Evidence map›Paper›PMID 41493521›Full record

ArticleJournal of clinical monitoring and computing2026

PVADet: fast patient-ventilator asynchrony detection on waveforms.

Longxiang Su, Yan Li, Yunping Lan, Qiang Sun, Fuhong Cai, Hongli He, Siyi Yuan, Song Zhang, Xianlong Liu, Elias Baedorf-Kassis and 2 more

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of clinical monitoring and computing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

12 authors.

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.
Yan LiShanghai SVM (Shumu) Medical Technology Co., Ltd, Shanghai, 200230, China.
Yunping LanIntensive Care Unit, Sichuan Provincial People's Hospital, School of Medicine of University of Electronic Science and Technology, Sichuan Academy of Medical Sciences, Chengdu, 610000, China.
Qiang SunIntensive Care Unit, Affiliated Hospital of Jining Medical University, Jining, 272029, China.
Fuhong CaiShanghai SVM (Shumu) Medical Technology Co., Ltd, Shanghai, 200230, China.
Hongli HeIntensive Care Unit, Sichuan Provincial People's Hospital, School of Medicine of University of Electronic Science and Technology, Sichuan Academy of Medical Sciences, Chengdu, 610000, China.
Siyi YuanDepartment 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.
Song ZhangDepartment of Automation, Tsinghua University, Beijing, 100084, China.
Xianlong LiuShanghai SVM (Shumu) Medical Technology Co., Ltd, Shanghai, 200230, China.
Elias Baedorf-KassisDepartment of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, United States of America. enbaedor@bidmc.harvard.edu.
Xiaobo HuangIntensive Care Unit, Sichuan Provincial People's Hospital, School of Medicine of University of Electronic Science and Technology, Sichuan Academy of Medical Sciences, Chengdu, 610000, China. drhuangxb@163.com.
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. ly_icu@aliyun.com.

Funding

CAMS Innovation Fund for Medical Sciences 2023-I2M-CT-B-031National High-Level Hospital Clinical Research Funding LY25A201 and 2022-PUMCH-D-005
6 · The paper itself

Abstract

Patient-ventilator asynchrony (PVA) is a common and critically import clinical problem in patients receiving mechanical ventilation. However, PVAs are often underrecognized, underestimated and delayed, and there has been minimal success in automating their detection. In this study, we develop an efficient and fast end-to-end model to recognize PVAs on ventilator waveforms: running the model costs 106.5ms on CPUs and 7.8ms on GPUs. We propose label striping and stripe-mask encoding for efficient multi-class multi-target detecting. The model innovatively integrates causal convolutional, depth-wise separable convolutional, and recurrent neural networks to memorize long short-term causal features. With 60s waveform segments, our model performs a cross-validation mean average precision (mAP) of 88.1% and a testing mAP of 65.7% for comprehensive PVA detection. Our approach might be implemented as a monitoring tool to automatically identify PVAs for improving bedside and remote care and prioritizing patient comfort.

Indexed as

Respiration, ArtificialVentilators, MechanicalAlgorithmsHumansMonitoring, PhysiologicNeural Networks, ComputerPatient-Ventilator AsynchronyReproducibility of ResultsSignal Processing, Computer-AssistedCritical CareDeep LearningMechenical VentilationPatient-ventilator asynchronyTime-series AnalysisWaveform Monitoring

Identifiers

PMID41493521

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.