Evidence map›Paper›PMID 30470999›Full record

ArticleSleep & breathing = Schlaf & Atmung2019

Utility of transthoracic impedance and novel algorithm for sleep apnea screening in pacemaker patient.

RuoHan Chen, KePing Chen, Yan Dai, Shu Zhang

Open access · hybridAbstract read
In one paragraph

Article in Sleep & breathing = Schlaf & Atmung, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.2field-weighted citation impact, top 20% of its field
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

9 citing papers in PubMed, 18 citations in OpenAlex.

  1. Review
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  7. Respiration Monitoring via Forcecardiography Sensors.Sensors (Basel, Switzerland) · 2021
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  8. Review
  9. Contact-Based Methods for Measuring Respiratory Rate.Sensors (Basel, Switzerland) · 2019
    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

4 authors at 1 institution in 1 country.

RuoHan ChenCenter of Arrhythmia, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beilishi Road 167#, Xicheng Qu, Beijing, 100037, China.
KePing ChenCenter of Arrhythmia, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beilishi Road 167#, Xicheng Qu, Beijing, 100037, China. ckpfuwai@sina.com.
Yan DaiCenter of Arrhythmia, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beilishi Road 167#, Xicheng Qu, Beijing, 100037, China.
Shu ZhangCenter of Arrhythmia, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beilishi Road 167#, Xicheng Qu, Beijing, 100037, China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPacing patients was revealed with a high prevalence of sleep disorder, but mostly undiagnosed. The pacemaker with transthoracic impedance sensor and novel algorithm could identify sleep apnea (SA) event. This study aimed to evaluate accuracy of pacemaker in sleep apnea diagnosis.

methodsThis is a prospective study which enrolled patients implanted with pacemakers integrated with transthoracic impedance sensor and SA-identified algorithm (AP Scan). All patients underwent a polysomnography (PSG). The apnea and hypopnea index (AHI) of the PSG (PSG-AHI) and the respiratory disturbance index (RDI) of the pacemaker (PM-RDI) were recorded on the same night. The correlation between two methods was evaluated by the kappa coefficient, receiver operating characteristic (ROC) curves, and Bland and Altman statistics.

resultsSixty-four patients were enrolled, who had never been diagnosed with SAS or underwent PSG exam. After PSG examination, 76.4% patients were diagnosed as combining with SA (20% severe, 18.2% moderate, and 38.2% mild). RDI calculated by PM has a strong positive correlation with PSG-AHI (r = 0.76, p < 0.001, 95% CI 0.61-0.85). The optimal cutoff value of PM-RDI for advanced SAS (PSG-AHI ≥ 15) diagnosis was 26, with AUC of 0.89 (95% CI 0.77 to 0.96, p < 0.001). The best cutoff value for severe SA (PSG-AHI ≥ 30) identification was 41, with a sensitivity of 81.6%, a specificity of 88.6%.

conclusionsPacemaker patients present a high prevalence of undiagnosed SA. Detection of SA by pacemaker is feasible and accurate in SA screening and monitoring.

Indexed as

AlgorithmsPacemaker, ArtificialAdultCardiography, ImpedanceFemaleHumansMaleMiddle AgedPolysomnographyProspective StudiesSleepSleep Apnea SyndromesPacemakersPolysomnographySleep apnea syndromes

Identifiers

PMID30470999
PMCPMC6700044
OpenAlexW2900737816

What OpenQuestion holds

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LicenceCC BY
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.