Evidence map›Paper›PMID 37103717›Full record

ArticleAnnals of intensive care2023

Assessment of lung injury severity using ultrasound in critically ill COVID-19 patients in resource limited settings.

Seydina Alioune Beye, Boubacar Diallo, Modibo Keita, Yacouba Cissoko, Khadidia Ouattara, Hammadoun Dicko, Majaliwa Shabani, Amadou Sidibé, Modibo Berthé, Yaya Ibrahim Coulibaly and 7 more

Abstract read
In one paragraph

Article in Annals of intensive care, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

17 authors.

Seydina Alioune BeyeDepartment of Intensive Care and Anesthesia, Point G Teaching Hospital, Bamako, Mali. beyealioune@gmail.com.ORCID http://orcid.org/0000-0002-0146-7273
Boubacar DialloDepartment of Intensive Care and Anesthesia, Point G Teaching Hospital, Bamako, Mali.
Modibo KeitaDepartment of Public Health, Teaching Hospital (CHU) Dermatology, Bamako, Mali.
Yacouba CissokoFaculty of Medicine and Odonto-Stomatology (FMOS)/University of Sciences, Technics and Technologies of Bamako (USTTB), Bamako, Mali.
Khadidia OuattaraFaculty of Medicine and Odonto-Stomatology (FMOS)/University of Sciences, Technics and Technologies of Bamako (USTTB), Bamako, Mali.
Hammadoun DickoDepartment of Intensive Care and Anesthesia, Point G Teaching Hospital, Bamako, Mali.
Majaliwa ShabaniDepartment of Intensive Care and Anesthesia, Point G Teaching Hospital, Bamako, Mali.
Amadou SidibéDepartment of Intensive Care and Anesthesia/University of Sciences, Technics and Technologies of Bamako (USTTB), Bamako, Mali.
Modibo BerthéDepartment of Intensive Care and Anesthesia, Point G Teaching Hospital, Bamako, Mali.
Yaya Ibrahim CoulibalyFaculty of Medicine and Odonto-Stomatology (FMOS)/University of Sciences, Technics and Technologies of Bamako (USTTB), Bamako, Mali.
Nouhoum DianiFaculty of Medicine and Odonto-Stomatology (FMOS)/University of Sciences, Technics and Technologies of Bamako (USTTB), Bamako, Mali.
Mohamed KeitaDepartment of Intensive Care and Anesthesia, Point G Teaching Hospital, Bamako, Mali.
Yacouba TolobaFaculty of Medicine and Odonto-Stomatology (FMOS)/University of Sciences, Technics and Technologies of Bamako (USTTB), Bamako, Mali.
Sounkalo DaoFaculty of Medicine and Odonto-Stomatology (FMOS)/University of Sciences, Technics and Technologies of Bamako (USTTB), Bamako, Mali.
Veronique SuttelsNational Teaching Hospital for Tuberculosis and Respiratory Diseases (CNHU-PPC), Cotonou, Benin.
Youssouf Coulibaly *Department of Intensive Care and Anesthesia, Point G Teaching Hospital, Bamako, Mali.
Armand Mekontso Dessap *Service de Médecine Intensive Réanimation, AP-HP, Hôpitaux Universitaires Henri-Mondor, 94010, Créteil, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung ultrasound is a non-invasive tool available at the bedside for the assessment of critically ill patients. The objective of this study was to evaluate the usefulness of lung ultrasound in assessing the severity of SARS-CoV-2 infection in critically-ill patients in a low-income setting.

methodsWe conducted a 12-month observational study in a university hospital intensive care unit (ICU) in Mali, on patients admitted for COVID-19 as diagnosed by a positive polymerase chain reaction for SARS-CoV-2 and/or typical lung computed tomography scan findings.

resultsThe inclusion criteria was met by 156 patients with a median age of 59 years. Almost all patients (96%) had respiratory failure at admission and many needed respiratory support (121/156, 78%). The feasibility of lung ultrasound was very good, with 1802/1872 (96%) quadrants assessed. The reproducibility was good with an intra-class correlation coefficient of elementary patterns of 0.74 (95% CI 0.65, 0.82) and a coefficient of repeatability of lung ultrasound score < 3 for an overall score of 24. Confluent B lines were the most common lesions found in patients (155/156). The overall mean ultrasound score was 23 ± 5.4, and was significantly correlated with oxygen saturation (Pearson correlation coefficient of - 0.38, p < 0.001). More than half of the patients died (86/156, 55.1%). The factors associated with mortality, as shown by multivariable analysis, were: the patients' age; number of organ failures; therapeutic anticoagulation, and lung ultrasound score.

conclusionLung ultrasound was feasible and contributed to characterize lung injury in critically-ill COVID-19 patients in a low income setting. Lung ultrasound score was associated with oxygenation impairment and mortality.

Indexed as

AssessmentCOVID-19 patientsICULung ultrasoundMali

Identifiers

PMID37103717
PMCPMC10134692

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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.