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
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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. Prediction models show consistency across international sites highlighting the potential generalizability of these models.
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SciScore for 10.1101/2020.12.16.20247684: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization The AUC estimate quantified the probability that the laboratory result of a randomly selected patient in the ever-severe group was sufficiently different from that of a randomly selected patient in the never-severe group. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Strengths and weaknesses: The nature and construction of the 4CE consortium offers a number of key strengths and requires …
SciScore for 10.1101/2020.12.16.20247684: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization The AUC estimate quantified the probability that the laboratory result of a randomly selected patient in the ever-severe group was sufficiently different from that of a randomly selected patient in the never-severe group. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Strengths and weaknesses: The nature and construction of the 4CE consortium offers a number of key strengths and requires acknowledgement of important limitations. The consortium approach enabled the pooling of laboratory values across 342 hospitals with diverse healthcare practices. This showed that site-level (within-site and between-site) differences were greater than country-level differences. Interestingly, the severity predictive performance (AUC) of each laboratory study is remarkably consistent between North America and Europe. Despite the differences in the composition of ever-severe and never-severe patients across sites and countries, the directionality of laboratory values and the threshold for ever-severe disease shared many similarities. The design of the consortium and these analyses highlight that these findings are unlikely to be site-specific or the result of health care system dynamics; the predictive nature of identified lab tests is more likely to generalize. Additionally, given the diversity of sites, the findings are unlikely to be biased by a majority population demographic. The federated nature of analyses presented several additional limitations the consortium acknowledged and took measures to mitigate. First, EHR data have intrinsic noise, variable levels and causes of missing data, and policy effects on available documentation that will result in differences between sites. By leveraging a federated system of common EHR data elements and capturing s...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
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- No protocol registration statement was detected.
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