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\begin{document}

\title{Utilisation of tracheostomy in patients with COVID-19 in England:
patient characteristics, timing and outcomes}



\author[1]{Annakan Navaratnam}%
\author[2]{William Gray}%
\author[2]{Josh Wall}%
\author[3]{Arun Takhar}%
\author[4]{Taran Tatla}%
\author[2]{Jamie Day}%
\author[2]{Anna Batchelor}%
\author[2]{Michael Swart }%
\author[2]{Christopher Snowden }%
\author[5]{Andrew Marshall}%
\author[2]{Tim Briggs}%
\affil[1]{Royal National Throat Nose and Ear Hospital}%
\affil[2]{NHS England}%
\affil[3]{Guy's and St Thomas' NHS Foundation Trust}%
\affil[4]{Northwick Park Hospital}%
\affil[5]{Queen's Medical Centre}%


\vspace{-1em}



  
  \date{May 15, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Objectives: We aimed to characterise the use of tracheostomy procedures
for all COVID-19 critical care patients in England and to understand how
patient factors and timing of tracheostomy affected outcomes. Design: A
retrospective observational study using exploratory analysis of hospital
administrative data. Setting: All 500 National Health Service hospitals
in England. Participants: All hospitalised COVID-19 patients aged [?] 18
years in England between March 1st and October 31st, 2020 were included.
Main outcomes and measures: This was a retrospective exploratory
analysis using the Hospital Episode Statistics administrative dataset.
Multilevel modelling was used to explore the relationship between
demographic factors, comorbidity and use of tracheostomy and the
association between tracheostomy use, tracheostomy timing and the
outcomes. Results: In total, 2,200 hospitalised COVID-19 patients had a
tracheostomy. Tracheostomy utilisation varied substantially across the
study period, peaking in April-June 2020. In multivariable modelling,
for those admitted to critical care, tracheostomy was most common in
those aged 40-79 years, in males and in people of Black and Asian ethnic
groups and those with a history of cerebrovascular disease. In critical
care patients, tracheostomy was associated with lower odds of mortality
(OR: 0.514 (95\% CI 0.443 to 0.596), but greater length of stay (OR:
41.143 (95\% CI 30.979 to 54.642). In patients that survived, earlier
timing of tracheostomy ([?] 14 days post admission to critical care) was
significantly associated with shorter length of stay. Conclusions:
Tracheostomy is safe and advantageous for critical care COVID-19
patients. Early tracheostomy may be associated with better outcomes,
such as shorter length of stay, compared to late tracheostomy.%
\end{abstract}%



\sloppy


Title

Utilisation of tracheostomy in patients with COVID-19 in England:
patient characteristics, timing and outcomes

\textbf{Abstract}

\textbf{Objectives:} We aimed to characterise the use of tracheostomy
procedures for all COVID-19 critical care patients in England and to
understand how patient factors and timing of tracheostomy affected
outcomes.

\textbf{Design:} A retrospective observational study using exploratory
analysis of hospital administrative data.

\textbf{Setting:} All 500 National Health Service hospitals in England.

\textbf{Participants:} All hospitalised COVID-19 patients aged [?] 18
years in England between March 1\textsuperscript{st} and October
31\textsuperscript{st}, 2020 were included.

\textbf{Main outcomes and measures:} This was a retrospective
exploratory analysis using the Hospital Episode Statistics
administrative dataset. Multilevel modelling was used to explore the
relationship between demographic factors, comorbidity and use of
tracheostomy and the association between tracheostomy use, tracheostomy
timing and the outcomes.

\textbf{Results:} In total, 2,200 hospitalised COVID-19 patients had a
tracheostomy. Tracheostomy utilisation varied across the study period,
peaking in April-June 2020. In multivariable modelling, for those
admitted to critical care, tracheostomy was most common in those aged
40-79 years, in males and in people of Black and Asian ethnic groups and
those with a history of cerebrovascular disease. In critical care
patients, tracheostomy was associated with lower odds of mortality (OR:
0.514 (95\% CI 0.443 to 0.596), but greater length of stay (OR: 41.143
(95\% CI 30.979 to 54.642). In patients that survived, earlier timing of
tracheostomy ([?] 14 days post admission to critical care) was
significantly associated with shorter length of stay.

\textbf{Conclusions:} Tracheostomy is safe and advantageous for critical
care COVID-19 patients. Early tracheostomy may be associated with better
outcomes, such as shorter length of stay, compared to late tracheostomy.

