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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 A; Clavien, Pierre-Alain (2013). Novel prediction score including pre- and intraoperative parameters best predicts acute kidney injury after liver surgery. World Journal of Surgery, 37(11):2618-2628.

Abstract

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.

Abstract

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.

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10 citations in Web of Science®
11 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Institute of Anesthesiology
04 Faculty of Medicine > University Hospital Zurich > Clinic for Visceral and Transplantation Surgery
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:August 2013
Deposited On:16 Oct 2013 15:24
Last Modified:07 Dec 2017 22:59
Publisher:Springer
ISSN:0364-2313
Publisher DOI:https://doi.org/10.1007/s00268-013-2159-6
PubMed ID:23959337

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