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Articles
www.thelancet.com Vol 398 July 17, 2021
223
Characterisation of in-hospital complications associated
with COVID-19 using the ISARIC WHO Clinical
Characterisation Protocol UK: a prospective, multicentre
cohort study
Thomas M Drake*, Aya M Riad*, Cameron J Fairfield, Conor Egan, Stephen R Knight, Riinu Pius, Hayley E Hardwick, Lisa Norman,
Catherine A Shaw, Kenneth A McLean, A A Roger Thompson, Antonia Ho, Olivia V Swann, Michael Sullivan, Felipe Soares, Karl A Holden,
Laura Merson, Daniel Plotkin, Louise Sigfrid, Thushan I de Silva, Michelle Girvan, Clare Jackson, Clark D Russell, Jake Dunning, Tom Solomon,
Gail Carson, Piero Olliaro, Jonathan S Nguyen-Van-Tam, Lance Turtle, Annemarie B Docherty, Peter JM Openshaw, J Kenneth Baillie,
Ewen M Harrison†, Malcolm G Semple†, on behalf of the ISARIC4C investigators‡
Summary
Background COVID-19 is a multisystem disease and patients who survive might have in-hospital complications.
These complications are likely to have important short-term and long-term consequences for patients, health-care
utilisation, health-care system preparedness, and society amidst the ongoing COVID-19 pandemic. Our aim was to
characterise the extent and eect of COVID-19 complications, particularly in those who survive, using the International
Severe Acute Respiratory and Emerging Infections Consortium WHO Clinical Characterisation Protocol UK.
Methods We did a prospective, multicentre cohort study in 302 UK health-care facilities. Adult patients aged 19 years
or older, with confirmed or highly suspected SARS-CoV-2 infection leading to COVID-19 were included in the study.
The primary outcome of this study was the incidence of in-hospital complications, defined as organ-specific diagnoses
occurring alone or in addition to any hallmarks of COVID-19 illness. We used multilevel logistic regression and
survival models to explore associations between these outcomes and in-hospital complications, age, and pre-existing
comorbidities.
Findings Between Jan 17 and Aug 4, 2020, 80 388 patients were included in the study. Of the patients admitted to
hospital for management of COVID-19, 49·7% (36 367 of 73 197) had at least one complication. The mean age of our
cohort was 71·1 years (SD 18·7), with 56·0% (41 025 of 73 197) being male and 81·0% (59 289 of 73 197) having at least
one comorbidity. Males and those aged older than 60 years were most likely to have a complication (aged ≥60 years:
54·5% [16 579 of 30 416] in males and 48·2% [11 707 of 24 288] in females; aged <60 years: 48·8% [5179 of 10 609] in
males and 36·6% [2814 of 7689] in females). Renal (24·3%, 17 752 of 73 197), complex respiratory (18·4%, 13 486 of
73 197), and systemic (16·3%, 11 895 of 73 197) complications were the most frequent. Cardiovascular (12·3%, 8973 of
73 197), neurological (4·3%, 3115 of 73 197), and gastrointestinal or liver (10·8%, 7901 of 73 197) complications were
also reported.
Interpretation Complications and worse functional outcomes in patients admitted to hospital with COVID-19 are
high, even in young, previously healthy individuals. Acute complications are associated with reduced ability to self-
care at discharge, with neurological complications being associated with the worst functional outcomes. COVID-19
complications are likely to cause a substantial strain on health and social care in the coming years. These data will
help in the design and provision of services aimed at the post-hospitalisation care of patients with COVID-19.
Funding National Institute for Health Research and the UK Medical Research Council.
Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0
license.
Lancet 2021; 398: 223–37
This online publication has been
corrected. The corrected version
first appeared at thelancet.com
on July 29, 2021
See Comment page 188
*Joint first authors
†Joint senior authors
‡Investigators listed at end of
paper
Centre for Medical Informatics,
Usher Institute
(T M Drake MBChB,
A M Riad BMedSci,
C J Fairfield MBChB, C Egan MSc,
S R Knight MBChB, R Pius PhD,
L Norman PhD, C A Shaw PhD,
K A McLean MBChB,
A B Docherty PhD,
Prof E M Harrison PhD),
Department of Child Life and
Health (O V Swann PhD), Roslin
Institute (C D Russell MBChB),
and Centre for Inflammation
Research, The Queen’s Medical
Research Institute (C D Russell),
University of Edinburgh,
Edinburgh, UK; Health
Protection Research Unit in
Emerging and Zoonotic
Infections, Institute of
Infection, Veterinary and
Ecological Sciences, Faculty of
Health and Life Sciences
(H E Hardwick,
K A Holden MBChB,
J Dunning PhD,
Prof T Solomon PhD,
L Turtle PhD, M G Semple PhD),
Liverpool Clinical Trials Centre
(M Girvan BSc, C Jackson,
J K Baillie PhD), and Clinical
Infection Microbiology and
Immunology, Institute of
Infection, Veterinary, and
Zoological Science
(Prof T Solomon), University of
Liverpool, Liverpool, UK;
Department of Infection,
Immunity and Cardiovascular
Introduction
Many people across the world have been hospitalised
with COVID-19 following SARS-CoV-2 infection. Evi-
dence has established that these patients have high
mortality rates (26%), and up to 17% of patients admitted
to hospital will require ventilatory support and critical
care.1 Several case reports, cross-sectional studies, and
case-control studies have described the presence of
non-respiratory complications in those with COVID-19
and suggest that these are likely to be associated with
poor outcomes.2–4
Understanding the possible complications of COVID-19
is important for patient management and provision in
health-care systems. For patients, information around in-
hospital complication rates are important for decision
making about treatment, long-term planning, possible
Articles
224
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Disease, University of
Sheffield, Sheffield, UK
(A A R Thompson PhD,
F Soares MPhil, T I de Silva PhD);
Medical Research Council-
University of Glasgow Centre
for Virus Research (A Ho PhD)
and Institute of Cardiovascular
and Medical Sciences
(M Sullivan MBChB), University
of Glasgow, Glasgow, UK;
Paediatric Infectious Diseases,
Royal Hospital for Sick
Children, Edinburgh, UK
(O V Swann, MG Semple);
Department of Respiratory
Medicine, Alder Hey Children’s
Hospital, Liverpool, UK
(K A Holden); Centre for Tropical
Medicine and Global Health,
Nuffield Department of
Medicine, University of Oxford,
Oxford, UK (L Merson BSc,
D Plotkin BA, L Sigfrid PhD,
G Carson MBChB,
Prof P Olliaro PhD); Emerging
Infections and Zoonoses Unit,
National Infection Service,
Public Health England, London,
UK (J Dunning); Department of
Neurology, The Walton Centre
NHS Foundation Trust,
Liverpool, UK (Prof T Solomon);
Division of Epidemiology and
Public Health, School of
Medicine, University of
Nottingham, Nottingham, UK
(Prof J S Nguyen-Van-Tam DM);
United Kingdom Department
of Health and Social Care,
London, UK
(Prof J S Nguyen-Van-Tam);
National Heart and Lung
Institute, Imperial College
London, London, UK
(Prof P JM Openshaw PhD)
Correspondence to:
Prof Ewen M Harrison, Centre for
Medical Informatics, Usher
Institute, University of
Edinburgh,
Edinburgh EH16 4UX, UK
ewen.harrison@ed.ac.uk
resumption of normal activity and, more recently,
vaccination. For health-care systems, these data are vital
to inform immediate preparedness measures (ie, alloca-
tion of resources, equipment, and stang) and also for
long-term planning of health-care delivery to a population
that might have incurred additional morbidity due to
COVID-19.
A substantial proportion of patients with COVID-19 go
on to develop critical illness and require organ support. It
is widely recognised that survival following critical illness
is accompanied by a substantial burden of additional
physical and mental health morbidity that cannot be
measured by mortality outcomes.5,6 Mortality has been
widely used as an outcome in epidemiological studies
and randomised controlled trials for patients with
COVID-19 but fails to capture the immediate short-term
health issues faced by survivors, including in-hospital
complications and functional outcomes. In patients
with COVID-19 undergoing surgery, high rates of post-
procedural mortality and complications have been noted,
but systematic characterisation of hospitalised patients
with COVID-19 is lacking.7 In other non-SARS-CoV-2
viral illnesses, for example influenza, short-term
complications such as myocardial infarction, acute
kidney injury, and stroke are common and can cause
greater morbidity than the initial infection itself.6,8–11
Understanding which patients develop short-term
complications might also allow clinicians and researchers
to develop care pathways and interventions to mitigate
the impact of complications. As many patients with
COVID-19 are critically unwell, identifying the burden
of short-term morbidity could be useful to understand
the long-term burden on health-care systems and society
for those who survive COVID-19.
We have previously characterised the clinical features
of patients admitted to hospital with COVID-19 using the
International Severe Acute Respiratory and Emerging
Infections Consortium (ISARIC) WHO Clinical Char-
acterisation Protocol UK (CCP-UK) for severe emerging
infections.1 The aim of this study was to describe the
short-term complications, beyond those associated with
the presenting features of COVID-19 and severe acute
respiratory infection.
Methods
Study design and participants
The ISARIC WHO CCP-UK protocol was developed by
an international consensus in 2012–14 and reactivated in
response to the COVID-19 pandemic on Jan 17, 2020.12
Our study is an actively recruiting prospective cohort
study across 302 health-care facilities in the UK. Adult
patients aged 19 years and older, who were admitted to
hospital between Jan 17 and Aug 4, 2020, with confirmed
or highly suspected SARS-CoV-2 infection leading to
COVID-19 were included in this analysis; overall study
recruitment is ongoing. We used this WHO age cuto13
as children exhibit other patterns of complica-
tions including multisystem inflammatory syndrome.
Confirmation of SARS-CoV-2 was done using RT-PCR.
