Prediction of remdesivir resistance in COVID-19 illness: Need for development of clinical laboratory test.
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ABSTRACT:
SUBMITTER: Kumar D
PROVIDER: S-EPMC8190719 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:COVID-19 has emerged as a major global health crisis since the first cases were reported in China in December 2019. Remdesivir is the only broad-spectrum antiviral approved by the US Food and Drug Administration to treat hospitalized patients with COVID-19 infection. Although the adverse effects of remdesivir are largely unknown, data from randomized controlled trials have demonstrated its deleterious effect on the liver. This review briefly addresses the hepatic manifestations of COVID-19 infection and the data regarding the efficacy and adverse effects of remdesivir on liver function when used in patients hospitalized with COVID-19. Through a literature search, we identified five randomized controlled trials, two case reports, and one case series, including a total of 2375 patients. Although mild transaminase elevation has been reported as a feature of COVID-19, there has been a concern of hepatotoxicity associated with the use of remdesivir. Based on the limited available data regarding the adverse effects of remdesivir on hepatic function, it is prudent to exercise caution by evaluating baseline liver function, avoiding the use of potentially hepatotoxic drugs, and closely monitoring liver function when using remdesivir in patients hospitalized with COVID-19.
| S-EPMC8224190 | biostudies-literature
Project description:Testing the uploader tool with COVID-19 data
| PRJEB38185 | ENA
Project description:COVID-19 is now pandemic throughout the world. Scientist, doctors are searching for effective therapy of this diseases. The remdesivir, an antiviral drug, is appeared as 'molecule of hope' for the treatment of this disease. USFDA gave emergency approval to this drug for the treatment of COVID-19. The molecular mechanism is unknown. In this paper, we tried to describe the probable molecular mechanism of remdesivir to inhibit the RNA synthesis of SARS-CoV-2. However, more detail mechanism is needed to understand mechanism of action of remdesivir.
| S-EPMC7214327 | biostudies-literature
Project description: Not available
| S-EPMC7837336 | biostudies-literature
Project description:BackgroundThe outbreak of coronavirus disease 2019 (COVID-19) has become a global public health concern. Many inpatients with COVID-19 have shown clinical symptoms related to sepsis, which will aggravate the deterioration of patients' condition. We aim to diagnose Viral Sepsis Caused by SARS-CoV-2 by analyzing laboratory test data of patients with COVID-19 and establish an early predictive model for sepsis risk among patients with COVID-19.MethodsThis study retrospectively investigated laboratory test data of 2,453 patients with COVID-19 from electronic health records. Extreme gradient boosting (XGBoost) was employed to build four models with different feature subsets of a total of 69 collected indicators. Meanwhile, the explainable Shapley Additive ePlanation (SHAP) method was adopted to interpret predictive results and to analyze the feature importance of risk factors.FindingsThe model for classifying COVID-19 viral sepsis with seven coagulation function indicators achieved the area under the receiver operating characteristic curve (AUC) 0.9213 (95% CI, 89.94-94.31%), sensitivity 97.17% (95% CI, 94.97-98.46%), and specificity 82.05% (95% CI, 77.24-86.06%). The model for identifying COVID-19 coagulation disorders with eight features provided an average of 3.68 (±) 4.60 days in advance for early warning prediction with 0.9298 AUC (95% CI, 86.91-99.04%), 82.22% sensitivity (95% CI, 67.41-91.49%), and 84.00% specificity (95% CI, 63.08-94.75%).InterpretationWe found that an abnormality of the coagulation function was related to the occurrence of sepsis and the other routine laboratory test represented by inflammatory factors had a moderate predictive value on coagulopathy, which indicated that early warning of sepsis in COVID-19 patients could be achieved by our established model to improve the patient's prognosis and to reduce mortality.
| S-EPMC7966961 | biostudies-literature
Project description:ObjectivesCoronavirus disease-19 (COVID-19) is associated with various clinical manifestations, ranging from asymptomatic infection to critical illness. The aim of this study is to evaluate the clinical and laboratory characteristics of hospitalised COVID-19 patients and construct a predictive model for the discrimination of patients at risk of disease progression.MethodsA single-centre cohort study was conducted including consecutively patients with COVID-19. Demographic, clinical and laboratory findings were prospectively collected at admission. The primary outcome of interest was the intensive care unit admission. A risk model was constructed by applying a Cox's proportional hazard's model with elastic net penalty. Its diagnostic performance was assessed by receiver operating characteristic analysis and was compared with conventional pneumonia severity scores.ResultsFrom a total of 67 patients 15 progressed to critical illness. The risk score included patients' gender, presence of hypertension and diabetes mellitus, fever, shortness of breath, serum glucose, aspartate aminotransferase, lactate dehydrogenase, C-reactive protein and fibrinogen. Its predictive accuracy was estimated to be high (area under the curve: 97.1%), performing better than CURB-65, CRB-65 and PSI/PORT scores. Its sensitivity and specificity were estimated to be 92.3% and 93.3%, respectively, at the optimal threshold of 1.6.ConclusionsA10-variable risk score was constructed based on clinical and laboratory characteristics in order to predict critical illness amongst hospitalised COVID-19 patients, achieving better discrimination compared with traditional pneumonia severity scores. The proposed risk model should be externally validated in independent cohorts in order to ensure its prognostic efficacy.
