OBJECTIVE: To determine efficacy and safety of PDA10 in treating renal anaemia in haemodialysis patients, in comparison to the originator epoetin-α, Eprex®.
METHODS: A phase 3, multicentre, multi-national, double-blind, randomised, active-controlled and parallel group study conducted over 40 weeks in Malaysia and Korea. End stage kidney disease patients undergoing regular haemodialysis who were on erythropoietin treatment were recruited. The study has 3 phases, which included a 12-week titration phase, followed by 28-week double-blind treatment phase and 24-week open-label extension phase.
RESULTS: The PDA10 and Eprex® were shown to be therapeutically equivalent (p
PATIENTS AND METHODS: The dataset encompassed patient data from a tertiary cardiothoracic center in Malaysia between 2011 and 2015, sourced from electronic health records. Extensive preprocessing and feature selection ensured data quality and relevance. Four machine learning algorithms were applied: Logistic Regression, Gradient Boosted Trees, Support Vector Machine, and Random Forest. The dataset was split into training and validation sets and the hyperparameters were tuned. Accuracy, Area Under the ROC Curve (AUC), precision, F-measure, sensitivity, and specificity were some of the evaluation criteria. Ethical guidelines for data use and patient privacy were rigorously followed throughout the study.
RESULTS: With the highest accuracy (88.66%), AUC (94.61%), and sensitivity (91.30%), Gradient Boosted Trees emerged as the top performance. Random Forest displayed strong AUC (94.78%) and accuracy (87.39%). In contrast, the Support Vector Machine showed higher sensitivity (98.57%) with lower specificity (59.55%), but lower accuracy (79.02%) and precision (70.81%). Sensitivity (87.70%) and specificity (87.05%) were maintained in balance via Logistic Regression.
CONCLUSION: These findings imply that Gradient Boosted Trees and Random Forest might be an effective method for identifying patients who would develop AKI following heart surgery. However specific goals, sensitivity/specificity trade-offs, and consideration of the practical ramifications should all be considered when choosing an algorithm.
METHODS: A 17-item questionnaire was developed to assess nutrition practices and administered to dialysis managers of 150 HD centers, identified through the National Renal Registry. Nutritional outcomes of 4362 patients enabled crosscutting comparisons as per dietitian accessibility and center sector.
RESULTS: Dedicated dietitian (18%) and visiting/shared dietitian (14.7%) service availability was limited, with greatest accessibility at government centers (82.4%) > non-governmental organization (NGO) centers (26.7%) > private centers (15.1%). Nutritional monitoring varied across HD centers as per albumin (100%) > normalized protein catabolic rate (32.7%) > body mass index (BMI, 30.7%) > dietary intake (6.0%). Both sector and dietitian accessibility was not associated with achieving albumin ≥40 g/L. However, NGO centers were 36% more likely (p = 0.030) to achieve pre-dialysis serum creatinine ≥884 μmol/L compared to government centers, whilst centers with dedicated dietitian service were 29% less likely (p = 0.017) to achieve pre-dialysis serum creatinine ≥884 μmol/L. In terms of BMI, private centers were 32% more likely (p = 0.022) to achieve BMI ≥ 25.0 kg/m2 compared to government centers. Private centers were 62% less likely (p
METHOD: Secondary data from a cross-sectional survey was utilized. HRQOL was assessed for 379 HD patients using the generic Short Form 36 (SF-36) and disease-specific Kidney-Disease Quality of Life-36 (KDQOL-36). Malnutrition was indicated by malnutrition inflammation score (MIS) ≥ 5, and presence of protein-energy wasting (PEW). The individual nutritional parameters included the domains of physical status, serum biomarkers, and dietary intake. Multivariate associations were assessed using the general linear model.
RESULTS: MIS ≥ 5 was negatively associated with SF-36 scores of physical functioning (MIS
METHODS: Using 3 d of dietary records, FA intakes of 333 recruited patients were calculated using a food database built from laboratory analyses of commonly consumed Malaysian foods. Plasma triacylglycerol (TG) and erythrocyte FAs were determined by gas chromatography.
RESULTS: High dietary saturated fatty acid (SFA) and monounsaturated fatty acid (MUFA) consumption trends were observed. Patients on HD also reported low dietary ω-3 and ω-6 polyunsaturated fatty acid (PUFA) consumptions and low levels of TG and erythrocyte FAs. TG and dietary FAs were significantly associated respective to total PUFA, total ω-6 PUFA, 18:2 ω-6, total ω-3 PUFA, 18:3 ω-3, 22:6 ω-3, and trans 18:2 isomers (P < 0.05). Contrarily, only dietary total ω-3 PUFA and 22:6 ω-3 were significantly associated with erythrocyte FAs (P < 0.01). The highest tertile of fish and shellfish consumption reflected a significantly higher proportion of TG 22:6 ω-3. Dietary SFAs were directly associated with TG and erythrocyte MUFA, whereas dietary PUFAs were not.
CONCLUSION: TG and erythrocyte FAs serve as biomarkers of dietary PUFA intake in patients on HD. Elevation of circulating MUFA may be attributed to inadequate intake of PUFAs.
METHODS: Twenty-six patients on HD underwent US and CT scans on the same day, postdialysis session. QMT for rectus femoris (RF) and vastus intermedius (VI) muscles was taken at the midpoint (MID) and two-thirds (2/3) of both thighs and CSA of the RF muscle (RFCSA ), respectively. Correlation between US and CT measurements was determined by intraclass correlation coefficient (ICC) and Bland-Altman plot.
RESULTS: ICC (95% CI) computed between US and CT was 0.94 (0.87-0.97), 0.97 (0.93-0.99), 0.94 (0.87-0.97), 0.94 (0.86-0.97), and 0.92 (0.83-0.97) for RFMID , VIMID, RF2/3, VI2/3 , and RFCSA , respectively (all P < 0.001). Bland-Altman analysis indicated no bias in agreement between both methods.
CONCLUSION: The US imaging offers a valid and quick bedside assessment approach to assess muscle wasting in HD patients.