TY - JOUR
T1 - Machine learning methods for perioperative anesthetic management in cardiac surgery patients
T2 - a scoping review
AU - Rellum, Santino R.
AU - Schuurmans, Jaap
AU - van der Ven, Ward H.
AU - Eberl, Susanne
AU - Driessen, Antoine H.G.
AU - Vlaar, Alexander P.J.
AU - Veelo, Denise P.
N1 - Funding Information: Peer Review File: Available at https://dx.doi.org/10.21037/ jtd-21-765 Conflicts of Interest: The authors have completed the ICMJE uniform disclosure form (available at https://dx.doi. org/10.21037/jtd-21-765). The series “Artificial Intelligence in Thoracic Disease: from Bench to Bed” was commissioned by the editorial office without any funding or sponsorship. APJV reports having received unrestricted research grants from Edwards Lifesciences and Philips. DPV reports having received research grants from Philips as well as consultancy, lecture, and travel expenses fees from Edwards Lifesciences. The authors have no other conflicts of interest to declare. Publisher Copyright: © Journal of Thoracic Disease. All rights reserved.
PY - 2021/12
Y1 - 2021/12
N2 - Background: Machine learning (ML) is developing fast with promising prospects within medicine and already has several applications in perioperative care. We conducted a scoping review to examine the extent and potential limitations of ML implementation in perioperative anesthetic care, specifically in cardiac surgery patients. Methods: We mapped the current literature by searching three databases: MEDLINE (Ovid), EMBASE (Ovid), and Cochrane Library. Articles were eligible if they reported on perioperative ML use in the field of cardiac surgery with relevance to anesthetic practices. Data on the applicability of ML and comparability to conventional statistical methods were extracted. Results: Forty-six articles on ML relevant to the work of the anesthesiologist in cardiac surgery were identified. Three main categories emerged: (I) event and risk prediction, (II) hemodynamic monitoring, and (III) automation of echocardiography. Prediction models based on ML tend to behave similarly to conventional statistical methods. Using dynamic hemodynamic or ultrasound data in ML models, however, shifts the potential to promising results. Conclusions: ML in cardiac surgery is increasingly used in perioperative anesthetic management. The majority is used for prediction purposes similar to conventional clinical scores. Remarkable ML model performances are achieved when using real-time dynamic parameters. However, beneficial clinical outcomes of ML integration have yet to be determined. Nonetheless, the first steps introducing ML in perioperative anesthetic care for cardiac surgery have been taken.
AB - Background: Machine learning (ML) is developing fast with promising prospects within medicine and already has several applications in perioperative care. We conducted a scoping review to examine the extent and potential limitations of ML implementation in perioperative anesthetic care, specifically in cardiac surgery patients. Methods: We mapped the current literature by searching three databases: MEDLINE (Ovid), EMBASE (Ovid), and Cochrane Library. Articles were eligible if they reported on perioperative ML use in the field of cardiac surgery with relevance to anesthetic practices. Data on the applicability of ML and comparability to conventional statistical methods were extracted. Results: Forty-six articles on ML relevant to the work of the anesthesiologist in cardiac surgery were identified. Three main categories emerged: (I) event and risk prediction, (II) hemodynamic monitoring, and (III) automation of echocardiography. Prediction models based on ML tend to behave similarly to conventional statistical methods. Using dynamic hemodynamic or ultrasound data in ML models, however, shifts the potential to promising results. Conclusions: ML in cardiac surgery is increasingly used in perioperative anesthetic management. The majority is used for prediction purposes similar to conventional clinical scores. Remarkable ML model performances are achieved when using real-time dynamic parameters. However, beneficial clinical outcomes of ML integration have yet to be determined. Nonetheless, the first steps introducing ML in perioperative anesthetic care for cardiac surgery have been taken.
KW - Anesthesiology
KW - Artificial intelligence
KW - Cardiac surgery
KW - Machine learning
KW - Perioperative care
UR - http://www.scopus.com/inward/record.url?scp=85122575976&partnerID=8YFLogxK
U2 - https://doi.org/10.21037/jtd-21-765
DO - https://doi.org/10.21037/jtd-21-765
M3 - Review article
C2 - 35070381
SN - 2072-1439
VL - 13
SP - 6976
EP - 6993
JO - Journal of thoracic disease
JF - Journal of thoracic disease
IS - 12
ER -