The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Pulmonary artery trunk enlargement on admission as a predictor of mortality in in-hospital patients with COVID-19
Open accessAbstract
Purpose To describe the prognostic value of pulmonary artery (PA) trunk enlargement on the admission of in-hospital patients with severe COVID-19 infection by unenhanced CT image.Materials and methods In-hospital patients confirmed COVID-19 from January 18, 2020, to March 7, 2020, were retrospectively enrolled. PA trunk diameters on admission and death events were collected to calculate the optimum cutoff using a receiver operating characteristic curve. According to the cutoff, the subjects on admission were divided into two groups. Then the in-hospital various parameters were compared between the two groups to assess the predictive value of PA trunk diameter.Results In the 180 enrolled in-hospital patients (46.99 ± 14.95 years; 93 (51.7%) female, 14 patients (7.8%) died during their hospitalization. The optimum cutoff PA trunk diameter to predict in-hospital mortality was > 29 mm with a sensitivity of 92.59% and a specificity of 91.11%. Kaplan-Meier survival curves for PA trunk diameter on admission showed that a PA trunk diameter > 29 mm was a significant predictor of subsequent death (log-rank < 0.001, median survival time of PA > 29 mm was 28 days).Conclusion PA trunk enlargement can be a useful predictive factor for distinguishing between mild and severe COVID-19 disease progression.
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“Furthermore, owing to the coronavirus disease 2019 (COVID-19) pandemic, the number of COVID-19-related studies from Japan has increased [54–61], with a significant increase in such research focusing on AI [62–64].”
From Fairness of artificial intelligence in healthcare: review and recommendations (2024) · cites this paper as [56]
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Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection
Bernheim, A., Mei, X., Huang, M., Yang, Y., Fayad, Z. A., Zhang, N., Diao, K., Lin, B., Zhu, X., Li, K., et al. (2020) · Radiology · doi:10.1148/radiol.2020200463 · Cited by 2,710
Procalcitonin in patients with severe coronavirus disease 2019 (COVID-19): A meta-analysis
Lippi, G., & Plebani, M. (2020) · Clinica Chimica Acta · doi:10.1016/j.cca.2020.03.004 · Cited by 664
Wu, J., Wu, X., Zeng, W., Guo, D., Zheng, F., Chen, L., Huang, H., & Li, C. (2020) · Investigative Radiology · doi:10.1097/rli.0000000000000670 · Cited by 638
Acute Pulmonary Embolism Associated with COVID-19 Pneumonia Detected with Pulmonary CT Angiography
Grillet, F., Behr, J., Calame, P., Aubry, S., & Delabrousse, É. (2020) · Radiology · doi:10.1148/radiol.2020201544 · Cited by 552
Treatment Options for Carbapenem-Resistant Enterobacteriaceae Infections
Morrill, H. J., Pogue, J. M., Kaye, K. S., & LaPlante, K. L. (2015) · Open Forum Infectious Diseases · doi:10.1093/ofid/ofv050 · Cited by 436
Tan, R. T., Kuzo, R. S., Goodman, L. R., Siegel, R., Haasler, G. R., & Presberg, K. W. (1998) · CHEST Journal · doi:10.1378/chest.113.5.1250 · Cited by 401
on behalf of the European Society of Radiology (ESR) and the European Society of Thoracic Imaging (ESTI), Revel, M., Parkar, A. P., Prosch, H., Silva, M., Sverzellati, N., Gleeson, F. V., & Brady, A. P. (2020) · European Radiology · doi:10.1007/s00330-020-06865-y · Cited by 394
CT Imaging and Differential Diagnosis of COVID-19
Dai, W.-C., Zhang, H., Yu, J., Xu, H.-J., Chen, H., Luo, S., Zhang, H., Liang, L.-H., Wu, X.-L., Yi, L., et al. (2020) · Canadian Association of Radiologists Journal · doi:10.1177/0846537120913033 · Cited by 367
Severe Acute Respiratory Syndrome: Temporal Lung Changes at Thin-Section CT in 30 Patients
OOI, G. C., Khong, P. L., Müller, N. L., Yiu, W. C., Zhou, L. J., Ho, J., LAM, B. K., Nicolaou, S., & TSANG, K. W. T. (2004) · Radiology · doi:10.1148/radiol.2303030853 · Cited by 329
Cardiac involvement in COVID‐19 patients: Risk factors, predictors, and complications: A review
Aghagoli, G., Marin, B. G., Soliman, L. B., & Sellke, F. W. (2020) · Journal of Cardiac Surgery · doi:10.1111/jocs.14538 · Cited by 193
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Fairness of artificial intelligence in healthcare: review and recommendations
Ueda, D., Kakinuma, T., Fujita, S., Kamagata, K., Fushimi, Y., Ito, R., Matsui, Y., Nozaki, T., Nakaura, T., Fujima, N., et al. (2024) · Japanese Journal of Radiology · doi:10.1007/s11604-023-01474-3 · Cited by 607
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Yanagawa, M., Ito, R., Nozaki, T., Fujioka, T., Yamada, A., Fujita, S., Kamagata, K., Fushimi, Y., Tsuboyama, T., Matsui, Y., et al. (2023) · La radiologia medica · doi:10.1007/s11547-023-01691-w · Cited by 33
Dalpiaz, G., Gamberini, L., Carnevale, A., Spadaro, S., Mazzoli, C. A., Piciucchi, S., Allegri, D., Capozzi, C., Neziri, E., Bartolucci, M., et al. (2022) · La radiologia medica · doi:10.1007/s11547-021-01444-7 · Cited by 17
Scarpato, B. M., Locke, B. W., Bledsoe, J. R., Knox, D., Conner, K. E., Stoddard, G. J., Cirulis, M. M., Elliott, C. G., & Dodson, M. W. (2023) · Pulmonary Circulation · doi:10.1002/pul2.12225 · Cited by 7
Riachy, M. A., Ismail, A., Awali, M., Farah, F., Haddad, S. F., Kerbage, A., Aoun, N. J., & Sleilaty, G. (2022) · Mayo Clinic Proceedings Innovations Quality & Outcomes · doi:10.1016/j.mayocpiqo.2022.07.001 · Cited by 4
Metadata
- Authors
- Zhu, Q.-Q., Gong, T., Huang, G.-Q., Niu, Z.-F., Yue, T., Xu, F.-Y., Chen, C., & Wang, G.-B.
- Affiliations
- Shandong University (CN)
- Sir Run Run Shaw Hospital (CN)
- Venue
- Japanese Journal of Radiology
- Published
- 2021-03-22
- Volume
- 39
- Issue
- 6
- Pages
- 589-597
- ISSN
- 1867-1071 (ISSN-L)
- 1867-108X
- PMC
- PMC7982766
- OpenAlex
- W3137553501
- Cited by
- 20
- References
- 15
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 4 · 2023: 4 · 2022: 8 · 2021: 1
- FWCI
- 1.35
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Japanese Journal of Radiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Zhejiang University special scientific research (2020XGZX051)
- Topics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Clinical Neurology › Long-Term Effects of COVID-19
- Keywords
- COVID-19 pandemic, in-hospital mortality, prognostic factors, unenhanced CT, pulmonary artery diameter, disease severity, cut-off values
- MeSH terms
- COVID-19, SARS-CoV-2, Adult, Dilatation, Pathologic, Female, Hospitalization, Humans, Male, Middle Aged, Prognosis, Pulmonary Artery, Retrospective Studies, ROC Curve, Tomography, X-Ray Computed, Hospital Mortality
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