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

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.