Evidence map›Paper›PMID 41107677›Full record

ArticleEmergency radiology2025

Prognostic validation of a simplified chest CT severity score in COVID-19 patients from resource-limited Gaza.

Husam H Mansour, Noor Khairiah A Karim, Noor Diyana Osman, Rohayu Hami, Yasser S Alajerami

Abstract readValidation Study
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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 1 paper.

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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Husam H MansourDepartment of Biomedical Imaging, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, 13200, Pulau Pinang, Kepala Batas, Malaysia.
Noor Khairiah A KarimDepartment of Biomedical Imaging, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, 13200, Pulau Pinang, Kepala Batas, Malaysia. drkhairiah@usm.my.ORCID http://orcid.org/0000-0001-8176-3939
Noor Diyana OsmanDepartment of Biomedical Imaging, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, 13200, Pulau Pinang, Kepala Batas, Malaysia.
Rohayu HamiDepartment of Community Health, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, 13200, Pulau Pinang, Kepala Batas, Malaysia.
Yasser S AlajeramiMedical Imaging Department, Applied Medical Sciences Faculty, Al Azhar University-Gaza, P.O. Box 1277, Gaza City, Palestine.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo validate the Simplified Chest CT Severity Score (SCCTSS) for prognostic stratification of COVID-19 pneumonia in a resource-limited Gaza cohort and compare its diagnostic accuracy and interobserver reliability against the established Chest CT Score (CCTS). MATERIALS AND

methodsIn this retrospective cohort study, 360 adult patients with RT-PCR-confirmed COVID-19 underwent non-contrast chest CT within 72 h of admission (September 2020-June 2022). SCCTSS assessed three parameters, opacity type, extent, and distribution, with scores ranging from 0 to 7 and classified into no involvement (0), mild (3), moderate (4-5), and severe (6-7) disease. CCTS scores ranged from 0 to 25 and were categorized as no involvement (0), mild (1-8), moderate (9-16), and severe (17-25). Interobserver agreement was measured by Cohen's kappa. Diagnostic accuracy for mortality prediction was evaluated via sensitivity, specificity, ROC curves, and Kaplan-Meier survival analyses.

resultsSCCTSS demonstrated almost perfect interobserver reliability (κ = 0.883), comparable to CCTS (κ = 0.870). Mortality was significantly associated with higher SCCTSS categories; 58.5% of non-survivors had severe scores (6-7), compared to 3.7% of survivors (p < 0.001). At the optimal cutoff (≥ 3.5), SCCTSS sensitivity was 93.2%, specificity 90.1%, with AUC 0.933. CCTS (cutoff ≥ 8.5) showed 89.8% sensitivity, 93.4% specificity, and AUC 0.948. Kaplan-Meier analysis confirmed significantly reduced 30-day survival in patients with scores above these thresholds (p < 0.001).

conclusionsSCCTSS is a simple, reproducible, and effective CT scoring tool for COVID-19 prognostication, suitable for rapid risk stratification in resource-limited environments.

Indexed as

COVID-19Severity of Illness IndexTomography, X-Ray ComputedAdultAgedFemaleHumansMaleMiddle AgedMiddle EastPrognosisReproducibility of ResultsRetrospective StudiesSensitivity and SpecificityChest CTCOVID-19GazaPrognostic stratificationResource-limited settingsSCCTSSSeverity score

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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.