Why the study?
Does the GLUCOSE reinforcement learning algorithm improve insulin dosing accuracy compared to clinicians in patients after cardiac surgery?
Does the GLUCOSE reinforcement learning algorithm improve insulin dosing accuracy compared to clinicians in patients after cardiac surgery?
The GLUCOSE reinforcement learning algorithm outperforms clinicians in optimizing insulin dosing after cardiac surgery, demonstrating lower mean absolute error and comparable or better safety and effectiveness.
Should not yet change post-cardiac surgery insulin dosing; hypothesis-generating for reinforcement learning in critical care.
This study introduces Glucose Level Understanding and Control Optimized for Safety and Efficacy (GLUCOSE), a distributional offline reinforcement learning algorithm for optimizing insulin dosing after cardiac surgery. Trained on 5,228 patients, tested on 920, and externally validated on 649, GLUCOSE achieved a mean estimated reward of 0.0 [-0.07, 0.06] in internal testing and -0.63 [-0.74, -0.52] in external validation, outperforming clinician returns of -1.29 [-1.37, -1.20] and -1.02 [-1.16, -0.89]. In multi-phase human validation, GLUCOSE first showed a significantly lower mean absolute error (MAE) in insulin dosing, with 0.9 units MAE versus clinicians’ 1.97 units (p < 0.001) in internal testing and 1.90 versus 2.24 units (p = 0.003) in external validation. The second and third phases found GLUCOSE‘s performance as comparable to or exceeding that of senior clinicians in MAE, safety, effectiveness, and acceptability. These findings suggest GLUCOSE as a robust tool for improving postoperative glucose management.
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Desman et al. (2025) studied this question.
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