Methods: This is a cohort study where prevalent ESRD patients' details were recorded between May 2012 and October 2012. Their records were matched with national death record at the end of year 2015 to identify the deceased patients within three years. Four models were formulated with two models were based on logistic regression models but with different number of predictors and two models were developed based on risk scoring technique. The preferred models were validated by using sensitivity and specificity analysis.

Results: A total of 1332 patients were included in the study. Majority succumbed due to cardiovascular disease (48.3%) and sepsis (41.3%). The identified risk factors were mode of dialysis (P < 0.001), diabetes mellitus (P < 0.001), chronic heart disease (P < 0.001) and leg amputation (P = 0.016). The accuracy of four models was almost similar with AUC between 0.680 and 0.711. The predictive models from logistic regression model and risk scoring model were selected as the preferred models based on both accuracy and simplicity. Besides the mode of dialysis, diabetes mellitus and its complications are the important predictors for early mortality among prevalent ESRD patients.

METHODS: This is a cohort study of T2DM patients in the national diabetes registry, Malaysia. Patients' particulars were derived from the database between 1st January 2009 and 31st December 2009. Their records were matched with the national death record at the end of year 2013 to determine the status after five years. The factors associated with mortality were investigated, and a prognostic model was developed based on logistic regression model.

RESULTS: There were 69,555 records analyzed. The mortality rate was 1.4 persons per 100 person-years. The major cause of death were diseases of the circulatory system (28.4%), infectious and parasitic diseases (19.7%), and respiratory system (16.0%). The risk factors of mortality within five years were age group (p < 0.001), body mass index category (p < 0.001), duration of diabetes (p < 0.001), retinopathy (p = 0.001), ischaemic heart disease (p < 0.001), cerebrovascular (p = 0.007), nephropathy (p = 0.001), and foot problem (p = 0.001). The sensitivity and specificity of the proposed model was fairly strong with 70.2% and 61.3%, respectively.

METHODS: A questionnaire-based survey using the Simple Lifestyle Indicator Questionnaire (SLIQ) was administered to, and anthropometric measurements were collected from, 494 healthcare workers.

RESULTS: The mean age of the subjects was 32.4±8.4, with a range of 19 to 59 years. The subjects were from the allied health (45.5%), management and professional (25.1%) and executive (29.4%) fields. Overall, 47.4% of the subjects were of normal weight, 30.2% were overweight, 17.2% were obese and 5.2% were underweight. The mean number of working hours per week for the subjects was 47.6±14.0 with the highest working hours found among the management and professional group, followed by the executive and allied health groups. Overall, 39.7% of the healthcare workers worked office hours, 36.6% worked within the shift system, 20.9% worked office hours and were on-call and the remaining 2.8% worked a mixture of office hours and shifts. Based on the SLIQ score, 58.1% were classified as at intermediate risk for CVD, 38.5% were in the healthy category and 3.4% were in the unhealthy category. Factors associated with a healthier lifestyle were being female (Odds Ratio [OR]= 12.1; CI=3.2-46.4), professional (mean score= 6.70), in the allied health group (mean score=7.33) and in the normal BMI group (OR= 9.3, CI= 1.8-47.0).

METHODS: In this systematic review and meta-analysis, we searched PubMed, Scopus, and Cochrane Library from database inception to Jan 18, 2021. We included randomised controlled trials and observational or cohort studies that evaluated the effects of a telemedicine intervention on cardiovascular outcomes for people either at risk (primary prevention) of cardiovascular disease or with established (secondary prevention) cardiovascular disease, and, for the meta-analysis, we included studies that evaluated the effects of a telemedicine intervention on cardiovascular outcomes and risk factors. We excluded studies if there was no clear telemedicine intervention described or if cardiovascular or risk factor outcomes were not clearly reported in relation to the intervention. Two reviewers independently assessed and extracted data from trials and observational and cohort studies using a standardised template. Our primary outcome was cardiovascular-related mortality. We evaluated study quality using Cochrane risk-of-bias and Newcastle-Ottawa scales. The systematic review and the meta-analysis protocol was registered with PROSPERO (CRD42021221010) and the Malaysian National Medical Research Register (NMRR-20-2471-57236).

FINDINGS: 72 studies, including 127 869 participants, met eligibility criteria, with 34 studies included in meta-analysis (n=13 269 with 6620 [50%] receiving telemedicine). Combined remote monitoring and consultation for patients with heart failure was associated with a reduced risk of cardiovascular-related mortality (risk ratio [RR] 0·83 [95% CI 0·70 to 0·99]; p=0·036) and hospitalisation for a cardiovascular cause (0·71 [0·58 to 0·87]; p=0·0002), mostly in studies with short-term follow-up. There was no effect of telemedicine on all-cause hospitalisation (1·02 [0·94 to 1·10]; p=0·71) or mortality (0·90 [0·77 to 1·06]; p=0·23) in these groups, and no benefits were observed with remote consultation in isolation. Small reductions were observed for systolic blood pressure (mean difference -3·59 [95% CI -5·35 to -1·83] mm Hg; p<0·0001) by remote monitoring and consultation in secondary prevention populations. Small reductions were also observed in body-mass index (mean difference -0·38 [-0·66 to -0·11] kg/m2; p=0·0064) by remote consultation in primary prevention settings.

INTERPRETATION: Telemedicine including both remote disease monitoring and consultation might reduce short-term cardiovascular-related hospitalisation and mortality risk among patients with heart failure. Future research should evaluate the sustained effects of telemedicine interventions.

METHODS: We recruited 81 travelers and 15 non-travelers (including ten controls) prospectively within a mean of 3·22 days of RT-PCR confirmed COVID-19. Each study participant provided 2 mls of early morning fresh drooled whole saliva separately into a sterile plastic container and GeneFiX™ saliva collection kit. The saliva specimens were processed within 4 h and tested for SARS-CoV-2 genes (E, RdRP, and N2) and the results compared to paired NPS RT-PCR for diagnostic accuracy.

RESULTS: Majority of travellers were asymptomatic (75·0%) with a mean age of 34·26 years. 77 travelers were RT-PCR positive at the time of hospitalization whilst three travelers had positive contacts. In this group, the detection rate for SARS-CoV-2 with NPS, whole saliva, and GeneFiX™ were comparable (89·3%, 50/56; 87·8%, 43/49; 89·6%, 43/48). Both saliva collection methods were in good agreement (Kappa = 0·69). There was no statistical difference between the detection rates of saliva and NPS (p > 0·05). Detection was highest for the N2 gene whilst the E gene provided the highest viral load (mean = 27·96 to 30·10, SD = 3·14 to 3·85). Saliva specimens have high sensitivity (80·4%) and specificity (90·0%) with a high positive predictive value of 91·8% for SARS-CoV-2 diagnosis.