\textbf{Key words:} tracheostomy, COVID-19, SARS-CoV2, intubation,
mechanical ventilation



\textbf{Key points}

\begin{itemize}
\tightlist
\item
  To our knowledge, our study is one of the largest published to date on
  the profile of COVID-19 tracheostomy patients.
\item
  There was increased utilisation of tracheostomy as the COVID-19
  pandemic progressed.
\item
  Tracheostomy was beneficial procedure for COVID-19 patients requiring
  ventilatory weaning.
\item
  Tracheostomy patients had lower odds of mortality and greater length
  of stay compared to non-tracheostomy critical care patients.
\item
  In patients that survived, earlier timing of tracheostomy was
  significantly associated with shorter length of stay.
\end{itemize}

\textbf{INTRODUCTION} Critical care admission is common in people
hospitalised with COVID-19.\textsuperscript{1} In the United Kingdom, it
has been reported that 17\% of COVID-19 hospital inpatients required
critical care support and 10\% required mechanical
ventilation.\textsuperscript{2} The management of these patients has
evolved as new evidence regarding treatment approaches has been
developed. However, an area of controversy that has remained is the
appropriate utilisation of tracheostomy and its subsequent management.

Early in the pandemic, guidelines were published based on expert opinion
before much was understood about the disease. Most of these guidelines
focused on minimising risk of nosocomial transmission to clinicians and
delaying or avoiding tracheostomies in these patients as the benefits of
the procedure were unknown.\textsuperscript{3,4} As further experience
was gained, tracheostomy use became common for critical care COVID-19
patients\textsuperscript{5} although optimal timing of tracheostomy
remains a subject of debate.\textsuperscript{6,7}

In England, reports of departments' experiences have been
described\textsuperscript{8--10} and a UK multi-centre prospective
cohort study, COVIDTrach has been evaluating outcomes of COVID-19
tracheostomy patients.\textsuperscript{11} However, capturing
information directly from hospitals can be constrained by inconsistent
reporting patterns, especially if individual hospitals are overwhelmed
by surges of critical care patients.

The National Health Service (NHS) Hospital Episodes Statistics (HES)
database is an administrative dataset that contains a wide range of
details regarding all NHS-funded hospital admissions in England. Using
HES data, the aim of this study was to characterise tracheostomy use for
COVID-19 critical care patients in England, understand the patient
factors associated with having a tracheostomy and determine how this
related to outcomes.

\textbf{METHODS}

\textbf{Ethics}

The analysis of data follows current NHS Digital guidance for the use of
HES data for research purposes. Reported data are anonymised to the
level required by ISB1523 Anonymisation Standard for Publishing Health
and Social Care Data.\textsuperscript{12}

\textbf{Study design and data collection}

This was a retrospective analysis of HES administrative data following
STROBE guidelines. HES data are collected by NHS Digital for all
NHS-funded patients admitted to hospitals in England.

\textbf{Timing, case ascertainment, inclusion and exclusion criteria}

We reviewed HES data for all completed episodes of hospital care in
England with a discharge date from 1\textsuperscript{st} March to
31\textsuperscript{st} October 2020 that involved a diagnosis of
COVID-19. Patients aged \textless{} 18 years were excluded. Cases of
COVID-19 were identified using the International Statistical
Classification of Disease and Related Health Problems
10\textsuperscript{th} edition (ICD-10) codes U071 and U072.

Critical care (high dependency unit (HDU) or intensive care unit (ICU))
admissions and those receiving advanced respiratory support were
identified.

\textbf{Outcomes}

Primary outcome: Tracheostomy use in critical care patients. Use of a
tracheostomy was recorded if the Office of Population Censuses and
Surveys Classification of Interventions and Procedures version 4
(OPCS-4) code E42- was used anywhere in the HES record of procedures.

Secondary outcomes: In-hospital mortality, length of hospital stay,
length of critical care stay and tracheostomy malfunction. In these
analyses, tracheostomy use was treated as the primary exposure variable.
Mortality was taken from the Office for National Statistics (ONS) date
linked to HES data at a patient level. An in-hospital death was recorded
if the date of death was the same as or +/- one day of the date of
hospital discharge recorded in HES. Tracheostomy malfunction was
recorded where the ICD-10 code J950 was used.

