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. 2025 Jul:193:110382.
doi: 10.1016/j.compbiomed.2025.110382. Epub 2025 May 22.

A rule-based clinical decision support system for detection of acute kidney injury after pediatric cardiac surgery

Janice Wachenbrunner  1 , Marcel Mast  2 , Julia Böhnke  3 , Nicole Rübsamen  3 , Louisa Bode  4 , André Karch  3 , Henning Rathert  1 , Alexander Horke  5 , Philipp Beerbaum  1 , Michael Marschollek  4 , Thomas Jack  6 , Martin Böhne  1
Affiliations
Free article

A rule-based clinical decision support system for detection of acute kidney injury after pediatric cardiac surgery

Janice Wachenbrunner et al. Comput Biol Med. 2025 Jul.
Free article

Abstract

Background: Acute kidney injury (AKI) is common in children with congenital heart disease following open-heart surgery with cardiopulmonary bypass (CPB). Early AKI detection in critically ill children requires clinician expertise to compile various data from different sources within a stressful and time-sensitive environment. However, as electronic health records provide data in a machine-readable format, this process could be supported by computerized systems. Therefore, we developed a time-aware, rule-based clinical decision support system (CDSS) to detect, stage, and track temporal AKI progression in children.

Methods: We integrated retrospective clinical routine data from n = 290 randomly selected cases (n = 263 patients, aged 0-17 years) who underwent cardiac surgery with CPB into a dataset. We adapted Kidney Disease: Improving Global Outcome (KDIGO) criteria, including serum creatinine, urine output, and estimated glomerular filtration rate, and translated them into computable rules for the CDSS. As a reference standard, patients were manually assessed by blinded clinical experts.

Results: The AKI incidence, according to the reference standard, was n = 146 cases for stage 1, n = 58 for stage 2, and n = 20 for stage 3. The CDSS achieved sensitivities of 92.2 % (95 % CI: 86.8-95.5 %) for AKI stage 1, 88.1 % (95 % CI: 77.2-94.2 %) for stage 2, and 95 % (95 % CI: 70.5-99.3 %) for stage 3. The specificities were 97.0 % (95 % CI: 94.4-98.4 %), 98.5 % (95 % CI: 96.5-99.4 %), and 99.3 % (95 % CI: 97.3-99.8 %), respectively.

Conclusions: We demonstrated that a CDSS is able to perform a complex AKI detection and staging process, including 11 criteria across three stages. For accurate automated AKI detection, standardized machine-readable data of high data quality are required. CDSS with high diagnostic accuracy, like presented, can support clinical management and be used for surveillance and quality management. The prototypical use for surveillance and further studies, such as the development of prediction models, should demonstrate the system's benefits in the future.

Keywords: Acute kidney injury; Clinical decision support systems; Diagnostic accuracy; KDIGO; Pediatric intensive care.

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Conflict of interest statement

Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Janice Wachenbrunner reports financial support was provided by Else Kroner-Fresenius Foundation. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.