Highly suspected cases were eligible for inclusion, given
Research in context
Evidence before this study
We did a systematic search of the MEDLINE and PubMed
databases on Dec 5, 2020, using the search terms
(“in-hospital” OR “hospital”) AND (“SARS-CoV-2” OR
“COVID” OR “COVID-19”) AND “complications”. We limited
dates of searches from Jan 1, 2020, to the date the search was
conducted. No language restrictions applied. Data from
other areas of health care, such as surgery, suggest that
patients with COVID-19 are at greater risk of subsequent
complications, but systematic characterisation of
complications in these patients has not yet been undertaken
in large multicentre studies of patients admitted to hospital.
Most COVID-19 studies have focused on mortality and
respiratory support outcomes. Characterising the burden of
complications is important for health-care system
preparedness for further waves of infection, determining
future population morbidity, understanding the full
repercussions of COVID-19 for society, and for informing
future research and clinical guidelines. The current literature
is comprised of several small cohort or case-control studies
that focus on specific organ systems or conditions. There are
few prospective systematically collected data describing the
in-hospital complications of COVID-19.
Added value of this study
Hospitalised adult patients aged 19 and over with COVID-19
frequently had complications, even in younger age groups and in
those with few pre-existing comorbidities. Occurrence of
complications was associated with a significantly reduced ability
to self-care at discharge, which was seen in all age and
comorbidity groups. Although patients aged younger than
50 years are at low risk of dying from COVID-19, we found high
rates of complications across all age groups.
Implications of all the available evidence
In patients admitted to hospital with COVID-19, there is a burden
of immediate complications affecting all age groups. Many of the
complications identified are likely to have important long-term
effects. Health-care systems and policy makers should prepare for
increases in population morbidity arising from COVID-19 and its
subsequent complications. As complications following COVID-19
are common across all age groups and comorbidities, public
health messaging around the risk COVID-19 poses to younger
otherwise healthy people should be considered alongside vaccine
prioritisation. Further studies are required to understand the
medium-term to long-term effects of COVID-19 and how
immediate complications may lead to lasting morbidity.
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225
that SARS-CoV-2 was an emergent pathogen at the time
of protocol activation and laboratory confirmation was
dependent on local availability of testing.
Study materials including protocol, revision history,
case report forms, study information, and consent forms
are available online.14 All patients who provided biological
samples were required to provide informed, written
consent. If patients only provided routinely collected
clinical data, written consent was not required. Ethical
approval was given by the South Central–Oxford C
Research Ethics Committee in England (reference
13/SC/0149) and the Scotland A Research Ethics
Committee (reference 20/SS/0028). The study is reported
in line with the Strengthening the Reporting of
Observational Studies in Epidemiology guidelines.15
Procedures
Data collected by research nurses and volunteer medical
students were entered into a standardised electronic
case report form within a secure research electronic
data capture database.16 Multiple timepoints were
captured, including admission, hospital stay at days 1, 3,
and 9, and discharge or status at 28 days if not
discharged. Data were collected according to a detailed
protocol, which was updated to reflect developments
over the course of the pandemic. Participant char-
acteristics including age, sex at birth, physiological
parameters at presentation, and comorbidities were also
recorded. Comorbidities included asthma, chronic
cardiac disease, chronic haematological disease, chronic
kidney disease, chronic neurological disease, chronic
pulmonary disease, HIV/AIDS, history of malignancy,
liver disease, clinician-defined obesity, rheumatological
disorders, and smoking. Deprivation was calculated by
mapping individual postcodes to their corresponding
Index of Multiple Deprivation (IMD) using the Oce
for National Statistics postcode data. Using national
data, we calculated deprivation quintiles, with the first
quintile being the least deprived and the fifth quintile
the most deprived. For patients where postcodes were
missing, the average IMD rank, weighted by population
in each lower super output area for a given hospital
catchment area, was used.
Outcomes
The primary outcome of this study was the incidence
of in-hospital complications, defined as organ-specific
diagnoses occurring alone or in addition to any
hallmarks of COVID-19 illness (appendix p 1–2). All
complications were recorded so that total morbidity
could be described, not just those directly attributable
to COVID-19. Although COVID-19 is a multisystem
disease, severe respiratory infection was considered
characteristic of COVID-19 and was not regarded as a
complication. Data were collected on organ-specific
complications including complex respiratory (bacterial
pneumonia, acute respiratory distress syndrome
[ARDS], empyema, pneumothorax, and pleural eusion),
neurological (meningitis, encephalitis, seizure, and
stroke), cardio vascular (thromboembolism, heart failure,
myocarditis, endocarditis, arrhythmia, cardiomyopathy,
myocardial ischaemia, and cardiac arrest), acute kidney
injury, gastrointestinal (acute liver injury, pancreatitis,
and gastrointestinal haemorrhage), and other systemic
complications (coagulopathy, disseminated intravascular
coagulation, anaemia, and bloodstream infection). The
occurrence of complications was determined from
routine clinical records by local investigators with
the exceptions of bloodstream infection and micro-
biologically confirmed bacterial pneumonia. These were
defined based on recorded results from sputum, deep
respiratory, or blood cultures and restricted to instances
where clinically signifi cant organisms were detected
in the sample. Blood stream infection was defined
as growth of clinically significant bacteria (excluding
coagulase-negative Staphylococci) or fungus recorded
from blood culture or PCR of the blood. Results
considered to represent contamination or colonisation
were excluded. Owing to the diculties of obtaining
lower respiratory tract samples to confirm bacterial
pneumonia and the low positivity rates, we present both
highly likely and suspected bacterial pneumonia in the
appendix (pp 1–2).
The existence of likely ARDS was described clinically or
defined as one of the following combinations: receiving
extracorporeal membrane oxygenation; being nursed in
a prone position and receiving invasive mechanical
ventilation; or receiving mechanical ventilation with a
ratio of partial pressure of arterial oxygen to fraction of
inspired air of 300 mm Hg or less. For acute kidney injury
and acute liver injury, we used laboratory measurements
with internationally recognised grading systems to detect
complications that could have been missed. Acute kidney
injury was defined as a creatinine rise which corresponded
to the Kidney Disease Improving Global Outcomes
stage 1 or above definition17 (creatinine rise ≥1·5 × baseline
value or by ≥26·5 µmol/L). We did not incorporate urine
output into this definition as this parameter is not
universally recorded for all patients, particularly outwith
critical care. Acute liver injury was defined as one of the
following: an international normalised ratio rise of
2·5 times or greater than the lowest entered value; an
international normalised ratio of more than 4·5 (in the
absence of warfarin therapy); an alanine amino transferase
rise of more than 10 times the lowest value; an alanine
aminotransferase of more than 150 IU/L; a bilirubin
rise of more than 15 µmol/L; or a bilirubin greater
than 55 µmol/L (in the absence of any pre-existing liver
disease). In those who survived, we also captured
information on whether self-care ability was the same or
worse than before hospital admission at time of discharge,
defined clinically as the change in support required
before and after hospital admission. For this outcome, if
patients required ongoing hospital care, we defined this
See Online for appendix
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outcome as worse than before onset of COVID-19 illness
due to these ongoing care requirements.
Statistical analysis
Continuous data are presented as a mean with SD where
data are normally distributed and as a median with
the 25th and 75th centiles for non-parametric data.
Categorical data are summarised as frequencies and
percentages. Dierences between groups for continuous
normally distributed data were tested using Welch’s
t test for two groups or ANOVA when there were more
than two groups. Non-parametric continuous data were
tested using a Mann-Whitney U test for two groups or
Kruskall-Wallis test for three or more groups. Dierences
across categorical data were tested using the χ² test or
Fisher’s exact test when expected cell counts were less
than five. Analysis of complication co-occurrence was
done using the Jaccard similarity index and represented
visually as heatmaps with dendrograms constructed
from complete hierarchical clustering results. We only
included patients who had completed outcomes, with at
least 2 months of follow-up. There were low rates of
missing data and therefore multiple imputation was not
used.
To explore if the number of complications and which
specific complications were associated with mortality
(dependent variable), complication variables were entered
independently into Cox proportional hazards models and
adjusted for other potentially confounding factors. These
data were described using Kaplan-Meier plots and
modelled using Cox proportional hazards regression.
Reported date of symptom onset was taken as day 0.
Discharge from hospital was considered an absorbing
state (once discharged, patients were considered no
longer at risk of death); thus discharge did not compete
with death. The proportional hazards assumption was
checked.
To observe whether complications were associated with
increased severity of initial disease, we used the ISARIC 4C
Mortality Score, quick sequential organ failure assessment
(qSOFA), and National Early Warning Score 2 (NEWS2)
on admission or time of symptom start to examine the
relationship between severity and presence of any in-
hospital complications.18 These scores are commonly used
in clinical practice to identify patients with deteriorating
or critical illness and risk of subsequent death in general
adult hospital popula tions (NEWS2 and qSOFA) or in
COVID-19 patients (4C Mortality Score). We calculated the
score for each adult patient in the dataset and plotted each
score against the observed incidence of complications in
each score group.
Multilevel logistic regression models were constructed
to identify associations between patient characteristics
(potential confounders, including patient demographics
and existing comorbidities) and the development of
specific complications, worse self-care ability on dis-
charge, and the requirement for ongoing hospital care.
For all models, variable selection was done based on
clinical plausibility, and final models were selected
based on clinical relevance guided by minimisation of
the Akaike information criterion. Centre-level variation
was accounted for using mixed-eects models that
included hospital as a random eect and patient-level
variables as fixed eects. We did stratified analyses to
focus on survivors and on those admitted to critical
care.
To identify which patient groups are at the highest risk of
complications and mortality, we used generalised additive
models and generated risk estimates by age, sex, and
comorbidity status. Generalised additive models accom-
modated potential non-linear relationships between
variables with the inclusion of penalised thin-plate
regression splines on continuous variables. We did this
for each organ-specific complication outcome, as well as
testing the associations between organ-specific complica-
tions and death. Models were adjusted for age, sex,
comorbidity status and deprivation (IMD quintile). First
and second order interactions were explored and included
where they significantly contributed to model fitting. We
ran 100 bootstrap replicates for each model to provide a
visual representation of the distribution.