\textbf{Covariates}

Age: Categorised as 18-39 years, 40-49 years, 50-59 years, 60-69 years,
70-79 years and [?] 80 years for exploratory analysis (in line with
previous reports)\textsuperscript{13} and treated as continuous in the
final multivariate model.

Sex: Male or female.

Ethnicity: Coded in categories used by NHS Digital: White, South Asian
(Bangladeshi, Indian, Pakistani), Other Asian, Black, Mixed, Other, not
stated.

Deprivation: Recorded using the Index of Multiple Deprivation (IMD) for
the Lower Super Output Area (LSOA) of the patients' home address, with
scores categorised into quintiles based on national averages.

Comorbidities: These were the 14 comorbidities used to construct the
Charlson Comorbidity Index (\textbf{Table 1)} .\textsuperscript{14} The
comorbidity was deemed present if it was recorded in HES as a secondary
diagnosis in the index admission or as a primary or secondary diagnosis
in any admission during the previous year, in accordance with the
recommendations of Quan \emph{et al} .\textsuperscript{15}

Obesity: Recorded as present if the ICD-10 code E66 was used as a
diagnostic code during the admission.

Discharge date: Categorised into day or month of discharge (starting
from 1\textsuperscript{st} March) depending on the analysis undertaken.
Monthly data were used for descriptive statistics. Daily data were used
for the final multivariable model.

Admission date: Categorised into month of admission. Only admissions
from 1\textsuperscript{st} February to 31\textsuperscript{st} August
2020 were used, to avoid biasing the data at the end of the study period
due to many patients admitted in September and October still being in
hospital on 31\textsuperscript{st} October.

\textbf{Grouping variable}

NHS region: London, South-East, South-West, East of England, Midlands,
North-West and North-East \& Yorkshire.

\textbf{Data management and statistical analyses}

Data were extracted onto a secure encrypted server controlled by NHS
England and NHS Improvement. Analysis within this secure environment
took place using standard statistical software: Microsoft Excel
(Microsoft Corp, Redmond, WA, USA), Stata (StataCorp LLC, College
Station, TX, USA) and Alteryx (Alteryx Inc, Irvine, CA, USA).

In descriptive analysis, data were categorised as detailed above and
summarised in terms of frequency and percentage. Length of stay was
non-normally distributed, with a right-skew, and summarised using the
median and inter-quartile range (IQR).

To explore variables associated with having a tracheostomy and the
association between tracheostomy and in-hospital mortality and length of
stay greater than the median (8 days) in critical care patients, a
series of multilevel logistic regression models were fitted using
the\emph{melogit} command. Two-level intercept only models were used,
allowing adjustment for clustering of patients within hospital trusts.
Covariates were categorised as described above. Adjusted tracheostomy
and mortality rates were calculated using the \emph{margins} command in
Stata based on the conditional probability across the entire dataset.

For the outcomes time from tracheostomy to hospital discharge and
critical care discharge a multi-level linear model was fitted based on
the natural logarithm of the outcome using the \emph{mixed} command in
Stata. The main exposure variable of interest (timing of tracheostomy
post-critical care admission) was modelled as a binary variable with a
threshold of [?] 14 days with all covariates modelled as described
previously.

Missing data were uncommon. No attempt was made to impute missing
values. Where data were missing the numbers involved are given.

\textbf{RESULTS}

The data extraction process yielded a dataset of 132,446 unique patients
who had a diagnosis of COVID-19 either on admission or during their
stay. Of these 13,401 (10.1\%) were admitted to critical care as part of
a hospital stay. Of those admitted to critical care, 7,993 (59.6\%) had
advanced respiratory support and 2,200 (16.4\%) had a tracheostomy
procedure recorded.

Tracheostomy use in critical care patients changed markedly over time.
Use of tracheostomy and associated mortality rates for month of
discharge are presented in Figure 1, tracheostomy use for each region
over time by month of discharge are presented in Figure 2. By discharge
date, tracheostomy rates were very low for patients discharged in March
(2.6\%), peaked for discharges in June (36.7\%) and declined thereafter
to 4.1\% in October. This trend was seen across all regions. Mortality
rates in those with a tracheostomy revealed a mirror image trend, with
the lowest death rates seen at times of highest tracheostomy use. When
plotted by admission month the pattern is similar, but with an earlier
and smaller peak in the proportion of patients with a tracheostomy
(11.9\% in February, 20.5\% in April). However, there was a similar
decline in the proportionate use of tracheostomy in late summer 2020
(3.8\% in August).

