A Novel Predictive Model for Hospital Survival in Patients who are Critically Ill with Dialysis-Dependent AKI: A Retrospective Single-Center Exploratory Study.

Background: Mortality of patients who are critically ill with AKI initiated on RRT is very high. Identifying modifiable and unmodifiable clinical variables at dialysis start that are associated with hospital survival can help, not only in prognostication, but also in clinical triaging. Methods: A retrospective observational study was conducted on patients with AKI-D who were initiated on RRT in the medical and surgical intensive care units (ICUs) of a high-acuity academic medical center from January 2010 through December 2015. We excluded patients with suspected poisoning, ESKD, stage 5 CKD not on dialysis, or patients with AKI-D initiated on RRT outside of the ICU setting. The primary outcome was in-hospital mortality. Results: Of the 416 patients who were critically ill with AKI-D admitted to the medical (38%), surgical (41%), and cardiac (21%) ICUs, with nearly 75% on artificial organ support, the mean age 62.1±14.8 years, mean SOFA score was 11.8±4.3, dialysis was initiated using continuous RRT in 261 (63%) and intermittent hemodialysis in 155 (37%) patients. Incidence of survival to hospital discharge was 48%. Using multivariable logistic regression with stepwise backward elimination, a prognostic model was created that included the variables age, CKD, COPD, admission, and within 24 hours of the start SOFA score, refractory hyperkalemia and uremic encephalopathy as dialysis indications, BUN >100 mg/dl, serum creatinine, serum lactate, serum albumin, CRRT as initial modality, severe volume overload, and abdominal surgery. The model exhibited good calibration (goodness of fit test, Conclusions: In this single-center, diverse, critically ill AKI-D population, a novel prognostic model that combined widely used ICU scores, clinical and biochemical data at dialysis start, and dialysis indication and modality, robustly predicted short-term survival. External validation is needed to prove the generalizability of the study findings.