AIM: To evaluate the impact of a Ramadan-focused diabetes education programme on hypoglycaemic risk and other clinical and metabolic parameters.
METHODS: A systematic literature search was performed using Scopus, PubMed, Embase, and Google Scholar to identify relevant studies meeting the inclusion criteria from inception. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and guidelines were followed when performing the search and identification of appropriate studies.
RESULTS: Seventeen studies were included in this systemic review; five of them met the criteria to compile for a meta-analysis. The included studies were with various study designs, including randomised controlled trials, quasi-experimental and non-randomised studies. Overall, the results revealed a significant reduction of hypoglycemia risk (81% reduction) for fasting patients in intervention groups who received Ramadan-focused education compared with patients receiving conventional care (OR 0.19, 95% CI: 0.08-0.46). Moreover, HbA1c significantly improved amongst patients who received a Ramadan-focused diabetes education intervention, compared with those receiving conventional care.
CONCLUSION: Ramadan-focused diabetes education had a significant impact on hypoglycemia and glycaemic control, with no significant effect on body weight, blood lipids or blood pressure.
METHODOLOGY: One thousand two hundred and sixteen prospectively enrolled patients with ACLF (males 98%, mean age 42.5 ± 9.4 years, mean CTP, MELD and AARC scores of 12 ± 1.4, 29.7 ± 7 and 9.8 ± 2 respectively) from the Asian Pacific Association for the Study of the Liver (APASL) ACLF Research Consortium (AARC) database were analysed retrospectively. Patients with or without metabolic risk factors were compared for severity (CTP, MELD, AARC scores) and day 30 and 90 mortality. Information on overweight/obesity, type 2 diabetes mellitus (T2DM), hypertension and dyslipidaemia were available in 1028 (85%), 1019 (84%), 1017 (84%) and 965 (79%) patients respectively.
RESULTS: Overall, 392 (32%) patients died at day 30 and 528 (43%) at day 90. Overweight/obesity, T2DM, hypertension and dyslipidaemia were present in 154 (15%), 142 (14%), 66 (7%) and 141 (15%) patients, respectively, with no risk factors in 809 (67%) patients. Patients with overweight/obesity had higher MELD scores (30.6 ± 7.1 vs 29.2 ± 6.9, P = .007) and those with dyslipidaemia had higher AARC scores (10.4 ± 1.2 vs 9.8 ± 2, P = .014). Overweight/obesity was associated with increased day 30 mortality (HR 1.54, 95% CI 1.06-2.24, P = .023). None of other metabolic risk factors, alone or in combination, had any impact on disease severity or mortality. On multivariate analysis, overweight or obesity was significantly associated with 30-day mortality (aHR 1.91, 95% CI 1.41-2.59, P
BACKGROUND: In 2014, almost two-thirds of Malaysia's adult population aged 18 years or older had T2DM, hypertension or hypercholesterolaemia. An analysis of health system performance from 2016 to 2018 revealed that the control and management of diabetes and hypertension in Malaysia was suboptimal with almost half of the patients not diagnosed and just one-quarter of patients with diabetes appropriately treated. EnPHC framework aims to improve diagnosis and effective management of T2DM, hypertension or hypercholesterolaemia and their risk factors by increasing prevention, optimising management and improving surveillance of diagnosed patients.
METHODS: This is a quasi-experimental controlled study which involves 20 intervention and 20 control clinics in two different states in Malaysia, namely Johor and Selangor. The clinics in the two states were matched and randomly allocated to 'intervention' and 'control' arms. The EnPHC framework targets different levels from community to primary healthcare clinics and integrated referral networks.Data are collected via a retrospective chart review (RCR), patient exit survey, healthcare provider survey and an intervention checklist. The data collected are entered into tablet computers which have installed in them an offline survey application. Interrupted time series and difference-in-differences (DiD) analyses will be conducted to report outcomes.
DESIGN: A population-based cross-sectional study.
SETTING: 13 states and 3 Federal Territories in Malaysia.
PARTICIPANTS: A total of 3966 adults aged 60 years and above were extracted from the nationwide National Health and Morbidity Survey (NHMS) 2018 data set.
PRIMARY OUTCOME MEASURES: Multimorbidity was defined as co-occurrence of at least two known chronic non-communicable diseases in the same individual. The chronic diseases included hypertension, type 2 diabetes mellitus, dyslipidaemia and cancer.
