Aim Acute kidney injury (AKI) after cardiac surgery increases morbidity and mortality. Different definitions for AKI have been used such as Acute Kidney Injury Network (AKIN), Kidney Disease: Improving Global Outcomes (KDIGO) or risk, injury, failure, loss, end‐stage kidney disease (RIFLE). The aim of this study is to determine the best definition of AKI after cardiac surgery with the largest impact on the outcome. Methods This retrospective study of cardiac surgery patients compared the incidence and effect on outcome (90‐day and 1‐year mortality) of different definitions of AKI: RIFLE, AKIN and KDIGO. Additionally, we defined transient AKI (increase in serum creatinine that resolved in <72 hours), sustained (increase in serum creatinine within 48 hours that remained for >72 hours), and late (increase in serum creatinine after 48 hours). Results Of the included 1551 patients, 410 patients developed AKI defined by AKIN criteria, 449 defined by KDIGO and 217 defined by RIFLE‐Risk criteria. Hundred and nine patients developed transient AKI (6.9%), 183 patients had sustained AKI (11.6%), and 106 patients had late AKI (6.7%). The best definition with the highest positive likelihood ratio was RIFLE‐Risk (positive likelihood ratio = 2.32) followed by “sustained AKI” (positive likelihood ratio = 2.27). AKI defined by AKIN criteria missed all 80 patients with late AKI and 39 patients with KDIGO AKI. Conclusion Risk, injury, failure, loss, end‐stage kidney disease‐risk was the best definition of AKI as determined by the ability to predict short‐term mortality. A substantial number of patients developed AKI only after 48 hours, and these were missed when using AKIN criteria. AKIN criteria are not sensitive enough to capture all episodes of AKI in this population.

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Background. COVID-19 may result in multiorgan failure and death. Early detection of patients at risk may allow triage and more intense monitoring. The aim of this study was to develop a simple, objective admission score, based on laboratory tests, that identifies patients who are likely going to deteriorate. Methods. This is a retrospective cohort study of all COVID-19 patients admitted to a tertiary academic medical center in New York City during the COVID-19 crisis in spring 2020. The primary combined endpoint included intubation, stage 3 acute kidney injury (AKI), or death. Laboratory tests available on admission in at least 70% of patients (and age) were included for univariate analysis. Tests that were statistically or clinically significant were then included in a multivariate binary logistic regression model using stepwise exclusion. 70% of all patients were used to train the model, and 30% were used as an internal validation cohort. The aim of this study was to develop and validate a model for COVID-19 severity based on biomarkers. Results. Out of 2545 patients, 833 (32.7%) experienced the primary endpoint. 53 laboratory tests were analyzed, and of these, 47 tests (and age) were significantly different between patients with and without the endpoint. The final multivariate model included age, albumin, creatinine, C-reactive protein, and lactate dehydrogenase. The area under the ROC curve was 0.850 (CI [95%]: 0.813, 0.889), with a sensitivity of 0.800 and specificity of 0.761. The probability of experiencing the primary endpoint can be calculated as p = e − 2.4475 + 0.02492 age − 0.6503 albumin + 0.81926 creat + 0.00388 CRP + 0.00143 LDH / 1 + e − 2.4475 +   0.02492 age − 0.6503 albumin + 0.81926 creat + 0.00388 CRP + 0.00143 LDH . Conclusions. Our study demonstrated that poor outcome in COVID-19 patients can be predicted with good sensitivity and specificity using a few laboratory tests. This is useful for identifying patients at risk during admission.

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