Evidence map›Paper›PMID 39055813›Full record

ArticleHeliyon2024

Application of a cloud platform that identifies patient-ventilator asynchrony and enables continuous monitoring of mechanical ventilation in intensive care unit.

Xiangyu Chen, Junping Fan, Wenxian Zhao, Ruochun Shi, Nan Guo, Zhigang Chang, Maifen Song, Xuedong Wang, Yan Chen, Tong Li and 4 more

Registry-linked trialAbstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07307066 (Real-Time Algorithm-Driven Ventilation Feedback to Improve Lung-Protective Ventilation in Patients With ARDS), which is not on this map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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.

NCT07307066 narecruitingnot on this mapstarted 2025, after this paper: background citation

Real-Time Algorithm-Driven Ventilation Feedback to Improve Lung-Protective Ventilation in Patients With ARDS (REALVENT-study)

TypeinterventionalSponsorPeking Union Medical College HospitalRan2025 to 2027Enrolled208ConditionsARDS (Acute Respiratory Distress Syndrome), VILI (Ventilator-induced Lung Injury), Respiratory Failure, Critical IllnessArmsREal-time Algorithm-driven Ventilation feedback to improve lung-protective ventilation in critically, Standard ICU care
3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. PVADet: fast patient-ventilator asynchrony detection on waveforms.Journal of clinical monitoring and computing · 2026
    Article
  2. Article
  3. Review
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

14 authors.

Xiangyu ChenDepartment of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China.
Junping FanDepartment of Pulmonary and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan Wangfujing, Dongcheng District, Beijing, China.
Wenxian ZhaoDepartment of Critical Care Medicine, Beijing Puren Hospital, Beijing, 100062, China.
Ruochun ShiDepartment of Critical Care Medicine, Beijing Sixth Hospital, Beijing, 100007, China.
Nan GuoIntensive Care Unit, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China.
Zhigang ChangIntensive Care Unit, Beijing Hospital, Beijing, 100005, China.
Maifen SongDepartment of Critical Care Medicine, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010, China.
Xuedong WangIntensive Care Unit, Beijing Hepingli Hospital, Beijing, 100013, China.
Yan ChenIntensive Care Unit, Beijing Longfu Hospital, Beijing, 100010, China.
Tong LiIntensive Care Unit, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China.
Guang-Gang LiDepartment of Critical Care Medicine, 7th Medical Center of PLA General Hospital, Beijing, China.
Longxiang SuDepartment of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China.
Yun LongDepartment of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China.
on bahalf of Beijing Dongcheng Critical Care Quality Control Centre Group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Patient-ventilator asynchrony (PVA) frequently occurs in mechanically ventilated patients within the ICU and has the potential for harm. Depending solely on the health care team cannot accurately and promptly identify PVA. To address this issue, our team has developed a cloud-based platform for monitoring mechanical ventilation (MV), comprising the PVA-RemoteMonitor system and the 24-h MV analysis report. We conducted a survey to evaluate physicians' satisfaction and acceptance of the platform in 14 ICUs. Methods: Data from medical records, clinical information systems, and ventilators were uploaded to the cloud platform and underwent data processing. The data were analyzed to monitor PVA and displayed in the front-end. The 24-h analysis report for MV was generated for clinical reference. Critical care physicians in 14 hospitals' ICUs that involved in the platform participated in a questionnaire survey, among whom 10 physicians were interviewed to investigate physicians' acceptance and opinions of this system. Results: The PVA-RemoteMonitor system exhibited a high level of specificity in detecting flow insufficiency, premature cycle, delayed cycle, reverse trigger, auto trigger, and overshoot, with sensitivities of 90.31 %, 98.76 %, 99.75 %, 99.97 %, 100 %, and 99.69 %, respectively. The 24-h analysis report supplied essential data about PVA and respiratory mechanics. 86.2 % (75/87) of physicians supported the application of this platform. Conclusions: The PVA-RemoteMonitor system accurately identified PVA, and the MV analysis report provided guidance in controlling PVA. Our platform can effectively assist ICU physicians in the management of ventilated patients.

Indexed as

Intensive care medicineMechanical ventilationMonitoring platformPatient-ventilator asynchronyRespiratory mechanics

Identifiers

PMID39055813
PMCPMC11269847

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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.