Derivation and validation of a prognostic model to predict mortality in patients with advanced chronic kidney disease

Background. Guiding patients with advanced chronic kidney disease (CKD) through advance care planning about future treatment obliges an assessment of prognosis. A patient-specific integrated model to predict mortality could inform shared decision-making for patients with CKD. Methods. Patients with Stages 4 and 5 CKD from Massachusetts (749) and West Virginia (437) were prospectively evaluated for clinical parameters, functional status [Karnofsky Performance Score (KPS)] and their provider's response to the Surprise Question (SQ). A predictive model for 12-month mortality was derived with the Massachusetts cohort and then validated externally on the West Virginia cohort. Logistic regression was used to create the model, and the c-statistic and Hosmer-Lemeshow statistic were used to assess model discrimination and calibration, respectively. Results. In the derivation cohort, the SQ, KPS and age were most predictive of 12-month mortality with odds ratios (ORs) [95% confidence interval (CI)] of 3.29 (1.87-5.78) for a 'No' response to the SQ, 2.09 (95% CI 1.19-3.66) for fair KPS and 1.41 (95% CI 1.15-1.74) per 10-year increase in age. The c-statistic for the 12-month mortality model for the derivation cohort was 0.80 (95% CI 0.75-0.84) and for the validation cohort was 0.74 (95% CI 0.66-0.83). Conclusions. Our integrated prognostic model for 12-month mortality in patients with advanced CKD had good discrimination and calibration. This model provides prognostic information to aid nephrologists in identifying and counseling advanced CKD patients with poor prognosis who are facing the decision to initiate dialysis or pursue medical management without dialysis.