An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis

Abstract

Background The National COVID-19 Chest Imaging Database (NCCID) is a centralized database containing mainly chest X-rays and computed tomography scans from patients across the UK. The objective of the initiative is to support a better understanding of the coronavirus SARS-CoV-2 disease (COVID-19) and the development of machine learning technologies that will improve care for patients hospitalized with a severe COVID-19 infection. This article introduces the training dataset, including a snapshot analysis covering the completeness of clinical data, and availability of image data for the various use-cases (diagnosis, prognosis, longitudinal risk). An additional cohort analysis measures how well the NCCID represents the wider COVID-19–affected UK population in terms of geographic, demographic, and temporal coverage. Findings The NCCID offers high-quality DICOM images acquired across a variety of imaging machinery; multiple time points including historical images are available for a subset of patients. This volume and variety make the database well suited to development of diagnostic/prognostic models for COVID-associated respiratory conditions. Historical images and clinical data may aid long-term risk stratification, particularly as availability of comorbidity data increases through linkage to other resources. The cohort analysis revealed good alignment to general UK COVID-19 statistics for some categories, e.g., sex, whilst identifying areas for improvements to data collection methods, particularly geographic coverage. Conclusion The NCCID is a growing resource that provides researchers with a large, high-quality database that can be leveraged both to support the response to the COVID-19 pandemic and as a test bed for building clinically viable medical imaging models.

Radiographic Assessment of Lung Edema (RALE) Scores are Highly Reproducible and Prognostic of Clinical Outcomes for Inpatients with COVID-19

“…Meanwhile, diagnosis of COVID-19 pneumonia relies on presence of radiographic consolidations on chest x-ray (CXR) or computed tomography (CT). Of the two modalities, CXR is the most widely available and routinely used, and CXRs are often repeated to determine pneumonia evolution or upon any new clinical indication 1,2 . However, radiographic severity has not been systematically integrated into risk predictions for COVID-19, and severity assessments are mostly qualitative and limited to narrative descriptions in diagnostic reports.…”

Bioinformatics, Computational Informatics, and Modeling Approaches to the Design of mRNA COVID-19 Vaccine Candidates

“…Some of the sources are in real time, for instance web-dashboards [92-95], while others are diverse. COVID-19 data are of different forms, namely genomic sequences of different variants of the SARS-CoV-2 virus [92-95], chest X-rays of COVID-19 patients [96], kidney replacement therapy data for COVID-19 patients [97], lungs data of patients [98], blood data of COVID-19 patients [99][100][101][102][103][104], medical data of patients that were infected and recovered [105], medical data of patients that were infected and died [106], patients' demographic data, patients' bio-data, COVID-19 health facility data for different regions of the world, and COVID-19 phylogenetic data, amongst others. Genomic data collected for the SARS-CoV-2 virus were stored in bioinformatics databases such as NCBI, EBL, and GISAID [20,107,108].…”

Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes

“…This study analysed data from the National COVID-19 Chest Imaging Database (NCCID), which comprised COVID-19 positive and negative patients 14 , 15 . All patients in the study were admitted with suspected COVID-19 infection.…”