Edmund Kong1, Nicoletta Nicolaou2,3, Marcela P Vizcaychipi1,4. 1. Department of Medicine, Imperial College London, London, UK. 2. Medical School, University of Nicosia, Nicosia, Cyprus - nicolaou.nic@unic.ac.cy. 3. Centre for Neuroscience and Interdisciplinary Brain Research (CENIBRE), Medical School, University of Nicosia, Nicosia, Cyprus. 4. Magill Department of Anaesthesia, Intensive Care Medicine and Pain Management, Chelsea & Westminster Hospital, London, UK.
Abstract
INTRODUCTION: This systematic review investigates the effect of closed-loop anesthesia delivery on the maintenance of cardiovascular parameters. The specific challenges arise from the fact that many physiological variables used for the control of anesthetic delivery and maintenance of hemodynamic stability are regulated by the autonomic nervous system, which is subject to high inter-individual variability. EVIDENCE ACQUISITION: A systematic database search (MEDLINE, EMBASE and Web of Science) was conducted following the PRISMA guidelines and the principles of the Cochrane Handbook for Systematic Reviews of Interventions. Identified articles were screened and studies that fulfilled the eligibility criteria using the PICO approach (Patient, Intervention, Comparison, Outcome) were included in a random effects model to calculate weighted mean and 95% confidence intervals. EVIDENCE SYNTHESIS: Twenty studies (1402 subjects: 706 intervention and 696 control) were included in this review. Meta-analysis showed that closed-loop systems achieved longer duration of heart rate and MAP control, at 90.9% (95% CI: 90.0-91.8%) and 88.2% (95% CI: 87.4-89.0%) respectively, compared to the respective manual control group at 86.6% (95% CI: 85.1-88.0%) and 85.1% (95% CI: 84.3-86.0%). Subgroup analysis demonstrated better performance and faster recovery compared to the control group. CONCLUSIONS: The findings support the use of closed-loop systems for anesthetic delivery. Interpretation should take into account limitations, such as the large variations in the selected studies in the type of parameters used to measure outcomes. In summary, this review provides evidence supporting the importance of considering cardiovascular variables in the design of automated anesthetic delivery systems.
INTRODUCTION: This systematic review investigates the effect of closed-loop anesthesia delivery on the maintenance of cardiovascular parameters. The specific challenges arise from the fact that many physiological variables used for the control of anesthetic delivery and maintenance of hemodynamic stability are regulated by the autonomic nervous system, which is subject to high inter-individual variability. EVIDENCE ACQUISITION: A systematic database search (MEDLINE, EMBASE and Web of Science) was conducted following the PRISMA guidelines and the principles of the Cochrane Handbook for Systematic Reviews of Interventions. Identified articles were screened and studies that fulfilled the eligibility criteria using the PICO approach (Patient, Intervention, Comparison, Outcome) were included in a random effects model to calculate weighted mean and 95% confidence intervals. EVIDENCE SYNTHESIS: Twenty studies (1402 subjects: 706 intervention and 696 control) were included in this review. Meta-analysis showed that closed-loop systems achieved longer duration of heart rate and MAP control, at 90.9% (95% CI: 90.0-91.8%) and 88.2% (95% CI: 87.4-89.0%) respectively, compared to the respective manual control group at 86.6% (95% CI: 85.1-88.0%) and 85.1% (95% CI: 84.3-86.0%). Subgroup analysis demonstrated better performance and faster recovery compared to the control group. CONCLUSIONS: The findings support the use of closed-loop systems for anesthetic delivery. Interpretation should take into account limitations, such as the large variations in the selected studies in the type of parameters used to measure outcomes. In summary, this review provides evidence supporting the importance of considering cardiovascular variables in the design of automated anesthetic delivery systems.
Authors: Eric J Snider; Saul J Vega; Evan Ross; David Berard; Sofia I Hernandez-Torres; Jose Salinas; Emily N Boice Journal: Sensors (Basel) Date: 2022-01-11 Impact factor: 3.576