Comparative Characterization and Risk Stratification of Asymptomatic and Presymptomatic Patients With COVID-19.
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ABSTRACT:
SUBMITTER: Shi L
PROVIDER: S-EPMC8301218 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: Not available
| S-EPMC8064900 | biostudies-literature
Project description:BackgroundThe outbreak of coronavirus disease 2019 (COVID-19) in December 2019 overlaps with the flu season.MethodsWe compared clinical and laboratory results from 719 influenza and 973 COVID-19 patients from January to April 2020. We compiled laboratory results from the first 14 days of the hospitalized patients using parameters that are most significantly different between COVID-19 and influenza and hierarchically clustered COVID-19 patients.FindingsCompared to influenza, patients with COVID-19 exhibited a continued increase in white blood cell count, rapid decline of hemoglobin, more rapid increase in blood urea nitrogen (BUN) and D-dimer, and higher level of alanine transaminase, C-reactive protein, ferritin, and fibrinogen. COVID-19 patients were sub-classified into 5 clusters through a hierarchical clustering analysis. Medical records were reviewed and patients were risk stratified based on the clinical outcomes. The cluster with the highest risk showed 27·8% fatality, 94% ICU admission, 94% intubation, and 28% discharge rates compared to 0%, 38%, 22%, and 88% in the lowest risk cluster, respectively. Patients in the highest risk cluster had leukocytosis including neutrophilia and monocytosis, severe anemia, increased red blood cell distribution width, higher BUN, creatinine, D-dimer, alkaline phosphatase, bilirubin, and troponin.InterpretationThere are significant differences in the clinical and laboratory courses between COVID-19 and influenza. Risk stratification in hospitalized COVID-19 patients using laboratory data could be useful to predict clinical outcomes and pathophysiology of these patients.FundingNational Institute of Diabetes and Digestive and Kidney Disease, Department of Defense, and National Heart, Lung, and Blood Institute.
| S-EPMC7564523 | biostudies-literature
Project description:Elderly people are more severely affected by COVID-19. Nevertheless scarce information about specific prognostic scores for this population is available. The main objective was to compare the accuracy of recently developed COVID-19 prognostic scores to that of CURB-65, Charlson and PROFUND indices in a cohort of 272 elderly patients from four nursing homes, affected by COVID-19. Accuracy was measured by calibration (calibration curves and Hosmer-Lemeshov (H-L) test), and discriminative power (area under the receiver operation curve (AUC-ROC). Negative and positive predictive values (NPV and PPV) were also obtained. Overall mortality rate was 22.4 %. Only ACP and Shi et al. out of 10 specific COVID-19 indices could be assessed. All indices but CURB-65 showed a good calibration by H-L test, whilst PROFUND, ACP and CURB-65 showed best results in calibration curves. Only CURB-65 (AUC-ROC = 0.81 [0.75-0.87])) and PROFUND (AUC-ROC = 0.67 [0.6-0.75])) showed good discrimination power. The highest NPV was obtained by CURB-65 (95 % [90-98%]), PROFUND (93 % [77-98%]), and their combination (100 % [82-100%]); whereas CURB-65 (74 % [51-88%]), and its combination with PROFUND (80 % [50-94%]) showed highest PPV. PROFUND and CURB-65 indices showed the highest accuracy in predicting death-risk of elderly patients affected by COVID-19, whereas Charlson and recent developed COVID-19 specific tools lacked it, or were not available to assess. A comprehensive clinical stratification on two-level basis (basal death risk due to chronic conditions by PROFUND index, plus current death risk due to COVID-19 by CURB-65), could be an appropriate approach.
| S-EPMC7446617 | biostudies-literature
Project description:Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has provoked, up to date, more than 5 million deaths worldwide. While asymptomatic individuals are responsible of many potential transmissions, the difficulty to identify and isolate them at the high peak of infection constitutes still a real challenge in the actual pandemic. Moreover, apart from the effect over the respiratory system, SARS-CoV-2 provokes severe vascular damage and thromboembolic events in critical COVID-19 patients, deriving in many related deaths and long-hauler symptoms. Understanding how these processes are triggered in response to the virus as well as the potential sequelae at the long term, even in asymptomatic donors, becomes essential. Methods: We have evaluated, by application of a proteomics-based quantitative approach, the effect of serum of COVID-19 asymptomatic individuals over circulating angiogenic cells (CACs). Thus, healthy CACs were incubated ex-vivo with the serum of either COVID-19 negative (PCR-/IgG-, n:8) or COVID-19 positive asymptomatic donors, at different infective stages: PCR+/ IgG- (n:8) and PCR-/IgG+ (n:8). Also, a label free quantitative approach was applied to identify and quantify protein differences between these serums. Results: Our results confirmed that SARS-CoV-2 promotes changes at the protein level in the serum of infected asymptomatic individuals, mainly correlated with altered coagulation and inflammatory processes (Fibrinogen, Von Willebrand Factor, Thrombospondin-1). At the cellular level, proteins like ICAM-1, TLR2 or Ezrin/Radixin were only up-regulated in CACs treated with the serum of asymptomatic patients at the highest peak of infection (PCR+/IgG-), but not with the serum of PCR-/IgG+ individuals. Many of these proteins correlate with the initial response of the endothelium against the virus. Conclusions: The incubation ex vivo of CACs with the serum of asymptomatic COVID-19 donors at different stages of infection promoted protein changes representative of the endothelial dysfunction and inflammatory response that occur after viral infection, together with activation of the coagulation process. The current approach constitutes an optimal model to study the response of vascular cells to SARS-CoV-2 infection, as well as an alternative to test potential inhibitors targeting either the virus entry pathway or molecules involved in the immune responses following SARS-CoV-2 infection.
