Multi-biomarker is an early-stage predictor for progression of Coronavirus disease 2019 (COVID-19) infection.
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ABSTRACT:
SUBMITTER: Zhou Z
PROVIDER: S-EPMC8241766 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The purpose of this study was to identify miRNAs that were dysregulated after the onset of COVID-19 and thus potentially be used for risk stratification (i.e., mortality). Therefore, we conducted a multi-center, retrospective longitudinal cohort study enrolling 142 patients with laboratory-confirmed SARS-CoV-2 infection who presented to two Canadian hospitals from May 2020 – December 2020 along with a cohort of 27 SARS-CoV-2 patients with mild upper respiratory tract symptoms and 69 SARS-CoV-2-negative patients from the ICU. Blood was biobanked from SARS-CoV-2 positive patients in the emergency department (mild), ward (moderate) or intensive care unit (severe). Assessment of miRNA expression and co-regulatory network generation revealed significant transcriptome dyregulation in pateints with severe COVID-19 that was largely different from SARS-CoV-2 negative patients in the ICU.
2022-04-08 | GSE178246 | GEO
Project description:As at 27 January 2020, 42 novel coronavirus (2019-nCoV) cases were confirmed outside China. We estimate the risk of case importation to Europe from affected areas in China via air travel. We consider travel restrictions in place, three reported cases in France, one in Germany. Estimated risk in Europe remains high. The United Kingdom, Germany and France are at highest risk. Importation from Beijing and Shanghai would lead to higher and widespread risk for Europe.
| S-EPMC7001240 | biostudies-literature
Project description:Background and Aim: Lymphocytes play an important role in fighting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Low total lymphocyte count (TLC), which contributes to poor clinical outcomes, is common in persons with coronavirus disease 2019 (COVID-19). The current explanation for the cause of low TLC is that it is directly related to the invasiveness of SARS-CoV-2, which attacks lymphocytes. We hypothesized that malnutrition contributes to the development of low TLC in early-stage COVID-19. Methods: We prospectively enrolled 101 patients with confirmed COVID-19. On their first day of hospitalization, we collected baseline and laboratory data, including clinical symptoms; the Sequential Organ Failure Assessment, Nutrition Risk Screening 2002 and Subjective Global Assessment were used to assess the malnutrition status of the patients. Multivariable logistic regression was used to identify independent risk factors for low TLC and severe COVID-19. Results: Malnutrition was associated with lower TLC in COVID-19. Fifty-nine (58.4%) of the patients showed low TLC, 41 (40.6%) were at risk for malnutrition, and 18 of them were malnourished. Low TLC was an independent risk factor for severe COVID-19. Compared to patients with normal TLC, those with low TLC more often presented with anorexia, malnutrition, higher SOFA scores (P < 0.05) and comorbidities (diabetes and malignancies). Malnutrition (OR: 3.05, 95% CI: 1.5-6.19, P = 0.006) and SOFA scores (OR: 1.51, 95% CI: 1.04-2.43, P = 0.042) were identified as independent risk factors for low TLC. Conclusions: Malnutrition was common among our patients with early-stage COVID-19, and it contributed to the occurrence of low TLC.
| S-EPMC8770860 | biostudies-literature
Project description:BackgroundCarbohydrate antigen 125 (CA125) is an indicator of inflammation, immune response, and impaired cardiac function. The aim was to investigate whether CA125 behaves as a biomarker of severity and poor clinical outcomes in hospitalized patients with coronavirus disease 2019 (COVID-19).MethodsSerum CA125 [Elecsys CA125 II assay-(Roche Diagnostics GmbH)] was measured in stored biobank samples from COVID-19 hospitalized patients between 01 March 2020 and 17 October 2021. Multiple logistic regression models were built to explore the association between CA125 and clinical outcomes [in-hospital all-cause mortality, need for invasive mechanical ventilation (IMV), or non-invasive respiratory support (non-IRS)], estimating odds ratios (ORs; 95% CI). The gradient of risk of CA125 was evaluated by fractional polynomials.ResultsA total of 691 patients were included, median age of 63 years (50-76), men (57.2%), with high comorbidity. At admission, 85.8% had pneumonia. Median CA125 was 10.33 U/ml (7.48-15.50). The in-hospital mortality rate was 7.2%. After adjusting for confounding factors, CA125 ≥ 15.5 U/ml (75th percentile) showed an increased risk of death [OR 2.85(1.21-6.71)], as age ≥ 65 years, diabetes, and immunosuppression. Furthermore, CA125 as a continuous variable was positive and significantly associated with the risk of death after multivariate adjustment. The mean hospital stay of the patients with CA125 ≥ 15.5 U/ml was longer than the rest of the study population.ConclusionCA125 in the first 72 h of hospital admission seems a useful biomarker of mortality in hospitalized patients with moderate-severe COVID-19. If our findings are confirmed, the wide availability of this biomarker would make easy its widespread implementation in clinical practice.
