Evidence map›Paper›PMID 42683445›Full record

ReviewFrontiers in medicine2026

Research progress in agitation early warning monitoring and new technologies for critically ill patients: a narrative review.

Jintao Wei, Shouyin Jiang, Mengting Yan, Qiang Li, Pengyuan Chen, Zhiying Kang, Shanxiang Xu, Mao Zhang

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jintao WeiDepartment of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Shouyin JiangDepartment of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Mengting YanDepartment of Nursing, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Qiang LiDepartment of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Pengyuan ChenDepartment of Nursing, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Zhiying KangDepartment of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Shanxiang XuDepartment of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Mao ZhangDepartment of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

agitationartificial intelligencecomputer visioncritical caredeep learningearly warning monitoringmultimodal

Identifiers

PMID42683445
PMCPMC13532560

What OpenQuestion holds

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Registered trials

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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.