| S-EPMC7883033 | biostudies-literature
Project description:Remdesivir (Veklury®), a nucleotide analogue prodrug with broad-spectrum antiviral activity, is approved for the treatment of coronavirus disease 2019 (COVID-19), the illness caused by severe acute respiratory syndrome coronavirus 2 infection. Unlike some antivirals, remdesivir has a low potential for drug-drug interactions. In the pivotal ACTT-1 trial in hospitalized patients with COVID-19, daily intravenous infusions of remdesivir significantly reduced time to recovery relative to placebo. Subsequent trials provided additional support for the efficacy of remdesivir in hospitalized patients with moderate or severe COVID-19, with a greater benefit seen in patients with minimal oxygen requirements at baseline. Clinical trials also demonstrated the efficacy of remdesivir in other patient populations, including outpatients at high risk for progression to severe COVID-19, as well as hospitalized paediatric patients. In terms of mortality, results were equivocal. However, remdesivir appeared to have a small mortality benefit in hospitalized patients who were not already being ventilated at baseline. Remdesivir was generally well tolerated in clinical trials, but pharmacovigilance data found an increased risk of hepatic, renal and cardiovascular adverse drug reactions in the real-world setting. In conclusion, remdesivir represents a useful treatment option for patients with COVID-19, particularly those who require supplemental oxygen.
| S-EPMC10474216 | biostudies-literature
Project description:Human coronaviruses (HCoV) were discovered in the 1960s and were originally thought to cause only mild upper respiratory tract diseases in immunocompetent hosts. This view changed since the beginning of this century, with the 2002 SARS (severe acute respiratory syndrome) epidemic and the 2012 MERS (Middle East respiratory syndrome) outbreak, two zoonotic infections that resulted in mortality rates of approximately 10% and 35%, respectively. Despite the importance of these pathogens, no approved antiviral drugs for the treatment of human coronavirus infections became available. However, remdesivir, a nucleotide analogue prodrug originally developed for the treatment of Ebola virus, was found to inhibit the replication of a wide range of human and animal coronaviruses in vitro and in preclinical studies. It is therefore not surprising that when the highly pathogenic SARS-CoV-2 coronavirus emerged in late 2019 in China, causing global health concern due to the virus strong human-to-human transmission ability, remdesivir was one of the first clinical candidates that received attention. After in vitro studies had shown its antiviral activity against SARS-CoV-2, and a first patient was successfully treated with the drug in the USA, a number of trials on remdesivir were initiated. Several had encouraging results, particularly the ACTT-1 double blind, randomized, and placebo controlled trial that has shown shortening of the time to recovery in hospitalized patients treated with remdesivir. The results of other trials were instead negative. Here, we provide an overview of remdesivir discovery, molecular mechanism of action, and initial and current clinical studies on its efficacy.
| S-EPMC7836944 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic is probably the greatest health catastrophe of the modern era. Spain's health care system has been exposed to uncontrollable numbers of patients over a short period, causing the system to collapse. Given that diagnosis is not immediate, and there is no effective treatment for COVID-19, other tools have had to be developed to identify patients at the risk of severe disease complications and thus optimize material and human resources in health care. There are no tools to identify patients who have a worse prognosis than others.ObjectiveThis study aimed to process a sample of electronic health records of patients with COVID-19 in order to develop a machine learning model to predict the severity of infection and mortality from among clinical laboratory parameters. Early patient classification can help optimize material and human resources, and analysis of the most important features of the model could provide more detailed insights into the disease.MethodsAfter an initial performance evaluation based on a comparison with several other well-known methods, the extreme gradient boosting algorithm was selected as the predictive method for this study. In addition, Shapley Additive Explanations was used to analyze the importance of the features of the resulting model.ResultsAfter data preprocessing, 1823 confirmed patients with COVID-19 and 32 predictor features were selected. On bootstrap validation, the extreme gradient boosting classifier yielded a value of 0.97 (95% CI 0.96-0.98) for the area under the receiver operator characteristic curve, 0.86 (95% CI 0.80-0.91) for the area under the precision-recall curve, 0.94 (95% CI 0.92-0.95) for accuracy, 0.77 (95% CI 0.72-0.83) for the F-score, 0.93 (95% CI 0.89-0.98) for sensitivity, and 0.91 (95% CI 0.86-0.96) for specificity. The 4 most relevant features for model prediction were lactate dehydrogenase activity, C-reactive protein levels, neutrophil counts, and urea levels.ConclusionsOur predictive model yielded excellent results in the differentiating among patients who died of COVID-19, primarily from among laboratory parameter values. Analysis of the resulting model identified a set of features with the most significant impact on the prediction, thus relating them to a higher risk of mortality.
| S-EPMC8048712 | biostudies-literature