Prediction of Mortality in the First Two Years of Hemodialysis: Results from a Validation Study

Abstract

Background: Chronic hemodialysis (HD) patients experience high rates of mortality. Alerting medical staff of patients at increased risk of death may support clinical decision making. Methods: A large cohort of incident HD patients was used to develop logistic regression models to predict death in the subsequent 6 months (‘derivation cohort’). Predictors were age, gender, race, ethnicity, vascular access type, diabetic status, pre-HD systolic and diastolic blood pressure, pre-HD weight, pre-HD temperature, relative interdialytic weight gain, serum albumin, hemoglobin, phosphorus, serum creatinine, serum sodium, urea reduction ratio, equilibrated normalized protein catabolic rate, and equilibrated dialytic and renal Kt/V. These logistic regression models were then applied to validation cohorts. Predictive performance of the models was described in terms of sensitivity, specificity, and area under receiver-operating characteristic curves (AUC-ROC). Results: A total of 6,838 incident HD patients were followed over 2 years. The derivation cohort initially comprised 4,512 patients. In the validation cohort of initially 2,326 patients, the logistic regression models were able to predict mortality in subsequent 6-month periods with a sensitivity between 58 and 69%, and a specificity of 74–77%; the respective AUC-ROC were 0.67–0.72 (all p < 0.0001). Pre-HD weight and serum albumin levels were consistently significant predictors of mortality in all models. Conclusion: The results indicate that logistic regression models are useful tools in estimating incident HD patients’ probability of death in 6-month intervals for at least up to 2 years after beginning dialysis. Model predictions may be used to alert medical staff to patients at increased risk of death and facilitate timely diagnostic and therapeutic interventions to improve outcomes.