Predictive Models for Acute Kidney Injury Following Cardiac Surgery

Demirjian, Sevag; Schold, Jesse D.; Navia, Jose; Mastracci, Tara M.; Paganini, Emil P.; Yared, Jean-Pierre; Bashour, Charles A.

doi:10.1053/j.ajkd.2011.10.046

« Previous Next »American Journal of Kidney Diseases
Volume 59, Issue 3 , Pages 382-389, March 2012

Predictive Models for Acute Kidney Injury Following Cardiac Surgery

Sevag Demirjian, MD
- Department of Nephrology and Hypertension, Cleveland Clinic, Cleveland, OH
- Address correspondence to Sevag Demirjian, MD, Department of Nephrology and Hypertension, Cleveland Clinic, 9500 Euclid Ave, Q7, Cleveland, OH 44195
Jesse D. Schold, PhD
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH
Jose Navia, MD
- Department of Cardiothoracic Surgery, Cleveland Clinic, Cleveland, OH
Tara M. Mastracci, MD
- Department of Vascular Surgery, Cleveland Clinic, Cleveland, OH
Emil P. Paganini, MD
- Department of Nephrology and Hypertension, Cleveland Clinic, Cleveland, OH
Jean-Pierre Yared, MD
- Department of Cardiothoracic Anesthesiology, Cleveland Clinic, Cleveland, OH
Charles A. Bashour, MD
- Department of Cardiothoracic Anesthesiology, Cleveland Clinic, Cleveland, OH

Received 4 May 2011; accepted 7 October 2011. published online 30 December 2011.

Background

Accurate prediction of cardiac surgery–associated acute kidney injury (AKI) would improve clinical decision making and facilitate timely diagnosis and treatment. The aim of the study was to develop predictive models for cardiac surgery–associated AKI using presurgical and combined pre- and intrasurgical variables.

Study Design

Prospective observational cohort.

Settings & Participants

25,898 patients who underwent cardiac surgery at Cleveland Clinic in 2000-2008.

Predictor

Presurgical and combined pre- and intrasurgical variables were used to develop predictive models.

Outcomes

Dialysis therapy and a composite of doubling of serum creatinine level or dialysis therapy within 2 weeks (or discharge if sooner) after cardiac surgery.

Results

Incidences of dialysis therapy and the composite of doubling of serum creatinine level or dialysis therapy were 1.7% and 4.3%, respectively. Kidney function parameters were strong independent predictors in all 4 models. Surgical complexity reflected by type and history of previous cardiac surgery were robust predictors in models based on presurgical variables. However, the inclusion of intrasurgical variables accounted for all explained variance by procedure-related information. Models predictive of dialysis therapy showed good calibration and superb discrimination; a combined (pre- and intrasurgical) model performed better than the presurgical model alone (C statistics, 0.910 and 0.875, respectively). Models predictive of the composite end point also had excellent discrimination with both presurgical and combined (pre- and intrasurgical) variables (C statistics, 0.797 and 0.825, respectively). However, the presurgical model predictive of the composite end point showed suboptimal calibration (P < 0.001).

Limitations

External validation of these predictive models in other cohorts is required before wide-scale application.

Conclusions

We developed and internally validated 4 new models that accurately predict cardiac surgery–associated AKI. These models are based on readily available clinical information and can be used for patient counseling, clinical management, risk adjustment, and enrichment of clinical trials with high-risk participants.

Index Words: Acute kidney injury , cardiac surgery , predictive models