Clinical Characteristics of confirmed and clinically diagnosed patients with 2019 novel coronavirus pneumonia: a single-center, retrospective, case-control study

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Med Clin (Barc). 2020 Jul 28:S0025-7753(20)30564-9. doi: 10.1016/j.medcli.2020.06.055. Online ahead of print.

ABSTRACT

BACKGROUND: Novel coronavirus disease 2019 (COVID-19) is an emerging infectious disease caused by SARS-CoV-2. At the peak of the outbreak in Wuhan (January and February), there are two types of COVID-19 patients: laboratory confirmation and clinical diagnosis. This study aims to compare and analyze the clinical outcomes and characteristics of confirmed and clinically diagnosed COVID-19 patients to determine whether they are of the same type and require equal treatment. More importantly, the prognostic factors of COVID-19 patients are explored.

METHODS: A total of 194 hospitalized patients with COVID-19 pneumonia were retrospectively studied. Demographic data, clinical characteristcs, laboratory results and prognostic information were collected by electronic medical record system and analyzed.

RESULTS: Among 194 subjects included, 173 were confirmed and 21 were clinically diagnosed. There were no significant differences in clinical outcomes (mortality rate 39[22.54%] vs 7[33.33%], P=0.272) and hospital stay (19.00 vs 16.90 days, P=0.411) between the confirmed and clinically diagnosed group, and prognostic factors were similar between them. Older age, lower albumin levels, higher serum Lactate dehydrogenase (LDH) levels, higher D-D levels, longer prothrombin time (PT), higher IL-6 levels, lower T cells indicated poor prognosis in patients with COVID-19 pneumonia. NK cell has the highest AUC among all measured indicators (NK AUC=0.926, P<0.001).

CONCLUSION: Laboratory-confirmed and clinically diagnosed COVID-19 patients are similar in clinical outcomes and most clinical characteristics. They are of the same type and require equal treatment. Age, AST, LDH, BUN, PT, D-D, IL6, white blood cell and neutrophil counts, T cell and T cell subset counts can efficiently predict clinical outcomes.

PMID:32782109 | DOI:10.1016/j.medcli.2020.06.055

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