Effect of a Machine Learning Algorithm to Guide Goal-Directed Therapy After Cardiac Surgery - Amanda Rea; Alexandra Deasel; Clifford Edwin Fonner; Rawn Salenger (American Journal of Critical Care, 2026)
Background Goal-directed therapy allows clinicians to optimize perfusion and volume status in patients postoperatively. Objective To evaluate the effect of a machine learning algorithm to guide postoperative goal-directed fluid therapy in cardiac surgery patients. Methods A goal-directed fluid therapy program was implemented in a single center for coronary artery bypass patients with ejection fraction greater than or equal to 45% (implementation period: May 15, 2023, to May 31, 2024). Patient outcomes were compared with outcomes in matched historical control patients (control period: January 3 to October 31, 2022). The primary outcome was acute kidney injury. Results A total of 479 eligible patients were evaluated (246 in the control group and 233 in the goal-directed therapy group). The incidence of acute kidney injury on postoperative day 2 (P = .01), on postoperative day 7(P = .02), and at discharge (P = .008) was lower in the goaldirected therapy group than in the control group. Conclusions Patients in the goal-directed therapy program had a lower incidence of acute kidney injury compared with historical control patients. Incorporating a machine learning algorithm to guide goal-directed fluid therapy was a safe and less invasive way to monitor selected patients in the intensive care unit after cardiac surgery.