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
Authors: Weber Griffin M , Hong Chuan , Palmer Nathan P , Avillach Paul , Murphy Shawn N , Gutiérrez-Sacristán Alba , Xia Zongqi , Serret-Larmande Arnaud , Neuraz Antoine , Omenn Gilbert S. , Visweswaran Shyam , Klann Jeffrey G , South Andrew M , Will Loh Ne Hooi , Cannataro Mario , Beaulieu-Jones Brett K , Bellazzi Riccardo , Agapito Giuseppe , Alessiani Mario , Aronow Bruce J , Bell Douglas S , Bellasi Antonio , Benoit Vincent , Beraghi Michele , Boeker Martin , Booth John , Bosari Silvano , Bourgeois Florence T , Brown Nicholas W , Bucalo Mauro , Chiovato Luca , Chiudinelli Lorenzo , Dagliati Arianna , Devkota Batsal , DuVall Scott L , Follett Robert W , Ganslandt Thomas , García Barrio Noelia , Gradinger Tobias , Griffier Romain , Hanauer David A , Holmes John H , Horki Petar , Huling Kenneth M , Issitt Richard W , Jouhet Vianney , Keller Mark S , Kraska Detlef , Liu Molei , Luo Yuan , Lynch Kristine E , Malovini Alberto , Mandl Kenneth D , Mao Chengsheng , Maram Anupama , Matheny Michael E , Maulhardt Thomas , Mazzitelli Maria , Milano Marianna , Moore Jason H , Morris Jeffrey S , Morris Michele , Mowery Danielle L , Naughton Thomas P , Ngiam Kee Yuan , Norman James B , Patel Lav P , Jimenez Miguel Pedrera , Ramoni Rachel B , Schriver Emily R , Scudeller Luigia , Sebire Neil J , Balazote Pablo Serrano , Spiridou Anastasia , Tan Amelia LM , Tan Byorn WL , Tibollo Valentina , Torti Carlo , Trecarichi Enrico M , Vitacca Michele , Zambelli Alberto , Zucco Chiara ,   The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), Kohane Isaac S , Cai Tianxi , Brat Gabriel A 
Published: December 2020

ABSTRACTObjectivesTo 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.DesignRetrospective cohort study.SettingThe 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.ParticipantsPatients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2.Primary and secondary outcome measuresPatients 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.ResultsOf 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.ConclusionsLaboratory 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.