scholarly journals Clinical Features and Laboratory Examination to Identify Severe Patients with COVID-19: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yan Meng ◽  
Jinpeng Wang ◽  
Kaicheng Wen ◽  
Wacili Da ◽  
Keda Yang ◽  
...  
Keyword(s):  
Clinical Features ◽  
Chronic Renal Disease ◽  
Early Stage ◽  
Meta Analysis ◽  
Severe Illness ◽  
Laboratory Examination ◽  
Bias Analysis ◽  
Protein Ratio ◽  
Reactive Protein ◽  
Sensitivity Specificity

Background. With the COVID-19 epidemic breakout in China, up to 25% of diagnosed cases are considered to be severe. To effectively predict the progression of COVID-19 via patients’ clinical features at an early stage, the prevalence of these clinical factors and their relationships with severe illness were assessed. Methods. In this study, electronic databases (PubMed, Embase, Web of Science, and Chinese database) were searched to obtain relevant studies, including information on severe patients. Publication bias analysis, sensitivity analysis, prevalence, sensitivity, specificity, likelihood ratio, diagnosis odds ratio calculation, and visualization graphics were achieved through software Review Manager 5.3, Stata 15, Meta-DiSc 1.4, and R. Results. Data of 3.547 patients from 24 studies were included in this study. The results revealed that patients with chronic respiratory system diseases (pooled positive likelihood 6.07, 95% CI: 3.12-11.82), chronic renal disease (4.79, 2.04-11.25), cardiovascular disease (3.45, 2.19-5.44), and symptoms of the onset of chest tightness (3.8, 1.44-10.05), shortness of breath (3.18, 2.24-4.51), and diarrhea (2.04, 1.38-3.04) exhibited increased probability of progressing to severe illness. C-reactive protein, ratio of neutrophils to lymphocytes, and erythrocyte sedimentation rate increased a lot in severe patients compared to nonsevere. Yet, it was found that clinical features including fever, cough, and headache, as well as some comorbidities, have little warning value. Conclusions. The clinical features and laboratory examination could be used to estimate the process of infection in COVID-19 patients. The findings contribute to the more efficient prediction of serious illness for patients with COVID-19 to reduce mortality.

scholarly journals Clinical Features and Laboratory Examination to Identify Severe Patients With COVID-19: A Systematic Review and Meta-analysis

2020 ◽  
Author(s):  
Yan Meng ◽  
Jinpeng Wang ◽  
Kaicheng Wen ◽  
Wacili Da ◽  
Keda Yang ◽  
...  
Keyword(s):  
Systematic Review ◽  
Clinical Features ◽  
Chronic Renal Disease ◽  
Early Stage ◽  
Meta Analysis ◽  
Severe Illness ◽  
Laboratory Examination ◽  
Bias Analysis ◽  
Protein Ratio ◽  
Sensitivity Specificity

Abstract Background With the COVID-19 epidemic breakout in China, up to 25% of diagnosed cases are considered to be severe. To effectively predict the progression of COVID-19 via patients’ clinical features at an early stage, the prevalence of these clinical factors and their relationships with severe illness were assessed.Methods In this study, electronic databases (PubMed, Embase, Web of Science and Chinese database) were searched to obtain relevant studies including information on severe patients. Publication bias analysis, sensitivity analysis, prevalence, sensitivity, specificity, likelihood ratio, diagnosis odds ratio calculation, and visualization graphics were achieved through software Review Manager 5.3, STATA 15, Meta-DiSc 1.4 and R.Results Data of 3.547 patients from 24 studies was included in this study. The results revealed that patients with chronic respiratory system diseases (pooled positive likelihood 6.07, 95% CI: 3.12-11.82), chronic renal disease (4.79, 2.04-11.25), cardiovascular disease (3.45, 2.19-5.44), and symptoms of the onset of chest tightness (3.8, 1.44-10.05), shortness of breath (3.18, 2.24-4.51), and diarrhea (2.04, 1.38-3.04) exhibited increased probability of progressing to severe illness. C-reactive protein, ratio of neutrophils to lymphocytes, and erythrocyte sedimentation rate increased a lot in severe patients compared to non-severe. Yet it was found that clinical features including fever, cough, headache, and so on, as well as some comorbidities have little warning value.Conclusions The clinical features, and laboratory examination could be used to estimate the process of infection in COVID-19 patients. The findings contribute to the more efficient prediction of serious illness for patients with COVID-19 to reduce mortality.Systematic Review registrations Not applicable.