The profile of those admitted to hospital, those admitted to critical
care and those who had advanced respiratory support and a tracheostomy
is summarised in \textbf{Table 1} . Those admitted to critical care were
more likely to be aged 40-69 years and less likely to be aged 70 years
and over than the general hospitalised population. They were also more
likely to be male and from a non-White ethnic background. The
deprivation profile of those admitted to critical care reflected the
wider population. Obese patients were over-represented in those admitted
to critical care and patients with dementia, cardiovascular disease,
renal disease and cancer were under-represented. The profile of those
who were recorded as having a tracheostomy was similar to the wider
critical care population, although there was a smaller percentage of
people aged 70 years and over with a tracheostomy.

Factors associated with having a tracheostomy for those admitted to
critical care were explored using multilevel logistic regression and the
results are presented in \textbf{Table 2} . Compared to the 18-39 years
age group, tracheostomy was significantly more common in the 40-79 years
age group and significantly less common in the 80 years and over age
group. Tracheostomy was significantly more common in males, in Asian and
Black ethnic groups and in patients with cerebrovascular disease.
Tracheostomy was less common in patients with peripheral vascular
disease, chronic heart failure, acute myocardial infarction, connective
tissue/rheumatic disease, moderate/severe liver disease, renal disease
and cancer.

In patients admitted to critical care, outcomes for those with and
without a tracheostomy are presented in \textbf{Table 3} with the
adjusted association of tracheostomy with each outcome. Tracheostomy was
significantly associated with reduced odds of in-hospital mortality and
increased odds of length of stay greater than the median after adjusting
for covariates.

The time from hospital admission to critical care admission was the same
for both those who survived to discharge and those who died during their
stay: median 1 day (IQR 0 to 3). Of those with a tracheostomy, 120
(5.5\%) had tracheostomy malfunction recorded during their stay. Of
those with a malfunction recorded, 36 died in hospital (30.0\%) and in
the 2080 without a malfunction 437 (21.0\%) died in hospital.

Of the 2,200 patients with a tracheostomy, data on time from critical
care admission to tracheostomy were available for 1,777 (80.8\%)
patients. Data on the timing of tracheostomy from critical care
admission are presented in \textbf{Table 4} for those who died and those
who survived to discharge. Patients who underwent a tracheostomy at [?] 14
days from critical care admission and survived to discharge had a
shorter hospital and critical care stay both overall and
post-tracheostomy. Undergoing a tracheostomy [?] 14 days from critical
care admission was associated with significantly shorter time from
tracheostomy to critical care discharge (\selectlanguage{greek}β \selectlanguage{english}= -0.100 (95\% CI -0.170 to
-0.031) and hospital discharge (\selectlanguage{greek}β \selectlanguage{english}= -0.061 (95\% CI -0.115 to -0.007).

\textbf{Discussion}

Our study is one of the largest published to date on the profile of
COVID-19 tracheostomy patients. Approximately one in six COVID-19
critical care patients had a tracheostomy. In critical care patients,
tracheostomy was associated with half the odds of mortality compared
with critical care patients without tracheostomy but was associated with
much longer stay.

\emph{Tracheostomy is a beneficial procedure for COVID-19 patients
requiring ventilatory weaning.}

In our study having a tracheostomy was associated with lower in-hospital
mortality after adjustment for covariates. Patients selected for
tracheostomy will have been expected to meet specific criteria in terms
of lower ventilatory requirements and potential for rehabilitation.
Therefore, this finding is caveated as patient selection will have
influenced it. Patients selected for tracheostomy insertion will be
expected to survive and consequently there will be an inherent bias in
comparing outcomes of patients with and without a tracheostomy. This is
demonstrated by reduced odds of COVID-19 patients with multiple
co-morbidities having a tracheostomy in our cohort (Table 2). However,
despite early scepticism for its role in critically ill COVID-19
patients, our study adds further evidence that tracheostomy is a
beneficial intervention for COVID-19 patients requiring ventilatory
weaning. \textsuperscript{16}

