van der Schaar Lab

Study of admission timing and mortality following COVID-19 infection published in BMJ Open

A paper on COVID-19 authored by Mihaela van der Schaar, Ahmed Alaa, and Zhaozhi Qian, alongside a number of the van der Schaar Lab’s clinical collaborators, has been accepted for publication in BMJ Open (editor-in-chief: Adrian Aldcroft).

The paper, entitled “Retrospective cohort study of admission timing and mortality following COVID-19 infection in England,” was published online on November 23, and was co-authored by Dr. Jem Rashbass and Prof. Jonathan Benger, both from NHS Digital.

In their study, the authors investigate whether the timing of hospital admission is associated with the risk of mortality for patients with COVID-19 in England, and the factors associated with a longer interval between symptom onset and hospital admission. Key findings include the discovery that timing of hospital admission is an independent predictor of mortality following adjustment for age, sex, comorbidities, ethnicity and obesity. Additionally, healthcare workers were found to be most likely to have an increased interval between symptom onset and hospital admission, as were people from Black, Asian and minority ethnic (BAME) backgrounds, and patients with obesity.

Retrospective cohort study of admission timing and mortality following COVID-19 infection in England

Ahmed Alaa, Zhaozhi Qian, Jem Rashbass, Jonathan Benger, Mihaela van der Schaar

Objectives
We investigated whether the timing of hospital admission is associated with the risk of mortality for patients with COVID-19 in England, and the factors associated with a longer interval between symptom onset and hospital admission.

Design
Retrospective observational cohort study of data collected by the COVID-19 Hospitalisation in England Surveillance System (CHESS). Data were analysed using multivariate regression analysis. Setting Acute hospital trusts in England that submit data to CHESS routinely.

Participants
Of 14 150 patients included in CHESS until 13 May 2020, 401 lacked a confirmed diagnosis of COVID-19 and 7666 lacked a recorded date of symptom onset. This left 6083 individuals, of whom 15 were excluded because the time between symptom onset and hospital admission exceeded 3 months. The study cohort therefore comprised 6068 unique individuals.

Main outcome measures
All-cause mortality during the study period.

Results
Timing of hospital admission was an independent predictor of mortality following adjustment for age, sex, comorbidities, ethnicity and obesity. Each additional day between symptom onset and hospital admission was associated with a 1% increase in mortality risk (HR 1.01; p<0.005). Healthcare workers were most likely to have an increased interval between symptom onset and hospital admission, as were people from Black, Asian and minority ethnic (BAME) backgrounds, and patients with obesity.

Conclusion
The timing of hospital admission is associated with mortality in patients with COVID-19. Healthcare workers and individuals from a BAME background are at greater risk of later admission, which may contribute to reports of poorer outcomes in these groups. Strategies to identify and admit patients with high-risk and those showing signs of deterioration in a timely way may reduce the consequent mortality from COVID-19, and should be explored.

For a full list of the van der Schaar Lab’s publications, click here.

To find out more about the van der Schaar Lab’s work related to the COVID-19 pandemic, visit our dedicated page here.

Ahmed Alaa

Ahmed Alaa

Ahmed M. Alaa is a Postdoctoral Scholar at the ECE Department, University of California, Los Angeles (UCLA), and an affiliated Postdoctoral Researcher at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge.

His primary research focus has been on causal inference, automated machine learning, uncertainty quantification and time-series analysis.

He has published papers in several leading machine learning conferences including NeurIPS, ICML, ICLR and AISTATS.

Zhaozhi Qian

Zhaozhi Qian

After obtaining a MSc in Machine Learning at UCL, Zhaozhi Qian started his career as a data scientist in the largest mobile gaming company in Europe. Three years later, he found it might be more fulfilling to apply AI to cure cancer than to make the gamers hit the purchase button 1% more often.

He thus joined the group in 2019 as a PhD student focusing on robust and interpretable learning for longitudinal data. So far, his work has included inferring latent disease interaction networks from Electronic Health Records, uncovering the causal structure between events that unfold over time, and calibrating the predictive uncertainty under domain shift.

Zhaozhi also worked as a contractor in the NHS during the COVID-19 pandemic contributing his analytical skills to the national response to the pandemic.

Mihaela van der Schaar

Mihaela van der Schaar

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA.

Mihaela has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise span signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.

Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine.

Nick Maxfield

Nick Maxfield

Nick oversees the van der Schaar Lab’s communications, including media relations, content creation, and maintenance of the lab’s online presence.

Nick studied Japanese (BA Hons.) at the University of Oxford, graduating in 2012. Nick previously worked in HQ communications roles at Toyota (2013-2016) and Nissan (2016-2020).

Given his humanities/languages background and experience in communications, Nick is well-positioned to highlight and explain the real-world impact of research that can often be quite esoteric. Thankfully, he is comfortable asking almost endless questions in order to understand a topic.