All statistical analyses were done with R (version 3.6.3)
using the tidyverse, finalfit, mcgv, survival, stringdist,
janitor, and Hmisc packages.
Role of the funding source
The funders of the study had no role in the study
design, data collection, data analysis, data interpretation,
or writing of the report.
Results
Between Jan 17 and Aug 4, 2020, 80 388 patients were
included in the CCP-UK study (figure 1). Of these,
75 276 were adults aged 19 years or older, of which 97·2%
(73 197 of 75 276) had any com plication outcome available
for analysis. The overall mortality rate was 31·5%
(23 092 of 73 197), and the overall complication rate was
49·7% (36 367 of 73 197 had at least one complication). In
surviving patients, 43·5% (21 784 of 50 105) had at least
one complication. Proportions of patients having at least
one complication were highest in age groups of over
60 years (table 1). Missing data for each variable were
under 10% for nearly all patient characteristics included
in the study (appendix pp 3–4). Of all patients included,
85·9% (62 894 of 73 197) had a positive SARS-CoV-2
RT-PCR test. Patients who did not have a positive swab
had the same or slightly lower rates of complications
overall and organ-specific complications (appendix p 3).
The mean age of patients included in our study was
71·1 years old (SD 18·7), with the majority of those included
being male (table 1). One or more comorbidities were
present in 81·0% (59 289 of 73 197) of the cohort. Chronic
cardiac disease was the most common comorbidity,
followed by chronic pulmonary disease and chronic kidney
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227
disease. Most of the study cohort consisted of White
people.
In adult patients with COVID-19, renal, complex
respiratory, car dio vascular, neurological, gastrointestinal,
and systemic complications were reported (table 1).
Specific complications within each organ system were
also reported, with acute kidney injury, probable ARDS,
liver injury, anaemia, and cardiac arrhythmia being the
most common (appendix pp 4–5). The incidence of acute
kidney injury increased with age and was most common
in patients aged between 60 and 90 years, with males at
greater risk. Patients with chronic kidney disease were at
the highest risk of acute kidney injury, with 39·8%
(4785 of 12 182) developing acute kidney injury versus
21·6% (11 962 of 55 458) in patients without chronic
kidney disease. Cardiac complications were more
frequently observed with increasing age and in patients
with existing cardiac disease. In those with existing
cardiac disease, 19·9% (4496 of 22 563) developed a
cardiac complication compared with 8·9% (4077 of 45 563)
in those without previous cardiac disease. In contrast,
liver injury was most frequently seen in younger age
groups (aged <60 years), with the highest rates occurring
in males. Liver injury was more common in patients
with pre-existing moderate or severe liver disease
(300 [22·4%] of 1340) compared with those without liver
injury (4097 [6·2%] of 65 646). Complication rates were
com parable across White, South Asian, and East Asian
ethnic and racial groups, but were highest in Black
people (57·8% [1433 of 2480] in Black patients vs
49·1% [26 431 of 53 780] in White patients; table 1). Rates
of acute kidney injury were highest in Black patients
(822 [33·1%] of 2480) compared with White patients
(12 896 [24·0%] of 53 780). Patients with obesity were
1·6 times more likely to have respiratory complications
(2059 [28·1%] of 7329) compared with those who did not
have obesity (9498 [17·8%] of 53 415; table 1). Patients who
had obesity were also 1·3 times more likely to have renal
complications (2208 [30·1%] of 7329) compared with
those who did not have obesity (12 656 [23·7%] of 53 415;
table 1).
Suspected bacterial pneumonia was the most common
respiratory complication (appendix pp 6–7), but when the
definition incorporated positive microbiological testing
(highly likely bacterial pneumonia), the incidence of
highly likely bacterial pneumonia was lower. Acute
kidney injury (Jaccard index 0·23), likely ARDS (Jaccard
index 0·17), anaemia (Jaccard index 0·13), and liver
injury (Jaccard index 0·10) were most likely to co-occur
with death (appendix p 35).
Having at least one complication was common across
all demographic groups, with the lowest rates in patients
aged 19–29 years with no comorbidity (178 [21·2%] of 839)
and the highest rates in patients aged 60–69 years who
had two or more comorbidities (3340 [57·9%] of 5767;
appendix pp 8–11). The incidence of com plications rose
with increasing age occurring in 38·9% (3596 of 9249) in
those aged 19–49 years and 51·3% (32 771 of 63 948) in
those aged 50 years and older (figure 2A). The number of
complications increased with the number of pre-existing
comorbidities, particularly in individuals aged 40 years
and older (figure 2A and appendix pp 8–11). Complications
were higher in males compared with females, and males
were more likely to have complications than females,
with males aged older than 60 years the most likely group
to have at least one complication (aged <60 years: 36·6%
[2814 of 7689] in females and 48·8% [5179 of 10 609] in
males; aged ≥60 years: 48·2% [11 707 of 24 288] in females
and 54·5% [16 579 of 30 416] in males; figure 2A and
appendix pp 4–5). Young males (aged 19–29 years) without
comorbidities were significantly more likely to have
complications than young females (aged 19–29 years)
without comorbidities (28·4% [94 of 331] in males and
16·6% [84 of 505] in females; figure 2A). When we
stratified by mortality, complications occurred more
frequently in patients who died (14 583 [63·2%] of 23 092),
but were still common in survivors (21 784 [43·5%]
of 50 105; appendix pp 12–13) and there were direct
Figure 1: Study profile
80
388 patients included using the WHO Clinical
Characterisation Protocol UK
76
744 patients confirmed or highly suspected
with SARS-CoV-2
3644 patients excluded
2915 duplicated patient records
729 found to be ineligible or withdrew
consent
75
276 patients aged ≥19 included in the analysis
36
367 patients had at least one in-hospital
complication
8973 cardiovascular
13
486 respiratory
17
752 renal
3115 neurological
7901 gastrointestinal and liver
11
895 systemic
1468 patients excluded as they were aged
<19 years
2079 had no outcome data available
73
197 patients had any complication outcome
available for analysis
36
830 patients had no in-hospital
complication
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Patients having complications Organ-specific complications
Total number of
patients
Any complication Systemic Renal Gastrointestinal
(including liver)
Cardiovascular Neurological Respiratory*
Total number of
patients
73 197 36 367 (49·7%) 11 895 (16·3%) 17 752 (24·3%) 7901 (10·8%) 8973 (12·3%) 3115 (4·3%) 13 486 (18·4%)
Age on admission, years
19–29 1500 (2·0%) 411 (1·1%) 147 (1·2%) 126 (0·7%) 139 (1·8%) 47 (0·5%) 38 (1·2%) 145 (1·1%)
30–39 2753 (3·8%) 1015 (2·8%) 376 (3·2%) 353 (2·0%) 365 (4·6%) 134 (1·5%) 91 (2·9%) 457 (3·4%)
40–49 4996 (6·8%) 2170 (6·0%) 731 (6·1%) 874 (4·9%) 740 (9·4%) 370 (4·1%) 162 (5·2%) 1169 (8·7%)
50–59 9101 (12·4%) 4418 (12·1%) 1504 (12·6%) 2078 (11·7%) 1468 (18·6%) 847 (9·4%) 352 (11·3%) 2263 (16·8%)
60–69 11 139 (15·2%) 5954 (16·4%) 2008 (16·9%) 3055 (17·2%) 1578 (20·0%) 1389 (15·5%) 500 (16·1%) 2767 (20·5%)
70–79 16 563 (22·6%) 8549 (23·5%) 2727 (22·9%) 4318 (24·3%) 1644 (20·8%) 2220 (24·7%) 725 (23·3%) 2978 (22·1%)
80–89 19 900 (27·2%) 10 207 (28·1%) 3241 (27·2%) 5161 (29·1%) 1478 (18·7%) 2888 (32·2%) 941 (30·2%) 2761 (20·5%)
≥90 7245 (9·9%) 3643 (10·0%) 1161 (9·8%) 1787 (10·1%) 489 (6·2%) 1078 (12·0%) 306 (9·8%) 946 (7·0%)
Sex at birth
Female 31 977 (43·7%) 14 521 (39·9%) 4872 (41·0%) 6612 (37·2%) 2690 (34·0%) 3539 (39·4%) 1289 (41·4%) 4951 (36·7%)
