ABSTRACT
Artificial intelligence (AI) is making giant strides in the medical domain, and the field of anaesthesia is not untouched. Enhancement in technology, especially AI, in many fields, including medicine, has proven to be far superior, safer and less erratic than human decision-making. The intersection of anaesthesia and AI holds the potential for augmenting constructive advances in anaesthesia care. AI can improve anaesthesiologists' efficiency, reduce costs and improve patient outcomes. Anaesthesiologists are well placed to harness the advantages of AI in various areas like perioperative monitoring, anaesthesia care, drug delivery, post-anaesthesia care unit, pain management and intensive care unit. Perioperative monitoring of the depth of anaesthesia, clinical decision support systems and closed-loop anaesthesia delivery aid in efficient and safer anaesthesia delivery. The effect of various AI interventions in clinical practice will need further research and validation, as well as the ethical implications of privacy and data handling. This paper aims to provide an overview of AI in perioperative monitoring in anaesthesia.
ABSTRACT
A pregnant patient presented with fever and desaturation, without breathlessness. She was suspected to have COVID-19 but SARS-CoV-2 was negative. She developed fetal distress and underwent an uneventful Cesarean section. Postoperatively, she developed respiratory distress and needed mechanical ventilation support. The clinical features suggested COVID-19 infection and antiviral treatment were empirically initiated. Repeat SARS-CoV-2 was negative. Echocardiography, computed tomography scans, and biochemical investigations supported a diagnosis of peripartum cardiomyopathy. She was successfully managed with decongestive therapy and could be discharged home on the fifth day.
ABSTRACT
BACKGROUND AND AIMS: More than 80% of delivered anesthetic gases get wasted at high fresh gas flows as they are vented out unused. Use of minimal flow anesthesia is associated with less waste anesthetic gas emission and environmental pollution. There is no approved or validated technique to initiate minimal flow anesthesia and simultaneously achieve denitrogenation of the breathing circuit. We studied the wash-in characteristics of desflurane, when delivered with 50% nitrous oxide, to reach a target end-tidal concentration at two different gas flow rates. MATERIAL AND METHODS: Patients were allocated randomly to two groups of 25 adults each. In Group A, with the vaporizer dial fixed at 4 vol %, after an initial fresh gas flow of 4 L/min was administered to wash-in of desflurane using the closed-circuit, with 50% N2O in O2, and in group B, 6 L/min was used. Minimal flow anesthesia, with 0.5 L/min, was initiated in both groups on attaining a target end-tidal desflurane concentration of 3.5 vol %. After initiation of desflurane delivery, the inspired/expired gas concentrations were noted every minute for 15 min. RESULTS: In Group A, the target desflurane end-tidal concentration was reached in 499.2 ± 68.6 s±, and in the Group B (P < 0.001), it was reached significantly faster in 314.4 ± 69.89 s. Denitrogenation of the circuit was adequate in both groups. CONCLUSION: Minimal flow anesthesia can be initiated, without any gas-volume deficit, in about 5 min with an initial fresh gas flow rate of 6 L/min and the vaporizer set at 4 vol%.
