Title : Perioperative myocardial injury: A comprehensive review of detection, mechanisms, and outcomes in anesthetized patients
Abstract:
Background and Objectives: Perioperative myocardial injury (PMI) is a significant contributor to postoperative morbidity and mortality, particularly in patients undergoing non-cardiac surgery. Despite its prevalence, PMI often goes unrecognized due to the absence of classic ischemic symptoms under anesthesia. This review explores the incidence, underlying mechanisms, and clinical outcomes associated with PMI, while emphasizing diagnostic advancements and anesthesia-related risk factors.
Materials and Methods: We conducted a comprehensive review of clinical trials, observational studies, and guideline documents related to PMI in surgical patients. Particular focus was placed on the role of high-sensitivity cardiac troponin (hs-cTn) assays in detection, intraoperative hemodynamic and electrocardiographic monitoring, and the influence of anesthetic agents on myocardial oxygen supply-demand balance.
Results: PMI affects approximately 8–18% of patients undergoing non-cardiac surgery, with increased incidence in those with pre-existing cardiovascular risk factors. Ischemia in the perioperative period may be precipitated by supply-demand mismatch due to hypotension, anemia, tachycardia, or surgical stress. The use of hs-cTn has significantly improved early detection and risk stratification, allowing for postoperative surveillance and timely intervention. Anesthetic factors such as depth of anesthesia, vasodilatory effects, and choice of agent can also modulate ischemic risk. Notably, volatile agents may provide cardioprotection through ischemic preconditioning mechanisms, although evidence remains heterogeneous.
Conclusions: Effective management of PMI requires a multidisciplinary approach, incorporating vigilant intraoperative monitoring, optimized hemodynamic management, and risk-guided postoperative care. Routine hs-cTn screening in high-risk populations may enhance detection and improve outcomes. Future research should focus on refining risk prediction models and validating standardized monitoring protocols.
