Evidence map›Paper›PMID 41136790›Full record

ArticleNPJ digital medicine2025

Real-time detection of respiratory circuit events in mechanical ventilation using deep learning.

Qian He, Tingting Pan, Haiyang Hou, Yue Yu, Guo Chen, Ling Zhang, Xiangqun Yan, Lanqi Niu, Yuxi Yang, Na Lv and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. 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

12 authors.

Qian He *Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Tingting Pan *Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Haiyang Hou *School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.
Yue YuDepartment of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Guo ChenSchool of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.
Ling ZhangDepartment of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiangqun YanDepartment of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Lanqi NiuDepartment of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yuxi YangDepartment of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Na LvSchool of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, China. nana414526@sjtu.edu.cn.
Ruoming TanDepartment of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. trm11714@rjh.com.cn.
Hongping QuDepartment of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. qhp10516@rjh.com.cn.

Funding

Shanghai Natural Science Foundation Project 24ZR1447500Shanghai 'Science and Technology Innovation Action Plan' Special Program for Medical Innovation Research No. 23Y11900100
6 · The paper itself

Abstract

Respiratory circuit events, including fluid accumulation and circuit or cuff leakage during mechanical ventilation, increase ventilator-associated event risks but often go undetected. We developed a convolutional neural network analyzing 57,296 annotated breaths (26,768 training/internal validation; 30,528 external validation) from 48 patients. The algorithm detected fluid-accumulation-like patterns with an F1-score of 99.90% internally and 92.35% externally, while leakage detection exceeded 99% accuracy. Clinically, 91.7% of patients exhibited circuit events, with fluid-accumulation-like patterns observed in 77.1% of cases and associated with measurable airway pressure increases (median ΔPaw = 2 cmH₂O). The algorithm demonstrated high accuracy and generalizability in detecting respiratory circuit events from waveform data and may allow earlier intervention to reduce ventilator-associated complications through real-time detection.

Identifiers

PMID41136790
PMCPMC12552622

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.