ReviewJournal of clinical monitoring and computing2024
Closed-loop anesthesia: foundations and applications in contemporary perioperative medicine.
Review in Journal of clinical monitoring and computing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 24 citations in OpenAlex.
- Trial
- Systemic fragility in European total intravenous anesthesia delivery and opportunities for resilient real-time decision support.Communications medicine · 2026Review
- Automation in perioperative medicine: perceptions, requirements and boundaries - a mixed methods study.European journal of anaesthesiology and intensive care · 2026Article
- Prediction of surgery start for automated anesthesia using draping detection from surveillance videos.Journal of clinical monitoring and computing · 2026Article
- Closed-Loop Anesthesia: Advances in Automated Drug Delivery and Perioperative Monitoring.Journal of pharmacy & bioallied sciences · 2026Review
- Flight rules for clinical AI: lessons from aviation for human-AI collaboration in medicine.NPJ digital medicine · 2026Review
- Artificial Intelligence in Adult Cardiovascular Medicine and Surgery: Real-World Deployments and Outcomes.Journal of personalized medicine · 2026Review
- Target-controlled infusion for analgesia and sedation in the intensive care unit: a scoping review.Annals of intensive care · 2026Review
- Time-segmented, feedback-guided tiered warming during radical esophagectomy: a single-center retrospective cohort study.Frontiers in oncology · 2026Article
- Sedation and analgesia strategies in the neuro intensive care unit.World journal of critical care medicine · 2025Review
- From static to dynamic: Artificial intelligence revolution in perioperative care through multimodal data fusion and closed-loop optimization.Journal of anesthesia and translational medicine · 2025Review
- Goal-directed therapy: what is the goal again?Perioperative medicine (London, England) · 2025Review
- Challenges in Transitioning from Controlled to Assisted Ventilation in Acute Respiratory Distress Syndrome (ARDS) Management.Journal of clinical medicine · 2024Article
- Bridging neural circuits and clinical outcomes: a novel framework for perioperative disturbances of consciousness and cognition.Frontiers in neurologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A closed-loop automatically controls a variable using the principle of feedback. Automation within anesthesia typically aims to improve the stability of a controlled variable and reduce workload associated with simple repetitive tasks. This approach attempts to limit errors due to distractions or fatigue while simultaneously increasing compliance to evidence based perioperative protocols. The ultimate goal is to use these advantages over manual care to improve patient outcome. For more than twenty years, clinical studies in anesthesia have demonstrated the superiority of closed-loop systems compared to manual control for stabilizing a single variable, reducing practitioner workload, and safely administering therapies. This research has focused on various closed-loops that coupled inputs and outputs such as the processed electroencephalogram with propofol, blood pressure with vasopressors, and dynamic predictors of fluid responsiveness with fluid therapy. Recently, multiple simultaneous independent closed-loop systems have been tested in practice and one study has demonstrated a clinical benefit on postoperative cognitive dysfunction. Despite their advantages, these tools still require that a well-trained practitioner maintains situation awareness, understands how closed-loop systems react to each variable, and is ready to retake control if the closed-loop systems fail. In the future, multiple input multiple output closed-loop systems will control anesthetic, fluid and vasopressor titration and may perhaps integrate other key systems, such as the anesthesia machine. Human supervision will nonetheless always be indispensable as situation awareness, communication, and prediction of events remain irreplaceable human factors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.