Developing Biomarker Panels to Predict Progression of Acute Kidney Injury After Cardiac Surgery

Introduction: Acute kidney injury (AKI) is a frequent complication of cardiac surgery, but only a fraction of cardiac surgery patients that experience postoperative AKI have progression to more severe stages. Biomarkers that can distinguish patients that will experience progression of AKI are potentially useful for clinical care and/or the development of therapies. Methods: Data come from a prospective cohort study of cardiac surgery patients; the analytic dataset contained data from 354 cardiac surgery patients meeting criteria for AKI following surgery. Candidate predictors were 38 biomarkers of kidney function, insult, or injury measured at the time of AKI diagnosis. The outcome was AKI progression, defined as worsening of AKI Network stage. We investigated combining biomarkers with Bayesian model averaging (BMA) and random forests of classification trees, with and without center transformation. For both approaches, we used resampling-based methods to avoid optimistic bias in our assessment of model performance. Results: BMA yielded a combination of 3 biomarkers and an optimism-corrected estimated area under the receiver operating characteristic curve (AUC) of 0.75 (95% confidence interval [CI]: 0.68, 0.82). The random forests approach, which nominally uses all biomarkers, had an estimated AUC of 0.74 (95% CI: 0.66, 0.82). A second application of random forests applied to biomarker values after a center-specific transformation had an estimated AUC of 0.80 (95% CI: 0.72, 0.88). Conclusion: These findings suggest that the application of advanced statistical techniques to combine biomarkers offers only modest improvements over use of single biomarkers alone. This exemplifies a common experience in biomarker research: combinations of modestly performing biomarkers often also have modest performance.