Mortality/Survival Prediction Models in Hospitalised SARS-CoV-2 Positive Patients

This project focuses on the development of simplified risk tool that enables rapid triaging of SARS CoV-2 positive patients during hospital admission, which complements current practice. Many predictive tools developed to date are complex, rely on multiple blood results and past medical history, do not include chest X ray results and rely on Artificial Intelligence rather than simplified algorithms. Our aim was to develop a simplified risk-tool based on five parameters and CXR image data that predicts the 60-day survival of adult SARS CoV-2 positive patients at hospital admission.

Updated on May 20, 2022 by Surajit Ray

SARS-CoV-2 Machine Learning Artificial Neural Network (ANN) Screening Full blood count Leukocytes Monocytes

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Clinical prediction models such as NEWS2 is currently used in practice as mortality risk assessment. In a rapid response to support COVID-19 patient assessment and resource management, published risk tools and models have been found to have a high risk of bias and therefore cannot be translated into clinical practice.


We have developed and validated risk tool (LUCAS) based on rapid and routine blood tests predicts the mortality of patients infected with SARS-CoV-2 virus. This prediction model has both high and robust predictive power and has been tested on an external set of patients and therefore can be used to effectively triage patients when resources are limited. In addition, LUCAS can be used with chest imaging information and NEWS2 score.


External Collaborators

Mortality Calculator:

Github Repositories:

From coarse to fine: a deep 3D probability volume contour framework for tumor segmentation and dose painting in PET images
Zhang. W and Ray S. Frontiers In Radiology,. 3
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LUCAS: A highly accurate yet simple risk calculator that predicts survival of COVID-19 patients using rapid routine tests
Ray S., Swift A., Fanstone JW., Banerjee A., Mamalakis M., Vorselaars B., Mackenzie LS., and Weeks S. Nature Scientific Reports. 12
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Development of a Mortality Prediction Model in Hospitalised SARS-CoV-2 Positive Patients Based on Routine Kidney Biomarkers
Boss A.N., Banerjee A., Mamalakis M., Ray S., Swift A.J., Wilkie C., Fanstone J.W., Vorselaars B., Cole J., Weeks S., and Mackenzie L.S. International Journal of Molecular Sciences. 23 (13)
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DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays
Mamalakis M., Swift A.J., Vorselaars B., Ray S., Weeks S., Ding W., Clayton R.H., Mackenzie L.S., and Banerjee A. Computerized Medical Imaging and Graphics. 94
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Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population
Banerjee A., Ray S., Vorselaars B., Kitson J., Mamalakis M., Weeks S., Baker M., and Mackenzie L.S. International Immunopharmacology. 86
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