scholarly journals Comparison of diagnostic accuracy of Midkine and AFP for detecting hepatocellular carcinoma: a systematic review and meta-analysis

2020 ◽  
Vol 40 (3) ◽  
Author(s):  
Qingqing Lu ◽  
Jie Li ◽  
Hui Cao ◽  
Chenlu Lv ◽  
Xiaolin Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Diagnostic Accuracy ◽  
Hepatitis Virus ◽  
Early Stage ◽  
Meta Analysis ◽  
Area Under The Curve ◽  
Cochrane Library ◽  
Sensitivity Specificity ◽  
The Cochrane Library

Abstract Objective: Midkine (MDK) has been proposed as one of the most promising markers for hepatocellular carcinoma (HCC). This meta-analysis was conducted to compare the diagnostic accuracy of MDK and α-fetoprotein (AFP) for HCC. Methods: We systematically searched PubMed/MEDLINE, Ovid/EMBASE, and the Cochrane Library for all relevant studies up to 18 May 2019. The Revised Quality Assessment for Studies of Diagnostic Accuracy tool (QUADAS-2) was used to assess the methodological quality of the included studies. The sensitivity, specificity, and the area under the curve (AUC) of MDK and AFP for detecting HCC were pooled using random-effects model. Results: Seventeen studies from five articles with a total of 1122 HCC patients and 2483 controls were included. The summary estimates using MDK and AFP for detecting HCC were as follows: sensitivity, 85 vs 52%, specificity, 82 vs 94%, and AUC, 0.90 vs 0.83. The summary estimates using MDK and AFP for detecting hepatitis virus-related HCC as follows: sensitivity, 93 vs 74%, specificity, 85 vs 97%, and AUC, 0.95 vs 0.97. The summary estimates using MDK and AFP for detecting early-stage HCC were as follows: sensitivity, 83.5 vs 44.4%, specificity, 81.7 vs 84.8%, and AUC, 0.87 vs 0.52. The summary estimates using MDK for detecting AFP-negative HCC as follows: sensitivity, 88.5%, specificity, 83.9%, and AUC, 0.91. Conclusion: MDK is more accurate than AFP in diagnosing HCC, especially for early-stage HCC and AFP-negative HCC. Both MDK and AFP had excellent diagnostic performance for hepatitis virus-related HCC.

scholarly journals Elevated serum ferritin level effectively discriminates severity illness and predicts prognosis of COVID-19 patients

2020 ◽  
Author(s):  
Peng Cao ◽  
Yuanjue Wu ◽  
Sanlan Wu ◽  
Tingting Wu ◽  
Qilin Zhang ◽  
...  
Keyword(s):  
Roc Curve ◽  
Early Stage ◽  
Ferritin Level ◽  
Elevated Serum ◽  
Hospital Length ◽  
Severe Illness ◽  
Reactive Protein ◽  
Kaplan Meier ◽  
Union Hospital ◽  
Clinical Records

Abstract During coronavirus disease 2019 (COVID-19) pandemic, medical resources in every country is in shortage. Efficacious indicators of discriminating severe illness and predicting outcome is in urgent need. We collected data and clinical records from 79 COVID-19 patients admitted between January 12, 2020 and February 21, 2020 at Wuhan Union hospital, China. Spearman’s correlation analysis, receiver operating characteristic (ROC) curve, logistic regression model, and Kaplan-Meier survival curves were employed in the analysis. Of 79 patients enrolled, 2 died in hospital, 8 were transferred to other hospitals, and 69 were discharged. Patients with elevated ferritin levels (> 200 ng/mL) had a higher incidence of severity illness when compared with those with normal ferritin levels (≤ 200 ng/mL) (50.0% vs 2.9%). In addition, severity illness manifested significantly higher level of ferritin as compared with non-severe ones (median 921.3 vs 130.7 ng/mL, p < 0.001). Furthermore, ferritin could effectively discriminate severity and non-severity, with an area under the ROC curve (AUC) reaching 0.873 (sensitivity 96%, specificity 70%), larger than that of age (0.697), C-reactive protein (0.730) and lymphocytes% (0.717). Combined model incorporating multivariate revealed a similar manner with ferritin alone (p = 0.981). Furthermore, elevated ferritin group showed longer viral clearance time (median 16 vs 6 days, p < 0.001) and in-hospital length (median 18 vs 10 days, p < 0.001). Our results suggest that ferritin could act as a simple and efficacious complementary tool to identify severe COVID-19 patients at early stage and predict their outcome. This indicator would provide guidance for subsequent clinical practice, alleviate the medical stress and reduce the mortality.

