Dataset Information

A systematic review and meta-analysis of children with coronavirus disease 2019 (COVID-19).

ABSTRACT: To provide a comprehensive and systematic analysis of demographic characteristics, clinical symptoms, laboratory findings, and imaging features of coronavirus disease 2019 (COVID-19) in pediatric patients. A meta-analysis was carried out to identify studies on COVID-19 from 25 December 2019 to 30 April 2020. A total of 48 studies with 5829 pediatric patients were included. Children of all ages were at risk for COVID-19. The main illness classification ranged as: 20% (95% confidence interval [CI]: 14%-26%; I² = 91.4%) asymptomatic, 33% (95% CI: 23%-43%; I² = 95.6%) mild and 51% (95% CI: 42%-61%; I² = 93.4%) moderate. The typical clinical manifestations were fever 51% (95% CI: 45%-57%; I² = 78.9%) and cough 41% (95% CI: 35%-47%, I² = 81.0%). The common laboratory findings were normal white blood cell 69% (95% CI: 64%-75%; I² = 58.5%), lymphopenia 16% (95% CI: 11%-21%; I² = 76.9%) and elevated creatine-kinase MB 37% (95% CI: 25%-48%; I² = 59.0%). The frequent imaging features were normal images 41% (95% CI: 30%-52%; I² = 93.4%) and ground-glass opacity 36% (95% CI: 25%-47%; I² = 92.9%). Among children under 1 year old, critical cases account for 14% (95% CI: 13%-34%; I² = 37.3%) that should be of concern. In addition, vomiting occurred in 33% (95% CI: 18%-67%; I² = 0.0%) cases that may also need attention. Pediatric patients with COVID-19 may experience milder illness with atypical clinical manifestations and rare lymphopenia. High incidence of critical illness and vomiting symptoms reward attention in children under 1 year old.

SUBMITTER: Cui X

PROVIDER: S-EPMC7436402 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A systematic review and meta-analysis of children with coronavirus disease 2019 (COVID-19).

Cui Xiaojian X Zhao Zhihu Z Zhang Tongqiang T Guo Wei W Guo Wenwei W Zheng Jiafeng J Zhang Jiayi J Dong Cuicui C Na Ren R Zheng Lisheng L Li Wenliang W Liu Zihui Z Ma Jia J Wang Jinhu J He Sijia S Xu Yongsheng Y Si Ping P Shen Yongming Y Cai Chunquan C

Journal of medical virology 20200928 2

To provide a comprehensive and systematic analysis of demographic characteristics, clinical symptoms, laboratory findings, and imaging features of coronavirus disease 2019 (COVID-19) in pediatric patients. A meta-analysis was carried out to identify studies on COVID-19 from 25 December 2019 to 30 April 2020. A total of 48 studies with 5829 pediatric patients were included. Children of all ages were at risk for COVID-19. The main illness classification ranged as: 20% (95% confidence interval [CI] ...[more]

