Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 5 de 5
Filter
1.
Front Med (Lausanne) ; 8: 638097, 2021.
Article in English | MEDLINE | ID: covidwho-1266664

ABSTRACT

It is not clear whether D-dimer can be an independent predictor of coronavirus disease 2019 (COVID-19) mortality, and the cut-off of D-dimer for clinical use remains to be determined. Therefore, a comprehensive analysis is still necessary to illuminate the clinical significance of plasma D-dimer in COVID-19 mortality. We searched PubMed, Embase, Cochrane Library, and Scopus databases until November 2020. STATA software was used for all the statistical analyses. The identifier of systematic review registration was PROSPERO CRD42020220927. A total of 66 studies involving 40,614 COVID-19 patients were included in our meta-analysis. Pooled data showed that patients in high D-dimer group had poor prognosis than those in low D-dimer group [OR = 4.52, 95% CI = (3.61, 5.67), P < 0.001; HR = 2.81, 95% CI = (1.85, 4.27), P < 0.001]. Sensitivity analysis, pooled data based on different effect models and the Duval and Tweedie trim-and-fill method did not change the conclusions. Subgroup analyses stratified by different countries, cutoffs, sample size, study design, and analysis of OR/HR still keep consistent conclusions. D-dimer was identified as an independent predictor for COVID-19 mortality. A series of values including 0.5 µg/ml, 1 µg/ml, and 2 µg/ml could be determined as cutoff of D-dimer for clinic use. Measurement and monitoring of D-dimer might assist clinicians to take immediate medical actions and predict the prognosis of COVID-19.

2.
Ann Hepatol ; 21: 100279, 2021.
Article in English | MEDLINE | ID: covidwho-907197

ABSTRACT

INTRODUCTION: Coronavirus disease 2019 (COVID-19) has brought great challenges to global public health. However, a comprehensive analysis of the relationship between liver biochemical parameters and COVID-19 mortality is quite limited. METHODS: We searched the following electronic databases: PubMed, Embase, Cochrane Library, Web of Science, Scopus, Wanfang and China National Knowledge Infrastructure database until May 5, 2020. STATA software was used for the statistical analyses. RESULTS: A total of 25 studies involving 5971 COVID-19 patients were included in our analysis. Compared with non-survivors, survivors had lower levels of aspartate aminotransferase (AST) (weighted mean difference [WMD]=-16.71U/L, 95%CI=[-21.03,-12.40], P<0.001), alanine transaminase (ALT) (WMD=-5.20U/L, 95%CI=[-8.00,-2.41], P<0.001), total bilirubin (TBIL) (WMD=4.40µmol/L, 95%CI=[-5.11,-3.70], P<0.001) and lactic dehydrogenase (LDH) (WMD=-252.44U/L, 95%CI=[-289.57,-215.30], P<0.001), and higher albumin (ALB) level (WMD=4.47g/L, 95%CI=[3.47,5.47], P<0.001). Besides, survivors had lower proportions of these abnormally increased parameters (AST: OR=0.25, 95%CI=[0.15,0.41], P<0.001; ALT: OR=0.49, 95%CI=[0.37,0.64], P<0.001; TBIL: (OR=0.20, 95%CI=[0.12,0.34], P<0.001; LDH, OR=0.09, 95%CI=[0.06,0.14], P<0.001), and lower proportion of abnormally decreased ALB (OR=0.16, 95%CI=[0.07,0.38], P<0.001). Meta-analysis based on standard mean difference and sensitivity analysis did not change the conclusions. Egger test did not detect the presence of publication bias. CONCLUSIONS: Liver biochemical parameters were strongly correlated with COVID-19 mortality. Measurement of these liver biochemical parameters might assist clinicians to evaluate the prognosis of COVID-19.


Subject(s)
Alanine Transaminase/blood , Aspartate Aminotransferases/blood , COVID-19/mortality , Prognosis , SARS-CoV-2 , Biomarkers/blood , COVID-19/enzymology , Global Health , Humans , Pandemics , Survival Rate/trends
3.
Biometrics ; 77(3): 929-941, 2021 09.
Article in English | MEDLINE | ID: covidwho-634693

ABSTRACT

The incubation period and generation time are key characteristics in the analysis of infectious diseases. The commonly used contact-tracing-based estimation of incubation distribution is highly influenced by the individuals' judgment on the possible date of exposure, and might lead to significant errors. On the other hand, interval censoring-based methods are able to utilize a much larger set of traveling data but may encounter biased sampling problems. The distribution of generation time is usually approximated by observed serial intervals. However, it may result in a biased estimation of generation time, especially when the disease is infectious during incubation. In this paper, the theory from renewal process is partially adopted by considering the incubation period as the interarrival time, and the duration between departure from Wuhan and onset of symptoms as the mixture of forward time and interarrival time with censored intervals. In addition, a consistent estimator for the distribution of generation time based on incubation period and serial interval is proposed for incubation-infectious diseases. A real case application to the current outbreak of COVID-19 is implemented. We find that the incubation period has a median of 8.50 days (95% confidence interval [CI] [7.22; 9.15]). The basic reproduction number in the early phase of COVID-19 outbreak based on the proposed generation time estimation is estimated to be 2.96 (95% CI [2.15; 3.86]).