scholarly journals Serum Prealbumin Concentrations, COVID-19 Severity, and Mortality: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 8 ◽  
Author(s):  
Angelo Zinellu ◽  
Arduino A. Mangoni
Keyword(s):  
Systematic Review ◽  
Risk Stratification ◽  
Meta Analysis ◽  
Severe Disease ◽  
Severe Illness ◽  
Lower Serum ◽  
Reactive Protein ◽  
Early Risk ◽  
Standard Mean Difference ◽  
Reporting Data

Excessive inflammation and malnutrition are associated with coronavirus disease 2019 (COVID-19) severity and mortality. Combined biomarkers of malnutrition and inflammation, such as serum prealbumin, might be particularly attractive for early risk stratification. We conducted a systematic review and meta-analysis of studies reporting serum prealbumin in patients with COVID-19. We searched PubMed, Web of Science and Scopus, between January and November 2020, for studies reporting data on serum prealbumin, COVID-19 severity, defined as severe illness, prolonged viral load, receiving mechanical ventilation or admitted to intensive care unit (ICU), and mortality. Nineteen studies in 4,616 COVID-19 patients were included in the meta-analysis. Pooled results showed that serum prealbumin concentrations were significantly lower in patients with severe disease and non-survivors (standard mean difference, SMD, −0.92, 95% CI, −1.10 to −0.74, P &lt; 0.001). Extreme heterogeneity was observed (I2 = 77.9%; P &lt; 0.001). In sensitivity analysis, the effect size was not significantly affected when each study was in turn removed (range between −0.86 and −0.95). The Begg's (P = 0.06) and Egger's t-tests (P = 0.26) did not show publication bias. Pooled SMD values were significantly and negatively associated with age (t = −2.18, P = 0.045) and C-reactive protein (t = −3.85, P = 0.002). In our meta-analysis, lower serum prealbumin concentrations were significantly associated with COVID-19 severity and mortality. This combined marker of malnutrition and inflammation might assist with early risk stratification and management in this group.

scholarly journals Post-operative serum procalcitonin vs C reactive Protein as a marker of post-operative infectious complications in pancreatic surgery – A systemic review and meta-analysis

2020 ◽  
Author(s):  
Bhavin Vasavada ◽  
Hardik Patel
Keyword(s):  
Diagnostic Accuracy ◽  
Odds Ratio ◽  
Area Under Curve ◽  
Pancreatic Surgery ◽  
Meta Analysis ◽  
Diagnostic Odds Ratio ◽  
Infectious Complications ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Sensitivity Specificity

AbstractAim of StudyAim of this meta-analysis was to compare diagnostic accuracy of C reactive Protein and Procalcitonin between postoperative day 3 to 5 in predicting infectious complications post pancreatic surgery.MethodsSystemic literature search was performed using MEDLINE, EMBASE and SCOPUS to identify studies evaluating the diagnostic accuracy of Procalcitonin (PCT) and C-Reactive Protein (CRP) as a predictor for detecting infectious complications between postoperative days (POD) 3 to 5 following pancreatic surgery. A meta-analysis was performed using random effect model and pooled predictive parameters. Geometric means were calculated for PCT cut offs. The work has been reported in line with PRISMA guidelines.ResultsAfter applying inclusion and exclusion criteria 15 studies consisting of 2212 patients were included in the final analysis according to PRISMA guidelines. Pooled sensitivity, specificity, Area under curve and diagnostic odds ratio (DOR)for day 3 C-reactive protein was respectively 62%,67% 0.772 and 6.54. Pooled sensitivity, specificity, Area under curve and diagnostic odds ratio (DOR)for day 3 procalcitonin was respectively 74%,79%,0.8453 and 11.03. Sensitivity, specificity, Area under curve, and Diagnostic odds ratio for day 4 C-reactive protein was respectively 60%,68%, 0.8022 and 11.90. Pooled Sensitivity, specificity and Diagnostic odds ratio of post-operative day 5 procalcitonin level in predicting infectious complications were respectively 83%,70% and 12.9. Pooled Sensitivity, specificity, AUROC and diagnostic odds ratio were respectively 50%,70%, 0.777 and 10.19.ConclusionPost-operative procalcitonin is better marker to predict post-operative infectious complications after pancreatic surgeries and post-operative day 3 procalcitonin has highest diagnostic accuracy.

