Predicting Mortality in Incident Dialysis Patients: An Analysis of the United Kingdom Renal Registry

Abstract

Background The risk of death in dialysis patients remains high, but varies significantly among patients. No prediction tool is widely used in current clinical practice. We aimed to predict long-term mortality in incident dialysis patients with 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 and Participants Patients initiating hemodialysis or peritoneal dialysis between 2002 and 2004, who survived at least three months on dialysis treatment, were followed for three years. Analyses were restricted to subjects in whom information on comorbid conditions and laboratory measurements were available (n=5447). The dataset was divided into datasets for model development (n=3631, training) and validation (n=1816) by random selection. Predictors Basic patient characteristics, comorbidity and laboratory variables. Outcomes All cause mortality censored for kidney transplant, recovery of kidney function, and loss to follow-up. Results In the training dataset, 1078 patients (29.7%) died within the observation period. The final model of the training dataset included patient characteristics (age, race, primary kidney disease, treatment modality), comorbidities (diabetes, history of cardiovascular disease, smoking) and laboratory variables (hemoglobin, serum albumin, creatinine, calcium) and reached a C-statistic of 0.75 (95% CI, 0.73–0.77) and could accurately discriminate between patients with low (6%), intermediate (19%), high (33%) and very high (59%) mortality risk. The model was further applied to the validation dataset 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 dataset. Conclusions Basic patient characteristics, comorbidity and laboratory variables can predict three-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.

Predicting First-Year Mortality in Incident Dialysis Patients with End-Stage Renal Disease - The UREA5 Study

“…The addition of relevant biochemistry may aid mortality prediction [18]. Hypoalbuminemia has been consistently shown by our and previous studies, to be strongly associated with death in ESRD [6,18,19].…”

PD solutions: New and old

“…99 The 2010 USRDS reports that among diabetic patients, the unadjusted 1-year survival in PD populations is 0.86 compared with 0.88 in non-diabetics. 93 Diabetic patients on peritoneal dialysis with standard PD solutions are at an increased risk of obesity, hyperlipidemia, loss of UF, increased peritoneal membrane permeability, and overhydration compared with non-diabetics.…”

Dialysis Start Timing: Development and Validation of Start Scoring Scale

“…Сами авторы подчеркивают, что популяционные форму-лы, включая предсказание летальности, не следует применять к конкретному пациенту [15]. Очевидно, ч то в сравнении с предсказанием риска летальности более полезным могло бы быть выявление модифи-цируемых факторов, влияющих на твердые исходы.…”

“…Только в малом чис-ле исследований этот фактор не включен в анализ (главным образом из-за недоступности данных) [8,15,16]. В наблюдательных исследованиях на заклю-чение могут влиять две противоположные почти неизбежные предвзятости (bias).…”

“…Модель достаточно точно распределяла пациентов по подгруппам с низким (6%), средним (19%), высоким (33%), очень высоким (59%) риском трехлетней летальности [15]. Более ранние оценки предикторов летальности включали главным образом или исключительно немодифици-руемые параметры, хотя они и демонстрировали хо-рошую согласованность в группах подтверждения в отношении однолетней летальности [31,32].…”