Acute kidney injury detection using refined and physiological-feature augmented urine output

Acute kidney injury (AKI) is common in the intensive care unit, where it is associated with increased mortality. AKI is often defined using creatinine and urine output criteria. The creatinine-based definition is more reliable but less expedient, whereas the urine output based definition is rapid but less reliable. Our goal is to examine the urine output criterion and augment it with physiological features for better agreement with creatinine-based definitions of AKI. The objectives are threefold: (1) to characterize the baseline agreement of urine output and creatinine definitions of AKI; (2) to refine the urine output criteria to identify the thresholds that best agree with the creatinine-based definition; and (3) to build generalized estimating equation (GEE) and generalized linear mixed-effects (GLME) models with static and time-varying features to improve the accuracy of a near-real-time marker for AKI. We performed a retrospective observational study using data from two independent critical care databases, MIMIC-III and eICU, for critically ill patients who developed AKI in intensive care units. We found that the conventional urine output criterion (6 hr, 0.5 ml/kg/h) has specificity and sensitivity of 0.49 and 0.54 for MIMIC-III database; and specificity and sensitivity of 0.38 and 0.56 for eICU. Secondly, urine output thresholds of 12 hours and 0.6 ml/kg/h have specificity and sensitivity of 0.58 and 0.48 for MIMIC-III; and urine output thresholds of 10 hours and 0.6 ml/kg/h have specificity and sensitivity of 0.49 and 0.48 for eICU. Thirdly, the GEE model of four hours duration augmented with static and time-varying features can achieve a specificity and sensitivity of 0.66 and 0.61 for MIMIC-III; and specificity and sensitivity of 0.66 and 0.64 for eICU. The GLME model of four hours duration augmented with static and time-varying features can achieve a specificity and sensitivity of 0.71 and 0.55 for MIMIC-III; and specificity and sensitivity of 0.66 and 0.60 for eICU. The GEE model has greater performance than the GLME model, however, the GLME model is more reflective of the variables as fixed effects or random effects. The significant improvement in performance, relative to current definitions, when augmenting with patient features, suggest the need of incorporating these features when detecting disease onset and modeling at window-level rather than patient-level.

Urine microscopy as a biomarker of Acute Kidney Injury following cardiac surgery with cardiopulmonary bypass

Introduction: Acute kidney injury (AKI) occurs in about 22% of the patients undergoing cardiac surgery and 2.3% requires renal replacement therapy (RRT). The current diagnostic criteria for AKI by increased serum creatinine levels have limitations and new biomarkers are being tested. Urine sediment may be considered a biomarker and it can help to differentiate pre-renal (functional) from renal (intrinsic) AKI. Aims: To investigate the microscopic urinalysis in the AKI diagnosis in patients undergoing cardiac surgery with cardiopulmonary bypass. Methods: One hundred and fourteen patients, mean age 62.3 years, 67.5 % male, with creatinine 0.91 mg/dL (SD 0.22) had a urine sample examined in the first 24 h after the surgery. We looked for renal tubular epithelial cells (RTEC) and granular casts (GC) and associated the results with AKI development as defined by KDIGO criteria. Results: Twenty three patients (20.17 %) developed AKI according to the serum creatinine criterion and 76 (66.67 %) by the urine output criterion. Four patients required RRT. Mortality was 3.51 %. The use of urine creatinine criterion to predict AKI showed a sensitivity of 34.78 % and specificity of 86.81 %, positive likelihood ratio of 2.64 and negative likelihood ratio of 0.75, AUC-ROC of 0.584 (95%CI: 0.445-0.723). For the urine output criterion sensitivity was 23.68 % and specificity 92.11 %, AUC-ROC was 0.573 (95%CI: 0.465-0.680). Conclusion: RTEC and GC in urine sample detected by microscopy is a highly specific biomarker for early AKI diagnosis after cardiac surgery.

Acute kidney injury following resection of hepatocellular carcinoma: prognostic value of the acute kidney injury network criteria

Background: Acute kidney injury (AKI) is associated with increased morbidity and mortality after liver resection. Patients with hepatocellular carcinoma (HCC) have a higher risk of AKI owing to the underlying association between hepatic and renal dysfunction. Use of the Acute Kidney Injury Network (AKIN) diagnostic criteria is recommended for patients with cirrhosis, but remains poorly studied following liver resection. We compared the prognostic value of the AKIN creatinine and urine output criteria in terms of postoperative outcomes following liver resection for HCC. Methods: All patients who underwent a liver resection for HCC from January 2010 to June 2016 were included. We used AKIN urine output and creatinine criteria to assess for AKI within 48 hours of surgery. Results: Eighty liver resections were performed during the study period. Cirrhosis was confirmed in 80%. Median hospital stay was 9 (interquartile range 7–12) days, and 30-day mortality was 2.5%. The incidence of AKI was higher based on the urine output than on the creatinine criterion (53.8% v. 20%), and was associated with prolonged hospitalization and 30-day postoperative mortality when defined by serum creatinine (hospital stay: 11.2 v. 20.1 d, p = 0.01; mortality: 12.5% v. 0%, p < 0.01), but not urine output (hospital stay: 15.6 v. 10 d, p = 0.05; mortality: 2.3% v. 2.7%, p > 0.99). Conclusion: The urine output criterion resulted in an overestimation of AKI and compromised the prognostic value of AKIN criteria. Revision may be required to account for the exacerbated physiologic postoperative reduction in urine output in patients with HCC.