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Increased risk of death in COVID-19 hospital admissions during the second wave as compared to the first epidemic wave: a prospective, single-centre cohort study in London, UK.

Martina Cusinato1, Jessica Gates2, Danyal Jajbhay2, Tim Planche1, Yee Ean Ong1, Yee Ean Ong2 •
St George's, University of London1, St George’s University Hospitals NHS Foundation Trust2
21 Oct 2021-Infection (Springer Berlin Heidelberg)-pp 1-9
TL;DR: The second coronavirus disease (COVID-19) epidemic wave in the UK progressed aggressively and was characterised by the emergence and circulation of variant of concern alpha (VOC 202012/01).
Abstract: The second coronavirus disease (COVID-19) epidemic wave in the UK progressed aggressively and was characterised by the emergence and circulation of variant of concern alpha (VOC 202012/01). The impact of this variant on in-hospital COVID-19-specific mortality has not been widely studied. We aimed to compare mortality, clinical characteristics, and management of COVID-19 patients across epidemic waves to better understand the progression of the epidemic at a hospital level and support resource planning. We conducted an analytical, dynamic cohort study in a large hospital in South London. We included all adults (≥ 18 years) with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) who required hospital admission to COVID-19-specific wards between January 2020 and March 2021 (n = 2701). Outcome was COVID-19-specific in-hospital mortality ascertained through Medical Certificate Cause of Death. In the second wave, the number of COVID-19 admissions doubled, and the crude mortality rate dropped 25% (1.66 versus 2.23 per 100 person-days in second and first wave, respectively). After accounting for age, sex, dexamethasone, oxygen requirements, symptoms at admission and Charlson Comorbidity Index, mortality hazard ratio associated with COVID-19 admissions was 1.62 (95% CI 1.26, 2.08) times higher in the second wave. Although crude mortality rates dropped during the second wave, the multivariable analysis suggests a higher underlying risk of death for COVID-19 admissions in the second wave. These findings are ecologically correlated with an increased circulation of SARS-CoV-2 variant of concern 202012/1 (alpha). Availability of improved management, particularly dexamethasone, was important in reducing risk of death.

Summary (2 min read)

Jump to: [INTRODUCTION] – [Variables] – [Statistical methods] – [Governance and ethics] – [DISCUSSION] – [Strengths and limitations] – [Generalisability] and [CONCLUSIONS]

INTRODUCTION

  • Since its emergence in December 2019, the spread of SARS-CoV-2 has increased exponentially leading to the declaration of a pandemic by the World Health Organization (WHO) on 11 March 2020, marking the beginning of an outbreak that has posed immense challenges for health care systems across the globe [1] .
  • The implementation of a second lockdown (04 November 2020) followed by tiered control measures, in place until the beginning of March 2021, were needed to reduce the transmission rates again [2] .
  • Since the start of the pandemic a great deal has been learned about treatment of covid -19 and there have been several important changes to the management of patients admitted with covid -19.
  • Study follow-up (from admission to discharge/outcome) was also carried out by clinicians, prospectively, through review of electronic medical records.

Variables

  • The outcome variable was in-hospital covid-19-associated mortality, ascertained from clinical records and Medical Certificate Cause of Death (MCCD).
  • The main explanatory variable for this analysis was covid-19 wave, and 31 June 2020 used as cut-off to separate both waves.
  • Covariates of interest for this analysis included demographics (sex, age at admission, ethnicity), symptoms at admission, Body Mass Index (BMI), treatment (dexamethasone, Remdesivir, Tocilizumab), oxygen requirement, HFNO/CPAP (High Flow Nasal Oxygen/Continuous Positive Airway Pressure), invasive ventilation, Intensive Care Unit (ICU) admission, Clinical Frailty Score (CFS), Charlson Comorbidity Index (CCI), social history.
  • Most variables were used in their original scale, others were recategorised using clinically relevant categories with a sufficient number of participants in each group to avoid sparsity.
  • CFS was expanded to include all age groups excepting those patients with disabilities which rendered it inappropriate.

Statistical methods

  • The distribution of covariates was assessed for the entire cohort and across waves.
  • The strength of the association was quantified using incidence rate ratios (IRR), and the statistical significance using 95%CIs and p-values.
  • Cox regression was used to estimate the effect of wave on mortality adjusting for multiple covariates.
  • Age and gender were considered a priori confounders (forced variables).
  • Problems will multicollinearity on the main effect in the full model, were resolved using RMSE (Root Mean Square Error) reduction for backward deletion of non-forced variables.

