Evidence map›Paper›PMID 34811572›Full record

ArticleDer Anaesthesist2022

[Development of a giant bulla under spontaneous breathing by self-inflicted lung injury in a patient with COVID-19 pneumonia].

Nicholas Moellhoff, Philipp Groene, Ludwig Ney, Daniela Hauer

Open access · hybridAbstract readCase Reports
In one paragraph

Article in Der Anaesthesist, 2022. 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, top 75% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

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

4 authors at 2 institutions in 1 country.

Nicholas MoellhoffAbteilung für Hand‑, Plastische und Ästhetische Chirurgie, Klinikum der Universität München, LMU München, München, Deutschland.
Philipp GroeneIntensivstation ANIS 5, Klinik für Anaesthesiologie, Klinikum der Universität München, LMU München, Nussbaumstraße 20, 80336, München, Deutschland.
Ludwig NeyIntensivstation ANIS 5, Klinik für Anaesthesiologie, Klinikum der Universität München, LMU München, Nussbaumstraße 20, 80336, München, Deutschland.
Daniela HauerIntensivstation ANIS 5, Klinik für Anaesthesiologie, Klinikum der Universität München, LMU München, Nussbaumstraße 20, 80336, München, Deutschland. daniela.hauer@med.uni-muenchen.de.
LMU Klinikum · DELudwig-Maximilians-Universität München · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The outbreak of SARS-CoV‑2 and the associated COVID-19 pandemic pose major challenges to healthcare systems worldwide. New data on diagnosis, clinical presentation and treatment of the disease are published on a daily basis. This case report describes the fatal course of severe COVID-19 pneumonia in an 81-year-old patient with no previous pulmonary disease who developed a giant bulla during non-invasive high-flow oxygen therapy. Virus-induced diffuse destruction of alveolar tissue or patient self-inflicted lung injury (P-SILI) are discussed as possible pathomechanisms. Future studies must determine whether lung-protective mechanical ventilation with high levels of sedation and paralysis to suppress spontaneous respiratory drive and to reduce transpulmonary pressure can prevent structural lung damage induced both by the virus and P‑SILI in COVID-19 patients with ARDS.

Indexed as

COVID-19Lung InjuryRespiratory Distress SyndromeAged, 80 and overBlisterHumansLungPandemicsRespiration, ArtificialSARS-CoV-2ARDSBullaCOVID-19High-flow therapyPatient self-inflicted lung injury

Identifiers

PMID34811572
PMCPMC8608363
OpenAlexW3216346982

What OpenQuestion holds

Textmetadata
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.