\emph{Timing of tracheostomy}

Our data demonstrated that patients that underwent early tracheostomy
had shorter subsequent lengths of hospital stay. There are a variety of
clinical factors including disease severity, medical therapy and
emerging evidence about viral load and infectivity, which will have
influenced decisions regarding timing of tracheostomy. The distinct
categorisation of patients into early and late tracheostomies, even when
outcomes are adjusted for patient factors, may be too simplistic.
However, our data suggests that there is no need to delay tracheostomy
insertion in the expectation that this will improve patient outcomes.
Indeed, there is evidence that delaying tracheostomy insertion may lead
to long-term complications such as tracheal
stenosis.\textsuperscript{17}

\emph{Increased utilisation of tracheostomy as the pandemic progressed.}

We found that the proportion of critical care patients having a
tracheostomy significantly increased during the early part of the
pandemic. Anecdotally, during February-March 2020 there was some
hesitation in performing tracheostomies for COVID-19 patients owing to
the uncertainly regarding clinical prognosis and concerns for healthcare
worker safety when performing an aerosol generating procedure. There
were low rates of tracheostomies internationally during this period with
early reports from the USA stating that only 8\% (17/203) of patients
had a tracheostomy in a multicentre cohort from March
2020.\textsuperscript{18}

Tracheostomy use rapidly increased in April-June following the peak of
critical care admissions during the first wave in England. This increase
in tracheostomy utilisation reflects rapidly changing critical care
practice as understanding of the disease improved and patients survived
longer whilst intubated and ventilated. Furthermore, outcomes for
tracheostomy patients improved as procedure numbers increased over time.
It is possible that a combination of improved overall critical care
management with new information and better patient selection for
tracheostomy contributed to improved outcomes during early summer 2020.

The differences when plotting the data by discharge and admission month
appear to be driven by longer stay for patients undergoing tracheostomy
compared to non-tracheostomy patients, with the peak in tracheostomy
patient discharges in June 2020. Nevertheless, the same broad trend is
evident when plotting the data by either method.

\emph{Strengths and limitations}

HES data covers all NHS-funded hospital activity in England. As such
they are the most complete and detailed record of hospital activity in
England related to COVID-19. The eight-month study period allowed us to
look at temporal trends over an extended period.

However, as with any administrative dataset, there are limitations in
using HES data. HES relies on individual hospital trusts compiling data
accurately and in a consistent manner. As such, some patients who
underwent a tracheostomy may not have been coded as such and so number
of tracheostomies reported here is likely to be an under-estimate.
Furthermore, tracheostomy complications are likely to be underreported
due to coding limitations.\textsuperscript{19} However, we have no
reason to suspect that our data are systematically biased in terms of
temporal trends or the profile of patients receiving tracheostomy.

\emph{Conclusions}

To the best of our knowledge, we present the largest published study of
COVID-19 tracheostomy patients. Our findings of better adjusted
in-hospital mortality rates for COVID-19 critical care patients that had
tracheostomies compared to patients without, provides evidence that the
procedure is safe and advantageous for this cohort of patients. Data on
tracheostomy use and outcomes for patients with a tracheostomy will
assist hospitals prepare for further waves of COVID-19 patients in the
future.

