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The variability of critical care bed numbers in Europe

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  • French Institute of Health and Medical Research U1136

Abstract and Figures

To quantify the numbers of critical care beds in Europe and to understand the differences in these numbers between countries when corrected for population size and gross domestic product. Prospective data collection of critical care bed numbers for each country in Europe from July 2010 to July 2011. Sources were identified in each country that could provide data on numbers of critical care beds (intensive care and intermediate care). These data were then cross-referenced with data from international databases describing population size and age, gross domestic product (GDP), expenditure on healthcare and numbers of acute care beds. We identified 2,068,892 acute care beds and 73,585 (2.8 %) critical care beds. Due to the heterogeneous descriptions of these beds in the individual countries it was not possible to discriminate between intensive care and intermediate care in most cases. On average there were 11.5 critical care beds per 100,000 head of population, with marked differences between countries (Germany 29.2, Portugal 4.2). The numbers of critical care beds per country corrected for population size were positively correlated with GDP (r (2) = 0.16, p = 0.05), numbers of acute care beds corrected for population (r (2) = 0.12, p = 0.05) and the percentage of acute care beds designated as critical care (r (2) = 0.59, p < 0.0001). They were not correlated with the proportion of GDP expended on healthcare. Critical care bed numbers vary considerably between countries in Europe. Better understanding of these numbers should facilitate improved planning for critical care capacity and utilization in the future.
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A. Rhodes
P. Ferdinande
H. Flaatten
B. Guidet
P. G. Metnitz
R. P. Moreno
The variability of critical care bed numbers
in Europe
Received: 9 March 2012
Accepted: 14 June 2012
ÓCopyright jointly held by Springer and
ESICM 2012
Electronic supplementary material
The online version of this article
(doi:10.1007/s00134-012-2627-8) contains
supplementary material, which is available
to authorized users.
A. Rhodes ())
Department of Intensive Care Medicine,
St George’s Healthcare NHS Trust
and University of London,
London SW17 0QT, UK
e-mail: andyr@sgul.ac.uk;
andrewrhodes@nhs.net
Tel.: ?44-208-7255699
P. Ferdinande
Surgical and Transplantation ICU,
University Hospital Gasthuisberg,
Leuven, Belgium
e-mail: patrick.ferdinande@uzleuven.be
H. Flaatten
ICU, Haukeland University Hospital,
Bergen, Norway
e-mail: hans.flaatten@kir.uib.no
B. Guidet
Assistance Publique-Ho
ˆpitaux de Paris,
Ho
ˆpital Saint-Antoine,
service de re
´animation me
´dicale,
Paris 75012, France
e-mail: bertrand.guidet@sat.aphp.fr
B. Guidet
Inserm, Unite
´de Recherche en
E
´pide
´miologie Syste
`mes d’Information et
Mode
´lisation (U707), Paris 75012, France
P. G. Metnitz
Department of Anesthesia and General
Intensive Care, AKH Wien, Medical
University of Vienna, Vienna, Austria
e-mail: philipp@metnitz.biz
R. P. Moreno
Unidade de Cuidados Intensivos
Polivalente, Hospital de St. Anto
´nio dos
Capuchos, Centro Hospitalar de Lisboa
Central, E.P.E., Lisbon, Portugal
e-mail: r.moreno@mail.telepac.pt
Abstract Purpose: To quantify
the numbers of critical care beds in
Europe and to understand the differ-
ences in these numbers between
countries when corrected for popula-
tion size and gross domestic product.