Male 41 025 (56·0%) 21 758 (59·8%) 7001 (58·9%) 11 097 (62·5%) 5199 (65·8%) 5415 (60·3%) 1822 (58·5%) 8504 (63·1%)
Data missing 195 (0·3%) 88 (0·2%) 22 (0·2%) 43 (0·2%) 12 (0·2%) 19 (0·2%) 4 (0·1%) 31 (0·2%)
Deprivation, IMD quintile†
110 408 (14·2%) 5201 (14·3%) 1773 (14·9%) 2437 (13·7%) 1152 (14·6%) 1384 (15·4%) 466 (15·0%) 1885 (14·0%)
212 853 (17·6%) 6439 (17·7%) 2147 (18·0%) 2996 (16·9%) 1431 (18·1%) 1634 (18·2%) 552 (17·7%) 2305 (17·1%)
315 822 (21·6%) 7855 (21·6%) 2595 (21·8%) 3793 (21·4%) 1631 (20·6%) 1986 (22·1%) 633 (20·3%) 3035 (22·5%)
416 104 (22·0%) 8069 (22·2%) 2621 (22·0%) 4101 (23·1%) 1748 (22·1%) 2012 (22·4%) 718 (23·0%) 3083 (22·9%)
517 997 (24·6%) 8801 (24·2%) 2759 (23·2%) 4424 (24·9%) 1939 (24·5%) 1956 (21·8%) 745 (23·9%) 3177 (23·6%)
Data missing 13 (<0·1%) 2 (<0·1%) 0 1 (<0·1%) 0 1 (<0·1%) 1 (<0·1%) 1 (<0·1%)
Race or ethnicity
White 53 780 (73·5%) 26 431 (72·7%) 8678 (73·0%) 12 896 (72·6%) 5438 (68·8%) 6624 (73·8%) 2282 (73·3%) 9173 (68·0%)
South Asian 3318 (4·5%) 1630 (4·5%) 593 (5·0%) 799 (4·5%) 441 (5·6%) 369 (4·1%) 102 (3·3%) 777 (5·8%)
East Asian 484 (0·7%) 249 (0·7%) 96 (0·8%) 113 (0·6%) 82 (1·0%) 55 (0·6%) 15 (0·5%) 142 (1·1%)
Black 2480 (3·4%) 1433 (3·9%) 508 (4·3%) 822 (4·6%) 346 (4·4%) 306 (3·4%) 114 (3·7%) 627 (4·6%)
Other ethnic
minority‡
4646 (6·3%) 2435 (6·7%) 751 (6·3%) 1145 (6·4%) 641 (8·1%) 491 (5·5%) 203 (6·5%) 1171 (8·7%)
Data missing 8489 (11·6%) 4189 (11·5%) 1269 (10·7%) 1977 (11·1%) 953 (12·1%) 1128 (12·6%) 399 (12·8%) 1596 (11·8%)
Diabetes
No 49 765 (75·8%) 24 481 (73·6%) 7878 (71·9%) 11 265 (69·7%) 5694 (77·9%) 5948 (72·4%) 2173 (77·7%) 9194 (74·3%)
Yes 15 855 (24·2%) 8792 (26·4%) 3081 (28·1%) 4891 (30·3%) 1615 (22·1%) 2266 (27·6%) 625 (22·3%) 3173 (25·7%)
Obesity
No 53 415 (73·0%) 26 397 (72·6%) 8476 (71·3%) 12 656 (71·3%) 5784 (73·2%) 6331 (70·6%) 2304 (74·0%) 9498 (70·4%)
Yes 7329 (10·0%) 4230 (11·6%) 1583 (13·3%) 2208 (12·4%) 985 (12·5%) 1226 (13·7%) 296 (9·5%) 2059 (15·3%)
Data missing 12 453 (17·0%) 5740 (15·8%) 1836 (15·4%) 2888 (16·3%) 1132 (14·3%) 1416 (15·8%) 515 (16·5%) 1929 (14·3%)
Chronic cardiac disease
No 45 563 (62·2%) 21 808 (60·0%) 7117 (59·8%) 10 400 (58·6%) 5332 (67·5%) 4077 (45·4%) 1923 (61·7%) 8787 (65·2%)
Yes 22 563 (30·8%) 12 758 (35·1%) 4235 (35·6%) 6436 (36·3%) 2201 (27·9%) 4496 (50·1%) 995 (31·9%) 4025 (29·8%)
Data missing 5071 (6·9%) 1801 (5·0%) 543 (4·6%) 916 (5·2%) 368 (4·7%) 400 (4·5%) 197 (6·3%) 674 (5·0%)
Chronic pulmonary disease
No 55 604 (76·0%) 27 916 (76·8%) 9261 (77·9%) 13 619 (76·7%) 6404 (81·1%) 6665 (74·3%) 2461 (79·0%) 10 468 (77·6%)
Yes 12 235 (16·7%) 6472 (17·8%) 2002 (16·8%) 3143 (17·7%) 1100 (13·9%) 1791 (20·0%) 444 (14·3%) 2289 (17·0%)
Data missing 5358 (7·3%) 1979 (5·4%) 632 (5·3%) 990 (5·6%) 397 (5·0%) 517 (5·8%) 210 (6·7%) 729 (5·4%)
Asthma
No 58 352 (79·7%) 29 806 (82·0%) 9782 (82·2%) 14 657 (82·6%) 6525 (82·6%) 7286 (81·2%) 2572 (82·6%) 10 852 (80·5%)
Yes 9298 (12·7%) 4447 (12·2%) 1482 (12·5%) 2039 (11·5%) 977 (12·4%) 1141 (12·7%) 320 (10·3%) 1849 (13·7%)
Data missing 5547 (7·6%) 2114 (5·8%) 631 (5·3%) 1056 (5·9%) 399 (5·0%) 546 (6·1%) 223 (7·2%) 785 (5·8%)
(Table 1 continues on next page)
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229
relationships between worse survival and increasing
numbers of complications (figure 2B).
After adjusting for age, sex, deprivation, comorbidities,
and study centre, increasing age and male sex were
significant independent predictors for developing any
complication and for all organ-specific complications
except for gastrointestinal and liver complications,
which younger patients were more likely to experience
(figure 3A and appendix pp 36–44). Those with pre-
existing comorbidities that aected a specific organ
Patients having complications Organ-specific complications
Total number of
patients
Any complication Systemic Renal Gastrointestinal
(including liver)
Cardiovascular Neurological Respiratory*
(Continued from previous page)
Chronic kidney disease
No 55 458 (75·8%) 26 793 (73·7%) 8582 (72·1%) 11 962 (67·4%) 6284 (79·5%) 6434 (71·7%) 2368 (76·0%) 10 654 (79·0%)
Yes 12 182 (16·6%) 7503 (20·6%) 2661 (22·4%) 4785 (27·0%) 1166 (14·8%) 2008 (22·4%) 525 (16·9%) 2070 (15·3%)
Data missing 5557 (7·6%) 2071 (5·7%) 652 (5·5%) 1005 (5·7%) 451 (5·7%) 531 (5·9%) 222 (7·1%) 762 (5·7%)
Moderate or severe liver disease
No 65 646 (89·7%) 33 005 (90·8%) 10 769 (90·5%) 16 111 (90·8%) 6879 (87·1%) 8162 (91·0%) 2764 (88·7%) 12 314 (91·3%)
Yes 1340 (1·8%) 916 (2·5%) 358 (3·0%) 413 (2·3%) 528 (6·7%) 179 (2·0%) 96 (3·1%) 281 (2·1%)
Data missing 6211 (8·5%) 2446 (6·7%) 768 (6·5%) 1228 (6·9%) 494 (6·3%) 632 (7·0%) 255 (8·2%) 891 (6·6%)
Mild liver disease
No 65 784 (89·9%) 33 164 (91·2%) 10 837 (91·1%) 16 169 (91·1%) 7096 (89·8%) 8178 (91·1%) 2792 (89·6%) 12 338 (91·5%)
Yes 1035 (1·4%) 635 (1·7%) 240 (2·0%) 294 (1·7%) 269 (3·4%) 132 (1·5%) 60 (1·9%) 222 (1·6%)
Data missing 6378 (8·7%) 2568 (7·1%) 818 (6·9%) 1289 (7·3%) 536 (6·8%) 663 (7·4%) 263 (8·4%) 926 (6·9%)
Chronic neurological disorder
No 58 511 (79·9%) 29 546 (81·2%) 9725 (81·8%) 14 440 (81·3%) 6700 (84·8%) 7357 (82·0%) 2048 (65·7%) 11 352 (84·2%)
Yes 8802 (12·0%) 4559 (12·5%) 1467 (12·3%) 2167 (12·2%) 729 (9·2%) 1024 (11·4%) 845 (27·1%) 1309 (9·7%)
Data missing 5884 (8·0%) 2262 (6·2%) 703 (5·9%) 1145 (6·4%) 472 (6·0%) 592 (6·6%) 222 (7·1%) 825 (6·1%)
Malignant neoplasm
No 60 050 (82·0%) 29 952 (82·4%) 9485 (79·7%) 14 643 (82·5%) 6620 (83·8%) 7378 (82·2%) 2564 (82·3%) 11 283 (83·7%)
Yes 7072 (9·7%) 4075 (11·2%) 1675 (14·1%) 1932 (10·9%) 819 (10·4%) 994 (11·1%) 307 (9·9%) 1341 (9·9%)
Data missing 6075 (8·3%) 2340 (6·4%) 735 (6·2%) 1177 (6·6%) 462 (5·8%) 601 (6·7%) 244 (7·8%) 862 (6·4%)
Chronic haematological disease
No 64 082 (87·5%) 32 079 (88·2%) 10 150 (85·3%) 15 622 (88·0%) 6958 (88·1%) 7906 (88·1%) 2737 (87·9%) 12 003 (89·0%)
Yes 2982 (4·1%) 1907 (5·2%) 1017 (8·5%) 942 (5·3%) 447 (5·7%) 461 (5·15) 122 (3·9%) 600 (4·4%)
Data missing 6133 (8·4%) 2381 (6·5%) 728 (6·1%) 1188 (6·7%) 496 (6·3%) 606 (6·8%) 256 (8·2%) 883 (6·5%)
HIV/AIDs
No 65 920 (90·1%) 33 268 (91·5%) 10 828 (91·0%) 16 190 (91·2%) 7256 (91·8%) 8195 (91·3%) 2809 (90·2%) 12 360 (91·7%)
Yes 256 (0·3%) 149 (0·4%) 57 (0·5%) 82 (0·5%) 42 (0·5%) 28 (0·3%) 13 (0·4%) 57 (0·4%)
Data missing 7021 (9·6%) 2950 (8·1%) 1010 (8·5%) 1480 (8·3%) 603 (7·6%) 750 (8·4%) 293 (9·4%) 1069 (7·9%)
Rheumatological disorder
No 59 168 (80·8%) 29 823 (82·0%) 9663 (81·2%) 14 540 (81·9%) 6708 (84·9%) 7294 (81·3%) 2512 (80·6%) 11 245 (83·4%)
Yes 7724 (10·6%) 4075 (11·2%) 1462 (12·3%) 1961 (11·0%) 701 (8·9%) 1061 (11·8%) 353 (11·3%) 1358 (10·1%)
Data missing 6305 (8·6%) 2469 (6·8%) 770 (6·5%) 1251 (7·0%) 492 (6·2%) 618 (6·9%) 250 (8·0%) 883 (6·5%)
Dementia
No 55 758 (76·2%) 28 473 (78·3%) 9548 (80·3%) 13 583 (76·5%) 6708 (84·9%) 7079 (78·9%) 2237 (71·8%) 11 449 (84·9%)
Yes 11 682 (16·0%) 5668 (15·6%) 1624 (13·7%) 3064 (17·3%) 750 (9·5%) 1306 (14·6%) 645 (20·7%) 1239 (9·2%)
Data missing 5757 (7·9%) 2226 (6·1%) 723 (6·1%) 1105 (6·2%) 443 (5·6%) 588 (6·6%) 233 (7·5%) 798 (5·9%)
Smoking
Never smoked 23 944 (32·7%) 11 976 (32·9%) 4071 (34·2%) 5577 (31·4%) 2811 (35·6%) 2872 (32·0%) 889 (28·5%) 4894 (36·3%)
Current smoker 3895 (5·3%) 1927 (5·3%) 677 (5·7%) 875 (4·9%) 508 (6·4%) 459 (5·1%) 188 (6·0%) 694 (5·1%)
Former smoker 15 834 (21·6%) 8533 (23·5%) 2914 (24·5%) 4179 (23·5%) 1740 (22·0%) 2317 (25·8%) 630 (20·2%) 3304 (24·5%)
Data missing 29 524 (40·3%) 13 931 (38·3%) 4233 (35·6%) 7121 (40·1%) 2842 (36·0%) 3325 (37·1%) 1408 (45·2%) 4594 (34·1%)
Data are n or n (%). No means patients didn’t have the comorbidity or characteristic, yes means they did. IMD=Index of Multiple Deprivation. *Severe acute respiratory infection was contained within case
definition so was not counted as a complication. †1=least deprived, 5=most deprived. ‡Includes West Asian, Latinx, Aboriginal, and First Nations People.