Governance and ethics

  • This study was approved by the Health Research Authority (20/SC/0220).
  • This manuscript follows the STROBE statement for reporting of cohort studies.

DISCUSSION

  • This cohort study examined differences in the risk of death of patients requiring in-hospital treatment for covid-19, during the first and second wave of the covid-19 pandemic in UK.
  • The multivariable analysis attempted to account for all the available factors unequally distributed across waves and also associated with mortality (while avoiding multicollinearity in the model).
  • This correlation, was only observed in the second wave in accordance with changes in the standard of care as evidence became available.
  • After accounting for the effect of age, sex, dexamethasone, oxygen requirement, symptoms at admission, CCI and wave, dexamethasone reduced the hazard of death in this population of patients by 53% (95%CI 40%, 63%).
  • This further supports the observation that risk of death in covid-19 hospitalised patients was higher in the second wave compared to the first wave, when differences in the standard of care and the characteristics of the patients were taken into account.

Strengths and limitations

  • This was a large analytical cohort study comparing groups of patients at different points in time.
  • The overall goal was to investigate if different standards of care and possible changes in the natural history of the disease (attributed to changes in SARS-CoV-2 variants), had an impact on in-hospital mortality.
  • The majority of the data were collected by experienced respiratory and ICU clinicians, and although some data inconsistencies were rectified early during data management, misclassification of covariates due transcription errors cannot be ruled out.
  • Laboratory variables such as oxygenation parameters were obtained through the informatic department, but due to the limited quality of the electronic records, data were inconsistent and, in many cases, missing.
  • In addition, temporal effects could also have explained some of the observed differences between waves, as fatality rates are known to be higher during winter months, when the second wave unfolded.

Generalisability

  • This study was looking at an overall population of hospitalised adults with covid-19 in a large reference teaching London hospital.
  • Findings are only generalisable to inpatient population.

CONCLUSIONS

  • Analysis of covid-19 admissions recorded in St. Georges Hospital between 01 Jan 2020 and 31 March 2021, shows a second epidemic wave twice as large as the first one.
  • Sex, dexamethasone use, oxygen requirement, symptoms at admission and comorbidities), suggests a higher risk of death during the second epidemic wave compared to the first.
  • The authors findings are temporally and ecologically correlated with an increased circulation of VOC 202012/01 , with estimates in agreement community-based studies.
  • The availability of improved management and new treatments, particularly dexamethasone, was important in reducing risk of death during the second wave.

