Intraoperative utilisation of high-resolution data for cerebral autoregulation: a feasibility study

Strengths-weaknesses-opportunities-threats analysis of artificial intelligence in anesthesiology and perioperative medicine

The use of artificial intelligence (AI) and machine learning (ML) in anesthesiology and perioperative medicine is quickly becoming a mainstay of clinical practice. Anesthesiology is a data-rich medical specialty that integrates multitudes of patient-specific information. Perioperative medicine is ripe for applications of AI and ML to facilitate data synthesis for precision medicine and predictive assessments. Examples of emergent AI models include those that assist in assessing depth and modulating control of anesthetic delivery, event and risk prediction, ultrasound guidance, pain management, and operating room logistics. AI and ML support analyzing integrated perioperative data at scale and can assess patterns to deliver optimal patient-specific care. By exploring the benefits and limitations of this technology, we provide a basis of considerations for evaluating the adoption of AI models into various anesthesiology workflows. This analysis of AI and ML in anesthesiology and perioperative medicine explores the current landscape to understand better the strengths, weaknesses, opportunities, and threats (SWOT) these tools offer.

Our initial experience of monitoring the autoregulation of cerebral blood flow during cardiopulmonary bypass

BACKGROUND

Cerebral blood flow (CBF) is believed to be relatively constant within an upper and lower blood pressure limit. Different methods are available to monitor CBF autoregulation during surgery. This study aims to critically analyze the application of the cerebral oxygenation index (COx), one of the commonly used techniques, using a reference to data from a series of clinical registrations.

METHOD

CBF was monitored using near-infrared spectroscopy, while cerebral blood pressure was estimated by recordings obtained from either the radial or femoral artery in 10 patients undergoing cardiopulmonary bypass. The association between CBF and blood pressure was calculated as a moving continuous correlation coefficient. A COx index > 0.4 was regarded as a sign of abnormal cerebral autoregulation (CA). Recordings were examined to discuss reliability measures and clinical feasibility of the measurements, followed by interpretation of individual results, identification of possible pitfalls, and suggestions of alternative methods.

RESULTS AND CONCLUSION

Monitoring of CA during cardiopulmonary bypass is intriguing and complex. A series of challenges and limitations should be considered before introducing this method into clinical practice.

High-resolution perioperative cerebral blood flow autoregulation measurement: a practical and feasible approach for widespread clinical monitoring

A growing body of evidence demonstrates that excursions of BP below or above the limits of cerebral blood flow autoregulation are associated with complications in patients with neurological injury or for those undergoing cardiac surgery. Moreover, recent evidence suggests that maintaining MAP above the lower limit of cerebral autoregulation during cardiopulmonary bypass reduces the frequency of postoperative delirium and is associated with improved memory 1 month after surgery. Continuous measurement of BP in relation to cerebral autoregulation limits using a virtual patient monitoring platform processing near-infrared spectroscopy digital signals offers the hope of bringing this application to the bedside.