Table 1: Patient characteristics by organ-specific complications
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system were at higher risk of developing a complication
aecting the same organ (appendix pp 45–46). The
relationship between increasing age, male sex, and
the risk of complications persisted independent of
the number of comorbidities (figure 3A and appendix
pp 39–44). The risk of complications and relationship
between age and risk of complications were comparable
across all comorbidity groups.
In patients who survived to 28 days from first symp -
toms to discharge, 44·9% (23 619 of 52 582) suered
complications, compared with 61·9% (12 624 of 20 384)
in those who died within 28 days. Complications were
more common in those requiring respiratory support
and were highest in patients who received critical care
(8267 [82·4%] of 10 034) or invasive mechanical ventila tion
(5619 [91·7%] of 6122; table 2). The presence and number of
complications was significantly associated with worse in-
hospital survival (figure 2B). Following adjustment for age,
sex, deprivation, and hospital, the occurrence of any
complication was significantly associated with poorer
overall survival (figure 2C). Cardiovascular (hazard ratio
1·98, 95% CI 1·85–2·11) and complex respiratory com-
plications (2·15, 2·04–2·27) were most strongly associated
with worse outcomes. After adjusting for age, sex, and
deprivation, patients having an acute kidney injury were
4 times more likely to be admitted to critical care, and
those with respiratory complications were 13 times more
likely to be admitted to critical care (figure 2D).
When the relationships between complications and
mor tality were modelled using generalised additive
models and plotted (figure 3B and appendix pp 39–44), the
presence of any complication, in addition to increasing age
and male sex, was associated with death. In younger
people, the presence of a complication was associated with
a large increase in the risk of mortality, compared with
older people, in which the presence of a complication was
associated with a much smaller increase in mortality.
Associations between complications and mortality were
similar across comorbidity groups overall, but we identified
that in younger people with comorbidities, mortality was
much higher in those who had complications compared
with people of the same age without complications.
Respiratory and cardiovascular complications were
associated with the largest increases in death across all
ages, whereas those with neurological or systemic
Figure 2: Outcomes and mortality after complications
(A) Differences in complication rates, age, sex, and comorbidity. (B) Kaplan-Meier
survival curve stratified by number of complications had. The hazard ratios are:
no complications 1 (reference level); one complication 1·50 (95% CI 1·45–1·55,
p<0·0001); two complications 1·87 (1·80–1·94, p<0·0001); three complications
2·39 (2·29–2·50, p<0·0001); four complications 2·64 (2·50–2·79, p<0·0001);
and five complications 2·81 (2·67–2·95, p< 0·0001). (C) Hazard ratios for effect of
organ-specific complications on overall survival, adjusted for age, sex, indices of
multiple deprivation quintile, and study centre (appendix pp 14–20). (D) Effect of
organ-specific complications on odds of being admitted to critical care (appendix
pp 21–27). Error bars represent 95% CIs.
No comorbidity One comorbidity Two or more comorbidities
B
A
Number at risk
No complications
One complication
Two complications
Three complications
Four complications
Five or more
complications
0 30 60 90
Time (days)
0
0·25
0·50
0·75
1·00
Survival probability
33
560
17
988
10
018
5432
2872
3494
26
419
12
746
6570
3183
1571
1947
25
954
12
288
6223
2924
1415
1583
25
894
12
253
6191
2897
1401
1550
Log-rank test: p<0·001
No complications
One complication
Two complications
Three complications
Four complications
Five or more complications
C
1 2 3
Cardiovascular
Gastrointestinal
Neurological
Renal
Respiratory
Systemic
Any complication
Complication
Survival (mortality)
Hazard ratio 95% CI
1·20
1·49
1·50
1·98
1·21
2·15
1·74
1·15–1·26, p<0·0001
1·42–1·55, p<0·0001
1·44–1·57, p<0·0001
1·85–2·11, p<0·0001
1·13–1·29, p<0·0001
2·04–2·27, p<0·0001
1·64–1·84, p<0·0001
D
1 3 10
Cardiovascular
Gastrointestinal
Neurological
Renal
Respiratory
Systemic
Any complication
Complication
Critical care admission
3·15
4·36
3·52
3·64
1·88
12·48
7·25
2·97–3·33, p<0·0001
4·14–4·58, p<0·0001
3·32–3·74, p<0·0001
3·42–3·88, p<0·0001
1·70–2·08, p<0·0001
11·81–13·18, p<0·0001
6·83–7·69, p<0·0001
Odds ratio 95% CI
≥90
80–89
70–79
60–69
50–59
40–49
30–39
19–29
Age (years)
Female
0 25 50 75 100
≥90
80–89
70–79
60–69
50–59
40–49
30–39
19–29
Age (years)
Proportion of patients
with any complication (%)
0 25 50 75 100
Proportion of patients
with any complication (%)
0 25 50 75 100
Proportion of patients
with any complication (%)
Male
Died
Alive
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231
complications were most likely to survive (appendix
pp 39–44).
Physiology-based early warning scores and the 4C
Mortality Score, calculated using parameters at hospital
admission, were associated with the occurrence of
complications in survivors. Higher 4C Mortality Score on
admission corresponded with an increased probability
of at least one complication (appendix p 47). Similarly,
higher NEWS2 and qSOFA scores on admission were
associated with an increased probability of one or more
complications (appendix p 47). The number of symptoms
on admission did not appear to be related to the incidence
of complications (appendix p 47).
In those who survived, 26·6% (13 309 of 50 105) of
patients had worse ability to self-care than they did before
their illness (figure 4A). This worsening of ability
increased with age, male sex, and in those who received
critical care support (figures 4A, B). Having a complication
was independently associated with an increased risk of
worse ability to self-care after discharge after adjusting
for age, sex, deprivation, and hospital (adjusted odds
ratio 2·42, 95% CI 2·31–2·54; figure 4C). Neurological
complications had the strongest associations with worse
functional outcome (4·39, 3·95–4·63; figure 4C).
Discussion
Hospitalisation with COVID-19 is associated with high
rates of morbidity in adults. Almost half of the survivors
had one or more complications, which were more
likely in patients who required critical care. Survivors of
COVID-19 who had suered at least one complication
had a lower ability to self-care on discharge from hospital.
The eect of complications on the ability to self-care was
most profound in younger patients (aged <50 years). We
found that complication rates were high in every age
group and increased with age. Unlike mortality, there
were only small dierences in complication rates in
groups stratified by pre-existing comorbidity. Males were
significantly more likely to develop complications than
females.
The most common complications in our data were
acute kidney injury, and complex respiratory and sys-
temic complications. Although our study only looked at
complications during the first admission for COVID-19,
many of the common complications identified are
associated with substantial long-term morbidity. Acute
kidney injury is known to be associated with increased
long-term hazards of mortality, requirement for dialysis,
and an increase in cardiovascular events.19–21 In addition
to the more common complications identified, rarer
complications including stroke, congestive heart failure,
and cardiac arrest were present in 1–5% of patients.22–24
Patients who received critical care had the highest
complication rates, compatible with previous observations
describing high levels of morbidity in those who require
critical care.6,8,25,26 The least commonly observed were
neurological complications, although these were the most
strongly associated with reduced ability to self-care.
Suspected bacterial pneumonia and likely ARDS were
the most common respiratory complications. When
compared with the published literature on influenza,
complications rates in patients with COVID-19 were the
same or higher.27–29 Notably, this higher rate of com-
plications appears to be primarily driven by non-infectious
complications, as the rates of secondary bacterial infection
in patients with COVID-19 were lower than described in
influenza.30 In particular, COVID-19 patients had up to 19
times the risk of developing likely ARDS when compared
with patients admitted with influenza.31
Most clinical studies of COVID-19 have focused on
associated mortality.1 Mortality is a hard endpoint, easily
measured, and of utmost importance. However, its use as
Figure 3: Relationship between age, sex, comorbidities, and adjusted outcomes using generalised additive
models
(A) Relationships for the outcome of adjusted risk of any complication. (B) Relationships for the outcome of
adjusted mortality risk, stratified by presence of complications. Each line represents one bootstrap replicate
(ie, one simulated patient). The appendix (pp 39–44) shows models for other organ-specific complications.