Methods: Prospective data collec-
tion of critical care bed numbers for
each country in Europe from July
2010 to July 2011. Sources were
identified in each country that could
provide data on numbers of critical
care beds (intensive care and inter-
mediate care). These data were then
cross-referenced with data from
international databases describing
population size and age, gross
domestic product (GDP), expenditure
on healthcare and numbers of acute
care beds. Results: We identified
2,068,892 acute care beds and 73,585
(2.8 %) critical care beds. Due to the
heterogeneous descriptions of these
beds in the individual countries it was
not possible to discriminate between
intensive care and intermediate care
in most cases. On average there were
11.5 critical care beds per 100,000
head of population, with marked dif-
ferences between countries (Germany
29.2, Portugal 4.2). The numbers of
critical care beds per country cor-
rected for population size were
positively correlated with GDP
(r
2
=0.16, p=0.05), numbers of
acute care beds corrected for popula-
tion (r
2
=0.12, p=0.05) and the
percentage of acute care beds desig-
nated as critical care (r
2
=0.59,
p\0.0001). They were not corre-
lated with the proportion of GDP
expended on healthcare. Conclu-
sions: Critical care bed numbers
vary considerably between countries
in Europe. Better understanding of
these numbers should facilitate
improved planning for critical care
capacity and utilization in the future.
Keywords Intensive care
Bed numbers Demand Supply
Intensive Care Med
DOI 10.1007/s00134-012-2627-8 ORIGINAL
Introduction
The need for critical care capacity worldwide is increas-
ing [1]. This has been described in the USA, where it is
recognized that future provision of critical care is unlikely
to be able to meet the estimated demands [2]. This
potential shortfall has also been described in other
countries, such as Norway, as a result of changes in
population demographics [3]. Similar patterns are being
described in many other countries, although most have
been unable to accurately quantify the problem. The
future increase in demand is due to a number of factors
that include significant changes in the size and age of the
population, together with increasing prevalence of rele-
vant comorbidities and changing perceptions as to what
critical care can offer [4,5].
The identification of mechanisms to prevent this
mismatch developing needs to take place with some
urgency. Several factors have to be taken into account, all
of which interact with each other at a variety of levels.
Unless admission and referral practices change, the
increased future demand can only be met by an increase
in total capacity [2,6]. Without an increase in capacity
there will need to be rationing or triaging of available
resource to ensure that patients who are most likely to
benefit can receive the care they need [7]. Although part
of this change may be met by increased provision of
outreach and intermediate care [8], there will also need to
be an increase in the number of critical care beds and
hence also an increase in the numbers of appropriately
skilled healthcare professionals to care for the increased
number of patients.
To plan for these changes there is a need to better
understand the current situation of critical care bed
availability [4,911]. Although several countries publish
the numbers of beds provided, little is known about how
this varies between countries even within a confined
geographical region such as Europe. This study therefore
aims to identify the total numbers of critical care beds for
each country in Europe and to adjust the bed numbers to
the population in order to illustrate the differences in
resource provided for this group of patients.
Materials and methods
This was an observational study assessing the numbers of
adult critical care beds in each country in Europe between
July 2010 and July 2011. For the purposes of this study
critical care includes intensive care (ICU) and interme-
diate care beds (IMCU). To be included in this study, the
bed had to be open, staffed and fulfil any relevant national
criteria, where available. The following were excluded
from the data collection: private healthcare providers,
neonatal and paediatric intensive care beds, coronary
care, stroke and pure renal units. The numbers of beds in
each country were obtained by assessing data from reli-
able governmental sources (websites and contacts),
national societies with a declared interest in intensive care
medicine, national training boards, faculties or colleges
and national registries where appropriate. Data obtained
were then cross-referenced with the national council
representative for the European Society of Intensive Care
Medicine (ESICM) and other personal contacts with
knowledge of their country, in order to provide face
validity for the numbers obtained. In countries where,
following this approach, data were still not forthcoming,
personal contacts were used and numbers were estimated
according to a local sample assessment.
Data describing the total population of each country
were identified from a series of publicly available dat-
abases. These included the European Commission
database (Eurostat) (ec.europa.eu/Eurostat), the World
Health Organization (WHO) regional office for Europe,
the Central Intelligence Agency (CIA) World Factbook
(https://www.cia.gov/library/publications/the-world-factbook/)
and the Organisation for Economic Co-operation and Devel-
opment (OECD). Data were analysed using Graphpad Prism
(version 5.1a) and are presented as numbers with a percent-
age. Linear regression analysis was performed in order to
assess likely associations. A pvalue of less than 0.05 was
taken to be significant.
