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Record W3016170816 · doi:10.1016/j.jinf.2020.03.038

Clinical and laboratory-derived parameters of 119 hospitalized patients with coronavirus disease 2019 in Xiangyang, Hubei Province, China

2020· letter· en· W3016170816 on OpenAlexaff
Liang Shen, Shichao Li, Yufang Zhu, Jianzhong Zhao, Xiaoyong Tang, Huiqin Li, Hui Xing, Mingqing Lu, Christina Frederick, Canping Huang, Gary Wong, Chunhua Wang, Jiaming Lan

Bibliographic record

VenueJournal of Infection · 2020
Typeletter
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)MedicineDiseaseChinaPneumoniaEpidemiologyBeijingScopusSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Public healthInternal medicineGeographyInfectious disease (medical specialty)MEDLINEPathologyBiology

Abstract

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The newly emergent Coronavirus disease 2019 (COVID-19) causes severe viral pneumonia in humans and poses a serious threat to public health worldwide, with cases reported from all 6 permanently inhabited continents. Effective clinical management, based on comprehensive laboratory findings, is critical for improving the survival rates of COVID-19 patients. By now, clinical and epidemiological characteristics of COVID-19 in cities outside of Wuhan, such as Beijing1Tian S. Hu N. Lou J. Chen K. Kang X. Xiang Z. et al.Characteristics of COVID-19 infection in Beijing.J Infect. 2020; (pii: S0163-4453(20)30101-8)https://doi.org/10.1016/j.jinf.2020.02.018Abstract Full Text Full Text PDF Scopus (772) Google Scholar and Wenzhou2Yang W. Cao Q. Qin L. Wang X. Cheng Z. Pan A. et al.Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19): A multi-center study in Wenzhou city, Zhejiang, China.J Infect. 2020; (pii: S0163-4453(20)30099-2)https://doi.org/10.1016/j.jinf.2020.02.016Abstract Full Text Full Text PDF Scopus (660) Google Scholar are described. However, it is currently unknown whether there are any markers that can be informative of mild vs. severe disease. The objective of this study is to describe the comprehensive clinical characteristics of confirmed patients with COVID-19 and explore the potential markers correlating with prognosis. We collected data from 119 hospitalized, symptomatic patients confirmed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) with throat swab specimens in Xiangyang, Hubei Province, between January and February 2020. The severe cases in this study refer to the patients who had enrolled to the intensive care unit (ICU) and received a treatment for more than 3 days, whereas the other confirmed cases were distributed to the mild group. As a control, we collected the laboratory results of 20 healthy subjects (normal cases) examined by the same laboratory department during early December 2019, when COVID-19 was not yet prevalent in Xiangyang. The epidemiological, clinical, laboratory and disease outcome data were obtained from data collection forms and electronic medical records. Information was collected on the date of illness onset, visits to clinical facilities, and hospital admissions. The date of disease onset was defined as the day when the symptom was first noticed. Laboratory tests were conducted at admission, including a complete blood count and serum biochemistry. As shown in Table 1, we found that 85% (101 cases) of the patients were infected by another COVID-19 patient, 46% (55 cases) of the patients were categorized as collective cases, and 30% (36 cases) of patients were also diagnosed with a pre-existing medical condition. After hospital admission, 16.8 % (20 cases) of these patients progressed to severe disease, 4.2% (5 cases) of the patients had complications such as respiratory failure and distress, and 2.5% (3) patients succumbed to COVID-19. Fever was the most common symptom (86%, 102 cases), followed by fatigue (75%, 89 cases) and dry cough (63%, 75 cases). Headache and diarrhea were also reported among 14% (17 cases) and 12% (14 cases) cases, respectively.Table 1Personal and clinical characteristics of patients with COVID-19 (n = 119).No. (%)CharacteristicsAll patients (n = 119)Mild disease (n = 99)Severe disease (n = 20)Median (IQR) age (Y)49 (38-61)45 (34-57)67.5 (60-77)Age groups (Y): ≤187 (6)7 (7)0 (0) 19-4035 (30)35 (35)0 (0) 41-6555 (46)46 (46)9 (45) ≥6622 (18)11 (11)11 (55)Gender Female63 (53)55 (56)8 (40) Male56 (47)44 (44)12 (60)Co-morbidities36 (30)18 (18)17 (85) Hypertension23 (19)10 (10)13 (65) Diabetes12 (10)7 (7)5 (25) Cardiovascular disease7 (6)3 (3)4 (20) Renal diseases2 (2)1 (1)1 (5) Liver disease2 (2)2 (2)0 (0)Travel history to Wuhan Yes18 (15)15 (15)3 (15) No101 (85)84 (85)17 (85)Cluster cases55 (46)47 (47)8 (40)Signs and symptoms Fever102 (86)86 (86)16 (80) Fatigue89 (75)75 (75)14 (70) Dry cough75 (63)63 (53)13 (65) Expectoration22 (18)16 (16)6 (30) Headache17 (14)15 (15)2 (10) Diarrhea14 (12)11 (11)3 (15) Pharyngalgia11 (9)10 (10)1 (5) Palpitation6 (5)4 (4)2 (10) Nausea and vomiting4 (3)3 (3)1 (5) Rhinobyon3 (3)2 (2)1 (5)Routine urinalysis Urine protein21 (18)21 (18)0 (0) Urinary occult blood14 (12)14 (12)0 (0)Symptom onset to hospital admission, median (IQR), days5 (3-7)5(3–7)5 (4-9)Symptom onset to laboratory confirmation via qRT-PCR, median (IQR), days6 (4-9)6 (4-8)7 (5-11)Symptom onset to negative detection via qRT-PCR, median (IQR), days21 (18-24)19 (15-21)25 (23-27)Abbreviations: IQR, interquartile