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Page 1 of 15
TITLE
1
Increased risk of death in covid-19 hospital admissions during the second wave as compared to the first
2
epidemic wave. A prospective dynamic cohort study in South London, UK.
3
AUTHORS
4
Martina Cusinato
1
, Jessica Gates
2
, Danyal Jajbhay
2
, Tim Planche
1
, Yee-Ean Ong
2,3
.
5
6
Affiliations:
7
1. Institute for Infection & Immunity, St. George’s University of London, London, UK.
8
2. Respiratory Medicine, St. Georges Hospital, London, UK.
9
3. Institute of Medical and Biomedical Education, St. George’s University of London, London, UK.
10
Corresponding author:
11
Martina Cusinato, mcusinat@sgul.ac.uk
12
ABSTRACT
13
Objective: To assess whether mortality of patients admitted for covid-19 treatment was different in the
14
second UK epidemic wave of covid-19 compared to the first wave accounting for improvements in the
15
standard of care available and differences in the distribution of risk factors between the two waves.
16
Design: Single-centre, analytical, dynamic cohort study.
17
Participants: 2,701 adults (18 years) with SARS-CoV-2 infection confirmed by polymerase chain reaction
18
(PCR) and/or clinico-radiological diagnosis of covid-19, who required hospital admission to covid-19 specific
19
wards, between January 2020 and March 2021. There were 884 covid-19 admissions during the first wave
20
(before 30 Jun 2020) and 1,817 during the second wave.
21
Outcome measures: in-hospital covid-19 associated mortality, ascertained from clinical records and Medical
22
Certificate Cause of Death.
23
Results: The crude mortality rate was 25% lower during the second wave (2.23 and 1.66 deaths per 100
24
person-days in first and second wave respectively). However, after accounting for age, sex, dexamethasone,
25
oxygen requirements, symptoms at admission and Charlson Comorbidity Index, mortality hazard ratio
26
associated with covid-19 hospital admissions was 1.62 (95% confidence interval 1.26, 2.08) times higher in
27
the second wave compared to the first.
28
Conclusions: Analysis of covid-19 admissions recorded in St. Georges Hospital, shows a larger second
29
epidemic wave, with a lower crude mortality in hospital admissions. Nevertheless, after accounting for other
30
factors underlying risk of death for covid-19 admissions was higher in the second wave. These findings are
31
temporally and ecologically correlated with an increased circulation of SARS-CoV-2 variant of concern
32
202012/1 (alpha).
33
34
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprintthis version posted June 12, 2021. ; https://doi.org/10.1101/2021.06.09.21258537doi: medRxiv preprint
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.
Page 2 of 15
INTRODUCTION
35
Since its emergence in December 2019, the spread of SARS-CoV-2 has increased exponentially leading to the
36
declaration of a pandemic by the World Health Organization (WHO) on 11 March 2020, marking the
37
beginning of an outbreak that has posed immense challenges for health care systems across the globe [1].
38
The first confirmed case in the United Kingdom (UK) was registered on 31 January 2020. At the beginning of
39
March 2020, the growing transmission rates lead to the introduction of a series of control measures that
40
escalated to a full national lockdown (23 March 2020). This was subsequently followed by a drop in
41
transmission and hospitalisation rates with restrictions being eased over the summer months (Jun – Aug
42
2020). However, in October 2020 infections began to increase again leading to a second wave of covid-19
43
cases. The implementation of a second lockdown (04 November 2020) followed by tiered control measures,
44
in place until the beginning of March 2021, were needed to reduce the transmission rates again [2]. As of 01
45
May 2021, the UK has recorded 4,418,819 confirmed cases, 463,485 hospital admissions, and 127,571
46
deaths [3].
47
48
During the first wave of covid-19, relatively little was known about this novel illness and management was
49
largely based upon experience of treating other viral infections. However, since the start of the pandemic a
50
great deal has been learned about treatment of covid -19 and there have been several important changes to
51
the management of patients admitted with covid -19. From the start of the second wave, dexamethasone
52
was prescribed to all patients requiring supplemental oxygen and Remdesivir was administered to
53
hypoxemic patients presenting within 10 days of symptoms onset. The indications for Tocilizumab changed
54
during the second wave where patients initially had access to this only within clinical trials. Differences in the
55
management and standard of care across waves need to be accounted for when analysing covid-19 mortality
56
over time [4-8].
57
A key feature of the second wave of covid-19 in the UK, was the emergence of a new SARS-CoV-2 variant
58
designated VOC 202012/01 or alpha (lineage B.1.1.7). This new variant was identified in December 2020 by
59
Public Health England through genomic sequencing of samples originally taken in south east England in early
60
October 2020. Since then, VOC 202012/01 has become the predominant variant circulating in the UK [9-11].
61
Several studies have established that VOC 202012/01 is more transmissible than pre-existing variants but its
62
impact on mortality has not been widely studied. To our knowledge, three studies have been recently
63
published, reporting an increase in mortality among those with a positive test in the community; however,
64
the impact on in-hospital mortality remains poorly understood [12-14].
65
66
St George’s University Hospital NHS Foundation Trust is one of the largest hospitals in the UK and is based in
67
South West London. It serves a local catchment population of 560,000 and specialist services to 3.4 million
68
people. The objective of this study is to assess whether mortality of patients admitted for covid-19
69
treatment was different in the second UK wave of covid-19 compared to the first wave accounting for
70
differences in the standard of care available in each wave.
71
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprintthis version posted June 12, 2021. ; https://doi.org/10.1101/2021.06.09.21258537doi: medRxiv preprint
Page 3 of 15
METHODS
72
Study design
73
This is a single-centre, analytical, dynamic cohort study using data extracted from routinely collected,
74
electronic medical records and hospital database.
75
Participants and setting
76
The study population for this cohort study comprised all adults (18 years) with SARS-CoV-2 infection
77
confirmed by polymerase chain reaction (PCR) and/or clinico-radiological diagnosis of covid-19, who
78
required hospital admission to covid-19 specific wards at St George’s University Hospitals NHS Foundation
79
Trust (London, UK). Patients seen in the Emergency Department or in Acute Medical Units (AMU) who were
80
discharged on the same day were not included. Although covid-19 wards opened in March 2020, the study
81
period encompasses admissions between 01 Jan 2020 and 31 March 2021, as some of the early patients
82
admitted to covid-19 wards were already hospitalised. All patients meeting the inclusion criteria during the
83
study period were included in the cohort. There was no a priori study size calculation.
84
Data sources and measurement
85
The study cohort was identified retrospectively using hospital records of admissions to active covid-19
86
wards. These lists included patient identifiers, hospital admission date, ward and administrative information.
87
Respiratory and Intensive Care clinicians within the study team and involved in the care of covid-19 patients,
88
reviewed the electronic medical records for all the patients in the initial list, confirming criteria for covid-19
89
admission. In case of multiple covid-19 admissions, only the most severe, as defined by the highest
90
respiratory support needed, was included [15].
91
Study follow-up (from admission to discharge/outcome) was also carried out by clinicians, prospectively,
92
through review of electronic medical records. Patient data was extracted manually using a standardised
93
electronic questionnaire and was supervised by a senior clinician within the Respiratory team. These data