| S-EPMC9631479 | biostudies-literature
Project description:Coronavirus disease 2019 (COVID-19) is an infection caused by the novel coronavirus severe acute respiratory coronavirus 2 (SARS-CoV-2). The infection manifests as a mild flu to severe acute respiratory infection. The World Health Organization (WHO) declared COVID-19 as a global pandemic on March 11, 2020. The disease spreads by droplet infection from person to person. Early diagnosis is the key for prompt management of cases and control of the spread of the virus. Currently, the laboratory diagnosis of SARS-CoV-2 is based on nucleic acid amplification tests (NAAT) like real-time reverse transcriptase (RT-PCR). Various genes like E, N, S, ORF and RdRp are targeted as a part of screening and confirmation of cases. Furthermore, nucleic acid sequencing may be done for the identification of mutation in the genome of SARS-CoV-2. The development of serological assays and point of care molecular test will further intensify the diagnostic modalities of SARS-CoV-2.
| S-EPMC7189402 | biostudies-literature
Project description:PurposeClinical scores to rapidly assess the severity illness of Coronavirus Disease 2019 (COVID-19) could be considered of help for clinicians. Recently, a specific score (named COVID-GRAM) for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection, based on a nationwide Chinese cohort, has been proposed. We routinely applied the National Early Warning Score 2 (NEWS2) to predict critical COVID-19. Aim of this study is to compare NEWS2 and COVID-GRAM score.MethodsWe retrospectively analysed data of 121 COVID-19 patients admitted in two Clinics of Infectious Diseases in the Umbria region, Italy. The primary outcome was critical COVID-19 illness defined as admission to the intensive care unit, invasive ventilation, or death. Accuracy of the scores was evaluated with the area under the receiver-operating characteristic curve (AUROC). Differences between scores were confirmed used Hanley-McNeil test.ResultsThe NEWS2 AUROC curve measured 0.87 (standard error, SE 0.03; 95% CI 0.80-0.93; p < 0.0001). The COVID-GRAM score AUROC curve measured 0.77 (SE 0.04; 95% CI 0.68-0.85; p < 0.0001). Hanley-McNeil test showed that NEWS2 better predicted severe COVID-19 (Z = 2.03).ConclusionsThe NEWS2 showed superior accuracy to COVID-GRAM score for prediction of critical COVID-19 illness.
| S-EPMC8108728 | biostudies-literature
Project description:BackgroundIdentifying individuals with high-risk Parkinson's disease (PD) at earlier stages is an urgent priority to delay disease onset and progression. In the present study, we aimed to develop and validate clinical risk models using non-motor predictors to distinguish between early PD and healthy individuals. In addition, we constructed prognostic models for predicting the progression of non-motor symptoms [cognitive impairment, Rapid-eye-movement sleep Behavior Disorder (RBD), and depression] in de novo PD patients at 5 years of follow-up.MethodsWe retrieved the data from the Parkinson's Progression Markers Initiative (PPMI) database. After a backward variable selection approach to identify predictors, logistic regression analyses were applied for diagnosis model construction, and cox proportional-hazards models were used to predict non-motor symptom progression. The predictive models were internally validated by correcting measures of predictive performance for "optimism" or overfitting with the bootstrap resampling approach.ResultsFor constructing diagnostic models, the final model reached a high accuracy with an area under the curve (AUC) of 0.93 (95% CI: 0.91-0.96), which included eight variables (age, gender, family history, University of Pennsylvania Smell Inventory Test score, Montreal Cognitive Assessment score, RBD Screening Questionnaire score, levels of cerebrospinal fluid α-synuclein, and SNCA rs356181 polymorphism). For the construction of prognostic models, our results showed that the AUC of the three prognostic models improved slightly with increasing follow-up time. The overall AUCs fluctuated around 0.70. The model validation established good discrimination and calibration for predicting PD onset and progression of non-motor symptoms.ConclusionThe findings of our study facilitate predicting the individual risk at an early stage based on the predictors derived from these models. These predictive models provide relatively reliable information to prevent PD onset and progression. However, future validation analysis is still needed to clarify these findings and provide more insight into the predictive models over more extended periods of disease progression in more diverse samples.