Results
In many countries, readily accessible data with regards to
the provision of critical care bed numbers were not
available. In some countries, for instance the UK, there
were governmental census data. In other countries, data
were available through national societies (for instance,
Germany). In others no data were found, and local cli-
nicians had to count the beds themselves (Portugal).
There were marked differences in how critical care ser-
vices were set up between countries, with some having
separate intermediate and intensive care (Table ESM 1),
whilst others manage both flexibly within single services.
Some countries also included higher levels of care in
acute general wards, for instance the Czech Republic.
We identified a total of 2,068,892 acute care hospital
beds in Europe, with marked differences in total numbers
of beds and also in the numbers of beds corrected per
100,000 of population between countries (Table 1). On
average there were 409 acute care beds per 100,000 head
of population. A total of 73,585 critical care beds were
identified in Europe. This equates to an average of 11.5
beds per 100,000 head of population for Europe as a
whole. The country with the highest number of beds was
Germany (23,890), and the country with the least number
of beds was Andorra (6). When the total numbers of beds
per country were corrected for the size of the population,
the differences were less marked although still present
(Table 2). Germany still remained the country with the
highest number of beds (29.2/100,000), whereas Portugal
had the lowest (4.2/100,000) (Fig. 1).
The total numbers of critical care beds per country
corrected for population size were positively correlated to
population size (r
2
=0.69, p\0.0001) but only weakly
related to the country’s gross domestic product (GDP) (in
millions of US dollars) (r
2
=0.16, p=0.05), the pro-
portion of GDP expended on healthcare (r
2
\0.0001,
p=0.91) (Fig. 2) or the proportion of elderly patients in
the population (r
2
=0.04, p=0.31).
On average there are 2.8 critical care beds for every
100 acute care beds across Europe. This again hides a
marked variation, however, with Germany and Luxem-
bourg having the highest percentage at 5.1/100,000 and
the Czech Republic the lowest (1.3/100,000) (Table 2).
The numbers of critical care beds were correlated with the
numbers of acute care beds corrected for population size
(r
2
=0.12, p=0.05) (Fig. 3) and also the percentage of
acute care beds as compared with critical care (r
2
=0.59,
p\0.0001) (Figure ESM 1).
Discussion
In this work we found marked heterogeneity in the
numbers of critical care beds between European coun-
tries, even when corrected for population size and age
distribution, gross domestic product, expenditure on
healthcare and numbers of total acute care beds. The
differences in provision can be exemplified by the fact the
Germany has 6.9 times the number of intensive care beds
compared with Portugal per head of population.
