Validation of an admission coagulation panel for risk stratification of COVID-19 patients.

BACKGROUND: There is limited data on the markers of coagulation and hemostatic activation (MOCHA) profile in Coronavirus disease 2019 (COVID-19) and its ability to identify COVID-19 patients at risk for thrombotic events and other complications. METHODS: Hospitalized patients with confirmed SARS-COV-2 from four Atlanta hospitals were included in this observational cohort study and underwent admission testing of MOCHA parameters (plasma d-dimer, prothrombin fragment 1.2, thrombin-antithrombin complex, fibrin monomer). Clinical outcomes included deep vein thrombosis, pulmonary embolism, myocardial infarction, ischemic stroke, access line thrombosis, ICU admission, intubation and mortality. MAIN RESULTS: Of 276 patients (mean age 59 ± 6.4 years, 47% female, 62% African American), 45 (16%) had a thrombotic endpoint. Each MOCHA parameter was independently associated with a thrombotic event (p<0.05) and ≥ 2 abnormalities was associated with thrombotic endpoints (OR 3.3, 95% CI 1.2-8.8) as were admission D-dimer ≥ 2000 ng/mL (OR 3.1, 95% CI 1.5-6.6) and ≥ 3000 ng/mL (OR 3.6, 95% CI 1.6-7.9). However, only ≥ 2 MOCHA abnormalities were associated with ICU admission (OR 3.0, 95% CI 1.7-5.2) and intubation (OR 3.2, 95% CI 1.6-6.4). MOCHA and D-dimer cutoffs were not associated with mortality. MOCHA with <2 abnormalities (26% of the cohort) had 89% sensitivity and 93% negative predictive value for a thrombotic endpoint. CONCLUSIONS: An admission MOCHA profile is useful to risk-stratify COVID-19 patients for thrombotic complications and more effective than isolated d-dimer for predicting risk of ICU admission and intubation.

Peptide Correlation Analysis (PeCorA) Reveals Differential Proteoform Regulation.

Shotgun proteomics techniques infer the presence and quantity of proteins using peptide proxies produced by cleavage of the proteome with a protease. Most protein quantitation strategies assume that multiple peptides derived from a protein will behave quantitatively similar across treatment groups, but this assumption may be false due to (1) heterogeneous proteoforms and (2) technical artifacts. Here we describe a strategy called peptide correlation analysis (PeCorA) that detects quantitative disagreements between peptides mapped to the same protein. PeCorA fits linear models to assess whether a peptide's change across treatment groups differs from all other peptides assigned to the same protein. PeCorA revealed that ∼15% of proteins in a mouse microglia stress data set contain at least one discordant peptide. Inspection of the discordant peptides shows the utility of PeCorA for the direct and indirect detection of regulated post-translational modifications (PTMs) and also for the discovery of poorly quantified peptides. The exclusion of poorly quantified peptides before protein quantity summarization decreased false-positives in a benchmark data set. Finally, PeCorA suggests that the inactive isoform of prothrombin, a coagulation cascade protease, is more abundant in plasma from COVID-19 patients relative to non-COVID-19 controls. PeCorA is freely available as an R package that works with arbitrary tables of quantified peptides.

A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.

Abnormal laboratory findings and plasma concentration monitoring of lopinavir and ritonavir in COVID-19.

It is not known whether the adverse events (AEs) associated with the administration of lopinavir and ritonavir (LPV/r) in the treatment of COVID-19 are concentration-dependent. In a retrospective study of 65 patients treated with LPV/r and therapeutic drug monitoring (TDM) for severe forms of COVID-19 (median age: 67; males: 41 [63.1%]), 33 (50.8%) displayed a grade ≥2 increase in plasma levels of hepatobiliary markers, lipase and/or triglycerides. A causal relationship between LPV/r and the AE was suspected in 9 of the 65 patients (13.8%). At 400 mg b.i.d., the plasma trough concentrations of LPV/r were high and showed marked interindividual variability (median [interquartile range]: 16,600 [11,430-20,842] ng/ml for lopinavir and 501 [247-891] ng/ml for ritonavir). The trough lopinavir concentration was negatively correlated with body mass index, while the trough ritonavir concentration was positively correlated with age and negatively correlated with prothrombin activity. However, the occurrence of abnormal laboratory values was not associated with higher trough plasma concentrations of LPV/r. Further studies will be needed to determine the value of TDM in LPV/r-treated patients with COVID-19.

Prominent changes in blood coagulation of patients with SARS-CoV-2 infection.

Background As the number of patients increases, there is a growing understanding of the form of pneumonia sustained by the 2019 novel coronavirus (SARS-CoV-2), which has caused an outbreak in China. Up to now, clinical features and treatment of patients infected with SARS-CoV-2 have been reported in detail. However, the relationship between SARS-CoV-2 and coagulation has been scarcely addressed. Our aim is to investigate the blood coagulation function of patients with SARS-CoV-2 infection. Methods In our study, 94 patients with confirmed SARS-CoV-2 infection were admitted in Renmin Hospital of Wuhan University. We prospectively collect blood coagulation data in these patients and in 40 healthy controls during the same period. Results Antithrombin values in patients were lower than that in the control group (p < 0.001). The values of D-dimer, fibrin/fibrinogen degradation products (FDP), and fibrinogen (FIB) in all SARS-CoV-2 cases were substantially higher than those in healthy controls. Moreover, D-dimer and FDP values in patients with severe SARS-CoV-2 infection were higher than those in patients with milder forms. Compared with healthy controls, prothrombin time activity (PT-act) was lower in SARS-CoV-2 patients. Thrombin time in critical SARS-CoV-2 patients was also shorter than that in controls. Conclusions The coagulation function in patients with SARS-CoV-2 is significantly deranged compared with healthy people, but monitoring D-dimer and FDP values may be helpful for the early identification of severe cases.