Medicine and Health Sciences Commons^™

Mortality Prediction Analysis Among Covid-19 Inpatients Using Clinical Variables And Deep Learning Chest Radiography Imaging Features., Xuan V Nguyen, Engin Dikici, Sema Candemir, Robyn L Ball, Luciano M Prevedello

Faculty Research 2022

The emergence of the COVID-19 pandemic over a relatively brief interval illustrates the need for rapid data-driven approaches to facilitate clinical decision making. We examined a machine learning process to predict inpatient mortality among COVID-19 patients using clinical and chest radiographic data. Modeling was performed with a de-identified dataset of encounters prior to widespread vaccine availability. Non-imaging predictors included demographics, pre-admission clinical history, and past medical history variables. Imaging features were extracted from chest radiographs by applying a deep convolutional neural network with transfer learning. A multi-layer perceptron combining 64 deep learning features from chest radiographs with 98 patient clinical …

Nsaid Use And Clinical Outcomes In Covid-19 Patients: A 38-Center Retrospective Cohort Study., Justin T Reese, Ben D Coleman, Lauren Chan, Hannah Blau, Tiffany J Callahan, Luca Cappelletti, Tommaso Fontana, Katie R Bradwell, Nomi L Harris, Elena Casiraghi, Giorgio Valentini, Guy Karlebach, Rachel Deer, Julie A Mcmurry, Melissa A Haendel, Christopher G Chute, Emily Pfaff, Richard Moffitt, Heidi Spratt, Jasvinder A Singh, Christopher J Mungall, Andrew E Williams, Peter N Robinson

Faculty Research 2022

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use.

METHODS: A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was …

Combining Genomic And Epidemiological Data To Compare The Transmissibility Of Sars-Cov-2 Variants Alpha And Iota., Mary E Petrone, Jessica E Rothman, Mallery I Breban, Isabel M Ott, Alexis Russell, Erica Lasek-Nesselquist, Hamada Badr, Kevin Kelly, Gregory Omerza, Nicholas Renzette, Anne E Watkins, Chaney C Kalinich, Tara Alpert, Anderson F Brito, Rebecca Earnest, Irina R Tikhonova, Christopher Castaldi, John P Kelly, Matthew Shudt, Jonathan Plitnick, Erasmus Schneider, Steven Murphy, Caleb Neal, Eva Laszlo, Ahmad Altajar, Claire Pearson, Anthony Muyombwe, Randy Downing, Jafar Razeq, Linda Niccolai, Madeline S Wilson, Margaret L Anderson, Jianhui Wang, Chen Liu, Pei Hui, Shrikant Mane, Bradford P Taylor, William P Hanage, Marie L Landry, David R Peaper, Kaya Bilguvar, Joseph R Fauver, Chantal B F Vogels, Lauren M Gardner, Virginia E Pitzer, Kirsten St George, Mark D Adams, Nathan D Grubaugh

Faculty Research 2022

SARS-CoV-2 variants shaped the second year of the COVID-19 pandemic and the discourse around effective control measures. Evaluating the threat posed by a new variant is essential for adapting response efforts when community transmission is detected. In this study, we compare the dynamics of two variants, Alpha and Iota, by integrating genomic surveillance data to estimate the effective reproduction number (R_t) of the variants. We use Connecticut, United States, in which Alpha and Iota co-circulated in 2021. We find that the R_t of these variants were up to 50% larger than that of other variants. We then …