94
were also obtained through the informatic department and linked using hospital identifiers (laboratory,
95
pathology results and ethnicity data).
96
The follow-up period for this study began at admission and ended at outcome occurrence (death) or
97
censoring. Participants were censored at hospital discharge or at 6 months if admissions exceeded this
98
period (1 patient only).
99
PCR pathology results were available for all tests requested during the study period, so we matched these
100
with our cohort of patients. Those with positive PCR results dated at least 15 days after their hospital
101
admission were considered probable hospital acquired infections (HAI) and had the start of their follow-up
102
(time at risk) amended to be 14 days (maximum incubation period [16]) before the date of the positive PCR
103
result, instead of the actual admission day.
104
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprintthis version posted June 12, 2021. ; https://doi.org/10.1101/2021.06.09.21258537doi: medRxiv preprint
Page 4 of 15
Variables
105
The outcome variable was in-hospital covid-19-associated mortality, ascertained from clinical records and
106
Medical Certificate Cause of Death (MCCD). The main explanatory variable for this analysis was covid-19
107
wave, and 31 June 2020 used as cut-off to separate both waves. First wave was used as baseline/reference.
108
Covariates of interest for this analysis included demographics (sex, age at admission, ethnicity), symptoms at
109
admission, Body Mass Index (BMI), treatment (dexamethasone, Remdesivir, Tocilizumab), oxygen
110
requirement, HFNO/CPAP (High Flow Nasal Oxygen/Continuous Positive Airway Pressure), invasive
111
ventilation, Intensive Care Unit (ICU) admission, Clinical Frailty Score (CFS), Charlson Comorbidity Index (CCI),
112
social history. Most variables were used in their original scale, others were recategorised using clinically
113
relevant categories with a sufficient number of participants in each group to avoid sparsity.
114
Where categorised, age groups in years were: [18,40), [40,60), [60,80), 80. BMI at admission was grouped
115
using categories derived from the WHO classification of BMI (in kg/m
2
): <18.5, [18.5,25), [25,30), 30[17].
116
Oxygen requirement was a dichotomous variable indicating whether the maximum FiO2 (Fraction of Inspired
117
Oxygen) was over 21%. ICU admission was defined as covid-19 pneumonitis admitted to ICU. Symptoms at
118
admission were respiratory or wider infective symptoms at time of presentation. The CFS level was collected
119
on a nine-point ordinal scale to assess frailty within two weeks of admission (1 being very fit, 2 well, 3
120
managing well, 4 vulnerable, 5 mildly frail, 6 moderately frail, 7 severely frail, 8 very severely frail, and 9
121
terminally ill); but to avoid sparsity categories 7 to 9 were grouped [18]. CFS was expanded to include all age
122
groups excepting those patients with disabilities which rendered it inappropriate. CCI is a widely used
123
comorbidity summary measure, based on age and a predefined number of conditions with an assigned
124
integer weight representing the severity of each condition; for this analysis scores of 8 or more were
125
grouped in one category [19]. All scores were calculated by clinicians experienced in the use of scales. Four
126
categories under social history reflected the level of assistance required for daily activities.
127
Statistical methods
128
The distribution of covariates was assessed for the entire cohort and across waves. Mortality rates and
129
person-time of observation were calculated for the main exposure groups and all covariates of interest. The
130
strength of the association was quantified using incidence rate ratios (IRR), and the statistical significance
131
using 95%CIs and p-values. Survival across the different waves was explored using time-to-event analysis and
132
log-rank to test the significance of the difference between the survival curves.
133
Cox regression was used to estimate the effect of wave on mortality adjusting for multiple covariates. The
134
proportional hazard assumption was explored graphically and by testing for a zero slope in Schoenfeld
135
residuals. Follow-up time was stratified using lexis expansion and to minimise bias, intervals were created so
136
they would contain the same number of events. The assumption of proportionality was supported.
137
A causal model was built using a stepwise backward approach where (non-forced) pre-defined covariates
138
were retained in the model unless there were problems with multicollinearity. Age and gender were
139
considered a priori confounders (forced variables). Age was fitted using restricted cubic splines, with knots
140
positioned so numbers of events between knots were approximately equally distributed. The full model
141
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprintthis version posted June 12, 2021. ; https://doi.org/10.1101/2021.06.09.21258537doi: medRxiv preprint
Page 5 of 15
included age, gender and all variables found to confound the crude association between wave and mortality
142
(non-forced variables). A change in the magnitude 5.5% was considered an indication of confounding. ICU
143
admission was included in the model as a non-forced variable regardless of the degree of confounding of the
144
main association. Problems will multicollinearity on the main effect in the full model, were resolved using
145
RMSE (Root Mean Square Error) reduction for backward deletion of non-forced variables. The RMSE for the
146
full model was used as reference for each step; and RMSE for each reduced model was calculated as √[
〖〖
147
(𝛽
〗
_(1 𝑟𝑒𝑑𝑢𝑐𝑒𝑑) − 𝛽_(1 𝑓𝑢𝑙𝑙))
〗
^2 +
〖
𝑆𝐸
〗
_𝑟𝑒𝑑𝑢𝑐𝑒𝑑^2][20].
148
Following the same methodology, we carried out a sub-analysis among those requiring ICU admission. Data
149
management and statistical analysis were carried out using R.
150
Governance and ethics
151
This study was approved by the Health Research Authority (20/SC/0220). This manuscript follows the
152
STROBE statement for reporting of cohort studies.
153
154
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprintthis version posted June 12, 2021. ; https://doi.org/10.1101/2021.06.09.21258537doi: medRxiv preprint
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25 Aug 2022-Open Forum Infectious Diseases
TL;DR: This work suggests that the increased in-hospital mortality rates observed during the first epidemic wave were partly due to strain on hospital resources, and Preparations for future epidemics should prioritize evidence-based patient risks, treatment paradigms, and approaches to augment hospital capacity.
Abstract: Abstract Background Many regions have experienced successive epidemic waves of coronavirus disease 2019 (COVID-19) since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with heterogeneous differences in mortality. Elucidating factors differentially associated with mortality between epidemic waves may inform clinical and public health strategies. Methods We examined clinical and demographic data among patients admitted with COVID-19 during the first (March–August 2020) and second (August 2020–March 2021) epidemic waves at an academic medical center in New York City. Results Hospitalized patients (n = 4631) had lower overall and 30-day in-hospital mortality, defined as death or discharge to hospice, during the second wave (14% and 11%) than the first (22% and 21%). The wave 2 in-hospital mortality decrease persisted after adjusting for several potential confounders. Adjusting for the volume of COVID-19 admissions, a measure of health system strain, accounted for the mortality difference between waves. Several demographic and clinical patient factors were associated with an increased risk of mortality independent of wave: SARS-CoV-2 cycle threshold, do-not-intubate status, oxygen requirement, and intensive care unit admission. Conclusions This work suggests that the increased in-hospital mortality rates observed during the first epidemic wave were partly due to strain on hospital resources. Preparations for future epidemics should prioritize evidence-based patient risks, treatment paradigms, and approaches to augment hospital capacity.

