Thermal heterogeneity after deep hypothermic circulatory arrest: a new framework for understanding rewarming and...

Deep hypothermic circulatory arrest (DHCA) is widely used during complex aortic arch surgery to reduce metabolic demand and protect vital organs. Conventional temperature management assumes that restoration of core temperature reflects systemic thermal homeostasis, and rewarming is generally considered complete once central monitoring sites return to normothermic values. However, emerging evidence suggests that thermal recovery may occur unevenly across different anatomical compartments. This review proposes an integrated physiological framework of thermal heterogeneity, defined as the persistence of regional temperature gradients despite restoration of core normothermia. Observations from thermal imaging and physiological assessments suggest that cranial and thoracic regions may rewarm more rapidly than abdominal and peripheral tissues, resulting in residual thermal gradients that remain undetected by conventional monitoring methods. Persistent thermal heterogeneity may contribute to physiological heterogeneity, characterized by asynchronous recovery of vascular, metabolic, myocardial, and organ function. Regional thermal differences may therefore be associated with delayed recovery of vascular function, metabolic homeostasis, microcirculatory perfusion, and myocardial performance, although these physiological systems likely recover along partially independent trajectories. Whether thermal heterogeneity contributes directly to these responses remains a hypothesis requiring validation in prospective DHCA-specific studies. Recognition of thermal heterogeneity broadens current concepts of postoperative recovery by providing an integrated physiological framework that extends beyond core temperature measurements alone. Future validation studies integrating regional thermal mapping, multisite temperature monitoring, Doppler-derived vascular indices, myocardial strain imaging, metabolic biomarkers, postoperative clinical outcomes, and artificial intelligence-guided predictive models may improve characterization of recovery and support individualized perioperative management after DHCA.