Multiobjective optimization challenges in perioperative anesthesia: A review.
Multiobjective optimization challenges in perioperative anesthesia: A review.
复制标题
围手术期麻醉的多目标优化挑战：综述。
DOI：
10.1016/j.surg.2020.11.005
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发表时间：
2021-07
期刊：
影响因子：
3.8
通讯作者：
Tighe PJ
中科院分区：
文献类型：
--
作者：
Brennan M;Hagan JD;Giordano C;Loftus TJ;Price CE;Aytug H;Tighe PJ
医生使用围手术期决策支持工具来降低风险并最大化受益，以实现患者最成功的结局。当代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增强了外科医生的判断和经验。这些算法通过解析大型数据集和执行复杂的计算来准确评估各种术后并发症的风险，而这些计算对于忙碌临床医生来说是很麻烦的。即使有了这些进步，围手术期风险评估仍然存在很大的差距;决策支持算法通常不能解释在患者围手术期过程中应用的风险降低疗法，并且不能量化护理的竞争目标之间的权衡（例如，平衡术后疼痛控制与呼吸抑制的风险或平衡术中容量复苏与肺水肿并发症的风险）。多目标优化方法已成功地应用于类似问题，但尚未应用于围手术期决策支持。鉴于通过电子病历提供的大量数据，包括术中数据，现在可以成功地在围手术期护理中应用多目标优化。多目标优化的临床应用将需要用于分析和报告模型输出的半自动化管道以及仔细的开发和验证过程。在这种情况下，多目标优化有可能支持个性化，以患者为中心，精确和平衡的共享决策。风险计算器和决策支持工具估计个体或复合结局的概率，但多目标优化的方法和技术对大多数临床医生来说是未知的。在这里，我们描述了潜在的多目标优化方法，以量化围手术期医学的竞争结果之间的权衡。
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.
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影响因子：
16.9
作者：
Shafi, Shahid;Collinsworth, Ashley W.;Masica, Andrew L.
通讯作者：
Masica, Andrew L.
DOI：
10.1002/cnm.2992
发表时间：
2018-08-01
影响因子：
2.1
作者：
Ramos-de-Miguel, Angel;Escobar, Jose M.;Ramos-Macias, Angel
通讯作者：
Ramos-Macias, Angel
影响因子：
9
作者：
Meguid, Robert A.;Bronsert, Michael R.;Henderson, William G.
通讯作者：
Henderson, William G.
影响因子：
2.1
作者：
Emmerich MTM;Deutz AH
通讯作者：
Deutz AH
DOI：
10.1001/jama.1995.03530230060032
发表时间：
1995-12-20
影响因子：
120.7
作者：
MAX, MB;DONOVAN, M;EDWARDS, T
通讯作者：
EDWARDS, T