1
5
25
100
75
A
Risk of complication (log odds scale), %
No comorbidities Two or more comorbiditiesOne comorbidity
38% 47% 55%
Female
Male
Overall risk of
complication
B
Risk of death with complication Risk of death without complication
Overall risk of death
No
Yes
Any complication
1
5
25
100
75
Risk of death (log odds scale), %
14%
24%
23% 34%
9%
17%
35%
42%
27%
Female
20 40 60 80
1
5
25
100
75
Risk of death (log odds scale), %
Age (years)
20 40 60 80
Age (years)
20 40 60 80
Age (years)
27% 38% 47%
13%
22%
33%
19%
30%
41%
Male
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a sole outcome in COVID-19 studies might under-
estimate the detrimental impact of COVID-19, particularly
in those who are younger or otherwise healthy. Our
analysis suggests that the odds of some complications
change little with increasing age in those older than
50 years. Therefore, when compared with mortality,
complications will aect many more people across a
range of dierent age groups. Notably, our data show only
small increases in the risk of complications by pre-
existing comorbidities. The eect of comorbidities on the
risk of complications and death was substantially higher
in younger people compared with people without comor-
bidities of the same age. We also observed the dierences
in number of complications decrease between those who
died and those who survived as age increased, suggesting
that although young people are less likely to die, they
might be proportionally more likely to survive and live
with complications. Patients with complications are
also likely to have impaired ability to self-care following
discharge from hospital. This finding contradicts current
narratives that COVID-19 is only dangerous in people
with existing comorbidities and the elderly. Dispelling
and contributing to the scientific debate around such
narratives has become increasingly important. Many
countries including the UK are experiencing further
waves of infection.32 Suggestions have been made around
using younger, healthy demo graphic groups who are less
likely to die, to help support economic output, and to
propagate herd immunity within a population.33 Policy
makers need to consider not just mortality when making
decisions around easing population-level interventions
designed to limit spread, but also the risk of both short-
term and long-term complications for those who survive
COVID-19.
Our data provide the most comprehensive, multicentre,
systematic analysis of the eect of COVID-19 on short-
term clinical outcomes in a hospitalised population,
including patient groups from both ward level and
critical care. Data were collected prospectively and
capture most people hospitalised with COVID-19 in the
UK. Recruitment to our study con tinues, enabling us to
capture trends and incidence of complications in near
real time. Other smaller, or single centre studies, have
typically focused either exclusively on patients who
received critical care, or on one type of complication and
lack systematic approaches to data collection.4,34–38 Our
study identifies high rates of com plications and the risk
factors for developing these, and describes severity,
which previous studies have been unable to do at scale.
In particular, we find that in the short term, respiratory
and cardiovascular complications have the strongest
association with mortality. A further strength is that our
study includes patients in both critical care and in
ward-level areas, whereas other groups have just studied
intensive care populations.39 In addition, the multicentre
nature of our study across 302 facilities in four countries
increases the generalisability of our findings, which is
particularly important to provide robust estimates of
Patients having complications Organ-specific complications
Total number of
patients
Any complication Systemic Renal Gastrointestinal
(including liver)
Cardiovascular Neurological Respiratory*
Total number of patients 73 197 36 367 (49·7%) 11 895 (16·3%) 17 752 (24·3%) 7901 (10·8%) 8973 (12·3%) 3115 (4·3%) 13 486 (18·4%)
Death
No 50 105 (68·5%) 21 784 (59·9%) 7423 (62·4%) 10 059 (56·7%) 4837 (61·2%) 4035 (45·0%) 1880 (60·4%) 7028 (52·1%)
Yes 23 092 (31·5%) 14 583 (40·1%) 4472 (37·6%) 7693 (43·3%) 3064 (38·8%) 4938 (55·0%) 1235 (39·6%) 6458 (47·9%)
Critical care admission
No 62 125 (84·9%) 28 092 (77·2%) 8804 (74·0%) 12 992 (73·2%) 5139 (65·0%) 6640 (74·0%) 2446 (78·5%) 7472 (55·4%)
Yes 10 034 (13·7%) 8267 (22·7%) 3090 (26·0%) 4755 (26·8%) 2760 (34·9%) 2333 (26·0%) 668 (21·4%) 6012 (44·6%)
Data missing 1038 (1·4%) 8 (<0·1%) 1 (<0·1%) 5 (<0·1%) 2 (<0·1%) 0 1 (<0·1%) 2 (<0·1%)
Any invasive ventilation
No 65 888 (90·0%) 30 710 (84·4%) 9556 (80·3%) 14 262 (80·3%) 5815 (73·6%) 7186 (80·1%) 2573 (82·6%) 8809 (65·3%)
Yes 6122 (8·4%) 5619 (15·5%) 2330 (19·6%) 3471 (19·6%) 2077 (26·3%) 1784 (19·9%) 542 (17·4%) 4670 (34·6%)
Data missing 1187 (1·6%) 38 (0·1%) 9 (0·1%) 19 (0·1%) 9 (0·1%) 3 (<0·1%) 0 7 (0·1%)
Any non-invasive ventilation
No 60 035 (84·7%) 28 202 (78·5%) 9228 (78·2%) 13 361 (76·1%) 5685 (72·8%) 6862 (77·1%) 2566 (83·3%) 8332 (62·3%)
Yes 10 827 (15·3%) 7741 (21·5%) 2567 (21·8%) 4194 (23·9%) 2124 (27·2%) 2034 (22·9%) 513 (16·7%) 5038 (37·7%)
Any oxygen
No 17 652 (24·7%) 5971 (16·5%) 2079 (17·6%) 2470 (14·0%) 1153 (14·7%) 1190 (13·3%) 737 (23·8%) 838 (6·2%)
Yes 53 695 (75·3%) 30 181 (83·5%) 9762 (82·4%) 15 189 (86·0%) 6705 (85·3%) 7744 (86·7%) 2358 (76·2%) 12 598 (93·8%)
Data are n or n (%). No means patients did not have the clinical outcome specified in the table rows, yes means they did. *Severe acute respiratory infection was contained within case definition so was not
counted as a complication.
Table 2: Outcomes by organ-specific complications
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233
Figure 4: Relationship between in-hospital complications and ability to self-care at time of discharge or transfer to other health-care facility
(A) Ability to self-care at discharge in patients who had complications by age group and sex. (B) Ability to self-care at discharge by disease severity. (C) Adjusted odds
of worse ability to self-care at discharge by organ-specific complications in adults admitted to hospital with severe COVID-19 (appendix pp 27–34). Error bars
represent 95% CIs.
C
1 3 5
Cardiovascular
Gastrointestinal
Neurological
Renal
Respiratory
Systemic
Any complication
Complication
Ability to self-care worse on discharge
≥90
80–89
70–79
60–69
50–59
40–49
30–39
19–29
A
Age (years)
No complication
≥90
80–89
70–79
60–69
50–59
40–49
30–39
19–29
Age (years)
Any complication
No complication
Any complication
Female Male
≥90
80–89
70–79
60–69
50–59
40–49
30–39
19–29
B
Age (years)
≥90
80–89
70–79
60–69
50–59
40–49
30–39
19–29
Age (years)
Proportion of patients worse than
before illness (%)
Ward-level care
0 25 50 75 100
0255075 100
Proportion of patients worse than
before illness (%)
0255075 100
Critical care admission
0 25 50 75 100
Mechanical ventilation
0 25 50
Proportion of patients worse
than before illness (%)
Proportion of patients worse
than before illness (%)
Proportion of patients worse
than before illness (%)
75 100
7% (41/595)
8% (67/826)
10% (104/1079)
11% (190/1751)
18% (296/1661)
29% (680/2357)
36% (1014/2843)
38% (471/1244)
12% (22/178)
24% (86/354)
32% (184/581)
31% (317/1021)
36% (456/1258)
41% (693/1692)
43% (927/2160)
46% (420/918)
20% (71/363)
12% (89/726)
12% (162/1400)
13% (300/2242)
17% (403/2346)
28% (771/2761)
33% (850/2571)
38% (232/612)
24% (40/169)
32% (154/475)
35% (405/1152)
34% (696/2034)
38% (851/2222)
39% (1001/2594)
44% (1026/2324)
47% (257/547)
11% (94/865)
10% (136/1405)
10% (219/2254)
11% (390/3581)
16% (592/3663)
28% (1337/4816)
34% (1776/5194)
38% (671/1784)
9% (21/233)
12% (60/481)
16% (143/919)
17% (284/1713)
25% (573/2259)
36% (1341/3683)
44% (1900/4367)
46% (665/1448)
15% (6/41)
12% (9/76)
16% (19/122)
18% (45/256)
22% (41/183)
38% (56/147)
47% (36/76)
33% (7/21)
18% (10/55)
29% (38/129)
27% (69/257)
29% (121/423)
36% (144/401)
46% (145/312)
44% (46/105)
56% (10/18)
37% (7/19)
16% (4/25)
47% (22/47)
39% (32/83)
53% (39/73)
56% (24/43)
25% (3/12)
52% (32/61)
64% (143/222)
67% (377/561)
66% (607/923)
72% (594/827)
71% (211/299)
45% (10/22)
100% (2/2)
Odds ratio
2·40
2·14
1·95
2·19
4·39
3·63
2·42
95% CI
2·26–2·55, p<0·0001
2·04–2·26, p<0·0001
1·82–2·10, p<0·0001
2·04–2·36, p<0·0001
3·95–4·89, p<0·0001
3·42–3·86, p<0·0001
2·31–2·54, p<0·0001
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short-term morbidity for health-care planners and policy
makers. The large sample size of our study allowed us to
do meaningful subgroup analyses and integrate blood
test and microbiology results to increase robustness.
This size also meant we could detect rare events in
important patient groups such as those receiving critical
care, younger patients, and survivors where complications
might have the biggest eect and be with patients for a
long period of time after the initial event.
This study has important implications for clinicians. It
was not possible for us to causally link complications and
consequent poor outcomes. However, it is plausible
that interventions targeted at preventing in-hospital
com plications or reducing their impact could plausibly
improve outcomes. We found respiratory and cardio-
vascular complications were associated with greatest
severity and acute kidney injury was one of the most
common. Treatments such as enhanced monitoring and
early treatment for patients for cardiac arrhythmias that
might lead to further problems such as stroke or cardiac
arrest might, therefore, be useful. Similarly, for acute
kidney injury, optimising fluid balance to ensure adequate
renal perfusion in patients with less severe respiratory
disease might lessen the impact of acute kidney injury.
