FBG (-1.62mmol/L) over the study period (p<0.05). A similar observation was noted in diastolic blood pressure (DBP) and total cholesterol (TC) but not in high-density lipoprotein (HDL), and anthropometric parameters. Both utility value and Michigan Diabetes Knowledge Test (MDKT) scores increased significantly over time. As for the intervention group, significant changes in PCAR (p<0.001) and the number of DRP (p<0.001) were noted.
Conclusions: HMR-CP significantly improved the glycaemic control, QoL, medication adherence, and knowledge of T2DM patients as well as reduced the number of DRP and cost of medication wastage. However, the impact of HMR-CP on certain clinical and anthropometric parameters remains inconclusive and further investigation is warranted.
Objective: This study aimed to identify the range of work activities of clinical pharmacists by observation and to estimate the proportion of time spent on different work activities by using the work sampling technique.
Methods: The time spent by clinical pharmacists on various activities was measured using the work sampling technique over 30 working days. The work activities of clinical pharmacists were pre-identified and customized into an activity checklist. Two observers were placed at the study site and took turns recording the activities performed by the clinical pharmacists by following a randomly generated observation schedule.
Results: 1,455 observations were made on five clinical pharmacists with a total of 3493 events recorded. Overall, clinical pharmacists spent 78.8% (n=2751) of their time providing clinical services whereas 12.3% (n=433) of their time was spent on non-clinical activities. They were found to be idle from work for 8.9% of the time. There was no difference in bed occupancy rate in the study site regardless of the presence of the observer (p=0.384). Clinical pharmacists were found to report a higher average daily cumulative work unit of 9.8 (SD=4.3) when under observation compared to an average daily cumulative work unit of 6.5 (SD=4.6) when no observer was present (p=0.005).
Conclusions: The results revealed that clinical pharmacists spent a significant amount of time on non-clinical work. Their responsibilities with non-clinical work should be properly taken care of so they can allocate more time to providing patient care.
AIM: To determine TTR and the predictors of poor TTR among atrial fibrillation patients who receive warfarin therapy.
METHODS: A retrospective observational study was conducted at a cardiology referral center in Selangor, Malaysia. A total of 420 patients with atrial fibrillation and under follow-up at the pharmacist led Warfarin Medication Therapeutic Adherence Clinic between January 2014 and December 2018 were included. Patients' clinical data, information related to warfarin therapy, and INR readings were traced through electronic Hospital Information system. A data collection form was used for data collection. The percentage of days when INR was within range was calculated using the Rosendaal method. The poor INR control category was defined as a TTR < 60%. Predictors for poor TTR were further determined by using logistic regression.
RESULTS: A total of 420 patients [54.0% male; mean age 65.7 (10.9) years] were included. The calculated mean and median TTR were 60.6% ± 20.6% and 64% (interquartile range 48%-75%), respectively. Of the included patients, 57.6% (n = 242) were in the good control category and 42.4% (n = 178) were in the poor control category. The annual calculated mean TTR between the year 2014 and 2018 ranged from 59.7% and 67.3%. A high HAS-BLED score of ≥ 3 was associated with poor TTR (adjusted odds ratio, 2.525; 95% confidence interval: 1.6-3.9, P < 0.001).
CONCLUSION: In our population, a high HAS-BLED score was associated with poor TTR. This could provide an important insight when initiating an oral anticoagulant for these patients. Patients with a high HAS-BLED score may obtain less benefit from warfarin therapy and should be considered for other available oral anticoagulants for maximum benefit.
Materials and Methods: In order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords (deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed.
Results: When dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects.
Conclusion: Clinical applications of deep learning convolutional neural networks in the dental field have shown significant accuracy in detecting and diagnosing dental caries, and these models hold promise in supporting dental practitioners to improve patient outcomes.
MATERIALS AND METHODS: A systematic review of studies comparing the fracture resistance and/or endodontic outcomes between different AC designs was conducted in two electronic search databases (PubMed and Web of Science) following the PRISMA guidelines. Study selection, data extraction, and quality assessment were performed. Meta-analyses were undertaken for fracture resistance and root canal detection, with the level of significance set at 0.05 (P = 0.05).
RESULTS: A total of 33 articles were included in this systematic review. The global evaluation of the risk of bias in the included studies was assessed as moderate, and the level of evidence was rated as low. Four types of AC designs were categorized: traditional (TradAC), conservative (ConsAC), ultraconservative (UltraAC), and truss (TrussAC). Their impact on fracture resistance, cleaning/disinfection, procedural errors, root canal detection, treatment time, apical debris extrusion, and root canal filling was discussed. Meta-analysis showed that compared to TradAC, (i) there is a significant higher fracture resistance of teeth with ConsAC, TrussAC, or ConsAC/TrussAC when all marginal ridges are preserved (P 0.05), and (iii) there is a significantly higher risk of undetected canals with ConsAC if not assisted by dental operating microscope and ultrasonic troughing (P