Predicting 3‐month and 1‐year mortality for patients initiating dialysis: a population‐based cohort study Mei‐Yi Wu1,2,3,4 · Ping‐Jen Hu5,6 · Yu‐Wei Chen1,2,7 · Li‐Chin Sung8,9,10 · Tzu‐Ting Chen4,11 · Mai‐Szu Wu1,2,3,7 · Yih‐Giun Cherng12,13

Background Despite the continual improvements in dialysis treatments, mortality in end-stage kidney disease (ESKD) remains high. Many mortality prediction models are available, but most of them are not precise enough to be used in the clinical practice. We aimed to develop and validate two prediction models for 3-month and 1-year patient mortality after dialysis initiation in our population. Methods Using population-based data of insurance claims in Taiwan, we included more than 210,000 patients who initiated dialysis between January 1, 2006, and June 30, 2015. We developed two prognostic models, which included 9 and 11 variables, respectively (including age, sex, myocardial infarction, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, peptic ulcer disease, malignancy, moderate to severe liver disease, and first dialysis in intensive care unit). Results The models showed adequate discrimination (C-statistics were 0.80 and 0.82 for 3-month and 1-year mortality, respectively) and good calibration. In both our models, the first dialysis in the intensive care unit and moderate-to-severe liver disease were the strongest risk factors for mortality. Conclusion The prediction models developed in our population had good predictive ability for short-term mortality in patients initiating dialysis in Taiwan and could help in decision-making regarding dialysis initiation, at least in our setting, supporting a patient-centered approach to care. * Yih-Giun Cherng stainless@s.tmu. edu.tw 1 Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan 2 TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei, Taiwan 3 Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan 4 Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan 5 Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taitung Mackay Memorial Hospital, Taitung, Taiwan 6 Division of Gastroenterology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan 7 Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan 8 Division of Cardiology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan 9 Division of Cardiology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan 10 Department of Primary Care Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan 11 Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli County, Taiwan 12 Department of Anesthesiology, Shuang Ho Hospital, Taipei Medical University, No. 291, Zhongzheng Road, Zhonghe District, New Taipei City 235, Taiwan 13 Department of Anesthesiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan Journal of Nephrology 1 3 Graphical abstract Reference: Mei-Yi Wu, et al. Journal of Nephrology (2021) Journal of Predicng 3-month and 1-year mortality for paents iniang dialysis: A populaon-based cohort study The presented predicon models had good predicve ability for short-term mortality in paents iniang dialysis. According to readily available data, the nomogram could help in decision-making regarding dialysis iniaon and provide a paent-centered approach to care. Raonale & Objecve develop and validate two accurate predicon models for 3-month and 1-year paent mortality aer dialysis iniaon Predicon model Main findings We developed two prognosc models, which included 9 and 11 variables, respecvely (including age, sex, myocardial infarcon, peripheral vascular disease, cerebrovascular disease, demena, chronic pulmonary disease, pepc ulcer disease, malignancy, moderate to severe liver disease, and first dialysis in intensive care unit). The model showed excellent discriminaon (C-stascs were 0.80 and 0.82 for 3-month and 1-year mortality, respecvely) and good calibraon. In both our models, the first dialysis in the intensive care unit and moderate-to-severe liver disease were the strongest risk factors for mortality. Nomogram for esmang mortality within the first three months of iniang dialysis Nomogram for esmang mortality within the first one year of iniang dialysis Naonal Health Insurance