BACKGROUND: The risk of death in dialysis patients is high, but varies significantly among patients. No prediction tool is used widely in current clinical practice. We aimed to predict long-term mortality in incident dialysis patients using easily obtainable variables. STUDY DESIGN: Prospective nationwide multicenter cohort study in the United Kingdom (UK Renal Registry); models were developed using Cox proportional hazards. SETTING & PARTICIPANTS: Patients initiating hemodialysis or peritoneal dialysis therapy in 2002-2004 who survived at least 3 months on dialysis treatment were followed up for 3 years. Analyses were restricted to participants for whom information for comorbid conditions and laboratory measurements were available (n = 5,447). The data set was divided into data sets for model development (n = 3,631; training) and validation (n = 1,816) using random selection. PREDICTORS: Basic patient characteristics, comorbid conditions, and laboratory variables. OUTCOMES: All-cause mortality censored for kidney transplant, recovery of kidney function, and loss to follow-up. RESULTS: In the training data set, 1,078 patients (29.7%) died within the observation period. The final model for the training data set included patient characteristics (age, race, primary kidney disease, and treatment modality), comorbid conditions (diabetes, history of cardiovascular disease, and smoking), and laboratory variables (hemoglobin, serum albumin, creatinine, and calcium levels); reached a C statistic of 0.75 (95% CI, 0.73-0.77); and could discriminate accurately among patients with low (6%), intermediate (19%), high (33%), and very high (59%) mortality risk. The model was applied further to the validation data set and achieved a C statistic of 0.73 (95% CI, 0.71-0.76). LIMITATIONS: Number of missing comorbidity data and lack of an external validation data set. CONCLUSIONS: Basic patient characteristics, comorbid conditions, and laboratory variables can predict 3-year mortality in incident dialysis patients with sufficient accuracy. Identification of subgroups of patients according to mortality risk can guide future research and subsequently target treatment decisions in individual patients.
BACKGROUND: The risk of death in dialysis patients is high, but varies significantly among patients. No prediction tool is used widely in current clinical practice. We aimed to predict long-term mortality in incident dialysis patients using easily obtainable variables. STUDY DESIGN: Prospective nationwide multicenter cohort study in the United Kingdom (UK Renal Registry); models were developed using Cox proportional hazards. SETTING & PARTICIPANTS: Patients initiating hemodialysis or peritoneal dialysis therapy in 2002-2004 who survived at least 3 months on dialysis treatment were followed up for 3 years. Analyses were restricted to participants for whom information for comorbid conditions and laboratory measurements were available (n = 5,447). The data set was divided into data sets for model development (n = 3,631; training) and validation (n = 1,816) using random selection. PREDICTORS: Basic patient characteristics, comorbid conditions, and laboratory variables. OUTCOMES: All-cause mortality censored for kidney transplant, recovery of kidney function, and loss to follow-up. RESULTS: In the training data set, 1,078 patients (29.7%) died within the observation period. The final model for the training data set included patient characteristics (age, race, primary kidney disease, and treatment modality), comorbid conditions (diabetes, history of cardiovascular disease, and smoking), and laboratory variables (hemoglobin, serum albumin, creatinine, and calcium levels); reached a C statistic of 0.75 (95% CI, 0.73-0.77); and could discriminate accurately among patients with low (6%), intermediate (19%), high (33%), and very high (59%) mortality risk. The model was applied further to the validation data set and achieved a C statistic of 0.73 (95% CI, 0.71-0.76). LIMITATIONS: Number of missing comorbidity data and lack of an external validation data set. CONCLUSIONS: Basic patient characteristics, comorbid conditions, and laboratory variables can predict 3-year mortality in incident dialysis patients with sufficient accuracy. Identification of subgroups of patients according to mortality risk can guide future research and subsequently target treatment decisions in individual patients.