The artificial split of critical care beds into either
intermediate or intensive care varies widely across Europe
despite the move to standardize descriptions across the
region [12,13]. This lack of a consistent definition
reduces our ability to compare clinical practice and
organizational models across borders and therefore will
Table 1 Descriptors of population size, economic strength and health expenditure in European countries
Total size of
population
Gross domestic
product (GDP)
($millions)
b
Gross domestic
product (GDP)
per inhabitant ($)
b
Total expenditure
on health as a
% of GDP
c
% of population
over 65 years
of age
a
Andorra 84,082 2,893 34,407 7.7 13.0
Austria 8,404,252 377,382 44,904 8.6 18.2
Belgium 11,007,020 467,779 42,498 8.2 18.0
Bulgaria 7,504,868 47,702 6,356 4.4 18.2
Croatia 4,425,747 60,834 13,745 7.8 16.9
Cyprus 804,435 23,174 28,808 6.0 10.4
Czech Republic 10,532,770 192,030 18,232 6.9 16.3
Denmark 5,560,628 309,866 55,725 9.8 17.1
Estonia 1,340,194 19,253 14,366 5.3 17.7
Finland 5,375,276 239,177 44,496 6.8 17.8
France 65,075,310 2,562,742 39,381 9.2 16.8
Germany 81,748,892 3,286,451 40,202 8.9 20.6
Greece 11,329,618 305,415 26,957 5.8 19.6
Hungary 9,986,000 130,421 13,060 5.2 16.9
Iceland 318,452 12,594 39,548 7.9 12.7
Ireland 4,480,176 206,985 46,200 7.2 11.6
Italy 60,626,508 2,055,114 33,898 7.4 20.3
Latvia 2,229,641 24,013 10,770 8.1 16.9
Lithuania 3,244,601 36,370 11,209 7.8 16.5
Luxembourg 511,840 54,950 107,358 4.1 14.9
The Netherlands 16,654,979 780,668 46,873 5.5 15.6
Norway 4,920,305 412,990 83,936 8.1 16.0
Poland 38,200,037 469,401 12,288 5.3 13.7
Portugal 10,636,979 229,154 21,543 5.7 18.0
Romania 21,413,815 161,629 7,548 5.4 14.8
Slovakia 5,435,273 87,450 16,089 6.0 12.8
Slovenia 2,050,189 47,733 23,282 6.8 16.8
Spain 46,152,926 1,409,946 30,549 7.0 17.1
Sweden 9,415,570 458,725 48,720 8.2 19.7
Switzerland 7,866,500 527920 67,110 6.8 17.0
UK 62,435,709 2,250,209 36,040 8.2 16.5
a
CIA World Factbook
b
Eurostat 2011
c
OECD 2009 %GDP
not help individual countries to build the case for addi-
tional capacity in future years. We believe that it would
be beneficial to have a European standard definition of
exactly what an intensive care bed is, which could then be
implemented within the different countries. This defini-
tion could include factors related to the unit’s ability to
address organ dysfunction/failure, availability of beds
throughout the day and week, patient/nurse and patient/
doctor ratios, severity of illness and the operative rather
than the planned mean level of care of the ICU [14].
The overall number of critical care beds for Europe was
11.5/100,000 head of population. This is in marked contrast
to the number for the USA, which Carr found to be
28/100,000 in 2010 [15]. The heterogeneity of the data
between European countries is consistent with the findings
reported by other groups. Wunsch and colleagues [9] pre-
sented similar data although only on a very limited number
(six) of European countries in addition to a number of other
non-European countries. The provision of intensive care
beds that they found within the European region was very
similar to the numbers presented in this study, despite their
collection of data being from 2005, 5 years earlier.
An interesting question that arises from examination
of this data is how the different countries cope with the
widely differing levels of critical care capacity. Presum-
ably, in a grossly homogeneous geographical and
developed region such as Europe, one would expect that
comparable numbers of patients would develop acute
critical illness in the different countries. One would hence
expect that the different levels of provision should have a
major impact on practice and hence presumably outcomes
