Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL.
Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL.
Surgery. 2021 Jul;170(1):320-324. doi: 10.1016/j.surg.2020.11.005. Epub 2020 Dec 14.
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 surgeons' 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 (eg, balancing postoperative pain control with the risk of respiratory depression or balancing intraoperative volume resuscitation with the risk for complications from pulmonary edema). Multiobjective 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 multiobjective optimization in perioperative care. Clinical application of multiobjective optimization would require semiautomated pipelines for analytics and reporting model outputs and a careful development and validation process. Under these circumstances, multiobjective optimization has the potential to support personalized, patient-centered, shared decision-making with precision and balance.
医生使用围手术期决策支持工具来降低风险并最大限度地提高收益，从而为患者实现最成功的结果。当代的风险评估实践通过大数据和机器学习驱动的决策支持算法来增强外科医生的判断和经验。这些算法通过解析大型数据集并进行复杂的计算，准确地评估了广泛的术后并发症风险，这些计算对于忙碌的临床医生来说非常繁琐。即使有了这些进步，围手术期风险评估仍然存在很大差距；决策支持算法通常无法考虑到患者围手术期过程中应用的降低风险治疗方法，也无法量化护理目标之间的权衡（例如，平衡术后疼痛控制与呼吸抑制风险或平衡术中容量复苏与肺水肿并发症风险）。多目标优化解决方案已成功应用于类似问题，但尚未应用于围手术期决策支持。鉴于通过电子病历可获得大量数据，包括术中数据，现在可以成功地将多目标优化应用于围手术期护理。多目标优化的临床应用需要用于分析和报告模型输出的半自动分析管道，以及仔细的开发和验证过程。在这种情况下，多目标优化有可能以精确和平衡的方式支持个性化、以患者为中心的共同决策。
