Clinical Characteristics Predicting Progression of&amp;nbsp;COVID-19

Ji, Dong; Zhang, Dawei; Zhu, Chen; Xu, Zhe; Zhao, Peng; Zhang, Mingjie; Zhang, Lunqing; Cheng, Gregory; Wang, Yudong; Yang, Guang; Liu, Hongxia; Li, Boyu; Jun-sheng, JI; Lau, George; Qin, Enqiang

doi:10.2139/ssrn.3539674

Cited by 36 publications

(46 citation statements)

References 9 publications

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“…For most people the Novel Coronavirus Disease 2019 (COVID-19) has caused mild illness, however, it can generate severe pneumonia in some patient, and it can be fatal in others [2][3][4][6][7][8][9]. Older people, and those with pre-existing medical conditions (such as cardiovascular disease, chronic respiratory disease or diabetes) are at risk for severe disease.…”

Section: Introductionmentioning

confidence: 99%

Impact of cardiovascular risk profile on COVID-19 outcome. A meta-analysis

et al. 2020

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Background The ongoing pandemic of Novel Coronavirus Disease 2019 (COVID-19) infection has created a global emergency. Despite the infection causes a mild illness to most people, some patients are severely affected, demanding an urgent need to better understand how to riskstratify infected subjects. Design This is a meta-analysis of observational studies evaluating cardiovascular (CV) complications in hospitalized COVID-19 patients and the impact of cardiovascular risk factors (RF) or comorbidities on mortality. Methods Data sources: PubMed, Scopus, and ISI from 1 December 2019 through 11 June 2020; references of eligible studies; scientific session abstracts; cardiology web sites. We selected studies reporting clinical outcomes of hospitalized patients with COVID-19. The main outcome was death. Secondary outcomes were cardiovascular symptoms and cardiovascular events developed during the COVID-19-related hospitalization. Extracted data were recorded in excel worksheets and analysed using statistical software (MedCalc, OpenMetanalyst, R). We used the proportion with 95% CI as the summary measure. A Freeman-Tukey transformation was used to calculate the weighted summary proportion under the random-effects model. Heterogeneity was assessed by using the Cochran Q test and I 2 values. Results Among 77317 hospitalized patients from 21 studies, 12.86% had cardiovascular comorbidities or RF. Cardiovascular complications were registered in 14.09% of cases during hospitalization. At meta-regression analysis, pre-existing cardiovascular comorbidities or RF were significantly associated to cardiovascular complications in COVID-19 patients (p = 0.019). Pre-existing cardiovascular comorbidities or RF (p<0.001), older age (p<0.001), and

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Section: Introductionmentioning

confidence: 99%

Impact of cardiovascular risk profile on COVID-19 outcome. A meta-analysis

et al. 2020

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“…Therefore, identifying risk factors at presentation that predict the likelihood of disease progression would help the physicians to decide which group of patients can be managed safely at district hospitals and who needs early transfer to tertiary centers. Age, comorbidities, lymphopenia, serum ferritin, d-dimer levels, cardiac troponin I, lactate dehydrogenase, IL-6, subsets had been shown to be associated with poor prognosis and increased [4][5][6][11][12][13]. Guan et al [14] described the clinical characteristics of 1,099 patients with laboratory-confirmed COVID-19 from 552 hospitals through 29 January 2020.…”

Section: Discussionmentioning

confidence: 99%

Prediction for Progression Risk in Patients With COVID-19 Pneumonia: The CALL Score

Zhang

et al. 2020

Clinical Infectious Diseases

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Abstract Background We aimed to clarify high-risk factors for coronavirus disease 2019 (COVID-19) with multivariate analysis and establish a predictive model of disease progression to help clinicians better choose a therapeutic strategy. Methods All consecutive patients with COVID-19 admitted to Fuyang Second People’s Hospital or the Fifth Medical Center of Chinese PLA General Hospital between 20 January and 22 February 2020 were enrolled and their clinical data were retrospectively collected. Multivariate Cox regression was used to identify risk factors associated with progression, which were then were incorporated into a nomogram to establish a novel prediction scoring model. ROC was used to assess the performance of the model. Results Overall, 208 patients were divided into a stable group (n = 168, 80.8%) and a progressive group (n = 40,19.2%) based on whether their conditions worsened during hospitalization. Univariate and multivariate analyses showed that comorbidity, older age, lower lymphocyte count, and higher lactate dehydrogenase at presentation were independent high-risk factors for COVID-19 progression. Incorporating these 4 factors, the nomogram achieved good concordance indexes of .86 (95% confidence interval [CI], .81–.91) and well-fitted calibration curves. A novel scoring model, named as CALL, was established; its area under the ROC was .91 (95% CI, .86–.94). Using a cutoff of 6 points, the positive and negative predictive values were 50.7% (38.9–62.4%) and 98.5% (94.7–99.8%), respectively. Conclusions Using the CALL score model, clinicians can improve the therapeutic effect and reduce the mortality of COVID-19 with more accurate and efficient use of medical resources.

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“…Studies on COVID-19 patients have reported the levels of some inflammatory markers such as procalcitonin, C-reactive protein, erythrocyte sedimentation rate and serum amyloid A. However, little attention has been paid to ferritin, even though hyperferritinemia has been shown to be associated with complications in other viral diseases such as dengue fever [3]. In order to determine if the circulating ferritin concentration could be used to predict COVID-19 progression, and to associate hyperferritinemia with the development of the cytokine storm, we reviewed all published studies that documented serum ferritin levels in patients with severe and non-severe COVID-19 disease, along with other inflammatory factors, which are summarized in Table 1 [1][2][3][4][5][6][7][8].…”

mentioning

confidence: 99%

“…These studies report ferritin concentrations of COVID-19 patients only at the time of hospital admission. It can be observed that the concentrations of ferritin are generally within the normal range (30-400 μg/L [3]) in patients with non-severe disease (according to the National Health Commission of China guidelines for COVID-19 severity classification). However, hyperferritinemia (ferritin level > 400 μg/L), was observed in patients with severe disease on admission.…”

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confidence: 99%