[6,1619]. Again comparing Portugal and Germany, it is
impossible that Portugal is able to admit the same amount
of patients to critical care as in Germany. The implication
must therefore be that either patients in Portugal with
need for critical care are unable to get it or that Germany
overprovides intensive care for its population. Only fur-
ther analysis of data that describe provision and practice
of critical care in detail across countries will enable us to
answer these questions. As a start, comparison of data
Table 2 Data describing numbers of adult acute care, intermediate care and intensive care beds per European country
Acute care
beds
a
Acute
care beds/
100,000
population
Intermediate
care (IMCU)
beds
Intensive
care (ICU)
beds
Critical
care
beds
ICU and
IMCU beds/
100,000
population
ICU beds
as % of
acute care
beds
GDP
($million)/
ICU beds
Andorra 188 224 6 7.1 3.2 482.2
Austria 48,446 635 569 1,264 1,833 21.8 3.4 205.9
Belgium 50,156 456 1,755 15.9 3.5 266.5
Bulgaria 57,460 766 913 12.2 1.6 52.2
Croatia 15,629 353 650 14.7 4.2 93.6
Cyprus 2,813 350 9 83 92 11.4 3.3 251.9
Czech Republic 91,068 865 1,227 11.6 1.3 156.5
Denmark 17,124 308 372 6.7 2.2 833.0
Estonia 5,096 380 72 124 196 14.6 3.8 98.2
Finland 12,442 231 28 301 329 6.1 2.6 727.0
France 232,821 358 3,471 4,069 7,540 11.6 3.2 339.9
Germany 469,791 575 23,890 29.2 5.1 137.6
Greece 44,411 392 30 650 680 6.0 1.5 449.1
Hungary 41,574 416 1,374 13.8 3.3 94.9
Iceland 1,169 367 29 9.1 2.5 434.3
Ireland 12,202 272 88 201 289 6.5 2.4 716.2
Italy 201,932 333 7,550 12.5 3.7 272.2
Latvia 11,833 531 217 9.7 1.8 110.7
Lithuania 17,061 526 502 15.5 2.9 72.5
Luxembourg 2,511 204 27 100 127 24.8 5.1 432.7
The Netherlands 56,085 337 1,065 6.4 1.9 733.0
Norway 13,639 277 395 8.0 2.9 1,045.5
Poland 156,662 410 2,635 6.9 1.7 178.1
Portugal 31,722 298 451 451 4.2 1.4 508.1
Romania 108,611 507 2,574 2,000 4,574 21.4 4.2 35.3
Slovakia 32,560 599 500 9.2 1.5 174.9
Slovenia 7,656 373 131 6.4 1.7 364.4
Spain 124,194 269 4,479 9.7 3.6 314.8
Sweden 26,131 278 550 5.8 2.1 834.0
Switzerland 28,096 357 866 11.0 3.1 609.6
UK 147,809 237 1,737 2,377 4,114 6.6 2.8 547.0
a
World Health Organization Regional Office for Europe (2009)
from European countries with established national
registries of intensive care could give more insight into
such details. At present such registries are operative in
England, Scotland, Norway, Finland and Sweden,
The Netherlands, and Austria. With the exception of
Austria, these are all countries with a relatively low
number of ICU beds per population unit, as can be seen
in Fig. 1.
We have been able to demonstrate that there are still
major differences within Europe regarding provision of
critical care services. These differences are too large to be
explained purely by differences in the characteristics of
the populations and are inadequately explained by the
economic strength of the country. In that respect, the three
wealthiest countries in Europe [measured in GDP ($)/
inhabitant]: Luxembourg (107,358), Norway (83,936),
and Switzerland (67,110) have respectively 21, 8, and 11
ICU beds/100,000 population. It seems likely that the
healthcare models present in each country have a major
impact on the development and prioritization of this
resource. This is likely to reflect a variety of factors that
range from specialty status, bed and patient models and
bed utilization (admission and discharge criteria) proto-
cols. In addition, the staffing of other hospital wards may
also play a major role. We restricted this study to the
provision of beds through the public healthcare systems,
excluding private providers. There may, therefore, be an
underestimation of numbers in some countries due to the
missing private sector.
