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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/58133
Title: Acute kidney injury risk prediction score for critically-ill surgical patients
Authors: Konlawij Trongtrakul
Jayanton Patumanond
Suneerat Kongsayreepong
Sunthiti Morakul
Tanyong Pipanmekaporn
Osaree Akaraborworn
Sujaree Poopipatpab
Vajira Hospital
Faculty of Medicine, Ramathibodi Hospital, Mahidol University
Thammasat University
Faculty of Medicine, Siriraj Hospital, Mahidol University
Prince of Songkla University
Chiang Mai University
Navamindradhiraj University
Keywords: Medicine
Issue Date: 3-Jun-2020
Citation: BMC Anesthesiology. Vol.20, No.1 (2020)
Abstract: © 2020 The Author(s). Background: There has been a global increase in the incidence of acute kidney injury (AKI), including among critically-ill surgical patients. AKI prediction score provides an opportunity for early detection of patients who are at risk of AKI; however, most of the AKI prediction scores were derived from cardiothoracic surgery. Therefore, we aimed to develop an AKI prediction score for major non-cardiothoracic surgery patients who were admitted to the intensive care unit (ICU). Methods: The data of critically-ill patients from non-cardiothoracic operations in the Thai Surgical Intensive Care Unit (THAI-SICU) study were used to develop an AKI prediction score. Independent prognostic factors from regression analysis were included as predictors in the model. The outcome of interest was AKI within 7 days after the ICU admission. The AKI diagnosis was made according to the Kidney Disease Improving Global Outcomes (KDIGO)-2012 serum creatinine criteria. Diagnostic function of the model was determined by area under the Receiver Operating Curve (AuROC). Risk scores were categorized into four risk probability levels: low (0-2.5), moderate (3.0-8.5), high (9.0-11.5), and very high (12.0-16.5) risk. Risk of AKI was presented as likelihood ratios of positive (LH+). Results: A total of 3474 critically-ill surgical patients were included in the model; 333 (9.6%) developed AKI. Using multivariable logistic regression analysis, older age, high Sequential Organ Failure Assessment (SOFA) non-renal score, emergency surgery, large volume of perioperative blood loss, less urine output, and sepsis were identified as independent predictors for AKI. Then AKI prediction score was created from these predictors. The summation of the score was 16.5 and had a discriminative ability for predicting AKI at AuROC = 0.839 (95% CI 0.825-0.852). LH+ for AKI were: low risk = 0.117 (0.063-0.200); moderate risk = 0.927 (0.745-1.148); high risk = 5.190 (3.881-6.910); and very high risk = 9.892 (6.230-15.695), respectively. Conclusions: The function of AKI prediction score to predict AKI among critically ill patients who underwent non-cardiothoracic surgery was good. It can aid in early recognition of critically-ill surgical patients who are at risk from ICU admission. The scores could guide decision making for aggressive strategies to prevent AKI during the perioperative period or at ICU admission.
URI: http://repository.li.mahidol.ac.th/dspace/handle/123456789/58133
metadata.dc.identifier.url: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85086001565&origin=inward
ISSN: 14712253
Appears in Collections:Scopus 2020

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