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Background Diabetes is one of the leading causes of chronic kidney disease (CKD) and end-stage renal disease. This study aims to develop and validate different risk predictive models for incident CKD and CKD progression in people with type 2 diabetes (T2D). Methods We reviewed a cohort of people with T2D seeking care from two tertiary hospitals in the metropolitan cities of the state of Selangor and Negeri Sembilan from January 2012 to May 2021. To identify the 3-year predictor of developing CKD (primary outcome) and CKD progression (secondary outcome), the dataset was randomly split into a training and test set. A Cox proportional hazards (CoxPH) model was developed to identify predictors of developing CKD. The resultant CoxPH model was compared with other machine learning models on their performance using C-statistic. Results The cohorts included 1992 participants, of which 295 had developed CKD and 442 reported worsening of kidney function. Equation for the 3-year risk of developing CKD included gender, haemoglobin A1c, triglyceride and serum creatinine levels, estimated glomerular filtration rate, history of cardiovascular disease and diabetes duration. For risk of CKD progression, the model included systolic blood pressure, retinopathy and proteinuria. The CoxPH model was better at prediction compared with other machine learning models examined for incident CKD (C-statistic: training 0.826; test 0.874) and CKD progression (C-statistic: training 0.611; test 0.655). The risk calculator can be found at https://rs59.shinyapps.io/071221/. Conclusions The Cox regression model was the best performing model to predict people with T2D who will develop a 3-year risk of incident CKD and CKD progression in a Malaysian cohort. [ABSTRACT FROM AUTHOR]

Introduction: Burnout is a growing trend among medical students worldwide. The aim of this study was to determine the prevalence and factors associated with burnout among medical students at a public university in Malaysia. Methods: A cross-sectional study was conducted among 1st through 5th year medical students at a public university using a simple random sampling method in recruiting participants. In this study, The Maslach Burnout Inventory-General Survey for Student (MBI-SS) was used and burnout is defined as severely emotionally exhausted and severely depersonalised. Results: A total of 328 medical students were recruited with a with response rate of 88.6%. The burnout prevalence was 10.1%. Based on multivariate logistic regression, presence of smartphone addiction with adjusted (odds ratio (OR) 7.37, 95% confidence interval (CI) = 1.67, 32.49), course choice not based on personal interest or due to family pressure (OR 2.72, 95% CI = 1.08, 6.85) and the presence of family relationship problems (OR = 3.58, 95% CI = 1.27, 10.04) are more likely to be associated with burnout among the medical students. Conclusion: Our study has shown that every tenth medical students suffers from burnout. Medical students who are addicted to smartphone, have chosen medical course against individual interest or because of family pressure and have family relationship problems are at risk of getting burnout. Intervention is required to address this issue for the future well-being of medical students. [ABSTRACT FROM AUTHOR]

Introduction: Diabetic control depends partially on self-management. Diabetes self-care is reported to be a strong predictor of glycemic control in Type 2 Diabetes Mellitus (T2DM) patients. Factors contributing to better self-care include self-efficacy and social support. We aim to determine effects of these factors on glucose control in T2DM patients in a primary care clinic. Methods: This study utilised data from a bigger study looking into factors affecting social support and self-efficacy and their correlation in T2DM patients. This study was done among 329 T2DM patients attending a primary care clinic in Selangor, Malaysia between November 2013- January 2014. Systematic random sampling was used. Instruments used were the MOS Social Support Survey and the Diabetic Management Self Efficacy Scale (DMSES). Patients' socio-demographic data and glucose monitoring records were obtained. The association between social support and self-efficacy with blood glucose control was determined using logistic regression. Results: The mean age of the respondents is 54.6 (+11.2), mostly female (55.3%) and Malay ethnicity (63.5%). Being older and retired were predictors for good glucose control (OR=1.05,p=0.01 and OR=2.2,p=0.023) respectively. There is also significant correlations between number of close friends/relatives with social support and self-efficacy (r=0.162,p=0.030 and r=0.190,p=0.01) respectively. The correlation of social support and self-efficacy with HbA1c levels are r= -0.044(p=0.426) and r= -0.08(p= 0.744) respectively. Conclusion: There were no correlations between social support and self-efficacy with the level of HbA1c among patients with T2DM in this study. We found older aged and retired patients to have better HbA1c levels. [ABSTRACT FROM AUTHOR]