Predicting six-month mortality for patients who are on maintenance hemodialysis

Abstract

Background and objectives: Prognostic information is rarely conveyed by nephrologists because of clinical uncertainty about accuracy. The objective of this study was to develop an integrated prognostic model of 6-mo survival for patients who receive hemodialysis (HD).

Design, setting, participants, & measurements: A short-term prognostic model was developed using prospective data from a derivation cohort of 512 patients who were receiving HD at five dialysis clinics. Patient charts were reviewed for actuarial predictors (e.g., Charlson Comorbidity), and nephrologists answered the "surprise" question (SQ), "Would I be surprised if this patient died within the next 6 mo?" Survival was monitored for up to 24 mo. The prognostic model was tested with a validation cohort of 514 patients from eight clinics.

Results: In a Cox multivariate analysis of the derivation cohort, five variables were independently associated with early mortality: Older age (hazard ratio [HR] for a 10-yr increase 1.36; 95% confidence interval [CI] 1.17 to 1.57), dementia (HR 2.24; 95% CI 1.11 to 4.48), peripheral vascular disease (HR 1.88; 95% CI 1.24 to 2.84), decreased albumin (HR for a 1-U increase 0.27; 95% CI 0.15 to 0.50), and SQ (HR 2.71; 95% CI 1.76 to 4.17). Area under the curve for the resulting prognostic model predictions of 6-mo mortality were 0.87 (95% CI 0.82 to 0.92) in the derivation cohort and 0.80 (95% CI 0.73 to 0.88) in the validation cohort.

Conclusions: An integrated 6-mo prognostic tool was developed and validated for the HD population. The instrument may be of value for researchers and clinicians to improve end-of-life care by providing more accurate prognostic information.

Figure 1.

Patient flow diagram for derivation and validation cohorts.

Figure 2.

Survival across quartiles of predicted risk. The model successfully predicted which patients had worse and better survival over time with patients in quintile 5 (q5)—the highest risk quartile—having the poorest survival and patients in q1—the highest quartile—having the best survival. The survival in q5 was significantly worse than all other quintiles pooled together (log-rank test P < 0.0001).