scholarly journals Laboratory features in severe vs. non-severe COVID-19 patients, a systematic review and meta-analysis

2020 ◽  
Author(s):  
Sulmaz Ghahramani ◽  
Reza Tabrizi ◽  
Kamran B Lankarani ◽  
Seyyed mohammad amin Kashani ◽  
Shahla Rezaei ◽  
...  
Keyword(s):  
Troponin I ◽  
Meta Analysis ◽  
White Blood Cells ◽  
C Reactive Protein ◽  
Protein Ratio ◽  
Reactive Protein ◽  
Laboratory Test Results ◽  
Laboratory Features ◽  
Severe Group ◽  
Mean Differences

Abstract OBJECTIVE: Understanding the common laboratory features of COVID-19 in severe cases versus non-severe patients could be quite useful for clinicians and might help to predict the model of disease progression. MATERIALS AND METHODS: Electronic databases were systematically searched in PubMed, EMBASE, Scopus, Web of ‎Science, and Google Scholar from inception to 3rd of March 2020. Heterogeneity across included ‎studies was determined using Cochrane’s Q test and the I2 statistic. We used the fixed or random-effect models to pool ‎the weighted mean differences (WMDs) or standardized mean differences and 95% confidence ‎intervals (CIs).‎RESULTS:‎ Out of a total of 3009 citations, 17 articles (22 studies, 21 from China and one study from Singapour) with 3396 ranging from 12-1099 patients, ‎were included. Our meta-analyses showed a significant decrease in ‎lymphocyte, monocyte, and eosinophil, hemoglobin, platelet, albumin, serum sodium, lymphocyte to C-reactive protein ratio (LCR), leukocyte to C-reactive protein ratio (LeCR), leukocyte to IL-6 ratio (LeIR), and an increase in the ‎neutrophil, alanine ‎aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin, blood urea nitrogen (BUN), creatinine (Cr), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), procalcitonin (PCT), lactate dehydrogenase (LDH), fibrinogen, prothrombin time (PT), D-dimer, glucose ‎level, and neutrophil to lymphocyte ratio (NLR) in the severe group compared with the non-severe group. However, no significant changes were observed in white blood cells (WBC), ‎ creatine kinase (CK), troponin I, myoglobin, interleukin-6 (IL-6), and potassium (K) between the two groups.‎CONCLUSIONS: This meta-analysis provides evidence for the differentiation of severe cases of COVID-19 based on laboratory test results at the time of hospital admission. Future well-methodologically designed studies from other populations are strongly recommended.

Baseline C-Reactive Protein Level Predicts Survival of Early-Stage Lung Cancer: Evidence from a Systematic Review and Meta-Analysis

2016 ◽  
Vol 102 (5) ◽  
pp. 441-449 ◽  
Author(s):  
Giovanni Leuzzi ◽  
Carlotta Galeone ◽  
Mara Gisabella ◽  
Leonardo Duranti ◽  
Francesca Taverna ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Systematic Review ◽  
Protein Level ◽  
Early Stage ◽  
Meta Analysis ◽  
C Reactive Protein ◽  
Early Stage Lung Cancer ◽  
Reactive Protein ◽  
Stage Lung Cancer

scholarly journals Clinical and laboratory characteristics in outpatient diagnosis of COVID-19 in healthcare professionals in Rio de Janeiro, Brazil

