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Acute Kidney Injury Classification for Critically Ill Cirrhotic Patients: A Comparison of the KDIGO, AKIN, and RIFLE Classifications.

ABSTRACT: Critically ill cirrhotic patients have high mortality rates, particularly when they present with acute kidney injury (AKI) on admission. The Kidney Disease: Improving Global Outcomes (KDIGO) group aimed to standardize the definition of AKI and recently published a new AKI classification. However, the efficacy of the KDIGO classification for predicting outcomes of critically ill cirrhotic patients is unclear. We prospectively enrolled 242 cirrhotic patients from a 10-bed specialized hepatogastroenterology intensive care unit (ICU) in a 2000-bed tertiary-care referral hospital. Demographic parameters and clinical variables on day 1 of admission were prospectively recorded. The overall in-hospital mortality rate was 62.8%. Liver diseases were usually attributed to hepatitis B viral infection (26.9%). The major cause of ICU admission was upper gastrointestinal bleeding (38.0%). Our result showed that the KDIGO classification had better discriminatory power than RIFLE and AKIN criteria in predicting in-hospital mortality. Cumulative survival rates at the 6-month after hospital discharge differed significantly between patients with and without AKI on ICU admission day. In summary, we identified that the outcome prediction performance of KDIGO classification is superior to that of AKIN or RIFLE classification in critically ill cirrhotic patients.

SUBMITTER: Pan HC

PROVIDER: S-EPMC4794801 | biostudies-other | 2016 Mar

REPOSITORIES: biostudies-other

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Acute Kidney Injury Classification for Critically Ill Cirrhotic Patients: A Comparison of the KDIGO, AKIN, and RIFLE Classifications.

Pan Heng-Chih HC Chien Yu-Shan YS Jenq Chang-Chyi CC Tsai Ming-Hung MH Fan Pei-Chun PC Chang Chih-Hsiang CH Chang Ming-Yang MY Tian Ya-Chung YC Fang Ji-Tseng JT Yang Chih-Wei CW Chen Yung-Chang YC

Scientific reports 20160317

Critically ill cirrhotic patients have high mortality rates, particularly when they present with acute kidney injury (AKI) on admission. The Kidney Disease: Improving Global Outcomes (KDIGO) group aimed to standardize the definition of AKI and recently published a new AKI classification. However, the efficacy of the KDIGO classification for predicting outcomes of critically ill cirrhotic patients is unclear. We prospectively enrolled 242 cirrhotic patients from a 10-bed specialized hepatogastroe ...[more]

PMID: 26983372

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Project description:INTRODUCTION: Recently, the Kidney Disease: Improving Global Outcomes (KDIGO) proposed a new definition and classification of acute kidney injury (AKI) on the basis of the RIFLE (Risk, Injury, Failure, Loss of kidney function, and End-stage renal failure) and AKIN (Acute Kidney Injury Network) criteria, but comparisons of the three criteria in critically ill patients are rare. METHODS: We prospectively analyzed a clinical database of 3,107 adult patients who were consecutively admitted to one of 30 intensive care units of 28 tertiary hospitals in Beijing from 1 March to 31 August 2012. AKI was defined by the RIFLE, AKIN, and KDIGO criteria. Receiver operating curves were used to compare the predictive ability for mortality, and logistic regression analysis was used for the calculation of odds ratios and 95% confidence intervals. RESULTS: The rates of incidence of AKI using the RIFLE, AKIN, and KDIGO criteria were 46.9%, 38.4%, and 51%, respectively. KDIGO identified more patients than did RIFLE (51% versus 46.9%, P?=?0.001) and AKIN (51% versus 38.4%, P <0.001). Compared with patients without AKI, in-hospital mortality was significantly higher for those diagnosed as AKI by using the RIFLE (27.8% versus 7%, P <0.001), AKIN (32.2% versus 7.1%, P <0.001), and KDIGO (27.4% versus 5.6%, P <0.001) criteria, respectively. There was no difference in AKI-related mortality between RIFLE and KDIGO (27.8% versus 27.4%, P?=?0.815), but there was significant difference between AKIN and KDIGO (32.2% versus 27.4%, P?=?0.006). The areas under the receiver operator characteristic curve for in-hospital mortality were 0.738 (P <0.001) for RIFLE, 0.746 (P <0.001) for AKIN, and 0.757 (P <0.001) for KDIGO. KDIGO was more predictive than RIFLE for in-hospital mortality (P <0.001), but there was no difference between KDIGO and AKIN (P?=?0.12). CONCLUSIONS: A higher incidence of AKI was diagnosed according to KDIGO criteria. Patients diagnosed as AKI had a significantly higher in-hospital mortality than non-AKI patients, no matter which criteria were used. Compared with the RIFLE criteria, KDIGO was more predictive for in-hospital mortality, but there was no significant difference between AKIN and KDIGO.

Project description: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. TRIAL REGISTRATION:TCTR20190408004, registered on April 4, 2019.

Dataset Information

Acute Kidney Injury Classification for Critically Ill Cirrhotic Patients: A Comparison of the KDIGO, AKIN, and RIFLE Classifications.

Publications

Acute Kidney Injury Classification for Critically Ill Cirrhotic Patients: A Comparison of the KDIGO, AKIN, and RIFLE Classifications.

Similar Datasets

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