Risk factors for 3-year-mortality and a tool to screen patient in dialysis population

• Main Authors: Bujang, M. A., Kuan, P. X., Sapri, F. E., Liu, W. J., Musa, Ramli
• Format: Article
• Language: English; English; English
• Published: 2019
• Subjects: RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry; RC627 Specialties of Internal Medicine-Metabolic Diseases
• Online Access: http://irep.iium.edu.my/74625/1/89.%20Indian%20J%20Nephrol.pdf; http://irep.iium.edu.my/74625/7/74625_Risk%20Factors%20for%203-Year-Mortality%20and%20a%20Tool%20to%20Screen%20Patient%20in%20Dialysis.pdf; http://irep.iium.edu.my/74625/13/74625_Risk%20Factors%20for%203-Year-Mortality%20and%20a%20Tool%20to%20Screen_wos.pdf; http://irep.iium.edu.my/74625/; http://www.indianjnephrol.org/
• Institution: Universiti Islam Antarabangsa Malaysia

Introduction: Clinical parameters especially co‑morbidities among end stage renal disease (ESRD) patients are associated with mortality. This study aims to determine the risk factors that are associated with mortality within three years among prevalent patients with ESRD. 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. Conclusions: The models either based on logistic regression or risk scoring model can be used to screen high risk prevalent ESRD patients.