2022-05-20 | PXD030860 | Pride
Project description:We used total RNA of nasopharyngeal swabs from COVID-19 patients to identify their gene expression profile. Multiple biological process were significantly enriched in either asymptomatic or mildly symptomatic patients. These significantly expressed genes were suggested to contribute to the severity of the disease. We also performed metagenomics analysis to identify differences in the microbiome profile of the two groups of patients.
2022-06-29 | GSE186651 | GEO
Project description:Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global pandemic worldwide. Long non-coding RNAs (lncRNAs) are a subclass of endogenous, non-protein-coding RNA, which lacks an open reading frame and is more than 200 nucleotides in length. However, the functions for lncRNAs in COVID-19 have not been unravelled. The present study aimed at identifying the related lncRNAs based on RNA sequencing of peripheral blood mononuclear cells from patients with SARS-CoV-2 infection as well as health individuals. Overall, 17 severe, 12 non-severe patients and 10 healthy controls were enrolled in this study. Firstly, we reported some altered lncRNAs between severe, non-severe COVID-19 patients and healthy controls. Next, we developed a 7-lncRNA panel with a good differential ability between severe and non-severe COVID-19 patients using least absolute shrinkage and selection operator regression. Finally, we observed that COVID-19 is a heterogeneous disease among which severe COVID-19 patients have two subtypes with similar risk score and immune score based on lncRNA panel using iCluster algorithm. As the roles of lncRNAs in COVID-19 have not yet been fully identified and understood, our analysis should provide valuable resource and information for the future studies.
| S-EPMC8107096 | biostudies-literature
Project description:Objective: To distinguish COVID-19 patients and non-COVID-19 viral pneumonia patients and classify COVID-19 patients into low-risk and high-risk at admission by laboratory indicators. Materials and methods: In this retrospective cohort, a total of 3,563 COVID-19 patients and 118 non-COVID-19 pneumonia patients were included. There are two cohorts of COVID-19 patients, including 548 patients in the training dataset, and 3,015 patients in the testing dataset. Laboratory indicators were measured during hospitalization for all patients. Based on laboratory indicators, we used the support vector machine and joint random sampling to risk stratification for COVID-19 patients at admission. Based on laboratory indicators detected within the 1st week after admission, we used logistic regression and joint random sampling to develop the survival mode. The laboratory indicators of COVID-10 and non-COVID-19 were also compared. Results: We first identified the significant laboratory indicators related to the severity of COVID-19 in the training dataset. Neutrophils percentage, lymphocytes percentage, creatinine, and blood urea nitrogen with AUC >0.7 were included in the model. These indicators were further used to build a support vector machine model to classify patients into low-risk and high-risk at admission in the testing dataset. Results showed that this model could stratify the patients in the testing dataset effectively (AUC = 0.89). Our model still has good performance at different times (Mean AUC: 0.71, 0.72, 0.72, respectively for 3, 5, and 7 days after admission). Moreover, laboratory indicators detected within the 1st week after admission were able to estimate the probability of death (AUC = 0.95). We identified six indicators with permutation p < 0.05, including eosinophil percentage (p = 0.007), white blood cell count (p = 0.045), albumin (p = 0.041), aspartate transaminase (p = 0.043), lactate dehydrogenase (p = 0.002), and hemoglobin (p = 0.031). We could diagnose COVID-19 and differentiate it from other kinds of viral pneumonia based on these laboratory indicators. Conclusions: Our risk-stratification model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage of COVID-19. In addition, laboratory findings could be used to distinguish COVID-19 and non-COVID-19.
| S-EPMC8414546 | biostudies-literature
Project description:PURPOSE:Cancer patients have many medical and psychosocial needs, which may increase during the COVID-19 pandemic. We sought to (1) risk-stratify hematology/oncology patients using general medicine and cancer-specific methods to identify those at high risk for acute care utilization, (2) measure the correlation between two risk stratification methods, and (3) perform a telephone-based needs assessment with intervention for high-risk patients. METHODS:Patients were risk-stratified using a general medical health composite score (HCS) and a cancer-specific risk (CSR) stratification based on disease and treatment characteristics. The correlation between HCS and CSR was measured using Spearman's correlation. A multi-disciplinary team developed a focused needs assessment script with recommended interventions for patients categorized as high-risk by either method. The number of patient needs identified and referrals for services made in the first month of outreach are reported. RESULTS:A total of 1697 patients were risk-stratified, with 17% high-risk using HCS and 22% high-risk using CSR. Correlation between HCS and CSR was modest (? = 0.41). During the first month of the pilot, 286 patients were called for outreach with 245 contacted (86%). Commonly identified needs were financial difficulties (17%), uncontrolled symptoms (15%), and interest in advance care planning (13%), resulting in referral for supportive services for 33% of patients. CONCLUSION:There is a high burden of unmet medical and psychosocial needs in hematology/oncology patients during the COVID-19 pandemic. A telephone-based outreach program results in the identification of and intervention for these needs; however, additional cancer-specific risk models are needed to improve targeting to high-risk patients.