2021 ◽  
pp. jclinpath-2020-206797
Author(s):  
Luis Cristovao Porto ◽  
Claudia H Costa ◽  
Alessandra S Nunes ◽  
Isabel Bouzas ◽  
Tiago F Ferreira ◽  
...  
Keyword(s):  
Reverse Transcriptase ◽  
Clinical Features ◽  
Rio De Janeiro ◽  
Healthcare Professionals ◽  
Early Stage ◽  
Laboratory Data ◽  
Nasopharyngeal Swab ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Body Pain

AimsThis study aimed to identify the symptoms associated with early stage SARS-CoV-2 (COVID-19) infections in healthcare professionals (HCPs) using both clinical and laboratory data.MethodsA total of 1297 patients, admitted between 18 March and 8 April 2020, were stratified according to their risk of developing COVID-19 using their responses to a questionnaire designed to evaluate symptoms and risk conditions.ResultsAnosmia/hyposmia (p<0.0001), fever (p<0.0001), body pain (p<0.0001) and chills (p=0.001) were all independent predictors for COVID-19, with a 72% estimated probability for detecting COVID-19 in nasopharyngeal swab samples. Leucopenia, relative monocytosis, decreased eosinophil values, C reactive protein (CRP) and platelets were also shown to be significant independent predictors for COVID-19.ConclusionsThe significant clinical features for COVID-19 were identified as anosmia, fever, chills and body pain. Elevated CRP, leucocytes under 5400×109/L and relative monocytosis (>9%) were common among patients with a confirmed COVID-19 diagnosis. These variables may help, in the absence of reverse transcriptase PCR tests, to identify possible COVID-19 infections during pandemic outbreaks.SummaryFrom 19 March to 8 April 2020, 1297 patients attended the Polyclinic Piquet Carneiro for COVID-19 detection. HCP data were analysed, and significant clinical features were anosmia, fever, chills and body pain. Elevated CRP, leucopenia and monocytosis were common in COVID-19.

scholarly journals Prognostic factors for COVID-19 pneumonia progression to severe symptom based on the earlier clinical features: a retrospective analysis

2020 ◽  
Author(s):  
Huang Huang ◽  
Shuijiang Cai ◽  
Yueping Li ◽  
Youxia Li ◽  
Yinqiang Fan ◽  
...  
Keyword(s):  
Respiratory Rate ◽  
Clinical Features ◽  
Prognostic Value ◽  
Characteristic Curve ◽  
Severe Pneumonia ◽  
Severe Illness ◽  
Regression Methods ◽  
Reactive Protein ◽  
Measurable Serum ◽  
Laboratory Changes

AbstractApproximately 15-20% of COVID-19 patients will develop severe pneumonia, about 10 % of which will die if not properly managed. Earlier discrimination of the potential severe patients basing on routine clinical and laboratory changes and commencement of prophylactical management will not only save their lives but also mitigate the otherwise overwhelmed health care burden. In this retrospective investigation, the clinical and laboratory features were collected from 125 COVID-19 patients, who were classified into mild (93 cases) or severe (32 cases) groups according to their clinical outcomes after 3 to 7-days post-admission. The subsequent analysis with single-factor and multivariate logistic regression methods indicated that 17 factors on admission differed significantly between mild and severe groups, but that only comorbid with underlying diseases, increased respiratory rate (>24/min), elevated C-reactive protein (CRP >10mg/liter), and lactate dehydrogenase (LDH >250U/liter), were independently associated with the later disease development. Finally, we evaluated their prognostic values with the receiver operating characteristic curve (ROC) analysis and found that the above four factors could not confidently predict the occurrence of severe pneumonia individually, but that a combination of fast respiratory rate and elevated LDH significantly increased the predictive confidence (AUC= 0.944, sensitivity= 0.941, and specificity= 0.902). A combination consisting of 3- or 4-factors could further increase the prognostic value. Additionally, measurable serum viral RNA post-admission independently predicted the severe illness occurrence. In conclusion, a combination of general clinical characteristics and laboratory tests could provide high confident prognostic value for identifying potential severe COVID-19 pneumonia patients.SummaryWith our successful experience of treating COVID-19 patients, we retrospectively found that routine clinical features could reliably predict severe pneumonia development, thus provide quick and affordable references for physicians to save the otherwise fatal patients with the limited medical resource.

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