Evidence map›Paper›PMID 33839587›Full record

Observational studyRespiratory medicine2021

Lung ultrasonography for long-term follow-up of COVID-19 survivors compared to chest CT scan.

Guido Giovannetti, Lucrezia De Michele, Michele De Ceglie, Paola Pierucci, Alessandra Mirabile, Marco Vita, Vincenzo Ostilio Palmieri, Giovanna Elisiana Carpagnano, Arnaldo Scardapane, Carlo D'Agostino

Open access · bronzeAbstract readObservational Study
In one paragraph

Observational study in Respiratory medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
5.4field-weighted citation impact, top 3% 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

31 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.

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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

10 authors at 2 institutions in 1 country.

Guido GiovannettiDepartment of Biomedical Sciences and Human Oncology, University of Bari Medical School "Aldo Moro", Piazza Giulio Cesare 11, 70124, Bari, Italy.
Lucrezia De MicheleCardio Thoracic and Vascular Department, University Hospital Policlinico of Bari, Piazza Giulio Cesare 11, 70124, Bari, Italy.
Michele De CeglieDIM, Interdisciplinary Department of Medicine, Section of Diagnostic Imaging, University of Bari Medical School "Aldo Moro", Piazza Giulio Cesare 11, 70124, Bari, Italy.
Paola PierucciCardiothoracic Department, Respiratory and Critical Care Unit Bari Policlinic University Hospital, Italy; "Aldo Moro" Bari University School of Medicine, Italy. Electronic address: paola.pierucci@policlinico.ba.it.
Alessandra MirabileDIM, Interdisciplinary Department of Medicine, Section of Diagnostic Imaging, University of Bari Medical School "Aldo Moro", Piazza Giulio Cesare 11, 70124, Bari, Italy.
Marco VitaAmmagamma Artificial Intelligence Solutions, Via S. Orsola 37, 41121, Modena, Italy.
Vincenzo Ostilio PalmieriDepartment of Biomedical Sciences and Human Oncology, Clinica Medica "A. Murri", University of Bari "Aldo Moro", Piazza Giulio Cesare 11, 70124, Bari, Italy.
Giovanna Elisiana CarpagnanoCardiothoracic Department, Respiratory and Critical Care Unit Bari Policlinic University Hospital, Italy; "Aldo Moro" Bari University School of Medicine, Italy.
Arnaldo ScardapaneDIM, Interdisciplinary Department of Medicine, Section of Diagnostic Imaging, University of Bari Medical School "Aldo Moro", Piazza Giulio Cesare 11, 70124, Bari, Italy.
Carlo D'AgostinoCardio Thoracic and Vascular Department, University Hospital Policlinico of Bari, Piazza Giulio Cesare 11, 70124, Bari, Italy.
University of Bari Aldo Moro · ITAzienda Universitaria Ospedaliera Consorziale - Policlinico Bari · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWhile lung ultrasonography (LUS) has utility for the evaluation of the acute phase of COVID-19 related lung disease, its role in long-term follow-up of this condition has not been well described. The objective of this study is to compare LUS and chest computed tomography (CT) results in COVID-19 survivors with the intent of defining the utility of LUS for long-term follow-up of COVID-19 respiratory disease.

methodsProspective observational study that enrolled consecutive survivors of COVID-19 with acute hypoxemic respiratory failure (HARF) admitted to the Respiratory Intensive Care Unit. Three months following hospital discharge, patients underwent LUS, chest CT, body plethysmography and laboratory testing, the comparison of which forms the basis of this report.

results38 patients were enrolled, with a total of 190 lobes analysed: men 27/38 (71.1%), mean age 60.6 y (SD 10.4). LUS findings and pulmonary function tests outcomes were compared between patients with and without ILD, showing a statistically significant difference in terms of LUS score (p: 0.0002), FEV1 (p: 0.0039) and FVC (p: 0.012). ROC curve both in lobe by lobe and in patient's overall analysis revealed an outstanding ILD discrimination ability of LUS (AUC: 0.94 and 0.95 respectively) with a substantial Cohen's coefficient (K: 0.74 and 0.69).

conclusionsLUS has an outstanding discrimination ability compared to CT in identifying an ILD of at least mild grade in the post COVID-19 follow-up. LUS should be considered as the first-line tool in follow-up programs, while chest CT could be performed based on LUS findings.

Indexed as

SurvivorsUltrasonographyAgedCOVID-19FemaleFollow-Up StudiesHumansLungLung Diseases, InterstitialMaleMiddle AgedProspective StudiesRespiratory Function TestsRespiratory InsufficiencyTime FactorsTomography, X-Ray ComputedChest computed tomographyCOVID-19 follow-upHypoxemic acute respiratory failureILDInterstitial lung diseaseLung ultrasonography

Identifiers

PMID33839587
PMCPMC8010331
OpenAlexW3147380512

What OpenQuestion holds

Textmetadata
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.