PMID: 32761898

Similar Datasets

Project description:ObjectiveClinical and laboratory biomarkers to predict the severity of coronavirus disease 2019 (COVID-19) are essential in this pandemic situation of which resource allocation must be urgently prepared especially in the context of respiratory support readiness. Lymphocyte count has been a marker of interest since the first COVID-19 publication. We conducted a systematic review and meta-analysis in order to investigate the association of lymphocyte count on admission and the severity of COVID-19. We would also like to analyze whether patient characteristics such as age and comorbidities affect the relationship between lymphocyte count and COVID-19.MethodsComprehensive and systematic literature search was performed from PubMed, SCOPUS, EuropePMC, ProQuest, Cochrane Central Databases, and Google Scholar. Research articles in adult patients diagnosed with COVID-19 with information on lymphocyte count and several outcomes of interest, including mortality, acute respiratory distress syndrome (ARDS), intensive care unit (ICU) care, and severe COVID-19, were included in the analysis. Inverse variance method was used to obtain mean differences and its standard deviations. Maentel-Haenszel formula was used to calculate dichotomous variables to obtain odds ratios (ORs) along with its 95% confidence intervals. Random-effect models were used for meta-analysis regardless of heterogeneity. Restricted-maximum likelihood random-effects meta-regression was performed for age, gender, cardiac comorbidity, hypertension, diabetes mellitus, COPD, and smoking.ResultsThere were a total of 3099 patients from 24 studies. Meta-analysis showed that patients with poor outcome have a lower lymphocyte count (mean difference - 361.06 μL [- 439.18, - 282.95], p < 0.001; I 2 84%) compared to those with good outcome. Subgroup analysis showed lower lymphocyte count in patients who died (mean difference - 395.35 μL [- 165.64, - 625.07], p < 0.001; I 2 87%), experienced ARDS (mean difference - 377.56 μL [- 271.89, - 483.22], p < 0.001; I 2 0%), received ICU care (mean difference - 376.53 μL [- 682.84, - 70.22], p = 0.02; I 2 89%), and have severe COVID-19 (mean difference - 353.34 μL [- 250.94, - 455.73], p < 0.001; I 2 85%). Lymphopenia was associated with severe COVID-19 (OR 3.70 [2.44, 5.63], p < 0.001; I 2 40%). Meta-regression showed that the association between lymphocyte count and composite poor outcome was affected by age (p = 0.034).ConclusionThis meta-analysis showed that lymphopenia on admission was associated with poor outcome in patients with COVID-19.

Project description:Importance: Coronavirus disease 2019 (COVID-19) is a newly emerging infectious disease that has caused a global pandemic. The presenting symptoms are mainly respiratory symptom, yet studies have reported nervous system involvement in the disease. A systematic review and meta-analysis of these studies are required to understanding the neurologic characteristic of the disease and help physicians with early diagnosis and management. Objective: To conduct a systematic review and meta-analysis on the neurologic characteristics in patients with COVID-19. Evidence Review: Authors conducted a literature search through PubMed from January 1st, 2020 to April 8th, 2020. Furthermore, the authors added additional sources by reviewing related references. Studies presenting the neurologic features of COVID-19 patients in their data were included. Case reports and case series were also included in this review. The quality of the studies was assessed based on the Oxford Center for Evidence-Based Medicine guidelines. Selected studies were included in the meta-analysis of proportion and the heterogeneity test. Finding: From 280 identified studies, 33 were eligible, with 7,559 participants included. Most of the included studies were from China (29 [88%]). Muscle injury or myalgia was the most common (19.2%, 95%CI 15.4-23.2%) neurologic symptom of COVID-19, followed by headache (10.9%, 95%CI 8.62-13.51%); dizziness (8.7%, 95%CI 5.02-13.43%); nausea with or without vomiting (4.6%, 95%CI 3.17-6.27%); concurrent cerebrovascular disease (4.4%, 95%CI 1.92-7.91%); and impaired consciousness (3.8%, 95%CI 0.16-12.04%). Underlying cerebrovascular disease was found in 8.5% (95%CI 4.5-13.5%) of the studies. Conclusion: Neurologic findings vary from non-specific to specific symptoms in COVID-19 patients. Some severe symptoms or diseases can present in the later stage of the disease. Physicians should be aware of the presence of neurologic signs and symptoms as a chief complaint of COVID-19, in order to improve management and prevent a worsening outcome of the patients.