Our data also present research opportunities for
preventing complications that contribute to substantial
disability. For example, further characterisation of
thromboembolic complications and stroke can help to
identify optimal anti coagulation strategies in patients with
COVID-19.40 We found initial disease severity, measured
using the 4C Mortality Score, qSOFA, and NEWS, were
associated with the presence of complications, and could
therefore be useful tools to stratify those at the highest
risk of developing complications in clinical practice and
interventional trials.
There are several limitations to our study, which relate
to the design and current unknowns in COVID-19
research. First, this dataset focuses on in-hospital
complications during the index admission for COVID-19
and does not provide longer-term outcome data or data
on quality of life. Nevertheless, our results suggest that
complications of COVID-19 might aect all survivor
groups, rather than just those who are older and have
comorbidities. Second, the complications that were
captured were predefined by a pragmatic outbreak
preparedness study protocol, and case report forms
developed for disease X, long before the emergence of
SARS-CoV-2. The outcomes we chose are both clinically
important and associated with complications observed in
other infec tious viral diseases. Local investigators could
enter other complications as free text, but this approach
might have missed some important outcomes that were
otherwise unexpected (ie, venous thromboembolism);
however, as these emerged we amended the case report
form to include these. This suggests that our estimates
are likely to be conservative, when compared with the
incidence of some complications (including pulmonary
embolism or deep vein thrombosis) found in other
smaller studies. Similarly, these studies are more likely
to focus on populations with higher COVID-19 severity,
where our study captured all hospital admis sions.41 This
protocol did not include a non-SARS-CoV-2 comparator
group, which could provide useful data to compare
complication burdens to other causes of critical illness or
viral infection. Third, owing to logistical constraints, we
did not capture data on the timings of each complication.
As our study was an urgent response to the emerging
pandemic, it would not have been possible to identify
exactly when each complication started for such a large
number of patients. Data around timings could in the
future help to identify sequences of events that lead to
further deterioration. Fourth, our data can only provide
estimates of who gets complications in a hospitalised
population. We found that even in previously healthy
adults with no recorded comorbidity, complications
aected more than four in ten hospitalised patients;
the eect and burden in the community remains
undescribed. For infection-related outcomes, we sys-
tematically classified microbiological culture results to
identify whether infections were caused by pathogenic
organisms. However, individuals might have acquired
these in the community, so our estimates encompass
both hospital and community acquired infection. In
addition to this, the UK health service was under
considerable pressure, which could have resulted in
preferential admission to hospital of patients with the
most severe disease. This might lead to an increase in the
observed complication rate, as individuals with milder
disease were managed at home. However, the risk of this
is reduced by the multicentre design of our study, as
peaks in hospital admissions varied in the UK over time.
Compared with other international cohorts, our study
had a higher observed hospital case fatality rate.42–45 The
reasons for this are multifactorial, and could relate to
dierences in testing strategy, thresholds for hospital
admission, pre-existing population morbidity, and
health-care system preparedness. Finally, our data were
collected from real-world observed clinical practice and
patients did not undergo any additional tests to detect the
presence of complications. Therefore, the true burden of
complications is likely to be higher. However, doing large
numbers of invasive tests might not be acceptable for
patients, particularly in patients who are unlikely to
survive or cannot tolerate investigations, and would be
logistically challenging in a study of this size.
Policy makers and health-care planners should
anticipate that large amounts of health and social care
resources will be required to support those who survive
COVID-19. This includes adequate provision of stang
and equipment; for example, provision of follow-up clinics
for those who have sustained in-hospital complications
such as acute kidney injury or respiratory tract infection.
Beyond the short term, further work is underway to
establish the consequences of these complications and
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235
whether these are transient or linked to worse long-term
outcomes. Data on long-term health diculties posed by
COVID-19 will be of great importance, particularly as a
large proportion of COVID-19 survivors come from
economically active age groups. This should be considered
on a policy level in terms of return to work and education;
but importantly, it could have eects on individual
behaviour around perceived benefits of engaging with
preventive measures including vaccination.
In summary, high rates of complications and poor
functional outcomes were present in survivors of
COVID-19, including in young and previously healthy
individuals. Those aged older than 50 years and admitted
to critical care were at the highest risk. Common
COVID-19 complications identified in this Article are
known to be associated with long-term morbidity and an
increased risk of death.
Contributors
TMD, AMR, EMH, ABD, and MGS were responsible for the conception
of analysis, data analysis, and data interpretation, as well as the writing
and revision of the manuscript. CE, RP, and LN analysed the data.
HEH, DP, KAH, LM, LS, MG, CJ, PO, and GC designed and coordinated
the study. CJF, SRK, CAS, KAM, AH, CDR, TS, LT, JSN-V-T, and PJMO
interpreted the data, and were responsible for writing and revising the
manuscript. AART, FS, OVS, MS, TIdS, JD, JKB, and MGS interpreted
the data, and wrote and critically reviewed the manuscript. All authors
critically reviewed and revised the draft the manuscript. TMD, AMR,
ABD, LN, RP, and EMH all had access to the underlying data and verified
the findings. TMD, EMH, and MGS were responsible for manuscript
submission. All authors have seen and approved the final version.
TMD, AMR, RP, JKB, ABD, MGS and EMH had access to the raw data.
The corresponding author had full access to all data and the final
responsibility to submit for publication.
ISARIC4C investigators
United Kingdom: J Kenneth Baillie, Fiona Griths, Wilna Oosthuyzen,
Andrew Law, Sara Clohisey, Ross Hendry (Roslin Institute, University of
Edinburgh). Malcolm G Semple, Tom Solomon, Lance CW Turtle,
Hayley Hardwick (National Institure for Health Research [NIHR] Health
Protection Research Unit, Institute of Infection, Veterinary and
Ecological Sciences, Faculty of Health and Life Sciences, University of
Liverpool). Peter JM Openshaw, Ryan S Thwaites (National Heart and
Lung Institute, Imperial College London). Gail Carson, Laura Merson,
Louise Sigfrid (ISARIC Global Support Centre, Centre for Tropical
Medicine and Global Health, Nueld Department of Medicine,
University of Oxford). Beatrice Alex, Benjamin Bach, James Scott-Brown
(School of Informatics, University of Edinburgh). Wendy S Barclay
(Section of Molecular Virology, Imperial College London). Debby Bogaert,
Clark D Russell (Centre for Inflammation Research, The Queen’s
Medical Research Institute, University of Edinburgh). Meera Chand
(Antimicrobial Resistance and Hospital Acquired Infection Department,
Public Health England). Graham S Cooke, Shiranee Sriskandan
(Department of Infectious Disease, Imperial College London).
Annemarie B Docherty, Ewen M Harrison, Lisa Norman, Riinu Pius,
Thomas M Drake, Cameron J Fairfield, Stephen R Knight,
Kenneth A Mclean, Derek Murphy, Catherine A Shaw (Centre for
Medical Informatics, The Usher Institute, University of Edinburgh).
Jake Dunning, Maria Zambon (National Infection Service, Public Health
England). Ana da Silva Filipe, Antonia Ying Wai Ho, Massimo Palmarini,
David L Robertson, Janet T Scott, Emma C Thomson, Sarah E McDonald
(Medical Research Council [MRC]-University of Glasgow Centre for Virus
Research, University of Glasgow). Tom Fletcher (Liverpool School of
Tropical Medicine). Christoper A Green (Institute of Microbiology and
Infection, University of Birmingham). Julian A Hiscox (Institute of
Infection and Global Health, University of Liverpool). Peter W Horby
(Centre for Tropical Medicine and Global Health, Nueld Department of
Medicine, University of Oxford). Samreen Ijaz (Virology Reference
Department, National Infection Service, Public Health England).
Saye Khoo (Department of Pharmacology, University of Liverpool).
Paul Klenerman (Nueld Department of Medicine, Peter Medawar
Building for Pathogen Research, University of Oxford). Andrew Law
(The Roslin Institute, University of Edinburgh). Wei Shen Lim
(Nottingham University Hospitals NHS Trust). Alexander J Mentzer
(Nueld Department of Medicine, John Radclie Hospital, Oxford).
Alison M Meynert, Murray Wham (MRC Human Genetics Unit,
MRC Institute of Genetics and Molecular Medicine, University of
Edinburgh). Mahdad Noursadeghi (Division of Infection and Immunity,
University College London). Shona C Moore, William A Paxton,
Georgios Pollakis (Institute of Infection, Veterinary and Ecological
Sciences, University of Liverpool). Nicholas Price (Centre for Clinical
Infection and Diagnostics Research, Department of Infectious Diseases,
School of Immunology and Microbial Sciences, King’s College London).
Andrew Rambaut (Institute of Evolutionary Biology, University of
Edinburgh). Vanessa Sancho-Shimizu (Department of Pediatrics and
Virology, Imperial College London). Thushan de Silva (The Florey
Institute for Host-Pathogen Interactions, Department of Infection,
Immunity and Cardiovascular Disease, University of Sheeld).
David Stuart (Division of Structural Biology, The Wellcome Centre for
Human Genetics, University of Oxford). Charlotte Summers
(Department of Medicine, University of Cambridge, Cambridge).
Richard S Tedder (Blood Borne Virus Unit, Virus Reference Department,
National Infection Service, Public Health England). AA Roger Thompson
(Department of Infection, Immunity and Cardiovascular Disease,
University of Sheeld, Sheeld). Rishi K Gupta (Institute for Global
Health, University College London). Carlo Palmieri (Molecular and
Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative
Biology, University of Liverpool). Olivia V Swann (Department of Child
Life and Health, University of Edinburgh). Marc-Emmanuel Dumas,
Julian L Grin, Zoltan Takats, Petros Andrikopoulos, Anthonia Osagie,
Michael Olanipekun, Sonia Liggi (Department of Metabolism, Digestion
and Reproduction, Imperial College London). Kanta Chechi (Department
of Epidemiology and Biostatistics, School of Public Health, Faculty of
Medicine, Imperial College London). Matthew R Lewis,
Gonçalo dos Santos Correia, Caroline J Sands, Panteleimon Takis,
Lynn Maslen (National Phenome Centre, Department of Metabolism,
Digestion and Reproduction, Imperial College London). Chloe Donohue,
Jo Dalton, Michelle Girvan, Egle Saviciute, Stephanie Roberts,
Janet Harrison, Laura Marsh, Marie Connor, Sophie Halpin,
Clare Jackson, Carrol Gamble (Liverpool Clinical Trials Centre, University
of Liverpool). Gary Leeming (Centre for Health Informatics, Division of
Informatics, Imaging and Data Science, School of Health Sciences,
University of Manchester). William Greenhalf (Department of Molecular
and Clinical Cancer Medicine, University of Liverpool). Victoria Shaw
(Institute of Translational Medicine, University of Liverpool, Liverpool,
Merseyside, United Kingdom). Seán Keating (Intensive Care Unit, Royal
Infirmary Edinburgh). Carlo Palmieri (University of Liverpool).
