Predicting survival in patients requiring renal replacement therapy after cardiac surgery.

BACKGROUND: We sought to develop and internally validate a prediction score for all-cause in-hospital mortality in patients who have acute renal failure and require renal replacement therapy after cardiac surgery.

METHODS: From January 1992 to July 2001, 136 of 14,000 patients (0.9%) had acute renal failure requiring renal replacement therapy after cardiac surgery. Multivariate logistic regression analysis, based on pre-renal replacement therapy variables, was used to construct a predictive score for all causes of in-hospital mortality. Subsequently, the score was validated in 27 patients who underwent surgery between August 2001 and March 2003.

RESULTS: In-hospital mortality was 58% (79 of 136). From the logistic regression model, we assigned a score (range, 0 to 6) based on the presence of independent predictors of operative mortality (preoperative creatinine < or = 1.5 mg/dL [odds ratio (OR) = 5.0], hypertension [OR = 4.4], predialysis coma [OR = 9.6], sepsis [OR = 6.4], and total bilirubin > or = 2 mg/dL [OR = 5.6]). Higher scores strongly predicted mortality: patients who scored 3 or higher before the initiation dialysis (n = 54), had a mortality rate of 94% (51 of 54). In contrast, patients who scored 1 or less on this scale (n = 36), had a mortality of 16% (6 of 36). In the validation cohort, the sensitivity of the new score at the cutoff of 2 or fewer points versus 3 or more points was 0.71, the specificity was 0.90, the positive predictive value was 0.92, and the negative predictive value was 0.64.

CONCLUSIONS: The prediction score represents a simple and accurate tool for predicting in-hospital mortality associated with renal replacement therapy for cardiac surgery patients before the institution of this resource-intensive treatment.