Cardiac surgery-associated acute kidney injury: risk factors analysis and comparison of prediction models.

Cardiac surgery-associated acute kidney injury (AKI) is a well-known factor influencing patients' long-term morbidity and mortality. Several prediction models of AKI requiring dialysis (AKI-D) have been developed. Only a few direct comparisons of these models have been done. Recently, a new, more uniform and objective definition of AKI has been proposed [Kidney Disease: Improve Global Outcomes (KDIGO)-AKI]. The performance of these prediction models has not yet been tested. Preoperative demographic and clinical characteristics of 1056 consecutive adult patients undergoing cardiac surgery were collected retrospectively for the period 2012-2014. Multivariable logistic regression analysis was used to determine the independent predictors of AKI-D and the KDIGO-AKI stages. Risk scores of five prediction models were calculated using corresponding subgroups of patients. The discrimination of these models was calculated by the c-statistics (area under curve, AUC) and the calibration was evaluated for the model with the highest AUC by calibration plots. The incidence of AKI-D was 3.5% and for KDIGO-AKI 23% (17.3% for Stage 1, 2.1% for Stage 2 and 3.6% for Stage 3). Older age, atrial fibrillation, NYHA class III or IV heart failure, previous cardiac surgery, higher preoperative serum creatinine and endocarditis were independently associated with the development of AKI-D. For KDIGO-AKI, higher body mass index, older age, female gender, chronic obstructive pulmonary disease, previous cardiac surgery, atrial fibrillation, NYHA class III or IV heart failure, higher preoperative serum creatinine and the use of cardiopulmonary bypass were independent predictors. The model by Thakar et al. showed the best performance in the prediction of AKI-D (AUC 0.837; 95% CI = 0.810-0.862) and also in the prediction of KDIGO-AKI stage 1 and higher (AUC = 0.731; 95% CI = 0.639-0.761), KDIGO-AKI stage 2 and higher (AUC = 0.811; 95% CI = 0.783-0.838) and for KDIGO-AKI stage 3 (AUC = 0.842; 95% CI = 0.816-0.867). The performance of known prediction models for AKI-D was found reasonably well in the prediction of KDIGO-AKI, with the model by Thakar having the highest predictive value in the discrimination of patients with risk for all KDIGO-AKI stages.

Kristovic D ，Horvatic I ，Husedzinovic I ，Sutlic Z ，Rudez I ，Baric D ，Unic D ，Blazekovic R ，Crnogorac M ... -  《-》

Preoperative plasma growth-differentiation factor-15 for prediction of acute kidney injury in patients undergoing cardiac surgery.

Heringlake M ，Charitos EI ，Erber K ，Berggreen AE ，Heinze H ，Paarmann H ... -  《CRITICAL CARE》

A comparison of the systems for the identification of postoperative acute kidney injury in pediatric cardiac patients.

The pediatric-modified Risk, Injury, Failure and Loss, and End-Stage (pRIFLE) criteria and a different but conceptually similar system termed Acute Kidney Injury Network (AKIN) were created to standardize the definition of acute kidney injury (AKI) in children. Kidney Disease: Improving Global Outcomes (KDIGO) currently recommends a combination of AKIN and pRIFLE in AKI. This study aimed to compare the three classifications for predicting AKI in pediatric patients undergoing cardiac operations. We analyzed the prospectively collected data of 1,489 consecutive pediatric patients undergoing cardiac operations between January 2004 and December 2008. AKI presence and severity was assessed for each classification using the change in serum creatinine and estimated creatinine clearance levels calculated by the Schwartz equation. AKI was present in 285 (20%), 481 (34%), and 409 (29%) patients according to the AKIN, pRIFLE, and KDIGO systems, respectively. The KDIGO classification categorized 121 patients (8%) who were placed in the AKIN 0 category, whereas the pRIFLE system categorized 74 (5%) in KDIGO 0 and 200 (14%) in AKIN 0 stages as having an AKI. The overall mortality rate was 3.9%. The KDIGO stage III (odds ratio [OR], 18.8; 95% confidence interval [CI], 9.6 to 36.6, p < 0.001), the AKIN stage III (OR, 38.3; 95% CI, 20.6 to 70.9, p < 0.001), and pRIFLE failure group (OR, 13.6, 95% CI, 7 to 26.3; p < 0.001) were associated with increased mortality. The pRIFLE system was the most sensitive test in detecting AKI, and this was especially so in the infant age group and also in the early identification of AKI in low-risk patients. The AKIN system was more specific and detected mostly high-risk patients across all age groups. The KDIGO classification system fell between pRIFLE and AKIN in performance. All three had increasing severity of AKI associated with mortality.

Lex DJ ，Tóth R ，Cserép Z ，Alexander SI ，Breuer T ，Sápi E ，Szatmári A ，Székely E ，Gál J ，Székely A ... -  《-》

Early serum cystatin C-enhanced risk prediction for acute kidney injury post cardiac surgery: a prospective, observational, cohort study.

Wang X ，Lin X ，Xie B ，Huang R ，Yan Y ，Liu S ，Zhu M ，Lu R ，Qian J ，Ni Z ，Xue S ，Che M ... -  《-》

Validation of renal-risk models for the prediction of non-renal replacement therapy cardiac surgery-associated acute kidney injury.

Cardiac surgery-associated acute kidney injury (AKI) is a serious complication of cardiac surgery, even when renal replacement therapy (RRT) is not required. The existing risk models for cardiac surgery associated AKI are designed to predict AKI requiring RRT (RRT-AKI). The aim of this study is to validate three risk models for the prediction of RRT-dependent and non-RRT AKI after cardiac surgery. Retrospective analysis on 7675 consecutive adult patients undergoing cardiac surgery with cardiopulmonary bypass. AKI was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) criteria for stage 1 and 2. RRT AKI and non-RRT AKI were defined according to the need for RRT. Three risk models were validated separately for RRT and non-RRT AKI: the Cleveland Risk Score, the Bedside Risk Score, and the Simplified Renal Index Scoring Scheme. Discrimination power was assessed with Receiver Operating Characteristics analysis and c-statistics. There were 502 (6.5%) non-RRT AKI events, 128 (1.7%) RRT-AKI events, and 7045 (91.8%) no-events. The three models performed well for predicting RRT-AKI (c-statistics 0.75-0.79) and poorly for predicting non-RRT AKI (c-statistics 0.54-0.59). The models had an excellent calibration for RRT-AKI but not for non-RRT AKI. Preoperative serum creatinine and estimated glomerular filtration rate were associated with RRT AKI but not with non-RRT AKI. Mortality was 12.2% in non-RRT AKI and 46.9% in RRT-AKI, significantly (P = 0.001) higher than in patients without AKI (1.3%). The existing risk models are inadequate for predicting non-RRT AKI following cardiac surgery, both in terms of discrimination and calibration.

Ranucci M ，Aloisio T ，Cazzaniga A ，Di Dedda U ，Gallazzi C ，Pistuddi V ... -  《-》