range; Y, year. Open table in a new tab Abbreviations: IQR, interquartile range; Y, year. Laboratory findings showed that decreased lymphocyte counts (Fig. 1A), as well as elevated levels D-dimer (Fig. 1B), may be early markers contributing to disease severity. Decreased albumin (Fig. 1C) and elevated CK (Fig. 1D) levels among severe patients indicate liver damage and shown as indicators of prognosis. Increased lactate dehydrogenase (LDH, Fig. 1E) and α-hydroxybutyrate dehydrogenase (HBDH, Fig. 1F) levels, indicative of heart damage, were detected in COVID-19 patients. Kidney damage in the COVID-19 patients was evidenced by urinary occult blood, increased C1q (Fig. 1G) and β2-MG (Fig. 1H) in COVID-19 patients. In the study, we analyzed 119 cases of COVID-19 patients from a local hospital, in which 101 people had no residence or travel history to Wuhan, meaning most of the subjects in this study are non-first-generation cases. While some studies for clinical examinations have been published, many were not comprehensive and the studies took place in Wuhan.3Huang C. Wang Y. Li X. Ren L. Zhao J. Hu Y. et al.Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China.Lancet. 2020; 395: 497-506https://doi.org/10.1016/S0140-6736(20)30183-5Abstract Full Text Full Text PDF PubMed Scopus (32456) Google Scholar, 4Wang D. Hu B. Hu C. Zhu F. Liu X. Zhang J. et al.Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China.JAMA. 2020; https://doi.org/10.1001/jama.2020.1585Crossref Scopus (16038) Google Scholar, 5Chen H. Guo J. Wang C. Luo F. Yu X. Zhang W. et al.Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: a retrospective review of medical records.Lancet. 2020; https://doi.org/10.1016/s0140-6736(20)30360-3Abstract Full Text Full Text PDF Google Scholar, 6Kui L. Fang Y.Y. Deng Y. Liu W. Wang M.F. Ma J.P. et al.Clinical characteristics of novel coronavirus cases in tertiary hospitals in Hubei Province.Chin Med J (Engl). 2020; https://doi.org/10.1097/CM9.0000000000000744Crossref PubMed Scopus (1000) Google Scholar Especially in the early stages of the outbreak, due to the overwhelmed medical system and lack of adequate medical resources and staff in Wuhan, clinical studies and laboratory examination results may not be reflective of the true nature of COVID-19 in patients. Indeed, this is reflected in the case fatality rates inside (4%) and outside of Wuhan (2.5%, according to our study). While other studies suggest that men are more susceptible to SARS-CoV-2 infection, there were no significant differences in susceptibility to the virus between men and women in our study, even though women had more mild disease cases. The results in this study support the suggestion that there are no significant differences in the levels of ACE2 (the receptor for SARS-CoV-2) expression between genders. As ACE2 is more highly expressed in elderly people, they theoretically would account for a higher percentage of the COVID-19 patients in this study. Biomarkers that serve as reliable prognostic indicators predicting progression to mild vs. severe disease are urgently needed to enhance the quality of clinical care. In this study, we explored the possibility of identifying markers from a routine comprehensive laboratory examination. Consistent with other studies, we found that decreased lymphocyte counts and increased D-dimer concentrations might be an indication of a negative prognosis and enhanced disease severity. In addition, we provide several newly discovered bio-markers: decreased albumin as well as elevated CK, LDH and HBDH levels serve as indicators of a negative prognosis for COVID-19; urinary occult blood, increased C1q and β2-MG were observed in COVID-19 patients, indicated kidney damage. The damage of SARS-CoV-2 to various major tissues and organs of the body during COVID-19 is an important area of investigation. In this study, we found that this virus can cause damage to the liver, heart and kidney, in which abnormal renal indicators may be caused by immunopathological damage. These findings are consistent with recent studies that the virus can damage multiple major organs including liver7.Chen N Z.M. Dong X. Qu J. Gong F. Han Y. Qiu Y. Wang J. Liu Y. Wei Y. Xia J. Yu T. Zhang X. Zhang L. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study.Lancet. 2020; 395: 507-513https://doi.org/10.1016/S0140-6736(20)30211-7Abstract Full Text Full Text PDF PubMed Scopus (14212) Google Scholar,8.Zhang L. Shen F.M. Chen F. Lin Z. Origin and evolution of the 2019 novel coronavirus.Clin Infect Dis. 2020; (pii: ciaa112)https://doi.org/10.1093/cid/ciaa112Crossref Scopus (120) Google Scholar kidney and heart8.Zhang L. Shen F.M. Chen F. Lin Z. Origin and evolution of the 2019 novel coronavirus.Clin Infect Dis. 2020; (pii: ciaa112)https://doi.org/10.1093/cid/ciaa112Crossref Scopus (120) Google Scholar. The exact mechanism of this viral or immune-induced damage should be investigated in future studies. This work was supported by the Doctoral Fund of Xiangyang Central Hospital (RC202001), the One Belt and One Road major project for infectious diseases (2018ZX10101004-003). Gary WONG is supported by a G4 grant from IP, FMX and CAS.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.381
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations16
Published2020
Admission routes1
Has abstractyes

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