| Autor: |
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, Schofield, Daniel |
| Předmět: |
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| Zdroj: |
Scientific Reports; 6/20/2023, Vol. 13 Issue 1, p1-14, 14p |
| Abstrakt: |
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] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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