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12 NHS Digital (2013) \emph{Information Standard Board for Health and
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\textbf{Table 1: Profile of patients admitted to hospital, admitted to
critical care and undergoing a tracheostomy}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Variable} & \textbf{Hospital admissions (n = 132446)} &
\textbf{Critical care admissions (n = 13401)} & \textbf{Advanced
respiratory support (n = 7993)} & \textbf{Tracheostomy (n =
2200)}\tabularnewline
\midrule
\endhead
\textbf{Age band (years)} & & & &\tabularnewline
18-39 & 11943 (9.0\%) & 1143 (8.5\%) & 593 (7.4\%) & 137
(6.2\%)\tabularnewline
40-49 & 10569 (8.0\%) & 1733 (12.9\%) & 1089 (13.6\%) & 353
(16.0\%)\tabularnewline
50-59 & 17485 (13.2\%) & 3333 (24.9\%) & 2175 (27.2\%) & 676
(30.7\%)\tabularnewline
60-69 & 20141 (15.2\%) & 3766 (28.1\%) & 2499 (31.3\%) & 697
(31.7\%)\tabularnewline
70-79 & 27951 (21.1\%) & 2684 (20.0\%) & 1467 (18.4\%) & 322
(14.6\%)\tabularnewline
[?] 80 & 44357 (33.5\%) & 742 (5.5\%) & 170 (2.1\%) & 15
(0.7\%)\tabularnewline
\textbf{Sex} & \textbf{Sex} & & &\tabularnewline
Female & 60139 (45.5\%) & 4272 (32.2\%) & 2276 (29.0\%) & 634
(29.3\%)\tabularnewline
Male & 72081 (54.5\%) & 8980 (67.8\%) & 5574 (71.0\%) & 1528
(70.7\%)\tabularnewline
\emph{Missing} & \emph{226} & \emph{149} & \emph{143} &
\emph{38}\tabularnewline
\textbf{Deprivation quintile} & \textbf{Deprivation quintile} & &
&\tabularnewline
1 (most deprived) & 35456 (27.4\%) & 3675 (28.0\%) & 2166 (27.8\%) & 602
(27.9\%)\tabularnewline
2 & 28758 (22.2\%) & 3042 (23.2\%) & 1887 (24.2\%) & 515
(23.9\%)\tabularnewline
3 & 24179 (18.7\%) & 2485 (19.0\%) & 1507 (19.3\%) & 436
(20.2\%)\tabularnewline
4 & 21750 (16.8\%) & 2008 (15.3\%) & 1165 (14.9\%) & 327
(15.2\%)\tabularnewline
5 (least deprived) & 19314 (14.9\%) & 1894 (14.5\%) & 1068 (13.7\%) &
278 (12.9\%)\tabularnewline
\emph{Missing} & \emph{2989} & \emph{297} & \emph{200} &
\emph{42}\tabularnewline
\textbf{Ethnicity} & \textbf{Ethnicity} & & &\tabularnewline
White & 94825 (80.6\%) & 7579 (67.5\%) & 4136 (63.6\%) & 1113
(62.3\%)\tabularnewline
South Asian or South Asian British & 8497 (7.2\%) & 1234 (11.0\%) & 732
(11.3\%) & 194 (10.9\%)\tabularnewline
Other Asian or other Asian British & 3155 (2.7\%) & 614 (5.5\%) & 421
(6.5\%) & 130 (7.3\%)\tabularnewline
Black or Black British & 6186 (5.3\%) & 959 (8.5\%) & 669 (10.3\%) & 177
(9.9\%)\tabularnewline
Mixed & 1031 (0.9\%) & 158 (1.4\%) & 99 (1.5\%) & 32
(1.8\%)\tabularnewline
Other ethnic groups & 3945 (3.4\%) & 682 (6.1\%) & 445 (6.8\%) & 140
(7.8\%)\tabularnewline
\emph{Missing} & \emph{14807} & \emph{2175} & \emph{1491} &
\emph{414}\tabularnewline
\textbf{Charlson Comorbidity Index items*} & \textbf{Charlson
Comorbidity Index items*} & & &\tabularnewline
Peripheral vascular disease & 6930 (5.2\%) & 560 (4.2\%) & 291 (3.6\%) &
73 (3.3\%)\tabularnewline
Congestive heart failure & 18970 (14.3\%) & 1405 (10.5\%) & 771 (9.6\%)
& 149 (6.8\%)\tabularnewline
Acute myocardial infarction & 12152 (9.2\%) & 990 (7.4\%) & 540 (6.8\%)
& 99 (4.5\%)\tabularnewline
Cerebrovascular disease & 12239 (9.2\%) & 736 (5.5\%) & 538 (6.7\%) &
210 (9.5\%)\tabularnewline
Dementia & 18931 (14.3\%) & 109 (0.8\%) & 32 (0.4\%) & 10
(0.5\%)\tabularnewline
Chronic pulmonary disease & 34889 (26.3\%) & 3132 (23.4\%) & 1693
(21.2\%) & 480 (21.8\%)\tabularnewline
Connective tissue disease/rheumatic disease & 3933 (3.0\%) & 322 (2.4\%)
& 180 (2.3\%) & 35 (1.6\%)\tabularnewline
Peptic ulcer & 896 (0.7\%) & 97 (0.7\%) & 64 (0.8\%) & 19
(0.9\%)\tabularnewline
Mild liver disease & 4446 (3.4\%) & 551 (4.1\%) & 325 (4.1\%) & 101
(4.6\%)\tabularnewline
Moderate or severe liver disease & 1467 (1.1\%) & 259 (1.9\%) & 190
(2.4\%) & 34 (1.5\%)\tabularnewline
Diabetes without chronic complications & 32078 (24.2\%) & 3762 (28.1\%)
& 2247 (28.1\%) & 591 (26.9\%)\tabularnewline
Diabetes with chronic complications & 3951 (3.0\%) & 375 (2.8\%) & 208
(2.6\%) & 51 (2.3\%)\tabularnewline
Paraplegia and hemiplegia & 3112 (2.3\%) & 189 (1.4\%) & 125 (1.6\%) &
46 (2.1\%)\tabularnewline
Renal disease & 23821 (18.0\%) & 1596 (11.9\%) & 856 (10.7\%) & 196
(8.9\%)\tabularnewline
Primary cancer & 7459 (5.6\%) & 517 (3.9\%) & 248 (3.1\%) & 53
(2.4\%)\tabularnewline
Metastatic carcinoma & 4076 (3.1\%) & 151 (1.1\%) & 58 (0.7\%) & 6
(0.3\%)\tabularnewline
HIV/AIDS & 197 (0.1\%) & 22 (0.2\%) & 15 (0.2\%) & 3
(0.1\%)\tabularnewline
Obesity & 12167 (9.2\%) & 2563 (19.1\%) & 1564 (19.6\%) & 397
(18.0\%)\tabularnewline
\bottomrule
\end{longtable}

