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Novel Prediction Score Including Pre- and Intraoperative Parameters Best Predicts Acute Kidney Injury after Liver Surgery

Slankamenac, Ksenija ; Beck-Schimmer, Beatrice ; Breitenstein, Stefan ; Puhan, Milo ; Clavien, Pierre-Alain

In: World Journal of Surgery, 2013, vol. 37, no. 11, p. 2618-2628

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    Titre
    • Novel Prediction Score Including Pre- and Intraoperative Parameters Best Predicts Acute Kidney Injury after Liver Surgery
    Auteur
    • Slankamenac, Ksenija. Department of Surgery, Swiss HPB (Hepato-Pancreato-Biliary) Center, University Hospital of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
    • Beck-Schimmer, Beatrice. Institute of Anesthesiology, University Hospital of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
    • Breitenstein, Stefan. Department of Surgery, Swiss HPB (Hepato-Pancreato-Biliary) Center, University Hospital of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
    • Puhan, Milo. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Mail Room E6153, 21205, Baltimore, MD, USA
    • Clavien, Pierre-Alain. Department of Surgery, Swiss HPB (Hepato-Pancreato-Biliary) Center, University Hospital of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
    Type de document
    • Postprint
    Langue
    • Anglais
    Publié dans
    • World Journal of Surgery, 2013, vol. 37, no. 11, p. 2618-2628. Springer US; http://www.springer-ny.com
    Autre version électronique
    Classification
    • Santé
    Identifiant OAI-PMH
    • oai:doc.rero.ch:313699
    Summary
    Background: A recently published score predicts the occurrence of acute kidney injury (AKI) after liver resection based on preoperative parameters (chronic renal failure, cardiovascular disease, diabetes, and alanine-aminotransferase levels). By inclusion of additional intraoperative parameters we aimed to develop a new prediction model. Methods: A series of 549 consecutive patients were enrolled. The preoperative score and intraoperative parameters (blood transfusion, hepaticojejunostomy, oliguria, cirrhosis, diuretics, colloids, and catecholamine) were included in a multivariate logistic regression model. We added the strongest predictors that improved prediction of AKI compared to the existing score. An internal validation by fivefold cross validation was performed, followed by a decision curve analysis to evaluate unnecessary special care unit admissions. Results: Blood transfusions, hepaticojejunostomy, and oliguria were the strongest intraoperative predictors of AKI after liver resection. The new score ranges from 0 to 64 points predicting postoperative AKI with a probability of 3.5-95%. Calibration was good in both models (15% predicted risk vs. 15% observed risk). The fivefold cross-validation indicated good accuracy of the new model (AUC 0.79 (95% CI 0.73-0.84)). Discrimination was substantially higher in the new model (AUCnew 0.81 (95% CI 0.76-0.86) versus AUCpreoperative 0.60 (95% CI 0.52-0.69), p<0.001). The new score could reduce up to 84 unnecessary special care unit admissions per 100 patients depending on the decision threshold. Conclusions: By combining three intraoperative parameters with the existing preoperative risk score, a new prediction model was developed that more accurately predicts postoperative AKI. It may reduce unnecessary admissions to the special care unit and support management of patients at higher risk