Evidence map›Paper›PMID 39964580›Full record

ArticleEmergency radiology2025

Prognostication and integration of bedside lung ultrasound and computed tomography imaging findings with clinical features to Predict COVID-19 In-hospital mortality and ICU admission.

Mohammad Reza Maghsoudi, Amirhesam Alirezaei, Atena Soltanzadi, Sepehr Aghajanian, Arvin Naeimi, Ayad Bahadori Monfared, Fateme Mohammadifard, Mahmood Bakhtiyari

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Article in Emergency radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Article
  2. Advances in bedside imaging: lung ultrasound.Intensive care medicine experimental · 2025
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4 · The record

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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.

Mohammad Reza MaghsoudiDepartment of Emergency Medicine & Toxicology, Alborz University of Medical Sciences, Karaj, Iran.
Amirhesam AlirezaeiClinical Research and Development Center, Department of Nephrology, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. amirhesam124@gmail.com.
Atena SoltanzadiSchool of Medicine, Alborz University of Medical Sciences, Karaj, Iran.
Sepehr AghajanianSchool of Medicine, Alborz University of Medical Sciences, Karaj, Iran. Sepehraghajanian2@gmail.com.ORCID http://orcid.org/0000-0001-6062-0138
Arvin NaeimiStudent Research Committee, School of Medicine, Guilan University of Medical Sciences, Rasht, Gilan, Iran.ORCID http://orcid.org/0000-0002-8659-9549
Ayad Bahadori MonfaredDepartment of Health & Community Medicine, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Fateme MohammadifardSchool of Medicine, Alborz University of Medical Sciences, Karaj, Iran.
Mahmood BakhtiyariDepartment of Community Medicine, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionBedside lung ultrasound (LUS) and computed tomography (CT) imaging are valuable modalities in screening and diagnosis of pulmonary diseases. This study aims to investigate the prognostic value of integrating LUS and CT imaging findings with clinical features to predict poor outcomes upon ER admission in COVID-19.

methodsPatients visiting the study center with clinical presentation and laboratory findings compatible with COVID-19 between April 2020 to January 2022 were considered for this study. Several imaging findings (ground glass opacity, consolidation, atelectatic bands, mosaic attenuation, ARDS pattern, crazy paving, pleural thickening in CT and A-line, comet-tail artifact, confluent B-Line in BLUS, pleural thickening and Consolidation in both modalities) were evaluated, alongside clinical assessments upon admission, to assess their prognostic value. The top radiological, LUS findings, and clinical signs were integrated in a nomogram for predicting mortality.

resultsA total of 1230 patients were included in the analyses. Among the findings, consolidation in BLUS and CT imaging, and absence of A-lines were associated with mortality. In addition to these findings, ground-glass opacities, atelectatic band, mosaic attenuation, crazy paving, and confluent B-line were also associated with ICU hospitalization. Although, the prognostic value of individual markers was poor and comparable (AUC < 0.65), the combined use of top clinical and imaging findings in the associated nomogram led to a high accuracy in predicting mortality (Area under curve: 87.3%).

conclusionsBLUS and CT imaging findings alone provide limited utility in stratifying patients for higher mortality and ICU admission risk and should not be used for risk stratification alone outside the context of each patient and their clinical presentations in suspected COVID-19 patients.

Indexed as

COVID-19Hospital MortalityTomography, X-Ray ComputedAgedFemaleHumansIntensive Care UnitsLungMaleMiddle AgedPoint-of-Care SystemsPoint-of-Care TestingPredictive Value of TestsPrognosisRetrospective StudiesSARS-CoV-2Computed tomographyEmergency roomLung ultrasoundMachine learningPrediction model

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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.