| S-EPMC9459236 | biostudies-literature
Project description:PurposeCardiovascular comorbidity anticipates severe progression of COVID-19 and becomes evident by coronary artery calcification (CAC) on low-dose chest computed tomography (LDCT). The purpose of this study was to predict a patient's obligation of intensive care treatment by evaluating the coronary calcium burden on the initial diagnostic LDCT.MethodsEighty-nine consecutive patients with parallel LDCT and positive RT-PCR for SARS-CoV-2 were included from three centers. The primary endpoint was admission to ICU, tracheal intubation, or death in the 22-day follow-up period. CAC burden was represented by the Agatston score. Multivariate logistic regression was modeled for prediction of the primary endpoint by the independent variables "Agatston score > 0", as well as the CT lung involvement score, patient sex, age, clinical predictors of severe COVID-19 progression (history of hypertension, diabetes, prior cardiovascular event, active smoking, or hyperlipidemia), and laboratory parameters (creatinine, C-reactive protein, leucocyte, as well as thrombocyte counts, relative lymphocyte count, d-dimer, and lactate dehydrogenase levels).ResultsAfter excluding multicollinearity, "Agatston score >0" was an independent regressor within multivariate analysis for prediction of the primary endpoint (p<0.01). Further independent regressors were creatinine (p = 0.02) and leucocyte count (p = 0.04). The Agatston score was significantly higher for COVID-19 cases which completed the primary endpoint (64.2 [interquartile range 1.7-409.4] vs. 0 [interquartile range 0-0]).ConclusionCAC scoring on LDCT might help to predict future obligation of intensive care treatment at the day of patient admission to the hospital.
| S-EPMC8294495 | biostudies-literature
Project description:AbstractCoronavirus disease 2019 (COVID-19) has been a rampant worldwide health threat and we aimed to develop a model for early prediction of disease progression.This retrospective study included 124 adult inpatients with COVID-19 who presented with severe illness at admission and had a definite outcome (recovered or progressed to critical illness) during February 2020. Eighty-four patients were used as training cohort and 40 patients as validation cohort. Logistic regression analysis and receiver operating characteristic curve (ROC) analysis were used to develop and evaluate the prognostic prediction model.In the training cohort, the mean age was 63.4 ± 1.5 years, and male patients (48, 57%) were predominant. Forty-three (52%) recovered, and 41 (49%) progressed to critical. Decreased lymphocyte count (LC, odds ratio [OR] = 4.40, P = .026), elevated lactate dehydrogenase levels (LDH, OR = 4.24, P = .030), and high-sensitivity C-reactive protein (hsCRP, OR = 1.01, P = .025) at admission were independently associated with higher odds of deteriorated outcome. Accordingly, we developed a predictive model for disease progression based on the levels of the 3 risk factors (LC, LDH, and hsCRP) with a satisfactory performance in ROC analysis (area under the ROC curve [AUC] = 0.88, P < .001) and the best cut-off value was 0.526 with the sensitivity and specificity of 75.0% and 90.7%, respectively. Then, the model was internally validated by leave-one-out cross-validation with value of AUC 0.85 (P < .001) and externally validated in another validation cohort (26 recovered patients and 14 progressed patients) with AUC 0.84 (P < .001).We identified 3 clinical indicators of risk of progression and developed a severe COVID-19 prognostic prediction model, allowing early identification and intervention of high-risk patients being critically illness.
| S-EPMC7909133 | biostudies-literature
Project description:SUMMARYIn recent decades, several new diseases have emerged in different geographical areas, with pathogens including Ebola virus, Zika virus, Nipah virus, and coronaviruses (CoVs). Recently, a new type of viral infection emerged in Wuhan City, China, and initial genomic sequencing data of this virus do not match with previously sequenced CoVs, suggesting a novel CoV strain (2019-nCoV), which has now been termed severe acute respiratory syndrome CoV-2 (SARS-CoV-2). Although coronavirus disease 2019 (COVID-19) is suspected to originate from an animal host (zoonotic origin) followed by human-to-human transmission, the possibility of other routes should not be ruled out. Compared to diseases caused by previously known human CoVs, COVID-19 shows less severe pathogenesis but higher transmission competence, as is evident from the continuously increasing number of confirmed cases globally. Compared to other emerging viruses, such as Ebola virus, avian H7N9, SARS-CoV, and Middle East respiratory syndrome coronavirus (MERS-CoV), SARS-CoV-2 has shown relatively low pathogenicity and moderate transmissibility. Codon usage studies suggest that this novel virus has been transferred from an animal source, such as bats. Early diagnosis by real-time PCR and next-generation sequencing has facilitated the identification of the pathogen at an early stage. Since no antiviral drug or vaccine exists to treat or prevent SARS-CoV-2, potential therapeutic strategies that are currently being evaluated predominantly stem from previous experience with treating SARS-CoV, MERS-CoV, and other emerging viral diseases. In this review, we address epidemiological, diagnostic, clinical, and therapeutic aspects, including perspectives of vaccines and preventive measures that have already been globally recommended to counter this pandemic virus.
| S-EPMC7405836 | biostudies-literature