More research is urgently needed to understand how
the differing numbers of critical care beds impact on
11.5
4.2
5.8
6
6.1
6.4
6.4
6.5
6.6
6.7
6.9
7.1
8
9.1
9.2
9.7
9.7
11
11.4
11.6
11.6
12.2
12.5
13.8
14.6
14.7
15.5
15.9
21.4
21.8
24.8
29.2
0 5 10 15 20 25 30
Europe (average)
Portugal
Sweden
Greece
Finland
Netherlands
Slovenia
Ireland
United Kingdom
Denmark
Poland
Andorra
Norway
Iceland
Slovakia
Latvia
Spain
Switzerland
Cyprus
Czech Republic
France
Bulgaria
Italy
Hungary
Estonia
Croatia
Lithuania
Belgium
Romania
Austria
Luxembourg
Germany
Total numbers of critical care beds per 100,000 capita of
population
Fig. 1 Numbers of critical care beds corrected for size of
population (per 100,000 inhabitants) for European countries
45678910
0
10
20
30
Andorra
Austria
Belgium
Bulgaria
Croatia
Cyprus
Czech
Denmar
k
Estonia
Fin la nd
Fran ce
Germa ny
Gree ce
Hungary
Icel an d
Ireland
Ital y
Latvia
Lithuania
Luxembourg
Netherlands Norway
Poland
Portugal
Romania
Slovaki a
Slovenia
Spain
Sweden
Switzerl and
United Kingdom
Health expenditure as % of Gross Domestic Product (in millions of US Dollars)
Critical Care beds / 100,000 population
Fig. 2 Comparison of the
numbers of critical care beds
per 100,000 against the
proportion of health expenditure
as a percentage of gross
domestic product (in millions of
US dollars). Lines represent
linear regression analysis
together with 95 % confidence
intervals around the line
(r
2
\0.0001, p=0.91)
practice and ultimately on patient outcomes. If the need
for these beds continues to grow, then the most effective
and cost-efficient use of this level of care must be
developed in order for most countries to be able to afford
this level of provision of healthcare.
Acknowledgments ESICM: Claudia Arena and Giulia Evolvi,
Andorra: Antoni Margarit, Austria: Philipp Metnitz, Austrian
Center of Documentation and Quality Assurance in Intensive Care
Medicine (ASDI), Belgium: Federal Government. Patrick Ferdi-
nande and Eric Hoste, Bulgaria: National Center of Health
Informatics-Public Health Statistics, Croatia: Ino Husedzinovich,
president of the Croatian Society of Intensive Care Medicine,
Czech Republic: Ivan Novak, scientific secretary of the Czech
Society of Anesthesia and Intensive Care Medicine, Cyprus:
Department of Medical and Public Health Service-Maria Psalti and
Theodorus Kyprianou, Denmark: National Board of Health, Esto-
nia: Indrek Ra
¨tsep, from the database of the Estonian Society of
Anaesthesiologists, Finland: Ville Pettila-Finnish Society of
Intensive Care Medicine, France: Agence technique de l’informa-
tion hospitalie
`re (ATIH), Germany: German Association of
Anaesthesiology and Intensive Care, Greece: Apostolos Armagan-
idis, president of the Greek Society of Intensive Care Medicine,
Hungary: Akos Csomos for the Hungary national health fund
database, Iceland: Kari Hreinsson, president of the Icelandic
Society of Anesthesiology and Intensive Care Medicine, Ireland:
Patrick Seigne, The Prospectus Report (A Review of Adult Critical
Care Services in Ireland, 2009), Italy: Ministry of Health, Latvia:
Indulis Vagas (president of the National Society of Anesthesiology
and Reanimation of Latvia), Lithuania: Jurate Sypilaite-Lithuanian
Society of Intensive Care Medicine, Luxembourg: Max Martin,
Malta: Carmel Abela, The Netherlands: Jozef Kesecioglu and Hans
van der Spoel, Norway: Hans Flaatten, Poland: Krystof Kusza-
Polish Ministry of Health, Portugal: Rui Moreno; Cole
´gio de
Medicina Intensiva da Ordem dos Me
´dicos; Sociedade Portuguesa
de Medicina Intensiva, Romania: National Center for Statistic of
the Ministry of Health-Ioana Grigoras and Natalia Hagau, Slovakia:
Jozef Firment Slovakian Society of Intensive Care Medicine,
Slovenia: Slovenian Health Statistic Yearbook, Spain: Cristobal
Leon Gil and the SEMICYUC and Cesar Aldecoa, Sweden: Goran
Karlstrom, Switzerland: Hans Rothen, National coordinator, data
from the Swiss Society of Intensive Care Medicine, UK: Depart-
ment of Health (2010), Scottish Intensive Care Society, Linda
Mulholland, Critical Care Networks of Wales and Northern Ireland.