2 citations

References
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Journal Article•DOI•
Remdesivir for the Treatment of Covid-19 - Final Report.

[...]

John H. Beigel1, Kay M. Tomashek1, Lori E. Dodd1, Aneesh K. Mehta1, Barry S. Zingman1, Andre C. Kalil1, Elizabeth L. Hohmann1, Helen Y. Chu1, Annie Luetkemeyer1, Susan Kline1, Diego Lopez de Castilla1, Robert W. Finberg1, Kerry Dierberg1, Victor F. Tapson1, Lanny Hsieh1, Thomas F. Patterson1, Roger Paredes1, Daniel A. Sweeney1, William R. Short1, Giota Touloumi1, David Chien Lye1, Norio Ohmagari1, Myoung-don Oh1, Guillermo M. Ruiz-Palacios1, Thomas Benfield1, Gerd Fätkenheuer1, Mark G. Kortepeter1, Robert L. Atmar1, C. Buddy Creech1, JD Lundgren1, Abdel G. Babiker1, Sarah Pett1, James D. Neaton1, Timothy Burgess1, Tyler Bonnett1, Michelle Green1, Mat Makowski1, Anu Osinusi1, Seema U Nayak1, H. Clifford Lane1 •
University of California1
22 May 2020-The New England Journal of Medicine
TL;DR: The data show that remdesivir was superior to placebo in shortening the time to recovery in adults who were hospitalized with Covid-19 and had evidence of lower respiratory tract infection.
Abstract: Background Although several therapeutic agents have been evaluated for the treatment of coronavirus disease 2019 (Covid-19), none have yet been shown to be efficacious. Methods We conducte...

