Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes
Authors: 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 , Jacob Joseph , Jefferson Emily , Lemarchand Francois , Sarellas Anastasios , Schofield Daniel , Sutherland James , Watt Mathew ,   NCCID Extended Team, Alexander Daniel , Aziz Hena , Hurst John R. , Lewis Emma , Lip Gerald , Manser Peter , Quinlan Philip , Sebire Neil , Swift Andrew , Shetty Smita , Williams Peter , Bennett Oscar , Dorgham Samie , Favaro Alberto , Gan Samantha , Ganepola Tara , Imreh Gergely , Puri Neha ,   NCCID NHS Trusts Teams, Rodrigues Jonathan Carl Luis , Oliver Helen , Hudson Benjamin , Robinson Graham , Wood Richard , Moreton Annette , Lomas Katy ,   Research & Development Team, Marchbank Nigel , Law Chinnoi , Chana Harmeet , Gandy Nemi , Sharif Ban , Ismail Leila , Patel Jaymini , Wai Debbie , Mathers Liz , Clark Rachel , Harrar Anisha , Bettany Alison , Foley Kieran , Pothecary Carla , Buckle Stephen , Roche Lisa , Shah Aarti , Kirkham Fiona , Bown Hannah , Seal Simon , Connoley Hayley , Tugwell-Allsup Jenna , Owen Bethan Wyn , Jones Mary , Moth Andrew , Colman Jordan , Maskell Giles , Kim Daniel , Sanchez-Cabello Alexander , Lewis Hannah , Thorley Matthew , Kruger Ross , Chifu Madalina , Ashley Nicholas , Spas Susanne , Bates Angela , Halson Peter , Heafey Chris , McCann Caroline , McCreavy David , Duvva Dileep , Siah Tze , Deane Janet , Pearlman Emily , MacKay James , Sia Melissa , Easter Esme , Brookes Doreen , Burford Paul , Barbara Ramona-Rita , Payne Thomas , Ingram Mark , Bhatia Bahadar , Yusuf Sarah , Rotherham Fiona , Warren Gayle , Heeney Angela , Bowen Angela , Wilson Adele , Hussain Zahida , Kellett Joanne , Harrison Rachael , Watkins Janet , Patterson Lisa , Welsh Tom , Redwood Dawn , Greig Natasha , Van Pelt Lindsay , Palmer Susan , Milne Kate , Tilley Joanna , Alexander Melissa , Frary Amy J. , Babar Judith L. , Sadler Timothy , Neil-Gallacher Edward , Cardona Sarah , Gill Avneet , Omeje Nnenna , Ridgeon Claire , Gleeson Fergus , Johnstone Annette , Frood Russell , Rabani Mohammed Atif , Scarsbrook Andrew , Lyttle Mark D. , Lyen Stephen , James Gareth , Sheedy Sarah , Homer Kiarna , Glover Alison , Gibbison Ben , Blazeby Jane , Baquedano Mai , Payne Thomas , Jacob Teresa , Grubnic Sisa , Crick Tony , Crawford Debbie , Prestwood Fiona , Cooper Margaret , Radon Mark 
Published: June 2023

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.