SARS-CoV-2 PCR cycle threshold at hospital admission associated with Patient Mortality

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cycle threshold (Ct) has been suggested as an approximate measure of initial viral burden. The relationship of initial Ct at hospitalization and patient mortality has not been thoroughly investigated. Methods and findings We conducted a retrospective study of all SARS-CoV-2 positive, hospitalized patients from 3/26/2020 to 8/5/2020 who had SARS CoV-2 Ct data within 48 hours of admission (n=1044). Only patients with complete survival data discharged (n=774) or died in hospital (n=270), were included in our analysis. Laboratory, demographic, and clinical data were extracted from electronic medical records. Multivariable logistic regression was applied to examine the relationship of patient mortality with Ct values while adjusting for established risk factors. Ct values were analyzed both as continuous variables and subdivided into quartiles to better illustrate their relationship with outcomes, and other covariates. Cumulative incidence curves were created to assess whether there was a survival difference in the setting of the competing risks of death versus patient discharge. In this cohort the mean Ct at admission was higher for survivors (28.6, SD=5.8) compared to non-survivors (24.8, SD=6.0, P<0.001). Patients with a lower Ct value on admission were found to have a higher odds ratio (0.91, CI 0.89-0.94, p<0.001) of in hospital mortality after adjusting for age, gender, body mass index (BMI) and history of hypertension and diabetes. Patients with Ct values in 3rd Quartile (Ct 27.4-32.8) and 4th Quartile (Ct >32.9) have a lower odds of in-hospital death (P<0.001) in comparison to the 1st Quartile. On comparing between Ct quartiles, the mortality, BMI and glomerular filtration rate (GFR) were significantly different (p<0.05) between the groups. The cumulative incidence of all-cause mortality and discharge was found to differ between Ct quartiles (Grays Test P<0.001 for both.) Conclusion: SARS-CoV-2 Ct at admission was found to be an independent predictor of in patient mortality. However, further study is needed on how to best clinically utilize such information given the result variation due to specimen quality, phase of disease, and the limited discriminative ability of the test.

Using Automated-Machine Learning to Predict COVID-19 Patient Survival: Identify Influential Biomarkers

BackgroundIn a pandemic, it is important for clinicians to stratify patients and decide who receives limited medical resources. In this study, we used automated machine learning (autoML) to develop and compare between multiple machine learning (ML) models that predict the chance of patient survival from COVID-19 infection and identified the best-performing model. In addition, we investigated which biomarkers are the most influential in generating an accurate model. We believe an ML model such as this could be a useful tool for clinicians stratifying hospitalized SARS-CoV-2 patients. MethodsThe data was retrospectively collected from Clinical Looking Glass (CLG) on all patients testing positive for COVID-19 through a nasopharyngeal specimen by real-time RT-PCR and admitted between 3/1/2020-7/3/2020 (4376 patients) at our institution. We collected 47 biomarkers from each patient within 36 hours before or after the index time: RT-PCR positivity, and tracked whether a patient survived or not for one month following this time. We utilized the autoML from H2O.ai, an open source package for R language. The autoML generated 20 ML models and ranked them by area under the precision-recall curve (AUCPR) on the test set. We selected the best model (model_var_47) and chose a threshold probability that maximized F2 score to make a binary classifier: dead or alive. Subsequently, we ranked the relative importance of variables that generated model_var_47 and chose the 10 most influential variables. Next, we reran the autoML with these 10 variables and likewise selected the model with the best AUCPR on the test set (model_var_10). Again, threshold probability that maximized F2 score for model_var_10 was chosen to make a binary classifier. We calculated and compared the sensitivity, specificity, and positive predicate value (PPV) for model_var_10 and model_var_47. ResultsThe best model that autoML generated using all 47 variables was the stacked ensemble model of all models (AUCPR = 0.836). The most influential variables were: systolic and diastolic blood pressure, age, respiratory rate, pulse oximetry, blood urea nitrogen, lactate dehydrogenase, d-dimer, troponin, and glucose. When the autoML was retrained with these 10 most important variables, it did not significantly affect the performance (AUCPR= 0.828). For the binary classifiers, sensitivity, specificity, and PPV of model_var_47 was 83.6%, 87.7%, and 69.8% respectively, while for model_var_10 they were 90.9%, 71.1%, and 51.8% respectively. ConclusionsBy using autoML, we developed high-performing models that predict patient mortality from COVID-19 infection. In addition, we identified the most important biomarkers correlated with mortality. This ML model can be used as a decision supporting tool for medical practitioners to efficiently triage COVID-19 infected patients. From our literature review, this will be the largest COVID-19 patient cohort to train ML models and the first to utilize autoML. The COVID-19 survival calculator based on this study can be found at https://www.tsubomitech.com/.

Development, clinical translation, and utility of a COVID-19 antibody test with qualitative and quantitative readouts

The COVID-19 global pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) continues to place an immense burden on societies and healthcare systems. A key component of COVID-19 control efforts is serologic testing to determine the community prevalence of SARS-CoV-2 exposure and quantify individual immune responses to prior infection or vaccination. Here, we describe a laboratory-developed antibody test that uses readily available research-grade reagents to detect SARS-CoV-2 exposure in patient blood samples with high sensitivity and specificity. We further show that this test affords the estimation of viral spike-specific IgG titers from a single sample measurement, thereby providing a simple and scalable method to measure the strength of an individuals immune response. The accuracy, adaptability, and cost-effectiveness of this test makes it an excellent option for clinical deployment in the ongoing COVID-19 pandemic.

Treatment of Severe COVID-19 with Convalescent Plasma in the Bronx, NYC

Convalescent plasma with severe acute respiratory disease coronavirus 2 (SARS-CoV-2) antibodies (CCP) may hold promise as treatment for Coronavirus Disease 2019 (COVID-19). We compared the mortality and clinical outcome of patients with COVID-19 who received 200mL of CCP with a Spike protein IgG titer [≥]1:2,430 (median 1:47,385) within 72 hours of admission to propensity score-matched controls cared for at a medical center in the Bronx, between April 13 to May 4, 2020. Matching criteria for controls were age, sex, body mass index, race, ethnicity, comorbidities, week of admission, oxygen requirement, D-dimer, lymphocyte counts, corticosteroids, and anticoagulation use. There was no difference in mortality or oxygenation between CCP recipients and controls at day 28. When stratified by age, compared to matched controls, CCP recipients <65 years had 4-fold lower mortality and 4-fold lower deterioration in oxygenation or mortality at day 28. For CCP recipients, pre-transfusion Spike protein IgG, IgM and IgA titers were associated with mortality at day 28 in univariate analyses. No adverse effects of CCP were observed. Our results suggest CCP may be beneficial for hospitalized patients <65 years, but data from controlled trials is needed to validate this finding and establish the effect of ageing on CCP efficacy.