The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Laboratory abnormalities in patients with COVID-2019 infection
Open accessAbstract
The aim of this article is to provide a brief overview on the most frequent laboratory abnormalities encountered in patients with COVID-2019 infection An electronic search was performed in Medline (PubMed interface), Scopus and Web of Science, using the keywords 2019 novel coronavirus or 2019-nCoVor without date or language restrictions The title, abstract and full text (when available) of all articles identified according to these search criteria were scrutinized by the authors, and those describing significant laboratory abnormalities in patients with severe COVID-19 infection were finally selected The references of identified documents were also crosschecked for detecting additional studies A particular mention shall be made for procalcitonin and coagulation tests The former test does not appear substantially altered in patients with COVID-19 at admission, but the progressive increase of its value seemingly mirrors a worse prognosis This is not unexpected, whereby serum procalcitonin levels are typically normal in patients with viral infections (or viral sepsis), whilst its gradual increase probably mirrors bacterial superinfection, which may then contribute to drive the clinical course towards unfavorable progression The measurement of other innovative sepsis biomarkers, such as presepsin, for example, would probably help in increasing the accuracy in identification of severe COVID-19 cases, as well as for improving the current approach used for mortality risk prediction As concerns hemostasis tests, the evidence that laboratory criteria for diagnosing DIC are present in nearly three-fourths of patients who died underscores the critical role of these tests in this and other clinical settings, thus suggesting that their assessment shall be considered a routine part of COVID-19 patient monitoring
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1 retracted
Wang, X. F., Yuan, J., Zheng, Y. J., Chen, J., Bao, Y., Wang, Y. R., Wang, L. F., Li, H., Zeng, J. X., Zhang, Y. H., et al. (2020) · PubMed · doi:10.3760/cma.j.issn.0578-1310.2020.0008 · Cited by 105
RetractedClinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30211-7 · Cited by 21,415
Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., Wang, B., Xiang, H., Cheng, Z., Xiong, Y., et al. (2020) · JAMA · doi:10.1001/jama.2020.1585 · Cited by 20,148
La certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19
Martínez González, M. (2020) · DSPACE System - Metalibrary (University of the Coast) · Cited by 18,463
Correspondence - Tranexamic acid for traumatic brain injury
(2005) · London School of Economics and Political Science Theses Online (London School of Economics and Political Science) · doi:10.1016/s0140-6736 · Cited by 11,894
Tang, N., Li, D., Wang, X., & Sun, Z. (2020) · Journal of Thrombosis and Haemostasis · doi:10.1111/jth.14768 · Cited by 5,662
Clinical characteristics of 140 patients infected with SARS‐CoV‐2 in Wuhan, China
Zhang, J.-J., Dong, X., Cao, Y.-Y., Yuan, Y.-D., Yang, Y.-B., Yan, Y.-Q., Akdis, C. A., & Gao, Y.-D. (2020) · Allergy · doi:10.1111/all.14238 · Cited by 4,004
Chen, H., Guo, J., Wang, C., Luo, F., Yu, X., Zhang, W., Li, J., Zhao, D., Xu, D., Gong, Q., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30360-3 · Cited by 3,919
Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19)
Pan, F., Ye, T., Sun, P., Gui, S., Liang, B., Li, L., Zheng, D., Wang, J., Hesketh, R. L., Yang, L., et al. (2020) · Radiology · doi:10.1148/radiol.2020200370 · Cited by 2,856
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Body Localization of ACE-2: On the Trail of the Keyhole of SARS-CoV-2
Salamanna, F., Maglio, M., Landini, M. P., & Fini, M. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.594495 · Cited by 281
Clinical characteristics and in-hospital mortality of COVID-19 adult patients in Saudi Arabia
Abohamr, S. I., Abazid, R. M., Aldossari, M. A., Amer, H. A., Badhawi, O. S., Aljunaidi, O. M., Alzarzour, S. H., Saadeddin, H. M., Bhat, F. A., & Elsheikh, E. (2020) · Saudi Medical Journal · doi:10.15537/smj.2020.11.25495 · Cited by 57
Deep learning with robustness to missing data: A novel approach to the detection of COVID-19
Çallı, E., Murphy, K., Kurstjens, S., Samson, T., Herpers, R., Smits, H., Rutten, M., & van Ginneken, B. (2021) · PLOS ONE · doi:10.1371/journal.pone.0255301 · Cited by 6
Metadata
- Authors
- Lippi, G., & Plebani, M.
- Venue
- Clinical Chemistry and Laboratory Medicine (CCLM)
- Publisher
- Walter de Gruyter GmbH
- Published
- 2020-03-03
- Volume
- 58
- Issue
- 7
- Pages
- 1131-1134
- ISSN
- 1434-6621 (ISSN-L)
- 1437-4331
- OpenAlex
- W3010026184
- Cited by
- 1,020
- References
- 19
- FWCI
- 28.40
- Type
- journal article
- Language
- en
- Open access
- bronze
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