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Background Estimating glomerular filtration rate (GFR) in acute kidney injury (AKI) is challenging, with limited data comparing estimated and gold standard methods to assess GFR. The objective of our study was to assess the performance of the kinetic estimated GFR (KeGFR) and Jelliffe equations to estimate GFR in AKI, using a radioisotopic method (technetium-diethylenetriaminepentaacetic acid) as a reference measure. Methods We conducted a prospective multicenter observational study in hospitalized patients with AKI. We computed the Jelliffe and KeGFR equations to estimate GFR and compared these estimations to measured GFR (mGFR) by a radioisotopic method. The performances were assessed by correlation, Bland–Altman plots and smoothed and linear regressions. We conducted stratified analyses by age and chronic kidney disease (CKD). Results The study included 119 patients with AKI, mostly from the intensive care unit (63%) and with Stage 1 AKI (71%). The eGFR obtained from the Jelliffe and KeGFR equations showed a good correlation with mGFR (r = 0.73 and 0.68, respectively). The median eGFR by the Jelliffe and KeGFR equations was less than the median mGFR, indicating that these equations underestimated the mGFR. On Bland–Altman plots, the Jelliffe and KeGFR equations displayed a considerable lack of agreement with mGFR, with limits of agreement >40 mL/min/1.73 m2. Both equations performed better in CKD and the KeGFR performed better in older patients. Results were similar across AKI stages. Conclusions In our study, the Jelliffe and KeGFR equations had good correlations with mGFR; however, they had wide limits of agreement. Further studies are needed to optimize the prediction of mGFR with estimatation equations.

Background Acute kidney injury (AKI) is common in critically ill patients and is associated with high morbidity and mortality. Early identification of high-risk patients provides an opportunity to develop strategies for prevention, early diagnosis and treatment of AKI. Methods We undertook this multicenter prospective cohort study to develop and validate a risk score for predicting AKI in patients admitted to an intensive care unit (ICU). Patients were screened for predictor variables within 48 h of ICU admission. Baseline and acute risk factors were recorded at the time of screening and serum creatinine was measured daily for up to 7 days. A risk score model for AKI was developed with multivariate regression analysis combining baseline and acute risk factors in the development cohort (573 patients) and the model was further evaluated on a test cohort (144 patients). Validation was performed on an independent prospective cohort of 1300 patients. The discriminative ability of the risk model was assessed by the area under the receiver operating characteristic curve (AUROC) and model calibration was evaluated by Hosmer-Lemeshow statistic. AKI was defined by the Kidney Disease: Improving Global Outcomes criteria (absolute change of 0.3 mg/dL or relative change of 50% from baseline serum creatinine in 48 h to 7 days, respectively). Results AKI developed in 754 (37.2%) patients. In the multivariate model, chronic kidney disease, chronic liver disease, congestive heart failure, hypertension, atherosclerotic coronary vascular disease, pH ≤ 7.30, nephrotoxin exposure, sepsis, mechanical ventilation and anemia were identified as independent predictors of AKI and the AUROC for the model in the test cohort was 0.79 [95% confidence interval (CI) 0.70-0.89]. On the external validation cohort, the AUROC value was 0.81 (95% CI 0.78-0.83). The risk model demonstrated good calibration in both cohorts. Positive and negative predictive values for the optimal cutoff value of ≥ 5 points in test and validation cohorts were 22.7 and 96.1% and 31.8 and 95.4%, respectively. Conclusions A risk score model integrating chronic comorbidities and acute events at ICU admission can identify patients at high risk to develop AKI. This risk assessment tool could help clinicians to stratify patients for primary prevention, surveillance and early therapeutic intervention to improve care and outcomes of ICU patients.

Background. In critically ill patients with acute kidney injury, estimates of kidney function are used to modify drug dosing, adjust nutritional therapy and provide dialytic support. However, estimating glomerular filtration rate is challenging due to fluctuations in kidney function, creatinine production and fluid balance. We hypothesized that commonly used glomerular filtration rate prediction equations overestimate kidney function in patients with acute kidney injury and that improved estimates could be obtained by methods incorporating changes in creatinine generation and fluid balance. Methods. We analysed data from a multicentre observational study of acute kidney injury in critically ill patients. We identified 12 non-dialysed, non-oliguric patients with consecutive increases in creatinine for at least 3 and up to 7 days who had measurements of urinary creatinine clearance. Glomerular filtration rate was estimated by Cockcroft–Gault, Modification of Diet in Renal Disease, Jelliffe equation and Jelliffe equation with creatinine adjusted for fluid balance (Modified Jelliffe) and compared to measured urinary creatinine clearance. Results. Glomerular filtration rate estimated by Jelliffe and Modification of Diet in Renal Disease equation correlated best with urinary creatinine clearances. Estimated glomerular filtration rate by Cockcroft–Gault, Modification of Diet in Renal Disease and Jelliffe overestimated urinary creatinine clearance was 80%, 33%, 10%, respectively, and Modified Jelliffe underestimated GFR by 2%. Conclusion. In patients with acute kidney injury, glomerular filtration rate estimating equations can be improved by incorporating data on creatinine generation and fluid balance. A better assessment of glomerular filtration rate in acute kidney injury could improve evaluation and management and guide interventions.