Effect of a Machine Learning Algorithm to Guide Goal-Directed Therapy After Cardiac Surgery.
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 th...
| Publicado en: | American Journal of Critical Care Vol. 35; no. 5; pp. 378 - 384 |
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| Autores principales: | , , , |
| Formato: | CEU research tables/charts Journal Article |
| Publicado: |
American Association of Critical-Care Nurses
Sep2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=196600370&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196600370 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10623264 44L jtl: American Journal of Critical Care issn: 10623264 maglogo: N pubinfo: dt: Sep2026 vid: 35 iid: 5 pid: 2559 pub: American Association of Critical-Care Nurses place: Alisa Veijo, California artinfo: ui: 196600370 196600370 196600370 10.4037/ajcc2026587 196600370 ppf: 378 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Effect of a Machine Learning Algorithm to Guide Goal-Directed Therapy After Cardiac Surgery. aug: au: Rea, Amanda Deasel, Alexandra Fonner, Clifford Edwin Salenger, Rawn affil: lead of advanced practice, clinical program manager, Division of Cardiac Surgery, University of Maryland St Joseph Medical Center, Towson, Maryland sug: subj: Machine Learning Algorithms Cardiac Surgery Fluid Therapy Surgical Patients Psychosocial Factors Postoperative Care Human Male Female Retrospective Design Prospective Studies Algorithms Unpaired T-Tests Chi Square Test Two-Tailed Test Comparative Studies Kidney Failure, Acute Incidence Patient Discharge Cost Savings Coronary Artery Bypass Education, Continuing (Credit) Male Female ab: 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. pubtype: Academic Journal doctype: CEU research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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