5,532 citations

Journal Article•DOI•
Validation of a combined comorbidity index.

[...]

Mary E. Charlson1, Ted P. Szatrowski1, Ted P. Szatrowski2, Janey C. Peterson2, Janey C. Peterson1, Jeffrey P. Gold2 •
Hospital for Special Surgery1, Cornell University2
01 Nov 1994-Journal of Clinical Epidemiology
TL;DR: The estimated relative risk of death from an increase of one in the comorbidity score proved approximately equal to that from an additional decade of age.

5,388 citations

Journal Article•DOI•
A global clinical measure of fitness and frailty in elderly people

[...]

Kenneth Rockwood1, Xiaowei Song, Chris MacKnight, Howard Bergman, David B. Hogan, Ian McDowell, Arnold Mitnitski •
Dalhousie University1
30 Aug 2005-Canadian Medical Association Journal
TL;DR: The ability of the Clinical Frailty Scale to predict death or need for institutional care, and correlated the results with those obtained from other established tools are determined.
Abstract: Background: There is no single generally accepted clinical definition of frailty. Previously developed tools to assess frailty that have been shown to be predictive of death or need for entry into an institutional facility have not gained acceptance among practising clinicians. We aimed to develop a tool that would be both predictive and easy to use. Methods: We developed the 7-point Clinical Frailty Scale and applied it and other established tools that measure frailty to 2305 elderly patients who participated in the second stage of the Canadian Study of Health and Aging (CSHA). We followed this cohort prospectively; after 5 years, we determined the ability of the Clinical Frailty Scale to predict death or need for institutional care, and correlated the results with those obtained from other established tools. Results: The CSHA Clinical Frailty Scale was highly correlated ( r = 0.80) with the Frailty Index. Each 1-category increment of our scale significantly increased the medium-term risks of death (21.2% within about 70 mo, 95% confidence interval [CI] 12.5%–30.6%) and entry into an institution (23.9%, 95% CI 8.8%–41.2%) in multivariable models that adjusted for age, sex and education. Analyses of receiver operating characteristic curves showed that our Clinical Frailty Scale performed better than measures of cognition, function or comorbidity in assessing risk for death (area under the curve 0.77 for 18-month and 0.70 for 70-month mortality). Interpretation: Frailty is a valid and clinically important construct that is recognizable by physicians. Clinical judgments about frailty can yield useful predictive information.

5,189 citations

Journal Article•DOI•
Dexamethasone in Hospitalized Patients with Covid-19

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Peter Horby, Wei Shen Lim, Jonathan Emberson, M Mafham, Jennifer L Bell, Louise Linsell, Natalie Staplin, Christopher E. Brightling, Andrew Ustianowski, E Elmahi, B Prudon, Christopher A Green, Timothy Felton, David Chadwick, K Rege, C Fegan, Lucy C Chappell, Saul N. Faust, Thomas Jaki, Katie Jeffery, Alan A Montgomery, Kathryn M Rowan, Edmund Juszczak, J K Baillie, Richard Haynes, Martin J Landray 
17 Jul 2020-The New England Journal of Medicine
TL;DR: In patients hospitalized with Covid-19, the use of dexamethasone resulted in lower 28-day mortality among those who were receiving either invasive mechanical ventilation or oxygen alone at randomization but not among those receiving no respiratory support.
Abstract: BackgroundCoronavirus disease 2019 (Covid-19) is associated with diffuse lung damage. Glucocorticoids may modulate inflammation-mediated lung injury and thereby reduce progression to respiratory failure and death.MethodsIn this controlled, open-label trial comparing a range of possible treatments in patients who were hospitalized with Covid-19, we randomly assigned patients to receive oral or intravenous dexamethasone (at a dose of 6 mg once daily) for up to 10 days or to receive usual care alone. The primary outcome was 28-day mortality. Here, we report the final results of this assessment.ResultsA total of 2104 patients were assigned to receive dexamethasone and 4321 to receive usual care. Overall, 482 patients (22.9%) in the dexamethasone group and 1110 patients (25.7%) in the usual care group died within 28 days after randomization (age-adjusted rate ratio, 0.83; 95% confidence interval [CI], 0.75 to 0.93; P<0.001). The proportional and absolute between-group differences in mortality varied considerably according to the level of respiratory support that the patients were receiving at the time of randomization. In the dexamethasone group, the incidence of death was lower than that in the usual care group among patients receiving invasive mechanical ventilation (29.3% vs. 41.4%; rate ratio, 0.64; 95% CI, 0.51 to 0.81) and among those receiving oxygen without invasive mechanical ventilation (23.3% vs. 26.2%; rate ratio, 0.82; 95% CI, 0.72 to 0.94) but not among those who were receiving no respiratory support at randomization (17.8% vs. 14.0%; rate ratio, 1.19; 95% CI, 0.92 to 1.55).ConclusionsIn patients hospitalized with Covid-19, the use of dexamethasone resulted in lower 28-day mortality among those who were receiving either invasive mechanical ventilation or oxygen alone at randomization but not among those receiving no respiratory support. (Funded by the Medical Research Council and National Institute for Health Research and others; RECOVERY ClinicalTrials.gov number, NCT04381936. opens in new tab; ISRCTN number, 50189673. opens in new tab.)