Katie A Ahmed, Jane A Armstrong, Milton Ashworth,
Innocent G Asiimwe, Siddharth Bakshi, Samantha L Barlow,
Laura Booth, Benjamin Brennan, Katie Bullock, Nicola Carlucci,
Emily Cass, Benjamin WA Catterall, Jordan J Clark, Emily A Clarke,
Sarah Cole, Louise Cooper, Helen Cox, Christopher Davis,
Oslem Dincarslan, Alejandra Doce Carracedo, Chris Dunn, Philip Dyer,
Angela Elliott, Anthony Evans, Lorna Finch, Lewis WS Fisher,
Lisa Flaherty, Terry Foster, Isabel Garcia-Dorival, William Greenhalf,
Philip Gunning, Catherine Hartley, Anthony Holmes, Rebecca L Jensen,
Christopher B Jones, Trevor R Jones, Shadia Khandaker, Katharine King,
Robyn T. Kiy, Chrysa Koukorava, Annette Lake, Suzannah Lant,
Diane Latawiec, Lara Lavelle-Langham, Daniella Lefteri, Lauren Lett,
Lucia A Livoti, Maria Mancini, Hannah Massey, Nicole Maziere,
Sarah McDonald, Laurence McEvoy, John McLauchlan,
Soeren Metelmann, Nahida S Miah, Joanna Middleton, Joyce Mitchell,
Shona C Moore, Ellen G Murphy, Rebekah Penrice-Randal, Jack Pilgrim,
Tessa Prince, Will Reynolds, P. Matthew Ridley, Debby Sales,
Victoria E Shaw, Rebecca K Shears, Benjamin Small,
Krishanthi S Subramaniam, Agnieska Szemiel, Aislynn Taggart,
Jolanta Tanianis-Hughes, Jordan Thomas, Erwan Trochu,
Libby van Tonder, Eve Wilcock, J. Eunice Zhang (Outbreak Laboratory,
University of Liverpool). Kayode Adeniji, Daniel Agrano, Ken Agwuh,
Articles
236
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Dhiraj Ail, Erin L Aldera, Ana Alegria, Brian Angus, Abdul Ashish,
Dougal Atkinson, Shahedal Bari, Gavin Barlow, Stella Barnass,
Nicholas Barrett, Christopher Bassford, Sneha Basude, David Baxter,
Michael Beadsworth, Jolanta Bernatoniene, John Berridge, Nicola Best,
Pieter Bothma, Robin Brittain-Long, Naomi Bulteel, Tom Burden,
Andrew Burtenshaw, Vikki Caruth, David Chadwick, David Chadwick,
Duncan Chambler, Nigel Chee, Jenny Child, Srikanth Chukkambotla,
Tom Clark, Paul Collini, Catherine Cosgrove, Jason Cupitt,
Maria-Teresa Cutino-Moguel, Paul Dark, Chris Dawson,
Samir Dervisevic, Phil Donnison, Sam Douthwaite, Andrew Drummond,
Ingrid DuRand, Ahilanadan Dushianthan, Tristan Dyer, Cariad Evans,
Chi Eziefula, Chrisopher Fegan, Adam Finn, Duncan Fullerton,
Sanjeev Garg, Sanjeev Garg, Atul Garg, Erossyni Gkrania-Klotsas,
Jo Godden, Arthur Goldsmith, Clive Graham, Elaine Hardy,
Stuart Hartshorn, Daniel Harvey, Peter Havalda, Daniel B Hawcutt,
Maria Hobrok, Luke Hodgson, Anil Hormis, Michael Jacobs, Susan Jain,
Paul Jennings, Agilan Kaliappan, Vidya Kasipandian, Stephen Kegg,
Michael Kelsey, Jason Kendall, Caroline Kerrison, Ian Kerslake,
Oliver Koch, Gouri Koduri, George Koshy, Shondipon Laha, Steven Laird,
Susan Larkin, Tamas Leiner, Patrick Lillie, James Limb, Vanessa Linnett,
Je Little, Mark Lyttle, Michael MacMahon, Emily MacNaughton,
Ravish Mankregod, Huw Masson, Elijah Matovu, Katherine McCullough,
Ruth McEwen, Manjula Meda, Gary Mills, Jane Minton,
Mariyam Mirfenderesky, Kavya Mohandas, Quen Mok, James Moon,
Elinoor Moore, Patrick Morgan, Craig Morris, Katherine Mortimore,
Samuel Moses, Mbiye Mpenge, Rohinton Mulla, Michael Murphy,
Thapas Nagarajan, Megan Nagel, Mark Nelson, Lillian Norris,
Matthew K. O’Shea, Marlies Ostermann, Igor Otahal, Mark Pais,
Selva Panchatsharam, Danai Papakonstantinou, Padmasayee Papineni,
Hassan Paraiso, Brij Patel, Natalie Pattison, Justin Pepperell, Mark Peters,
Mandeep Phull, Stefania Pintus, Frank Post, David Price, Rachel Prout,
Nikolas Rae, Henrik Reschreiter, Tim Reynolds, Neil Richardson,
Mark Roberts, Devender Roberts, Alistair Rose, Guy Rousseau,
Brendan Ryan, Taranprit Saluja, Sarah Cole, Aarti Shah,
Manu Shankar-Hari, Prad Shanmuga, Anil Sharma, Anna Shawcross,
Jagtur Singh Pooni, Jeremy Sizer, Richard Smith, Catherine Snelson,
Nick Spittle, Nikki Staines, Tom Stambach, Richard Stewart,
Pradeep Subudhi, Tamas Szakmany, Kate Tatham, Jo Thomas,
Chris Thompson, Robert Thompson, Ascanio Tridente,
Darell Tupper-Carey, Mary Twagira, Nick Vallotton,
Rama Vancheeswaran, Lisa Vincent-Smith, Shico Visuvanathan,
Alan Vuylsteke, Sam Waddy, Rachel Wake, Andrew Walden,
Ingeborg Welters, Tony Whitehouse, Paul Whittaker, Ashley Whittington,
Meme Wijesinghe, Martin Williams, Lawrence Wilson,
Stephen Winchester, Martin Wiselka, Adam Wolverson,
Daniel G Wootton, Andrew Workman, Bryan Yates, Peter Young
(local principal investigators).
Declaration of interests
ABD reports grants from the Department of Health and Social Care
(DHSC), during the conduct of the study; and grants from Wellcome
Trust, outside the submitted work. PJMO reports institutional fees from
consultancies from Janssen, Oxford Immunotech, Nestle, Pfizer, and the
European Respiratory Society; grants from the MRC, MRC Global
Challenge Research Fund, EU, NIHR Biomedical Research Centre,
MRC, GlaxoSmithKline, Wellcome Trust, and NIHR Health Protection
Research Unit (HPRU) in Respiratory Infection; and is NIHR senior
investigator outside the submitted work. PJMO’s role as president of the
British Society for Immunology was unpaid but travel and
accommodation at some meetings was provided by the Society.
JKB reports grants from MRC UK. MGS reports grants from DHSC,
NIHR UK, MRC UK, HPRU in Emerging and Zoonotic Infections, and
University of Liverpool, during the conduct of the study; and is chair of
the Infectious Diseases Science Advisory Board and minority
shareholder of Integrum Scientific, Greensboro NC, outside the
submitted work. All other authors declare no competing interests.
Data sharing
Data, protocols, and all documentation around this analysis will be made
available to academic researchers after authorisation from the
independent data access and sharing committee. Data and analysis scripts
are available on request to the Independent Data Management and Access
Committee at https://isaric4c.net/ sample_access.
Acknowledgments
This work is supported by grants from: the NIHR (award CO-CIN-01),
MRC (grant MC_PC_19059), NIHR Imperial Biomedical Research
Centre (grant P45058), HPRU in Respiratory Infections at Imperial
College London, and NIHR HPRU in Emerging and Zoonotic Infections
at the University of Liverpool, in partnership with Public Health England
(NIHR award 200907), Wellcome Trust, Department for International
Development (215091/Z/18/Z), Bill & Melinda Gates Foundation
(OPP1209135), Liverpool Experimental Cancer Medicine Centre (grant
C18616/A25153), NIHR Biomedical Research Centre at Imperial College
London (IS-BRC-1215–20013), EU Platform for European Preparedness
Against (Re-) Emerging Epidemics (PREPARE; FP7 project 602525).
NIHR Clinical Research Network provided the infrastructure support for
this research. LT is a Wellcome Trust clinical career development fellow,
supported by grant number 205228/Z/16/Z. This research was funded
in part by the Wellcome Trust. PJMO is supported by an NIHR Senior
Investigator Award (award 201385). The views expressed are those of the
authors and not necessarily those of the DHSC, Department for
International Development, NIHR, MRC, Wellcome Trust, or Public
Health England. This work uses data provided by patients and collected
by the National Health Service (NHS) as part of their care and support.
We are extremely grateful to the 2648 front-line NHS clinical and
research sta and volunteer medical students, who collected this data in
challenging circumstances, and the generosity of the participants and
their families for their individual contributions in these dicult times.
We also acknowledge the support of Jeremy J Farrar and Nahoko Shindo.
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