| S-EPMC7549726 | biostudies-literature
Project description:BackgroundCOVID-19 has resulted in high mortality worldwide. Information regarding cardiac markers for precise risk-stratification is limited. We aim to discover sensitive and reliable early-warning biomarkers for optimizing management and improving the prognosis of COVID-19 patients.MethodsA total of 2954 consecutive COVID-19 patients who were receiving treatment from the Wuhan Huoshenshan Hospital in China from February 4 to April 10 were included in this retrospective cohort. Serum levels of cardiac markers were collected after admission. Coronary artery disease diagnosis and survival status were recorded. Single-cell RNA-sequencing and bulk RNA-sequencing from different cohorts of non-COVID-19 were performed to analyze SARS-CoV-2 receptor expression.ResultsAmong 2954 COVID-19 patients in the analysis, the median age was 60 years (50-68 years), 1461 (49.5%) were female, and 1515 (51.3%) were severe/critical. Compared to mild/moderate (1439, 48.7%) patients, severe/critical patients showed significantly higher levels of cardiac markers within the first week after admission. In severe/critical COVID-19 patients, those with abnormal serum levels of BNP (42 [24.6%] vs 7 [1.1%]), hs-TNI (38 [48.1%] vs 6 [1.0%]), α- HBDH (55 [10.4%] vs 2 [0.2%]), CK-MB (45 [36.3%] vs 12 [0.9%]), and LDH (56 [12.5%] vs 1 [0.1%]) had a significantly higher mortality rate compared to patients with normal levels. The same trend was observed in the ICU admission rate. Severe/critical COVID-19 patients with pre-existing coronary artery disease (165/1,155 [10.9%]) had more cases of BNP (52 [46.5%] vs 119 [16.5%]), hs-TNI (24 [26.7%] vs 9.6 [%], α- HBDH (86 [55.5%] vs 443 [34.4%]), CK-MB (27 [17.4%] vs 97 [7.5%]), and LDH (65 [41.9%] vs 382 [29.7%]), when compared with those without coronary artery disease. There was enhanced SARS-CoV-2 receptor expression in coronary artery disease compared with healthy controls. From regression analysis, patients with five elevated cardiac markers were at a higher risk of death (hazards ratio 3.4 [95% CI 2.4-4.8]).ConclusionsCOVID-19 patients with pre-existing coronary artery disease represented a higher abnormal percentage of cardiac markers, accompanied by high mortality and ICU admission rate. BNP together with hs-TNI, α- HBDH, CK-MB and LDH act as a prognostic biomarker in COVID-19 patients with or without pre-existing coronary artery disease.
| S-EPMC8074282 | biostudies-literature
Project description:BackgroundDespite being associated with worse prognosis in patients with COVID-19, systematic determination of myocardial injury is not recommended. The aim of the study was to study the effect of myocardial injury assessment on risk stratification of COVID-19 patients.MethodsSeven hundred seven consecutive adult patients admitted to a large tertiary hospital with confirmed COVID-19 were included. Demographic data, comorbidities, laboratory results and clinical outcomes were recorded. Charlson comorbidity index (CCI) was calculated in order to quantify the degree of comorbidities. Independent association of cardiac troponin I (cTnI) increase with outcomes was evaluated by multivariate regression analyses and area under curve. In addition, propensity-score matching was performed to assemble a cohort of patients with similar baseline characteristics.ResultsIn the matched cohort (mean age 66.76 ± 15.7 years, 37.3% females), cTnI increase above the upper limit was present in 20.9% of the population and was associated with worse clinical outcomes, including all-cause mortality within 30 days (45.1% vs. 23.2%; p = 0.005). The addition of cTnI to a multivariate prediction model showed a significant improvement in the area under the time-dependent receiver operating characteristic curve (0.775 vs. 0.756, DC-statistic = 0.019; 95% confidence interval 0.001-0.037). Use of renin-angiotensin-aldosterone system inhibitors was not associated with mortality after adjusting by baseline risk factors.ConclusionsMyocardial injury is independently associated with adverse outcomes irrespective of baseline comorbidities and its addition to multivariate regression models significantly improves their performance in predicting mortality. The determination of myocardial injury biomarkers on hospital admission and its combination with CCI can classify patients in three risk groups (high, intermediate and low) with a clearly distinct 30-day mortality.
| S-EPMC8078990 | biostudies-literature