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
Abstract
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. There is a need for prediction models that will perform well over the course of the disease in hospitalized patients.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
GW reports funding from NCATS UL1TR002541, NCATS UL1TR000005, and NLM R01LM013345. SM and JK report funding from NCATS 5UL1TR001857-05 and NHGRI 5R01HG009174-04. ZX reports funding from NINDS R01NS098023. GO reports funding from NIH grants NIEHS P30ES017885 and NCI U24CA210967. SV reports funding from NLM R01LM012095 and NCATS UL1TR001857. AS reports funding from NHLBI K23HL148394 and L40HL148910, and NCATS UL1TR001420. BA reports funding from NHLBI U24 HL148865. DB and RF report funding from NCATS UL1TR001881. TG and TG report funding from 01ZZ1801E German Federal Ministry of Education and Research. DH reports funding from NCATS UL1TR002240. MK reports funding from NHGRI 5T32HG002295-18. DK reports funding from MIRACUM Consortium grant 01ZZ1801A. YL reports funding from NLM R01LM01333. JM reports funding from NCATS UL1TR001878. DM reports funding from NCATS UL1-TR001878 Institutional Clinical and Translational Science Award (University of Pennsylvania). LP reports funding from NCATS CTSA Award #UL1TR002366.