Multi-objective optimization challenges in perioperative anesthesia: a review

• Published in: Surgery 2020-12, Vol.170 (1), p.320-324
• Main Authors: Brennan, Meghan, Hagan, Jack D., Giordano, Chris, Loftus, Tyler J., Price, Catherine E., Aytug, Haldun, Tighe, Patrick J.
• Format: Article
• Language: English
• ISSN: 0039-6060; 1532-7361
• DOI: 10.1016/j.surg.2020.11.005

Physicians use perioperative decision-support tools to mitigate risks and maximize benefits to achieve the most successful outcome for patients. Contemporary risk-assessment practices augment surgeon’s judgement and experience with decision-support algorithms driven by big data and machine learning. These algorithms accurately assess risk for a wide range of postoperative complications by parsing large datasets and performing complex calculations that would be cumbersome for busy clinicians. Even with these advancements, large gaps in perioperative risk assessment remain; decision-support algorithms often cannot account for risk-reduction therapies applied during a patient’s perioperative course, and do not quantify tradeoffs between competing goals of care (e.g., balancing postoperative pain control with the risk of respiratory depression or balancing intraoperative volume resuscitation with risk for complications from pulmonary edema). Multi-objective optimization solutions have been applied to similar problems successfully, but have not yet been applied to perioperative decision-support. Given the large volume of data available via electronic medical records, including intraoperative data, it is now feasible to successfully apply multi-objective optimization in perioperative care. Clinical application of multi-objective optimization would require semiautomated pipelines for analytics and reporting model outputs and a careful development and validation process. Under these circumstances, multi-objective optimization has the potential to support personalized, patient-centered, shared decision-making with precision and balance. Risk calculators and decision-support tools estimate the probability of individual or composite outcomes, yet approaches and techniques in multi-objective optimization are unknown to most clinicians. Here we describe the potential for multi-objective optimization methods to quantify tradeoffs among competing outcomes in perioperative medicine.