RESULTS: The prevalence of multimorbidity among Malaysian older adults was 40.6% (95% CI: 37.9 to 43.3). The factors associated with multimorbidity were those aged 70-79 years (adjusted OR (AOR)=1.30; 95% CI=1.04 to 1.63; p=0.019), of Indian (AOR=1.69; 95% CI=1.14 to 2.52; p=0.010) and Bumiputera Sarawak ethnicities (AOR=1.81; 95% CI=1.14 to 2.89; p=0.013), unemployed (AOR=1.53; 95% CI=1.20 to 1.95; p=0.001), with functional limitation from activities of daily livings (AOR=1.66; 95% CI=1.17 to 2.37; p=0.005), physically inactive (AOR=1.28; 95% CI=1.03 to 1.60; p=0.026), being overweight (AOR=1.62; 95% CI=1.11 to 2.36; p=0.014), obese (AOR=1.88; 95% CI=1.27 to 2.77; p=0.002) and with abdominal obesity (AOR=1.52; 95% CI=1.11 to 2.07; p=0.009).
CONCLUSION: This study highlighted that multimorbidity was prevalent among older adults in the community. Thus, there is a need for future studies to evaluate preventive strategies to prevent or delay multimorbidity among older adults in order to promote healthy and productive ageing.
METHODS: A systematic review was conducted according to the PRISMA guidelines. The study protocol was registered with PROSPERO (CRD42017056150). We searched MEDLINE, EMBASE, PsycINFO, CINAHL and ERIC for articles published up to January 2017. Articles that measured HL levels in adult patients with T2DM; that used validated HL tools; and that were reported in English were included. Two reviewers assessed studies for eligibility and quality, and extracted the data. Prevalence of limited HL is calculated from the number of patients with less than adequate HL over the total number of patients with T2DM in the study. Meta-analysis and meta-regression analysis were conducted using the Open Meta-analyst software.
RESULTS: Twenty-nine studies involving 13,457 patients with T2DM from seven countries were included. In total, seven different HL measurement tools were used. The prevalence of limited HL ranged from 7.3% to 82%, lowest in Switzerland and the highest in Taiwan. Meta-regression analysis of all included studies showed the country of study (p<0.001), HL tool used (p = 0.002), and the country's region (p<0.001) contributed to the variation findings. Thirteen studies in the USA measured functional HL. The pooled prevalence of inadequate functional HL among patients with T2DM in the USA was 28.9% (95% CI: 20.4-37.3), with high heterogeneity (I2 = 97.9%, p <0.001). Studies were done in the community as opposed to a hospital or primary care (p = 0.005) and populations with education level lower than high school education (p = 0.009) reported a higher prevalence of limited HL.
CONCLUSION: The prevalence of limited HL in patients with T2DM varied widely between countries, HL tools used and the country's region. Pooled prevalence showed nearly one in three patients with T2DM in the USA had limited functional HL. Interactions with healthcare providers and educational attainment were associated with reported of prevalence in the USA.
METHOD: For designing and modeling the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials were used. Different Machine learning-based feature ranking techniques were investigated to identify the important MNSI features associated with DSPN diagnosis. A multivariable logistic regression-based nomogram was generated and validated for DSPN severity grading using the best performing top-ranked MNSI features.
RESULTS: Top-10 ranked features from MNSI features: Appearance of Feet (R), Ankle Reflexes (R), Vibration perception (L), Vibration perception (R), Appearance of Feet (L), 10-gm filament (L), Ankle Reflexes (L), 10-gm filament (R), Bed Cover Touch, and Ulceration (R) were identified as important features for identifying DSPN by Multi-Tree Extreme Gradient Boost model. The nomogram-based prediction model exhibited an accuracy of 97.95% and 98.84% for the EDIC test set and an independent test set, respectively. A DSPN severity score technique was generated for MNSI from the DSPN severity prediction model. DSPN patients were stratified into four severity levels: absent, mild, moderate, and severe using the cut-off values of 17.6, 19.1, 20.5 for the DSPN probability less than 50%, 75%-90%, and above 90%, respectively.
CONCLUSIONS: The findings of this work provide a machine learning-based MNSI severity grading system which has the potential to be used as a secondary decision support system by health professionals in clinical applications and large clinical trials to identify high-risk DSPN patients.