Department of Anesthesia Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Curr Opin Crit Care. 2018 Dec;24(6):547-553. doi: 10.1097/MCC.0000000000000556.
Timely identification of high-risk surgical candidates facilitate surgical decision-making and allows appropriate tailoring of perioperative management strategies. This review aims to summarize the recent advances in perioperative risk stratification.
Use of indices which include various combinations of preoperative and postoperative variables remain the most commonly used risk-stratification strategy. Incorporation of biomarkers (troponin and natriuretic peptides), comprehensive objective assessment of functional capacity, and frailty into the current framework enhance perioperative risk estimation. Intraoperative hemodynamic parameters can provide further signals towards identifying patients at risk of adverse postoperative outcomes. Implementation of machine-learning algorithms is showing promising results in real-time forecasting of perioperative outcomes.
Perioperative risk estimation is multidimensional including validated indices, biomarkers, functional capacity estimation, and intraoperative hemodynamics. Identification and implementation of targeted strategies which mitigate predicted risk remains a greater challenge.
及时识别高危手术患者有助于手术决策，并能适当调整围手术期管理策略。本文旨在总结围手术期风险分层的最新进展。
目前最常用的风险分层策略仍然是使用包含术前和术后各种变量组合的指数。将生物标志物（肌钙蛋白和利钠肽）、对功能能力的全面客观评估以及脆弱性纳入现有框架可提高围手术期风险评估。术中血流动力学参数可提供进一步的信号，以识别术后不良结局风险增加的患者。机器学习算法的实施在实时预测围手术期结局方面显示出良好的效果。
围手术期风险评估是多维度的，包括经过验证的指数、生物标志物、功能能力评估和术中血流动力学。确定和实施可降低预测风险的针对性策略仍然是一个更大的挑战。
