Evidence map›Paper›PMID 36183064›Full record

Observational studyBMC anesthesiology2022

Breathing pattern, accessory respiratory muscles work, and gas exchange evaluation for prediction of NIV failure in moderate-to-severe COVID-19-associated ARDS after deterioration of respiratory failure outside ICU: the COVID-NIV observational study.

Andrey I Yaroshetskiy, Zamira M Merzhoeva, Natalia A Tsareva, Natalia V Trushenko, Galia S Nuralieva, Vasily D Konanykhin, Anna P Krasnoshchekova, Sergey N Avdeev

Registry-linked trialOpen access · goldAbstract readObservational Study
In one paragraph

Observational study in BMC anesthesiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04667923 (Noninvasive Ventilation in Moderate-to-severe COVID-19-associated Acute Respiratory Distress-syndrome to Prevent Tracheal Intubation), which is not on this map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.4field-weighted citation impact, top 18% 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.

NCT04667923 completednot on this map

Noninvasive Ventilation in Moderate-to-severe COVID-19-associated Acute Respiratory Distress-syndrome to Prevent Tracheal Intubation: the COVID-NIV Study

TypeobservationalSponsorI.M. Sechenov First Moscow State Medical UniversityRan2020 to 2021Enrolled80ConditionsCovid19, ARDSArmsRespiratory monitoring, Respiratory muscles ultrasound, Electro impedance tomography, Capnography, Arterial blood gas
3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
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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 at 2 institutions in 1 country.

Andrey I YaroshetskiySechenov First Moscow State Medical University (Sechenov University), Moscow, Russia. dr.intensivist@gmail.com.ORCID 0000-0002-1484-092X
Zamira M MerzhoevaSechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.ORCID 0000-0002-3174-5000
Natalia A TsarevaSechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.ORCID 0000-0001-9357-4924
Natalia V TrushenkoSechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.ORCID 0000-0002-0685-4133
Galia S NuralievaSechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.ORCID 0000-0002-4726-4906
Vasily D KonanykhinAnesthesiology and Critical Care Department, Research Institution for Clinical Surgery Division, Pirogov Russian National Research Medical University, 8/2 Trubetskaya Str., Moscow, 119991, Russia.ORCID 0000-0002-6558-1227
Anna P KrasnoshchekovaSechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.ORCID 0000-0001-6986-1368
Sergey N AvdeevSechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.ORCID 0000-0002-5999-2150
Sechenov University · RUPirogov Russian National Research Medical University · RU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundData on the efficacy of non-invasive ventilation (NIV) after progression of respiratory failure in patients who have already received oxygen therapy, or CPAP outside ICU is limited. The study aimed to find predictors of NIV failure based on breathing pattern, gas exchange, and accessory respiratory muscles evaluation in patients who progressed to moderate-to-severe COVID-19 ARDS.

methodsThis was a prospective observational study in patients with moderate-to-severe COVID-19-ARDS on NIV (n = 80) admitted to COVID-ICU of Sechenov University. The combined success rate for conventional oxygen and CPAP outside ICU was 78.6% (440 of 560 patients). The primary endpoints were intubation rate and mortality. We measured respiratory rate, exhaled tidal volume (Vte), mean peak inspiratory flow (PIF), inspiratory time (Ti), PaO

resultsNIV failure rate in ICU after deterioration of respiratory failure outside ICU was 71.3% (n = 57). Patients with the subsequent NIV failure were older at inclusion, more frail, had longer duration of disease before ICU admission, and higher rate of CPAP use outside ICU. ROC-analysis revealed that the following respiratory parameters after 48 h of NIV can serve as a predictors for NIV failure in moderate-to-severe COVID-19-associated ARDS: PaO

conclusionIn patients who progressed to moderate-to-severe COVID-19-ARDS probability of NIV success rate was about 1/3. Prediction of the NIV failure can be made after 48 h based on ROX index < 5.02, PaO

trial registrationClinicalTrials.gov identifier: NCT04667923 , registered on 16/12/2020.

Indexed as

COVID-19Noninvasive VentilationRespiratory Distress SyndromeRespiratory InsufficiencyCarbon DioxideHumansIntensive Care UnitsOxygenRespiratory MusclesRespiratory RateCarbon DioxideOxygenAccessory respiratory musclesAlveolar dead spaceARDSCOVID-19NIVNoninvasive ventilationROX-index

Identifiers

PMID36183064
PMCPMC9525938
OpenAlexW4298623098

What OpenQuestion holds

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LicenceCC BY
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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.