Conflicts of interest None.
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... In European reports published well before and during the pandemic, there were similar numbers of critical care beds per 100 000 population between Denmark, the UK and Greece (approx. 6 per 100 000) [20,21]. Thus, perhaps it is more about a culture for rationale allocation of hospital resources to those who are at highest risk rather than their general availability in the health care system [22]. ...
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Background Emergency laparotomy (EL) is accompanied by high post-operative morbidity and mortality which varies significantly between countries and populations. The aim of this study is to report outcomes of emergency laparotomy in Greece and to compare them with the results of the National Emergency Laparotomy Audit (NELA). Methods This is a multicentre prospective cohort study undertaken between 01.2019 and 05.2020 including consecutive patients subjected to EL in 11 Greek hospitals. EL was defined according to NELA criteria. Demographics, clinical variables, and post-operative outcomes were prospectively registered in an online database. Multivariable logistic regression analysis was used to identify independent predictors of post-operative mortality. Results There were 633 patients, 53.9% males, ASA class III/IV 43.6%, older than 65 years 58.6%. The most common operations were small bowel resection (20.5%), peptic ulcer repair (12.0%), adhesiolysis (11.8%) and Hartmann’s procedure (11.5%). 30-day post-operative mortality reached 16.3% and serious complications occurred in 10.9%. Factors associated with post-operative mortality were increasing age and ASA class, dependent functional status, ascites, severe sepsis, septic shock, and diabetes. HELAS cohort showed similarities with NELA patients in terms of demographics and preoperative risk. Post-operative utilisation of ICU was significantly lower in the Greek cohort (25.8% vs 56.8%) whereas 30-day post-operative mortality was significantly higher (16.3% vs 8.7%). Conclusion In this study, Greek patients experienced markedly worse mortality after emergency laparotomy compared with their British counterparts. This can be at least partly explained by underutilisation of critical care by surgical patients who are at high risk for death.
... Although speculative, one contributing factor might be a difference in pre-hospital and in-hospital triage systems between the two countries. For example, the number of ICU beds in the UK (6.6 ICU beds/100 000 people) 27 is considerably lower than in the US (28 ICU beds/100 000 people), 28 so it seems conceivable that the threshold to admit patients with a poor prognosis to the ICU (and maintain ICU support) is higher in the UK than in the USA, which might decrease the overall frequency of coma in the UK. Coincidence or not, the ratio of comatose UK and US family members on the day of the survey was very similar to the ratio of ICU beds in the two countries (1-5 versus 1-4.3). ...
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The epidemiology of coma is unknown because case ascertainment with traditional methods is difficult. Here, we used crowdsourcing methodology to estimate the incidence and prevalence of coma in the UK and the USA. We recruited UK and US laypeople (aged ≥18 years) who were nationally representative (i.e. matched for age, gender and ethnicity according to census data) of the UK and the USA, respectively, utilizing a crowdsourcing platform. We provided a description of coma and asked survey participants if they—‘right now’ or ‘within the last year’—had a family member in coma. These participants (UK n = 994, USA n = 977) provided data on 30 387 family members (UK n = 14 124, USA n = 16 263). We found more coma cases in the USA (n = 47) than in the UK (n = 20; P = 0.009). We identified one coma case in the UK (0.007%, 95% confidence interval 0.00–0.04%) on the day of the survey and 19 new coma cases (0.13%, 95% confidence interval 0.08–0.21%) within the preceding year, resulting in an annual incidence of 135/100 000 (95% confidence interval 81–210) and a point prevalence of 7 cases per 100 000 population (95% confidence interval 0.18–39.44) in the UK. We identified five cases in the USA (0.031%, 95% confidence interval 0.01–0.07%) on the day of the survey and 42 new cases (0.26%, 95% confidence interval 0.19–0.35%) within the preceding year, resulting in an annual incidence of 258/100 000 (95% confidence interval 186–349) and a point prevalence of 31 cases per 100 000 population (95% confidence interval 9.98–71.73) in the USA. The five most common causes were stroke, medically induced coma, COVID-19, traumatic brain injury and cardiac arrest. To summarize, for the first time, we report incidence and prevalence estimates for coma across diagnosis types and settings in the UK and the USA using crowdsourcing methods. Coma may be more prevalent in the USA than in the UK, which requires further investigation. These data are urgently needed to expand the public health perspective on coma and disorders of consciousness.