Authors: Dana C Miskulin; Klemens B Meyer; Alice A Martin; Nancy E Fink; Josef Coresh; Neil R Powe; Michael J Klag; Andrew S Levey Journal: Am J Kidney Dis Date: 2003-01 Impact factor: 8.860
Authors: B J Barrett; P S Parfrey; J Morgan; P Barré; A Fine; M B Goldstein; S P Handa; K K Jindal; C M Kjellstrand; A Levin; H Mandin; N Muirhead; R M Richardson Journal: Am J Kidney Dis Date: 1997-02 Impact factor: 8.860
Authors: Vandana Menon; Xuelei Wang; Tom Greene; Gerald J Beck; John W Kusek; Santica M Marcovina; Andrew S Levey; Mark J Sarnak Journal: Am J Kidney Dis Date: 2003-07 Impact factor: 8.860
Authors: Dana C Miskulin; Alice A Martin; Richard Brown; Nancy E Fink; Josef Coresh; Neil R Powe; Philip G Zager; Klemens B Meyer; Andrew S Levey Journal: Nephrol Dial Transplant Date: 2004-02 Impact factor: 5.992
Authors: Garabed Eknoyan; Gerald J Beck; Alfred K Cheung; John T Daugirdas; Tom Greene; John W Kusek; Michael Allon; James Bailey; James A Delmez; Thomas A Depner; Johanna T Dwyer; Andrew S Levey; Nathan W Levin; Edgar Milford; Daniel B Ornt; Michael V Rocco; Gerald Schulman; Steve J Schwab; Brendan P Teehan; Robert Toto Journal: N Engl J Med Date: 2002-12-19 Impact factor: 91.245
Authors: Ryan T Anderson; Hailey Cleek; Atieh S Pajouhi; M Fernanda Bellolio; Ananya Mayukha; Allyson Hart; LaTonya J Hickson; Molly A Feely; Michael E Wilson; Ryan M Giddings Connolly; Patricia J Erwin; Abdul M Majzoub; Navdeep Tangri; Bjorg Thorsteinsdottir Journal: Clin J Am Soc Nephrol Date: 2019-07-30 Impact factor: 8.237
Authors: Mara A McAdams-Demarco; Andrew Law; Jacqueline M Garonzik-Wang; Luis Gimenez; Bernard G Jaar; Jeremy D Walston; Dorry L Segev Journal: J Am Geriatr Soc Date: 2012-10 Impact factor: 5.562
Authors: Mae Thamer; James S Kaufman; Yi Zhang; Qian Zhang; Dennis J Cotter; Heejung Bang Journal: Am J Kidney Dis Date: 2015-06-26 Impact factor: 8.860
Authors: Gabriele Röhrig; Maria Cristina Polidori; Katherine Rascher; Mathias Schaller; Thomas Benzing; Gero von Gersdorff Journal: Z Gerontol Geriatr Date: 2016-11-10 Impact factor: 1.281
Authors: Lilia R Lukowsky; Leeka Kheifets; Onyebuchi A Arah; Allen R Nissenson; Kamyar Kalantar-Zadeh Journal: Am J Nephrol Date: 2012-06-06 Impact factor: 3.754
Authors: Cécile G Couchoud; Jean-Baptiste R Beuscart; Jean-Claude Aldigier; Philippe J Brunet; Olivier P Moranne Journal: Kidney Int Date: 2015-09-02 Impact factor: 10.612
Authors: Joshua Lang; Rebecca Scherzer; Phyllis C Tien; Chirag R Parikh; Kathryn Anastos; Michelle M Estrella; Alison G Abraham; Anjali Sharma; Mardge H Cohen; Anthony W Butch; Marek Nowicki; Carl Grunfeld; Michael G Shlipak Journal: Am J Kidney Dis Date: 2014-07-22 Impact factor: 8.860
Authors: Joshua Lang; Ronit Katz; Joachim H Ix; Orlando M Gutierrez; Carmen A Peralta; Chirag R Parikh; Suzanne Satterfield; Snezana Petrovic; Prasad Devarajan; Michael Bennett; Linda F Fried; Steven R Cummings; Mark J Sarnak; Michael G Shlipak Journal: Nephrol Dial Transplant Date: 2018-06-01 Impact factor: 5.992