Hemodynamics and cutaneous microcirculation during induction of general anesthesia with and without esketamine.

OBJECTIVE: Currently, there are limited data on the effect of macrocirculatory hemodynamic changes on human microcirculation, especially during the induction of general anesthesia (GA). METHODS: We performed a non-randomized observational trial on patients receiving GA for elective surgery. In the control group (CG), for GA induction sufentanil, propofol, and rocuronium was administered. Patients assigned to the esketamine group (EG) received additional esketamine for GA induction. Invasive blood pressure (IBP) and pulse contour cardiac output (CO) measurement were performed continuously. Microcirculation was assessed using cutaneous Laser Doppler Flowmetry (forehead and sternum LDF), peripheral and central Capillary Refill Time (pCRT, cCRT), as well as brachial temperature gradient (Tskin-diff) at baseline, 5, 10 and 15 minutes after induction of GA. RESULTS: 42 patients were included in the analysis (CG n = 22, EG n = 20). pCRT, cCRT, Tskin-diff, forehead and sternum LDF decreased following GA induction in both groups. IBP and CO were significantly more stable in esketamine group. However, the changes in the microcirculatory parameters were not significantly different between the groups. CONCLUSIONS: The addition of esketamine for GA induction warranted better hemodynamic stability for the first five minutes, but had no significant effect on any of the cutaneous microcirculatory parameters measured.

Bayesian networks identify determinants of outcomes following cardiac surgery in a UK population.

BACKGROUND: Traditional risk stratification tools do not describe the complex principle determinant relationships that exist amongst pre-operative and peri-operative factors and their influence on cardiac surgical outcomes. This paper reports on the use of Bayesian networks to investigate such outcomes. METHODS: Data were prospectively collected from 4776 adult patients undergoing cardiac surgery at a single UK institute between April 2012 and May 2019. Machine learning techniques were used to construct Bayesian networks for four key short-term outcomes including death, stroke and renal failure. RESULTS: Duration of operation was the most important determinant of death irrespective of EuroSCORE. Duration of cardiopulmonary bypass was the most important determinant of re-operation for bleeding. EuroSCORE was predictive of new renal replacement therapy but not mortality. CONCLUSIONS: Machine-learning algorithms have allowed us to analyse the significance of dynamic processes that occur between pre-operative and peri-operative elements. Length of procedure and duration of cardiopulmonary bypass predicted mortality and morbidity in patients undergoing cardiac surgery in the UK. Bayesian networks can be used to explore potential principle determinant mechanisms underlying outcomes and be used to help develop future risk models.