4,501 citations

Journal Article•DOI•
Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study.

[...]

Annemarie B Docherty1, Ewen M Harrison1, Christopher A Green2, Hayley E Hardwick3, Riinu Pius1, Lisa Norman1, Karl A Holden3, Jonathan M. Read4, Frank Dondelinger4, Gail Carson5, Laura Merson5, James A. Lee5, Daniel Plotkin5, Louise Sigfrid5, Sophie Halpin3, Clare Jackson3, Carrol Gamble3, Peter Horby5, Jonathan S. Nguyen-Van-Tam6, Antonia Ho7, Clark D Russell1, Jake Dunning8, Jake Dunning9, Peter J. M. Openshaw10, J Kenneth Baillie1, Malcolm G Semple3 •
University of Edinburgh1, University of Birmingham2, University of Liverpool3, Lancaster University4, University of Oxford5, University of Nottingham6, University of Glasgow7, Public Health England8, Imperial College London9, National Institutes of Health10
22 May 2020-BMJ
TL;DR: In study participants, mortality was high, independent risk factors were increasing age, male sex, and chronic comorbidity, including obesity, and the importance of pandemic preparedness and the need to maintain readiness to launch research studies in response to outbreaks is shown.
Abstract: Objective To characterise the clinical features of patients admitted to hospital with coronavirus disease 2019 (covid-19) in the United Kingdom during the growth phase of the first wave of this outbreak who were enrolled in the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study, and to explore risk factors associated with mortality in hospital. Design Prospective observational cohort study with rapid data gathering and near real time analysis. Setting 208 acute care hospitals in England, Wales, and Scotland between 6 February and 19 April 2020. A case report form developed by ISARIC and WHO was used to collect clinical data. A minimal follow-up time of two weeks (to 3 May 2020) allowed most patients to complete their hospital admission. Participants 20 133 hospital inpatients with covid-19. Main outcome measures Admission to critical care (high dependency unit or intensive care unit) and mortality in hospital. Results The median age of patients admitted to hospital with covid-19, or with a diagnosis of covid-19 made in hospital, was 73 years (interquartile range 58-82, range 0-104). More men were admitted than women (men 60%, n=12 068; women 40%, n=8065). The median duration of symptoms before admission was 4 days (interquartile range 1-8). The commonest comorbidities were chronic cardiac disease (31%, 5469/17 702), uncomplicated diabetes (21%, 3650/17 599), non-asthmatic chronic pulmonary disease (18%, 3128/17 634), and chronic kidney disease (16%, 2830/17 506); 23% (4161/18 525) had no reported major comorbidity. Overall, 41% (8199/20 133) of patients were discharged alive, 26% (5165/20 133) died, and 34% (6769/20 133) continued to receive care at the reporting date. 17% (3001/18 183) required admission to high dependency or intensive care units; of these, 28% (826/3001) were discharged alive, 32% (958/3001) died, and 41% (1217/3001) continued to receive care at the reporting date. Of those receiving mechanical ventilation, 17% (276/1658) were discharged alive, 37% (618/1658) died, and 46% (764/1658) remained in hospital. Increasing age, male sex, and comorbidities including chronic cardiac disease, non-asthmatic chronic pulmonary disease, chronic kidney disease, liver disease and obesity were associated with higher mortality in hospital. Conclusions ISARIC WHO CCP-UK is a large prospective cohort study of patients in hospital with covid-19. The study continues to enrol at the time of this report. In study participants, mortality was high, independent risk factors were increasing age, male sex, and chronic comorbidity, including obesity. This study has shown the importance of pandemic preparedness and the need to maintain readiness to launch research studies in response to outbreaks. Study registration ISRCTN66726260.

