Effect of a Machine Learning Algorithm to Guide Goal-Directed Therapy After Cardiac Surgery.

Authors

Amanda Rea, Alexandra Deasel, Clifford Edwin Fonner, Rawn Salenger

Published in

American journal of critical care : an official publication, American Association of Critical-Care Nurses. Volume 35. Issue 5. Pages 378-383. Sep 01, 2026.

Abstract

Goal-directed therapy allows clinicians to optimize perfusion and volume status in patients postoperatively.
To evaluate the effect of a machine learning algorithm to guide postoperative goal-directed fluid therapy in cardiac surgery patients.
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.
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.
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.

PMID:
42676141
Bibliographic data and abstract were imported from PubMed on 01 Sep 2026.

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