Chen Zhao1, Qimei Luo, Xi Xia, Feng He, Fenfen Peng, Xueqing Yu, Fengxian Huang. 1. Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Key Laboratory of Nephrology, Ministry of Health of China, Guangdong Provincial Key Laboratory of Nephrology Guangzhou, Guangdong, China.
Abstract
BACKGROUND: Patients with continuous ambulatory peritoneal dialysis (CAPD) have high all-cause mortality risk that varies extensively among different conditions. The objective of this study was to develop and validate risk models to predict the 2-year all-cause mortality risks of CAPD patients. MATERIAL AND METHODS: A total of 1354 patients who received CAPD treatment > 3 months from a single dialysis centre were enrolled into the study from January 1, 2006 to December 31, 2011 and followed up until June 30, 2013. The dataset was randomly divided into the derivation dataset (2/3, n = 903) and the validation dataset (1/3, n = 451). Baseline information, including demographic characteristics, comorbid conditions and laboratory data, was recorded and included in the models. Risk models were developed using Cox proportional hazards regression. C-statistic, Akaike Information Criterion, Hosmer-Lemeshow χ(2) test and net reclassification improvement (NRI) were performed to evaluate model prediction and validation. RESULTS: During the entire follow-up period, 175 (19·38%) and 85 (18·85%) patients died in the derivation and validation datasets respectively. A model that included age, diabetes mellitus, hypertension, cardiovascular disease, diastolic blood pressure, serum albumin, serum creatinine, phosphate, haemoglobin and fasting blood glucose demonstrated good discrimination in the derivation and validation datasets to predict 2-year all-cause mortality (C-statistic, 0·790 and 0·759, respectively). In the validation dataset, the above model performed good calibration (χ(2) = 2·08, P = 0·98) and NRI (7·37% compared with model 2, P = 0·05). CONCLUSIONS: The risk model can accurately predict 2-year all-cause mortality in Chinese CAPD patients and external validation is needed in future.
BACKGROUND:Patients with continuous ambulatory peritoneal dialysis (CAPD) have high all-cause mortality risk that varies extensively among different conditions. The objective of this study was to develop and validate risk models to predict the 2-year all-cause mortality risks of CAPD patients. MATERIAL AND METHODS: A total of 1354 patients who received CAPD treatment > 3 months from a single dialysis centre were enrolled into the study from January 1, 2006 to December 31, 2011 and followed up until June 30, 2013. The dataset was randomly divided into the derivation dataset (2/3, n = 903) and the validation dataset (1/3, n = 451). Baseline information, including demographic characteristics, comorbid conditions and laboratory data, was recorded and included in the models. Risk models were developed using Cox proportional hazards regression. C-statistic, Akaike Information Criterion, Hosmer-Lemeshow χ(2) test and net reclassification improvement (NRI) were performed to evaluate model prediction and validation. RESULTS: During the entire follow-up period, 175 (19·38%) and 85 (18·85%) patients died in the derivation and validation datasets respectively. A model that included age, diabetes mellitus, hypertension, cardiovascular disease, diastolic blood pressure, serum albumin, serum creatinine, phosphate, haemoglobin and fasting blood glucose demonstrated good discrimination in the derivation and validation datasets to predict 2-year all-cause mortality (C-statistic, 0·790 and 0·759, respectively). In the validation dataset, the above model performed good calibration (χ(2) = 2·08, P = 0·98) and NRI (7·37% compared with model 2, P = 0·05). CONCLUSIONS: The risk model can accurately predict 2-year all-cause mortality in Chinese CAPD patients and external validation is needed in future.
Authors: Jillian Pintye; Alison L Drake; John Kinuthia; Jennifer A Unger; Daniel Matemo; Renee A Heffron; Ruanne V Barnabas; Pamela Kohler; R Scott McClelland; Grace John-Stewart Journal: Clin Infect Dis Date: 2017-03-15 Impact factor: 9.079
Authors: Chava L Ramspek; Pauline Wm Voskamp; Frans J van Ittersum; Raymond T Krediet; Friedo W Dekker; Merel van Diepen Journal: Clin Epidemiol Date: 2017-09-05 Impact factor: 4.790