Background Randomised controlled trials have demonstrated the benefits of sodium-glucose co-transporter 2 inhibitors (SGLT2is) in people with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD). However, real-world data on CKD progression and the development of end-stage kidney disease (ESKD) remains scarce. We aim to study renal outcomes of people with diabetic kidney disease (DKD) using SGLT2is in a highly prevalent DKD population. Methods Between 2016 and 2019, we recruited T2DM patients in the renal or diabetic clinic in a regional hospital in Singapore. Patients prescribed SGLT2is were compared with those on standard anti-diabetic and reno-protective treatment. The outcome measures were CKD progression (a > = 25% drop from baseline and worsening of estimated glomerular filtration rate (eGFR) categories according to KDIGO guidelines) and ESKD (eGFR < 15ml/min/1.73m2). Results We analysed a total of 4446 subjects; 1598 were on SGLT2is. There was a significant reduction in CKD progression (hazard ratio [HR] 0.60; 95% confidence interval [CI] 0.49–0.74) with SGLT2is. The HR for eGFR ≥ 45 ml/min/1.73m2 and eGFR 15–44ml/min/1.73m2 was 0.60 (95% CI 0.47–0.76) and 0.43 (95% CI 0.23–0.66) respectively. There was also a reduction in risk for developing ESKD for the entire cohort (HR 0.33; 95% CI 0.17–0.65) and eGFR 15–44ml/min/1.73m2 (HR 0.24; 95% CI 0.09–0.66). Compared to canagliflozin and dapagliflozin, empagliflozin showed a sustained risk reduction of renal outcomes across stages 1–4 CKD. Conclusions This real-world study demonstrates the benefits of SGLT2is on CKD progression and ESKD. The effect is more pronounced in moderate to advanced CKD patients.

Background Acute kidney injury (AKI) is common in hospitalised patients. The relationship between body mass index (BMI) and the risk of having AKI for patients in the acute hospital setting is not known, particularly in the Asian population. Methods This was a retrospective, single-centre, observational study conducted in Singapore, a multiethnic population. All patients aged ≥21 years and hospitalised from January to December 2013 were recruited. Results A total of 12,555 patients were eligible for the analysis. A BMI of <18.5 kg/m2 was independently associated with the development of AKI in hospitalised patients (odds ratio (OR): 1.23 [95% confidence interval [CI]: 1.04–1.44, P = 0.01]) but not for overweight and obesity. Subgroup analysis further revealed that underweight patients aged ≥75 and repeated hospitalisation posed a higher risk of AKI (OR: 1.25 [CI: 1.01–1.56], P = 0.04; OR: 1.23 [CI: 1.04–1.44], P = 0.01, resp.). Analyses by interactions between different age groups and BMI using continuous or categorised variables did not affect the overall probability of developing AKI. Conclusions Underweight Asian patients are susceptible to AKI in acute hospital settings. Identification of this novel risk factor for AKI allows us to optimise patient care by prevention, early detection, and timely intervention.

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This is one of the first reports of TDC insertion by nephrologists from SEA. Our outcomes compare favourably with those reported in the literature.

Background Fluid assessment is challenging, and fluid overload poses a significant problem among dialysis patients, with pulmonary oedema being the most serious consequence. Our study aims to develop a simple objective fluid assessment strategy using lung ultrasound (LUS) and artificial intelligence (AI) to assess the fluid status of dialysis patients. Methods This was a single-centre study of 76 hemodialysis and peritoneal dialysis patients carried out between July 2020 to May 2022. The fluid status of dialysis patients was assessed via a simplified 8-point LUS method using a portable handheld ultrasound device (HHUSD), clinical examination and bioimpedance analysis (BIA). The primary outcome was the performance of 8-point LUS using a portable HHUSD in diagnosing fluid overload compared to physical examination and BIA. The secondary outcome was to develop and validate a novel AI software program to quantify B-line count and assess the fluid status of dialysis patients. Results Our study showed a moderate correlation between LUS B-line count and fluid overload assessed by clinical examination (r = 0.475, p < 0.001) and BIA (r = 0.356. p < 0.001). The use of AI to detect B-lines on LUS in our study for dialysis patients was shown to have good agreement with LUS B lines observed by physicians; (r = 0.825, p < 0.001) for the training dataset and (r = 0.844, p < 0.001) for the validation dataset. Conclusion Our study confirms that 8-point LUS using HHUSD, with AI-based detection of B lines, can provide clinically useful information on the assessment of hydration status and diagnosis of fluid overload for dialysis patients in a user-friendly and time-efficient way.