Algorithm-Based Detection of Acute Kidney Injury According to Full KDIGO Criteria Including Urine Output following Cardiac Surgery: A Descriptive Analysis

Background: Automated data analysis and processing has the potential to assist, improve and guide decision-making in clinical practice. However, by now it has not yet entered widely the clinical routine. Herein, we present the first results of applying algorithm-based detection to the diagnosis of postoperative acute kidney injury (AKI) comprising data from a cardiac surgical intensive care unit (ICU).

Method: First, we generated a well-defined study population of cardiac surgical ICU patients by implementing an application programming interface to extract, clean and select relevant data from the archived digital patient management system. Health records of N = 21,045 adult patients admitted to the ICU following cardiac surgery between 2012 and 2022 were analyzed. Second, we built a software functionality to detect the incidence of AKI according to the Kidney Disease: Improving Global Outcomes (KDIGO) criteria, including urine output. Incidence, severity, and temporal evolution of AKI were assessed.

Results: With the use of our automated data analyzing model, the overall incidence of postoperative AKI was 65.4% (N = 13,755). Divided by stages, AKI 2 was the most frequent maximum disease stage with 30.5% of patients (stage 1 in 17.6%, stage 3 in 17.2%). We observed considerable temporal divergence between first detections and maximum AKI stages: 51% of patients developed AKI stage 2 or 3 after a previously identified lower stage. Length of ICU stay was significantly prolonged in AKI patients (8.8 vs. 6.6 days, p < 0.001) and increased for higher AKI stages up to 10.1 days on average. In terms of AKI criteria, urine output proved to be most relevant, contributing to detection in 87.3% (N = 12,004) of cases.

Conclusion: Incidence of postoperative AKI following cardiac surgery is strikingly high with 65.4% when using full KDIGO criteria including urine output. Automated data analysis demonstrated reliable detection of AKI with progressive deterioration of renal function in the majority of patients, therefore allowing for potential earlier therapeutic intervention for the prevention or attenuation of disease progression, reduction of length of ICU stay, and finally improvement of patient outcomes.

Publikationsverlauf

Artikel online veröffentlicht:
28. Januar 2023