Fluid overload is common after neonatal congenital #cardiac surgery (CCS) and is frequently managed with continuous furosemide infusions requiring iterative dose titration. An interpretable prediction model could support more consistent early postoperative dosing decisions. We hypothesized that a novel, interpretable machine learning approach could accurately predict furosemide dosing decisions in neonates following CCS. We identified term neonates admitted to the Pediatric Cardiothoracic ICU at a large academic children’s hospital between 8/1/2014 and 3/1/2023 following CCS with cardiopulmonary bypass. Demographic and clinical data from the first 48 postoperative hours were used to train, validate, and test a Tropical Geometry-Based Fuzzy Neural Network Regressor (TGFNN-R) tasked with predicting furosemide infusion dose changes after CCS. The TGFNN-R was primed with clinician heuristics and provides transparent explanations behind predictions. A held-out internal validation/testing cohort was drawn from the same single-center population. Data from 506 neonates were extracted; 398 received a continuous furosemide infusion. Mean age at surgery was 6.2 (± 5.1) days; 67.3% were White. The most common surgeries were Stage I (Norwood) (25.1%) and arterial switch operation (18.6%). There were 783 furosemide dose increases and 224 dose decreases. Test set performance was R²=0.515, mean absolute error = 0.119 mg/kg/hr, and false positive rate = 0.062. In this retrospective single-center cohort of neonates following CCS, an interpretable TGFNN-R model predicted and explained furosemide dose changes with good test performance. Next steps include external validation and nonclinical studies evaluating the model within clinical decision support and closed-loop paradigms to achieve prespecified fluid balance goals.
https://link.springer.com/article/10.1007/s00246-026-04289-x
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