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Your search keyword '"Nccid Collaborative"' showing total 3 results
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3 results on '"Nccid Collaborative"'

1. Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors

Author

NCCID Collaborative, Aslani, Shahab, Lilaonitkul, Watjana, Gnanananthan, Vaishnavi, Raj, Divya, Rangelov, Bojidar, Young, Alexandra L., Hu, Yipeng, Taylor, Paul, Alexander, Daniel C., Jacob, Joseph, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Su, Ruidan, editor, Zhang, Yudong, editor, Liu, Han, editor, and F Frangi, Alejandro, editor

Published
2023
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2. Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes.

Author

Rangelov, Bojidar, Young, Alexandra, Lilaonitkul, Watjana, Aslani, Shahab, Taylor, Paul, Guðmundsson, Eyjólfur, Yang, Qianye, Hu, Yipeng, Hurst, John R., Hawkes, David J., Jacob, Joseph, Тhe NCCID Collaborative, NCCID Core Team, Bains, Pardeep, Cushnan, Dominic, Halling-Brown, Mark, Jefferson, Emily, Lemarchand, Francois, Sarellas, Anastasios, and Schofield, Daniel

Subjects
PROPORTIONAL hazards models, COVID-19, COVID-19 pandemic, COMMUNICABLE diseases
Abstract

The COVID-19 pandemic has been a great challenge to healthcare systems worldwide. It highlighted the need for robust predictive models which can be readily deployed to uncover heterogeneities in disease course, aid decision-making and prioritise treatment. We adapted an unsupervised data-driven model—SuStaIn, to be utilised for short-term infectious disease like COVID-19, based on 11 commonly recorded clinical measures. We used 1344 patients from the National COVID-19 Chest Imaging Database (NCCID), hospitalised for RT-PCR confirmed COVID-19 disease, splitting them equally into a training and an independent validation cohort. We discovered three COVID-19 subtypes (General Haemodynamic, Renal and Immunological) and introduced disease severity stages, both of which were predictive of distinct risks of in-hospital mortality or escalation of treatment, when analysed using Cox Proportional Hazards models. A low-risk Normal-appearing subtype was also discovered. The model and our full pipeline are available online and can be adapted for future outbreaks of COVID-19 or other infectious disease. [ABSTRACT FROM AUTHOR]

Published
2023
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3. Erratum to: An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis

Author

Mark D. Halling-Brown, Daniel Schofield, Rosalind Berka, Ottavia Bertolli, Joseph Jacob, Ashwin Chopra, Dominic Cushnan, Emily Jefferson, François Lemarchand, Jeremy C Wyatt, Alberto Favaro, Nccid Collaborative, Tara Ganepola, Gergely Imreh, Samie Dorgham, and Oscar Bennett

Subjects
medicine.medical_specialty, Coronavirus disease 2019 (COVID-19), AcademicSubjects/SCI02254, medical imaging, Health Informatics, Data Note, Cohort Studies, thoracic imaging, medicine, Humans, Medical physics, Pandemics, Chest imaging, SARS-CoV-2, business.industry, COVID-19, Data Accuracy, Computer Science Applications, machine learning, Data quality, SARS-CoV2, AcademicSubjects/SCI00960, Erratum, Tomography, X-Ray Computed, business, Cohort study
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.

Published
2021
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