2,459 citations

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Frequently Asked Questions (20)
Q1. What are the contributions in "Increased risk of death in covid-19 hospital admissions during the second wave as compared to the first epidemic wave. a prospective dynamic cohort study in south london, uk" ?

It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. 

The strongest confounders of the association in this cohort were dexamethasone, oxygen 207 requirement, symptomatic at admission, CCI, and HFNO/CPAP. 

During the second wave, patients were more likely to lead an independent 165 life (1,101, 61.1%) or have some level of family assistance (369, 20.5%); intermediate levels of frailty (3 166 managing well, 4 vulnerable, 5 mildly frail) were also more prevalent in the second wave (1,103, 60.7%) than 167 in the first wave (392, 44.3%). 

The multivariable analysis attempted to account for all the available factors unequally distributed across 238 waves and also associated with mortality (while avoiding multicollinearity in the model). 

161 Covid-19 patients admitted during the second wave, were more likely to be younger, with patients aged 40 162 to 60 years being more prevalent in the second wave (495, 27.2%) and patients aged over 80 years being 163 more prevalent in the first wave (273, 30.9%). 

181 182 A total of 752 patients died over the total time at risk (40,777 person-days); 297 (33.6%) deaths occurred 183 during the first wave and 455 (25.3%) during the second wave. 

155Between 01 January 2020 and 31 March 2021, there were 3,376 covid-19 positive adult patients registered 156 at St. Georges Hospital. 

241 Dexamethasone therapy and oxygen requirement were strong confounders of the association of interest 242 and removing either variable from the model would cause a change in the direction of the main effect. 

In the subgroup analyses of covid-19 patients requiring ICU, the hazard of death during the second wave was 221 2.00 (95%CI 1.10, 3.62) after conditioning on age, sex, dexamethasone, Remdesivir, Tocilizumab, and 222 HFNO/CPAP. 

The use of HFNO/CPAP was more prevalent in the second wave (400, 176 22.2%) than during the first wave (81, 9.3%), whilst invasive ventilation was more prevalent in the first wave. 

Deaths in people from 403 Black, Asian and minority ethnic communities from both COVID-19 and non-COVID causes in the first weeks 404 of the pandemic in London: a hospital case note review. 

Absence of 171 respiratory or wider infective symptoms at onset (initial diagnosis through PCR) was more prevalent in the 172 second wave (361, 19.9%) compared to the first wave (50, 5.7%). 

The RMSE for the 146full model was used as reference for each step; and RMSE for each reduced model was calculated as √[〖〖147 (𝛽〗_(1 𝑟𝑒𝑑𝑢𝑐𝑒𝑑) − 𝛽_(1 𝑓𝑢𝑙𝑙))〗^2 +〖𝑆𝐸〗_𝑟𝑒𝑑𝑢𝑐𝑒𝑑^2][20]. 

oxygen 211 requirement is acting as partial positive confounder whereas dexamethasone is acting as a negative 212 confounder in this cohort. 

218 Dexamethasone reduced the hazard of death by 53% (95%CI 40%, 63%) when accounting for all the other 219 factors in the model. 

In this cohort, patients admitted during the second wave of the covid-19 pandemic, had a (crude) mortality 191 rate 25% lower than that of patients admitted during the first wave (IRR 0.75, 95%CI 0.64, 0.86). 

Admissions scoring 0-3 in CCI were more 169 prevalent during the second wave (891, 49.0% vs. 386, 43.7%); the reverse occurred for CCI scores 4-5 and 170 over 6 (29.3% and 27.0% respectively for first wave, vs. 25.3% and 25.7% for second wave). 

The crude mortality rate was 25% (95%CI 14%, 36%) 230 lower for those admitted during the second wave compared to those admitted during the first wave (IRR 231 0.75 95%CI 0.64, 0.86). 

The median probability of survival was 29 days (95%CI 30-41 days) for the first 189 wave, and 37 days (95%CI 32-47 days) for the second. 

Survival across the different waves was explored using time-to-event analysis and 132 log-rank to test the significance of the difference between the survival curves.