Prognostic and discriminatory abilities of imaging scoring systems in predicting COVID‐19 adverse outcomes

Author:

Kandil Omneya1ORCID,Elgenidy Anas2,Saba Patrick3,Hasan Mohamed Tarek4,Galbraith Kenneth3,Spooner Mark3,Ajao Demi3,Yaipen Omar3,Ayad Elyas5,Nassar Abdelrahman2,Hamka Khalil3,Hasan Walaa6,Shah Jaffer7,Shawkat Ahmed8,Hakim Diaa9,Aiash Hani10111213

Affiliation:

1. Faculty of Medicine Alexandria University Alexandria Egypt

2. Faculty of Medicine Cairo University Cairo Egypt

3. State University of New York Upstate Medical University Syracuse New York USA

4. Faculty of Medicine Al Azhar University Cairo Egypt

5. SUNY Oswego Oswego New York USA

6. Clinical Oncology and Nuclear Medicine Suez Canal University Ismailia Egypt

7. Weill Cornell Medicine New York New York USA

8. Pulmonary and Critical Care SUNY Upstate Medical University Syracuse New York USA

9. Brigham and Women's Hospital, Harvard Medical School Boston MA USA

10. Department of Family Medicine Suez Canal University Ismailia Egypt

11. Department of Cardiovascular Perfusion State University of New York Upstate Medical University Syracuse New York USA

12. Department of Medicine State University of New York Upstate Medical University Syracuse New York USA

13. Department of Surgery State University of New York Upstate Medical University Syracuse New York USA

Abstract

AbstractBackgroundTo evaluate the discriminatory ability of imaging modalities' scoring systems in the prediction of COVID‐19 adverse outcomes like ICU admission, ventilatory support, or mortality.MethodsWe searched PUBMED, EBSCO, WEB OF SCIENCE, and SCOPUS. Two authors independently screened the resulting papers for fulfillment criteria. Meta‐DiSc version 1.4, RevMan version 5.4, and MedCalc version 19.1 were used for test accuracy analysis, sensitivity and specificity analysis, and pooling Area under the curve for discriminatory assessment, respectively.ResultsRegarding mortality prediction, the computed tomography (CT) showed significantly higher sensitivity [80%; 95% CI 0.74–0.85] and positive likelihood ratio (PLR) [4.41 95% CI 2.94–6.61] relative to the Lung Ultrasound Score (LUS) approach, while the LUS approached the CT scan with specificity of 81% [95% CI 0.78–0.83] and negative likelihood ratio (NLR) of [0.32; 95% CI 0.16–0.64]. The pooled area under ROC for LUS was [AUC = 0.777, 95% CI 0.701–0.852; p < 0.001, I2 = 74.86%, p = 0.019] while the pooled area under ROC for CT severity score was [AUC = 0.855, 95% CI 0.78–0.93; p < 0.001, I2 = 93.73%, p < 0.001]. Regarding adverse outcomes prediction, the LUS had a slightly higher specificity of [78%; 95% CI 0.75–0.80] and PLR of [3.60; 95% CI 2.28–5.68] compared to CT score. The pooled AUC using LUS was (0.77, 95% CI 0.719–0.832; p < 0.001), while using CT severity score was (0.843, 95% CI 0.787–0.898; p < 0.001), and using X‐ray scores was (0.814, 95% CI 0.751–0.878; p < 0.001).ConclusionCT severity score showed a better discriminatory ability in predicting COVID‐19 adverse outcomes, as in‐hospital mortality, ICU admission, and need for ventilatory support compared to LUS and X‐RAY scores, while the LUS, being more specific, had a slightly better prognostic value.

Publisher

Wiley

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