Algorithm-based detection of acute kidney injury according to full KDIGO criteria including urine output following cardiac surgery: a descriptive analysis

(2023) 16:12 Schmid et al. BioData Mining https://doi.org/10.1186/s13040-023-00323-3 RESEARCH BioData Mining Open Access Algorithm‑based detection of acute kidney injury according to full KDIGO criteria including urine output following cardiac surgery: a descriptive analysis Nico Schmid1, Mihnea Ghinescu2, Moritz Schanz3*, Micha Christ1, Severin Schricker3, Markus Ketteler3, Mark Dominik Alscher4, Ulrich Franke2 and Nora Goebel2 *Correspondence: 1 Department of Medical Informatics, Robert Bosch Society for Medical Research, Stuttgart, Germany 2 Department of Cardiovascular Surgery, Robert Bosch Hospital, Stuttgart, Germany 3 Division of General Internal Medicine and Nephrology, Department of Internal Medicine, Robert Bosch Hospital, Stuttgart, Germany 4 Executive Chief Physician of Robert Bosch Hospital and director of Robert Bosch Society for Medical Research, Stuttgart, Germany Abstract Background: Automated data analysis and processing has the potential to assist, improve and guide decision making in medical practice. However, by now it has not yet been fully integrated in a clinical setting. Herein we present the first results of applying algorithm-based detection to the diagnosis of postoperative acute kidney injury (AKI) comprising patient data from a cardiac surgical intensive care unit (ICU). Methods: First, we generated a well-defined study population of cardiac surgical ICU patients by implementing an application programming interface (API) 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. Secondly, we developed a software functionality to detect the incidence of AKI according to 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: The 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 early detection of AKI with progressive deterioration of renal function in the majority of patients, therefore allowing for potential earlier therapeutic intervention for preventing or lessening disease progression, reducing the length of ICU stay, and ultimately improving overall patient outcomes. © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publi cdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Schmid et al. BioData Mining (2023) 16:12 Keywords: Acute kidney injury, Cardiac surgery, Intensive care, Automated detection, Big data analysis, Algorithm-based detection Graphical Abstract Background The application of tools from modern medical informatics to existing data sets from routine care is an emerging field in medicine. Promises in this area range from the improvement in diagnosing conditions with subtle clinical representation to an accurately description of patient populations. Basis for such improvements is the automated extraction and accurate analysis of this data in a privacy-compliant manner [1]. Furthermore, automated data extraction from an existing database can serve as a solid basis for training machine learning algorithms. These have proven to be an exceedingly useful tool in the clinical setting over the past years with regards to early diagnosis, recognizing developing complications and ultimately improving patient outcomes especially in an intensive care setting [2]. Diagnosis itself is partially subjective and often based on subjective reasoning, therefore directly dependent on the physician and individual experience. This integrated process will most likely never become fully automated, at least not in the near future. On the other hand automated data processing can assist the clinician in arranging, and highlighting the relevant information in a timely manner. Current efforts are geared toward reducing the physicians’ workload and minimizing human error [3]. The current project relates to the implementation of a software to automatically detect AKI according to full KDIGO-criteria including urine output in a postoperative ICU-setting following cardiac surgery. It’s known that AKI is associated with a high mortality rate up to 60% on ICU and up to 1 year after discharge, making early detection and prevention crucial [4–7]. In the last 50 years, the mortality of ICU patients on kidney replacing therapy (KRT) has unfortunately not significantly improved and remains very high [8]. Although the incidence of Page 2 of 15 Schmid et al. BioData Mining (2023) 16:12 Page 3 of 15 AKI is high and associated with worse outcomes, it is often underestimated and frequently under-reported [9]. In addition, reported AKI rates show wide variations as the diagnosis is often based on serum creatinine values alone as is often seen in studies of automated AKI detection [10]. However, not considering urine output in the diagnosis of AKI can significantly underestimate incidence and mortality [11]. Previous studies state that automated AKI detection and prediction can outperform human predictive performances [3]. In this study, we performed a retrospective descriptive analysis o (...truncated)