ReviewFrontiers in medicine2026
Research progress in agitation early warning monitoring and new technologies for critically ill patients: a narrative review.
Review in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Agitation is a common and clinically significant problem in critically ill patients, closely associated with unplanned extubation, device removal, prolonged hospital stay, and increased mortality. Traditional assessment tools such as the richmond agitation-sedation scale (RASS) and sedation-agitation scale (SAS), despite their widespread use, are limited by intermittent assessment, subjectivity, and inability to provide continuous monitoring. In recent years, the rapid development of artificial intelligence and sensing technologies has opened new avenues for objective, continuous, and real-time agitation monitoring through vital sign-based parameters, video surveillance with computer vision, wearable devices, and multimodal fusion approaches. This narrative review systematically examines the epidemiological characteristics and clinical consequences of agitation in critically ill patients, analyzes the limitations of traditional assessment tools, and highlights emerging technologies based on physiological signals, computer vision, deep learning, and multimodal integration. The current status and challenges of agitation monitoring in various clinical settings-including intensive care units, emergency departments, prehospital environments, and inter-hospital transport-are discussed. Finally, future directions for multimodal early warning systems in complex environments are proposed, providing a theoretical foundation for constructing a comprehensive agitation monitoring system spanning prehospital, in-hospital, and transport settings based on vital signs and video-based behavioral analysis.
Indexed as
Identifiers
What OpenQuestion holds
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.