Somnath Bose1, Daniel Talmor. 1. Department of Anesthesia Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Abstract
PURPOSE OF REVIEW: 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. RECENT FINDINGS: 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. SUMMARY: 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.
PURPOSE OF REVIEW: 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. RECENT FINDINGS: 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. SUMMARY: 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.
Authors: Rasmus D Bojesen; Camilla Grube; Fatima Buzquurz; Rebecca E G Miedzianogora; Jens R Eriksen; Ismail Gögenur Journal: BJS Open Date: 2022-05-02