Clinical characteristics of 225 patients with COVID-19 in a tertiary Hospital near Wuhan, China

Li, Ruoqing; Tian, Jigang; Yang, Fang; Lv, Lei; Yu, Jie; Sun, Guangyan; Ma, Yu; Yang, Xiaojuan; Ding, Jianqiang

doi:10.1016/j.jcv.2020.104363

Cited by 116 publications

(133 citation statements)

References 13 publications

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“…Ninety‐two articles (of which four included pregnant patients) reported adult patients infected with SARS‐CoV‐2. Eleven articles included paediatric population, and four articles reported both adults and children infected with SARS‐CoV‐2 2,7,14‐118 . (Tables S1, S2 and S3) A total of 20 874 patients were included, of which 38 were pregnant patients, 395 were paediatric patients and the rest were adult patients.…”

Section: Resultsmentioning

confidence: 99%

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Systematic review with meta‐analysis: liver manifestations and outcomes in COVID‐19

Kulkarni

Kumar

Tevethia

et al. 2020

Aliment Pharmacol Ther

208

287

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Summary Background The incidence of elevated liver chemistries and the presence of pre‐existing chronic liver disease (CLD) have been variably reported in COVID‐19. Aims To assess the prevalence of CLD, the incidence of elevated liver chemistries and the outcomes of patients with and without underlying CLD/elevated liver chemistries in COVID‐19. Methods A comprehensive search of electronic databases from 1 December 2019 to 24 April 2020 was done. We included studies reporting underlying CLD or elevated liver chemistries and patient outcomes in COVID‐19. Results 107 articles (n = 20 874 patients) were included for the systematic review. The pooled prevalence of underlying CLD was 3.6% (95% CI, 2.5‐5.1) among the 15 407 COVID‐19 patients. The pooled incidence of elevated liver chemistries in COVID‐19 was 23.1% (19.3‐27.3) at initial presentation. Additionally, 24.4% (13.5‐40) developed elevated liver chemistries during the illness. The pooled incidence of drug‐induced liver injury was 25.4% (14.2‐41.4). The pooled prevalence of CLD among 1587 severely infected patients was 3.9% (3%‐5.2%). The odds of developing severe COVID‐19 in CLD patients was 0.81 (0.31‐2.09; P = 0.67) compared to non‐CLD patients. COVID‐19 patients with elevated liver chemistries had increased risk of mortality (OR‐3.46 [2.42‐4.95, P < 0.001]) and severe disease (OR‐2.87 [95% CI, 2.29‐3.6, P < 0.001]) compared to patients without elevated liver chemistries. Conclusions Elevated liver chemistries are common at presentation and during COVID‐19. The severity of elevated liver chemistries correlates with the outcome of COVID‐19. The presence of CLD does not alter the outcome of COVID‐19. Further studies are needed to analyse the outcomes of compensated and decompensated liver disease.

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Section: Resultsmentioning

confidence: 99%

“…Seventy‐six articles, including 13 141 patients, described elevated liver chemistries at initial presentation 7,14‐34,46,47,49‐52,54‐75,77‐84,86,88‐93,102‐112 . The incidence of elevated liver chemistries varied from 1.1% to 68% in COVID‐19 patients 84,89 .…”

Section: Resultsmentioning

confidence: 99%

Systematic review with meta‐analysis: liver manifestations and outcomes in COVID‐19

Kulkarni

Kumar

Tevethia

et al. 2020

Aliment Pharmacol Ther

208

287

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“…The finding was similar to earlier studies. 11,18,19 Sharma et al explained that symptoms of COVID-19 mostly began with dry cough, fever, fatigue and myalgia in the first four days. Then, dyspnea and purulent cough followed as the next naturally occurring symptoms until the 6 th day.…”

Section: Discussionmentioning

confidence: 99%

Current prevalence, characteristics, and comorbidities of patients with COVID-19 in Indonesia