Project description:BackgroundThe spectrum of neurological involvement in COVID-19 is not thoroughly understood. To the best of our knowledge, no systematic review with meta-analysis and a sub-group comparison between severe and non-severe cases has been published. The aim of this study is to assess the frequency of neurological manifestations and complications, identify the neurodiagnostic findings, and compare these aspects between severe and non-severe COVID-19 cases.MethodsA systematic search of PubMed, Scopus, EBSCO, Web of Science, and Google Scholar databases was conducted for studies published between the 1st of January 2020 and 22nd of April 2020. In addition, we scanned the bibliography of included studies to identify other potentially eligible studies. The criteria for eligibility included studies published in English language (or translated to English), those involving patients with COVID-19 of all age groups, and reporting neurological findings. Data were extracted from eligible studies. Meta-analyses were conducted using comprehensive meta-analysis software. Random-effects model was used to calculate the pooled percentages and means with their 95% confidence intervals (CIs). Sensitivity analysis was performed to assess the effect of individual studies on the summary estimate. A subgroup analysis was conducted according to severity. The main outcomes of the study were to identify the frequency and nature of neurological manifestations and complications, and the neuro-diagnostic findings in COVID-19 patients.Results44 articles were included with a pooled sample size of 13,480 patients. The mean age was 50.3 years and 53% were males. The most common neurological manifestations were: Myalgia (22.2, 95% CI, 17.2 to 28.1%), taste impairment (19.6, 95% CI, 3.8 to 60.1%), smell impairment (18.3, 95% CI, 15.4 to 76.2%), headache (12.1, 95% CI, 9.1 to 15.8%), dizziness (11.3, 95% CI, 8.5 to 15.0%), and encephalopathy (9.4, 95% CI, 2.8 to 26.6%). Nearly 2.5% (95% CI, 1 to 6.1%) of patients had acute cerebrovascular diseases (CVD). Myalgia, elevated CK and LDH, and acute CVD were significantly more common in severe cases. Moreover, 20 case reports were assessed qualitatively, and their data presented separately.ConclusionsNeurological involvement is common in COVID-19 patients. Early recognition and vigilance of such involvement might impact their overall outcomes.

Project description:BackgroundThe confirmed coronavirus disease 2019 (COVID-19) cases, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have exceeded 21 million (with more than 775,000 fatalities), and the number of children with COVID-19 is also increasing. This study aimed to summarize the chest imaging characteristics of pediatric COVID-19 cases and provide a reference for the diagnosis and control of pediatric COVID-19.MethodsThe study protocol was registered in PROSPERO, number CRD42020177391. Studies related to pediatric COVID-19 imaging manifestations were accessed from PubMed, Web of Science, and the Cochrane library databases, without language limitations. The publication date was limited to April 1, 2020, and it was updated on May 1 and May 27, 2020. Data normalization was determined with the Freeman-Tukey double arcsine transformation. Summarized incidences with 95% confidence intervals of various imaging manifestations were assessed by random-effects models. Heterogeneity was assessed with meta-regression and subgroup analyses, robustness with sensitivity analyses; and publication biases with Egger's test.ResultsTwenty-three with 517 cases were included in this study. The summarized incidence of chest computed tomography abnormalities in pediatric COVID-19 cases was 70%, which was lower than what has been seen in adults. The incidence of halo signs in pediatric COVID-19 cases was 26%, which is rarely seen in adult COVID-19 cases. The incidences of ground-glass opacities (GGOs), GGOs and consolidations, consolidations, reverse halo signs, crazy paving signs, pleural effusion, bronchopneumonia-like signs, air bronchograms, and increased lung markings were 40%, 25%, 10%, 2%, 4%, 1%, 15%, 12%, and 31%, respectively. Pericardial effusions were found in the computed tomography images of adult COVID-19 cases but were scarcely seen in the computed tomography images of pediatric COVID-19 cases. The incidences of bilateral lesions, unilateral lesions, and peripheral lesions were 35%, 22%, and 26%, respectively.ConclusionsChest computed tomography imaging of pediatric COVID-19 cases resulted in various abnormalities that were milder than those of adults. This study will hopefully provide a reference to help identify pediatric COVID-19 cases.

Dataset Information

A systematic review and meta-analysis of children with coronavirus disease 2019 (COVID-19).

Publications

A systematic review and meta-analysis of children with coronavirus disease 2019 (COVID-19).

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets