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</style><h1>N3C Data Help Validate Two Clinical Tools to Predict COVID-19 Mortality Risk</h1><div class="sidebar-right image-box"><img src="/sites/default/files/2023-11/N3C_data_feature_nurse_image_1920x1080.jpg" alt="A nurse is talking with an older patient and writing down answers. <strong><em>(Shutterstock)</em><strong>" width="1920" height="1080"><div class="caption-text"><p>A nurse is talking with an older patient and writing down answers.<strong> </strong>(<em>Shutterstock</em>)</p></div></div><p>November 20, 2023</p><p>For people hospitalized with COVID-19, two new risk-prediction tools, tested in part using the NCATS <a href="/n3c" target="_blank">National COVID Cohort Collaborative (N3C)</a>, could help clinicians make faster, better treatment decisions. The tools use a patient’s age and five common lab tests to analyze their risk of death more rapidly and accurately.</p><p>Scientists at Rutgers University reviewed health records from 969 adults hospitalized with COVID-19 from March to May 2020. The researchers used machine learning to examine 77 patient-health variables and find optimal combinations that best predicted severe COVID-19 outcomes.</p><p>The machine-learning analysis led to the discovery of two particularly predictive models, named PLABAC and PRABLE. Each model includes four shared patient-health variables: age, platelet count, lactate and blood urea nitrogen. PLABAC adds aspartate aminotransferase and C-reactive protein, and PRABLE uses red cell distribution width and eosinophil count.</p><p>The two models topped the traditional five-variable CURB-65 pneumonia severity prediction tool at assessing mortality risk among the people hospitalized with COVID-19, which is often used to predict COVID-19 outcomes. PLABAC and PRABLE performed on par with CURB-65 at identifying patients at greater risk of death. However, they surpassed CURB-65 at identifying patients at lower risk of death. That difference could help clinicians rapidly decide which patients hospitalized with COVID-19 face the greatest risk of death and require more intensive treatment.</p><p>To show how effective their prediction tools were, the researchers then tested PLABAC using N3C’s clinical health data, which represents more than 8 million COVID-19-positive patients. PRABLE could not be proven using N3C because the N3C data set lacked two of the PRABLE tool’s variables: red cell distribution width and eosinophil count. PLABAC performed well in predicting mortality using the N3C data from the periods both before widespread vaccination and after.</p><p>The researchers explained that the two prediction tools are “a practical first step for the inclusion of machine learning into clinical decision-making for COVID-19 that can serve as a template for future prognostic models.”</p><p>The PLABAC clinical predictive tool is available <a href="https://plabac-bc6be52803c8.herokuapp.com/" target="_blank">online</a>. Learn more about the study findings in the journal <a href="https://pubmed.ncbi.nlm.nih.gov/37681966/" target="_blank"><em>mBio</em></a>.</p><hr><div class="sharethis-inline-share-buttons"> </div>
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