\textbf{*} For the Charlson Comorbidity Index items: Only those with the
disease are listed. There were no missing data. Individual patients can
appear in multiple disease categories.

\textbf{Table 2: Multilevel logistic regression models of factors
associated with critical care patients with a tracheostomy}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Variable} & \textbf{Odds ratios (95\% CIs)}\tabularnewline
\midrule
\endhead
\textbf{Age band (years)} &\tabularnewline
18-39 (reference) & 1 (reference)\tabularnewline
40-49 & \textbf{2.118 (1.605 to 2.795)}\tabularnewline
50-59 & \textbf{2.375 (1.837 to 3.071)}\tabularnewline
60-69 & \textbf{2.193 (1.693 to 2.840)}\tabularnewline
70-79 & \textbf{1.590 (1.200 to 2.107)}\tabularnewline
[?] 80 & \textbf{0.249 (0.134 to 0.464)}\tabularnewline
\textbf{Sex} &\tabularnewline
Female & 1 (reference)\tabularnewline
Male & \textbf{1.222 (1.073 to 1.392)}\tabularnewline
\textbf{Deprivation quintile} & \textbf{Deprivation
quintile}\tabularnewline
5 (least deprived) & 1 (reference)\tabularnewline
4 & 1.040 (0.830 to 1.304)\tabularnewline
3 & 1.021 (0.822 to 1.269)\tabularnewline
2 & 0.954 (0.771 to 1.180)\tabularnewline
1 (most deprived) & 0.857 (0.691 to 1.062)\tabularnewline
\textbf{Ethnicity} &\tabularnewline
White & 1 (reference)\tabularnewline
South Asian & \textbf{1.328 (1.071 to 1.647)}\tabularnewline
Other Asian & \textbf{1.418 (1.102 to 1.824)}\tabularnewline
Black & \textbf{1.279 (1.018 to 1.607)}\tabularnewline
Mixed & 1.469 (0.931 to 2.317)\tabularnewline
Other & 1.258 (0.986 to 1.605)\tabularnewline
\textbf{Discharge month} &\tabularnewline
March & 1 (reference)\tabularnewline
April & \textbf{3.356 (2.020 to 5.574)}\tabularnewline
May & \textbf{19.067 (11.502 to 31.608)}\tabularnewline
June & \textbf{31.394 (18.734 to 52.609)}\tabularnewline
July & \textbf{23.842 (13.927 to 40.816)}\tabularnewline
August & \textbf{14.988 (8.190 to 27.428)}\tabularnewline
September & \textbf{5.200 (2.764 to 9.783)}\tabularnewline
October & \textbf{1.863 (1.033 to 3.362)}\tabularnewline
\textbf{Charlson Comorbidity Index items*} &\tabularnewline
Peripheral vascular disease & \textbf{0.723 (0.524 to
0.997)}\tabularnewline
Congestive heart failure & \textbf{0.498 (0.393 to
0.631)}\tabularnewline
Acute myocardial infarction & \textbf{0.581 (0.444 to
0.759)}\tabularnewline
Cerebrovascular disease & \textbf{1.946 (1.539 to 2.462)}\tabularnewline
Dementia & 0.657 (0.294 to 1.471)\tabularnewline
Chronic pulmonary disease & 1.031 (0.894 to 1.189)\tabularnewline
Connective tissue disease/rheumatic disease & \textbf{0.636 (0.413 to
0.978)}\tabularnewline
Peptic ulcer & 1.214 (0.651 to 2.262)\tabularnewline
Mild liver disease & 0.861 (0.648 to 1.144)\tabularnewline
Moderate or severe liver disease & \textbf{0.606 (0.374 to
0.980)}\tabularnewline
Diabetes without chronic complications & 0.876 (0.762 to
1.005)\tabularnewline
Diabetes with chronic complications & 0.795 (0.550 to
1.150)\tabularnewline
Paraplegia and hemiplegia & 0.973 (0.778 to 1.217)\tabularnewline
Renal disease & \textbf{0.839 (0.755 to 0.933)}\tabularnewline
Primary cancer & \textbf{0.495 (0.348 to 0.705)}\tabularnewline
Metastatic carcinoma & \textbf{0.141 (0.055 to 0.363)}\tabularnewline
Obesity & 0.923 (0.788 to 1.081)\tabularnewline
\bottomrule
\end{longtable}

