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. Prediction models show consistency across international sites highlighting the potential generalizability of these models.

International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries

“…Sites with implausible laboratory values or values consistently lower or higher than other sites were contacted for further investigation and correction as needed. 11 The local investigations and final assessment of accepted values considered age-specific reference ranges as well as clinical assays and site ranges.…”

“…9 To construct the required data files, sites used the Informatics for Integrating Biology and the Bedside uploaded their files to a central 4CE data upload tool, where quality control and validation steps were performed before analysis. 11 Patient-level files remained at each site and were not centrally shared at any point.…”

“… 9 The use of common data elements across a federated network allows for integration and harmonization of data to enable analyses of the disease manifestation and epidemiology of COVID-19 across health care sites. Focusing on adult populations, studies have used data from the 4CE initiative to measure the prevalence of specific types of clinical complications, develop EHR-based severity algorithms, 10 identify laboratory tests predicting severity in patients with COVID-19, 11 and define country-level differences in demographic and epidemiological presentation. 9 Leveraging data from this collaborative, our objective was to demonstrate large-scale, multinational use of EHR data to study COVID-19 in children and youth and describe hospitalization trends and key epidemiological and clinical features of the disease.…”

Bioinformatics Accelerates the Major Tetrad: A Real Boost for the Pharmaceutical Industry

“…In particular with the therapeutic interventions, the major research themes comprise the identification of biomarkers associated with COVID-19, discovery of drug targets, and bioinformatics methods for drug repurposing, which is employing already-developed and available drugs for COVID-19 management [143]. The primary research themes at the public health and epidemiological level comprising ordered assemblage and release of information about the infection spread, such as regular reports of number of cases, number of deaths, number of patients hospitalized and in ICU, etc., may aid the general population to realize the severity of the infection [144], followed by biological tests for testing and evaluation and computational tools and techniques to track the infected individuals, storage of large amount of clinical data in electronic health records of infected patients [145], evaluation and assessment of the impact and effects of complete lockdown at a socioeconomic level, and techniques to aid quarantined people by using local services, virtual assistants, and advanced technologies such as robots [143].…”

A hybrid approach to forecast the COVID-19 epidemic trend

“…They compared the changing trends of suspension of work and school without restrictions on travel and restrictions on travel on susceptible, latent, infected and removed persons. Hao X et al [ 14 ] and others [ 15 , 16 ] used two model laboratory cases for the estimation of the overall COVID-19 spread situation. According to those model’s, the actual cases can be monitored using the total registered cases in hospitals, and forecasting will be effective if that data is utilised to estimate future results.…”