Karyono¹,

Wicaksana

2020

J Community Empowerment for Health

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Indonesia is currently fighting against a novel coronavirus disease, known as COVID-19. Current information of COVID-19 is crucial for healthcare providers. This study aimed to explore the current prevalence, characteristics, and comorbidities of patients with COVID-19 in Indonesia. We obtained data of the confirmed cases of COVID-19, characteristics and comorbidities from the official website of Indonesia COVID-19 Task Force. The data were extracted, explored and discussed to respond to the research aims. Up to June 3rd, 2020, it was reported there were 28,233 confirmed cases of COVID-19 in Indonesia. The current prevalence of COVID-19 case was 0.11‰ and transmission was distributed to all provinces in Indonesia. Almost one-third of the COVID-19 infections were in the age group of 31-45 years (29.3%) but the highest mortality rate occurred in elderly people (17.68%). Overall, males slightly dominated and contributed only 6.84% to the mortality rate. Cough (76.2%), history of fever (50.4%), and current fever (47.1%) were the most common symptoms among the patients with COVID-19. For comorbidities, patients with COVID-19 had higher numbers of hypertension (52.1%), diabetes (33.6%), and other cardiovascular diseases (20.9%). Those three comorbidities led the greater proportion of deaths among other comorbidities. The COVID-19 pandemic is still a new challenge for Indonesia.

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“…Since the COVID-19 outbroke in Hubei, even with the optimal control in China, the cumulative con rmed cases in the globe had overpassed three million and the threat of coronavirus is still out there. In the patients who suffered COVID-19, CRP was increased in 86.22% of them, and ESR in 90.22% [13]. During the disease course, longitudinal evaluation of lymphocyte count dynamics and in ammatory indices, including LDH, CRP and IL-6 may help to identify cases with dismal prognosis and prompt intervention in order to improve outcomes [14].…”

Section: Discussionmentioning

confidence: 99%

Construction and Validation of a Machine Learning-based Nomogram: A TOOL to Predict the Risk of Getting Severe Corona Virus Disease 2019 (COVID-19)

Zheng

Yao

Zheng

et al. 2020

Preprint

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Background Identifying patients who may develop severe coronavirus disease 2019 (COVID-19) will facilitate personalized treatment and optimize the distribution of medical resources. Methods In this study, 590 COVID-19 patients during hospitalization were enrolled (Training set: n = 285; Internal validation set: n = 127; Prospective set: n = 178). After filtered by 2 machine learning methods in the training set, 5 out of 31 clinical features were selected into model building to predict the risk of developing severe COVID-19 disease. Multivariate logistic regression was applied to build the prediction nomogram and validated in 2 different sets. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were used to evaluate its performance. Results From 31 potential predictors in the training set, 5 independent predictive factors were identified and included in the risk score: C-reactive protein (CRP), Lactate dehydrogenase (LDH), Age, Charlson/Deyo comorbidity score (CDCS) and Erythrocyte sedimentation rate (ESR). Subsequently, we generated the nomogram based on the above features for predicting severe COVID-19. In the training cohort, the Area under curves (AUCs) were 0.822 (95% CI 0.765–0.875) and the internal validation cohort was 0.762 (95% CI 0.768–0.844). Further, we validated it in a prospective cohort with the AUCs of 0.705 (95% CI 0.627–0.778). The internally bootstrapped calibration curve showed favorable consistency between prediction by nomogram and actual situation. And DCA analysis also conferred high clinical net benefit. Conclusion In this study, our predicting model based on 5 clinical characteristics of COVID-19 patients will enable clinicians to predict the potential risk of developing critical illness and thus optimize medical management.

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Clinical characteristics of 225 patients with COVID-19 in a tertiary Hospital near Wuhan, China

Cited by 116 publications

References 13 publications

Systematic review with meta‐analysis: liver manifestations and outcomes in COVID‐19

Systematic review with meta‐analysis: liver manifestations and outcomes in COVID‐19

Current prevalence, characteristics, and comorbidities of patients with COVID-19 in Indonesia

Construction and Validation of a Machine Learning-based Nomogram: A TOOL to Predict the Risk of Getting Severe Corona Virus Disease 2019 (COVID-19)

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