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International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries: A Retrospective Cohort Study

By Griffin Weber, Chuan Hong, Nathan P Palmer, Paul Avillach, Shawn N Murphy, Alba Gutiérrez-Sacristán, Zongqi Xia, Arnaud Serret-Larmande, Antoine Neuraz, Gilbert S Omenn, Shyam Visweswaran, Jeffrey G. Klann, Andrew M South, Ne Hooi Will Loh, Mario Cannataro, Brett K. Beaulieu-Jones, Riccardo Bellazzi, Giuseppe Agapito, Mario Alessiani, Bruce J Aronow, Douglas S Bell, Antonio Bellasi, Vincent Benoit, Michele Beraghi, Martin Boeker, John Booth, Silvano Bosari, Florence T Bourgeois, Nicholas W Brown, Mauro Bucalo, Luca Chiovato, Lorenzo Chiudinelli, Arianna Dagliati, Batsal Devkota, Scott Duvall, Robert W Follett, Thomas Ganslandt, Noelia Garcia Barrio, Tobias Gradinger, Romain Griffier, David A Hanauer, John H Holmes, Petar Horki, Kenneth M Huling, Richard W Issitt, Vianney Jouhet, Mark S Keller, Detlef Kraska, Molei Liu, Yuan Luo, Kristine E Lynch, Alberto Malovini, Kenneth D Mandl, Chengsheng Mao, Anupama Maram, Michael E Matheny, Thomas Maulhardt, Maria Mazzitelli, Marianna Milano, Jason H Moore, Jeffrey S Morris, Michele Morris, Danielle Mowery, Thomas P Naughton, Kee Yuan Ngiam, James B Norman, Lav P Patel, Miguel Pedrera Jimenez, Rachel B Ramoni, Emily R Schriver, Luigia Scudeller, Neil J Sebire, Pablo Serrano Balazote, Anastasia Spiridou, Amelia LM Tan, Byorn W.L. Tan, Valentina Tibollo, Carlo Torti, Enrico M Trecarichi, Michele Vitacca, Alberto Zambelli, Chiara Zucco, The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), Isaac S. Kohane, Tianxi Cai, Gabriel A. Brat

Posted 18 Dec 2020
medRxiv DOI: 10.1101/2020.12.16.20247684

Objectives: To perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions. Design: Retrospective cohort study. Setting: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe. Participants: Patients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2. Primary and secondary outcome measures: Patients were categorized as ''ever-severe'' or ''never-severe'' using the validated 4CE severity criteria. Eighteen laboratory tests associated with poor COVID-19-related outcomes were evaluated for predictive accuracy by area under the curve (AUC), compared between the severity categories. Subgroup analysis was performed to validate a subset of laboratory values as predictive of severity against a published algorithm. A subset of laboratory values (CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin) was compared between North American and European sites for severity prediction. Results: Of 36,447 patients with COVID-19, 19,953 (43.7%) were categorized as ever-severe. Most patients (78.7%) were 50 years of age or older and male (60.5%). Longitudinal trajectories of CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin showed association with disease severity. Significant differences of laboratory values at admission were found between the two groups. With the exception of D-dimer, predictive discrimination of laboratory values did not improve after admission. Sub-group analysis using age, D-dimer, CRP, and lymphocyte count as predictive of severity at admission showed similar discrimination to a published algorithm (AUC=0.88 and 0.91, respectively). Both models deteriorated in predictive accuracy as the disease progressed. On average, no difference in severity prediction was found between North American and European sites. Conclusions: Laboratory test values at admission can be used to predict severity in patients with COVID-19. Prediction models show consistency across international sites highlighting the potential generalizability of these models.

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