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The Pattern of Longitudinal Change in Serum Creatinine and 90-Day Mortality After Major Surgery

Korenkevych, Dmytro PhD; Ozrazgat-Baslanti, Tezcan PhD; Thottakkara, Paul M Eng; Hobson, Charles E. MD, MHA; Pardalos, Panos PhD; Momcilovic, Petar PhD; Bihorac, Azra MD, MS

doi: 10.1097/SLA.0000000000001362
ORIGINAL ARTICLES
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Objective: Calculate mortality risk that accounts for both severity and recovery of postoperative kidney dysfunction using the pattern of longitudinal change in creatinine.

Background: Although the importance of renal recovery after acute kidney injury (AKI) is increasingly recognized, the complex association that accounts for longitudinal creatinine changes and mortality is not fully described.

Methods: We used routinely collected clinical information for 46,299 adult patients undergoing major surgery to develop a multivariable probabilistic model optimized for nonlinearity of serum creatinine time series that calculates the risk function for 90-day mortality. We performed a 70/30 cross validation analysis to assess the accuracy of the model.

Results: All creatinine time series exhibited nonlinear risk function in relation to 90-day mortality and their addition to other clinical factors improved the model discrimination. For any given severity of AKI, patients with complete renal recovery, as manifested by the return of the discharge creatinine to the baseline value, experienced a significant decrease in the odds of dying within 90 days of admission compared with patients with partial recovery. Yet, for any severity of AKI, even complete renal recovery did not entirely mitigate the increased odds of dying, as patients with mild AKI and complete renal recovery still had significantly increased odds for dying compared with patients without AKI [odds ratio: 1.48 (95% confidence interval: 1.30–1.68)].

Conclusions: We demonstrate the nonlinear relationship between both severity and recovery of renal dysfunction and 90-day mortality after major surgery. We have developed an easily applicable computer algorithm that calculates this complex relationship.

*Center for Applied Optimization, Industrial and Systems Engineering, University of Florida, Gainesville, FL

Department of Anesthesiology, College of Medicine, University of Florida, Gainesville, FL

Department of Surgery, Malcom Randall VA Medical Center, Gainesville, FL

§Department of Health Services Research, Management and Policy, University of Florida, Gainesville, FL

Reprints: Azra Bihorac, MD, MS, FASN, Department of Anesthesiology, College of Medicine, University of Florida, 1600 SW Archer Rd, PO Box 100254, Gainesville, FL 32610. E-mail: abihorac@anest.ufl.edu.

Disclosure: Azra Bihorac and Tezcan Ozrazgat-Baslanti were supported by Center for Sepsis and Critical Illness Award P50 GM-111152 from the National Institute of General Medical Sciences. Azra Bihorac has received Vision Grant from the Society of Critical Care Medicine (SCCM) and research grants from Astute Medical, Inc. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or SCCM. The authors declare no conflicts of interest.

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