Models are based on data for 10,935 patients with no missing
data.\textbf{*} For the Charlson Comorbidity Index items: Only those
with the disease are listed. Individual patients can appear in multiple
disease categories. HIV/AIDS was not included in the model due to the
small number of patients with this condition receiving tracheostomy.



\textbf{Table 3: Outcomes for those with and without a tracheostomy
within the critical care population}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
& \textbf{Tracheostomy} & \textbf{No tracheostomy} & \textbf{Adjusted
association}\tabularnewline
\midrule
\endhead
In-hospital mortality (\%) & 473 (21.5\%) & 4718 (42.1\%) & \textbf{Odds
ratio: 0.514 (95\% CI 0.443 to 0.596)}\tabularnewline
Median length of critical care stay (IQR) & 31 (22 to 43) & 6 (3 to 12)
& \textbf{Odds ratio for length of stay \textgreater{} 8 days (median):
41.143 (95\% CI 30.979 to 54.642)}\tabularnewline
\bottomrule
\end{longtable}

CI = confidence interval.



\textbf{Table 4: Timing of tracheostomy for those who survived to
discharge and those who died}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
& \textbf{Survived to discharge (n = 1,421)} & \textbf{Survived to
discharge (n = 1,421)} & \textbf{Died in hospital (n = 356)} &
\textbf{Died in hospital (n = 356)}\tabularnewline
\midrule
\endhead
& Tracheostomy [?] 14 days post admission to critical care (n = 657) &
Tracheostomy \textgreater{} 14 days post admission to critical care (n =
764) & Tracheostomy \textless{} 14 days post admission to critical care
(n = 194) & Tracheostomy [?] 14 days post admission to critical care (n =
162)\tabularnewline
Median days hospital stay (IQR) & 37 (27 to 52) & 54 (41 to 70) & 20 (16
to 27) & 31 (25 to 41)\tabularnewline
Median days critical care stay (IQR) & 28 (22 to 38) & 40 (32 to 51) &
20 (16 to 25) & 31 (25 to 40)\tabularnewline
Median days from tracheostomy to critical care discharge/death (IQR) &
15.5 (10 to 26) & 18 (12 to 27) & 9 (5 to 14.75) & 9 (4 to
15)\tabularnewline
Median days from tracheostomy to discharge/death (IQR) & 27 (18 to 41) &
33 (23 to 48) & 10 (6 to 15) & 9 (4 to 17)\tabularnewline
\bottomrule
\end{longtable}

IQR = inter-quartile range

\textbf{Figure 1: Adjusted percentage tracheostomy use in critical care
patients and adjusted mortality rate in those with a tracheostomy by
month of discharge}

\textbf{Figure 2: Percentage tracheostomy use in critical care patients
over time by region}

\textbf{Hosted file}

\verb`Figure 1.docx`  available at \url{https://authorea.com/users/335032/articles/548454-utilisation-of-tracheostomy-in-patients-with-covid-19-in-england-patient-characteristics-timing-and-outcomes}

\textbf{Hosted file}

\verb`Figure 2.docx`  available at \url{https://authorea.com/users/335032/articles/548454-utilisation-of-tracheostomy-in-patients-with-covid-19-in-england-patient-characteristics-timing-and-outcomes}

\selectlanguage{english}
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\end{document}