Subject(s)
COVID-19 , Epidemics , Infectious Disease Incubation Period , COVID-19/epidemiology , China/epidemiology , Disease Outbreaks , Humans , SARS-CoV-2
4.
Pol Arch Intern Med ; 130(5): 400-406, 2020 05 29.
Article in English | MEDLINE | ID: covidwho-622201

ABSTRACT

INTRODUCTION: The ongoing worldwide pandemic of coronavirus disease 2019 (COVID­19) has posed a huge threat to global public health. However, the issue as to whether routine blood tests could be used to monitor and predict the severity and prognosis of COVID­19 has not been comprehensively investigated so far. OBJECTIVES: This study aimed to provide an overview of the association of markers in the routine blood test with the severity of COVID­19. METHODS: PubMed, Embase, Cochrane Library, Wanfang, and China National Knowledge Infrastructure (CNKI) databases were searched to identify studies reporting data on markers in the routine blood test and the severity of COVID­19, published until March 20, 2020. The STATA software was used for meta­analysis. RESULTS: A total of 15 studies with 3090 patients with COVID­19 were included in this analysis. Patients in the nonsevere group, compared with those in the severe group, had lower counts of white blood cells (weighted mean difference [WMD], -0.85 [×109/l]; 95% CI, -1.54 to -0.16; P = 0.02) and neutrophils (WMD, -1.57 [×109/l]; 95% CI, -2.6 to -0.54; P = 0.003), greater counts of lymphocytes (WMD, 0.29 [×109/l]; 95% CI, 0.22-0.36; P <0.001) and platelets (WMD, 19.05 [×109/l]; 95% CI, 3.04-35.06; P = 0.02), and a lower neutrophil­to­lymphocyte (NLR) ratio (WMD, -2.48; 95% CI, -3.81 to -1.15; P <0.001). There was no difference in the monocyte count (WMD, 0.01 [×109/l]; 95% CI, -0.01 to 0.03; P = 0.029) between these 2 groups. Sensitivity analysis and meta­analysis based on standard mean difference did not change the conclusions regarding neutrophils, lymphocytes, and NLR, but yielded inconsistent results for white blood cells and platelets. CONCLUSIONS: Severe patients had more neutrophils, higher NLR level, and fewer lymphocytes than non-severe patients with COVID-19. Measurement of these markers might assist clinicians to monitor and predict the severity and prognosis of COVID-19.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/blood , Coronavirus Infections/diagnosis , Pneumonia, Viral/blood , Pneumonia, Viral/diagnosis , Adult , COVID-19 , Humans , Leukocyte Count , Middle Aged , Pandemics/prevention & control , Platelet Count , Prognosis , Risk Factors , SARS-CoV-2
5.
Int J Hyg Environ Health ; 228: 113555, 2020 07.
Article in English | MEDLINE | ID: covidwho-410479

ABSTRACT

BACKGROUND: The 2019 novel coronavirus (COVID-19) outbreak in Wuhan, China has attracted world-wide attention. As of March 31, 2020, a total of 82,631 cases of COVID-19 in China were confirmed by the National Health Commission (NHC) of China. METHODS: Three approaches, namely Poisson likelihood-based method (ML), exponential growth rate-based method (EGR) and stochastic Susceptible-Infected-Removed dynamic model-based method (SIR), were implemented to estimate the basic and controlled reproduction numbers. RESULTS: A total of 198 chains of transmission together with dates of symptoms onset and 139 dates of infections were identified among 14,829 confirmed cases outside Hubei Province as reported as of March 31, 2020. Based on this information, we found that the serial interval had an average of 4.60 days with a standard deviation of 5.55 days, the incubation period had an average of 8.00 days with a standard deviation of 4.75 days and the infectious period had an average of 13.96 days with a standard deviation of 5.20 days. The estimated controlled reproduction numbers, Rc, produced by all three methods in all analyzed regions of China are significantly smaller compared with the basic reproduction numbers R0. CONCLUSIONS: The controlled reproduction number in China is much lower than one in all regions of China by now. It fell below one within 30 days from the implementations of unprecedent containment measures, which indicates that the strong measures taken by China government was effective to contain the epidemic. Nonetheless, efforts are still needed in order to end the current epidemic as imported cases from overseas pose a high risk of a second outbreak.


Subject(s)
Basic Reproduction Number/statistics & numerical data , Betacoronavirus , Coronavirus Infections/epidemiology , Disease Outbreaks/statistics & numerical data , Models, Statistical , Pneumonia, Viral/epidemiology , COVID-19 , China/epidemiology , Coronavirus Infections/virology , Humans , Pandemics , Pneumonia, Viral/virology , SARS-CoV-2
SELECTION OF CITATIONS
SEARCH DETAIL