... 4,19e21 Rapid demographic and societal changes have led to an escalating burden of noncommunicable diseases. Supply of healthcare varies broadly worldwide, 22,23 including within Latin America, with a mixture of social, private, and government-funded systems. Latin America describes a geographic area, including 25 nations, with countries presenting some of the highest income disparities worldwide. ...
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Background: Reported data suggest that 4.2 million deaths will occur within 30 days of surgery worldwide each year, half of which are in low-and middle-income countries. Postoperative complications are a leading cause of long-term morbidity and mortality. Patients who survive and leave the hospital after surgical complications regularly experience reductions in long-term survival and functional independence, resulting in increased costs. With a high volume of surgery performed, there is a growing perception of the substantial impact of even minor enhancements in perioperative care. The Latin American Surgical Outcomes Study (LASOS) is an international, multicentre, prospective cohort study of adults submitted to in-patient surgery in Latin America aiming to provide detailed data describing postoperative complications and surgical mortality. Methods: LASOS is a 7 day cohort study of adults undergoing surgery in Latin America. Details of preoperative risk factors, intraoperative care, and postoperative outcomes will be collected. The primary outcome will be in-hospital postoperative complications of any cause. Secondary outcomes include in-hospital all-cause mortality, duration of hospital stay after surgery, and admission to a critical care unit within 30 days after surgery during the index hospitalisation. Results: The LASOS results will be published in peer-reviewed journals, reported and presented at international meetings , and widely disseminated to patients and public in participating countries via mainstream and social media. Conclusions: The LASOS may augment our understanding of postoperative complications and surgial mortality in Latin America. Clinical trial registration: NCT05169164.
... Although the number of beds is rapidly increasing in response to the COVID-19 pandemic, the Japanese Society of Intensive Care Medicine reported that there were 7015 ICU beds and 13,003 HDU beds nationwide in 2020, with an overall intensive care bed count of approximately 15.9 beds per 100,000 population [20]. In contrast, the number of intensive care beds per 100,000 population in the United States and Germany was 34.7 (as of 2009) and 29.2 (as of 2010), respectively [21,22]. Moreover, Japan has only 2115 certified intensivists (as of April 1, 2021) compared with approximately 12,000 certified intensivists in the United States. ...
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Background Septic shock is a common and life-threatening condition that requires intensive care. Intensive care units (ICUs) in Japan are classified into ICUs and high-dependency care units (HDUs), depending on presence of full-time certified intensivists and the number of assigned nurses. Compared with other developed countries, there are fewer intensive care beds and certified intensivists in Japan; therefore, non-intensivists often treat patients with septic shock in HDUs. It is unknown where we should treat patients with septic shock because no studies have compared the clinical outcomes between ICU and HDU treatment. This study aimed to elucidate which units should admit patients with septic shock by comparing mortality data and resource use between ICU and HDU admissions. Methods In this retrospective cohort study, we used a nationwide Japanese administrative database to identify adult patients with septic shock who were admitted to ICUs or HDUs between January 2010 and February 2021. The patients were divided into two groups, based on admittance to ICU or HDU on the day of hospitalization. The primary outcome was 30-day all-cause mortality adjusted for covariates using Cox regression analyses; the secondary outcomes were the length of ICU or HDU stay and length of hospital stay. Results Of the 10,818 eligible hospitalizations for septic shock, 6584 were in the ICU group, and 4234 were in the HDU group. Cox regression analyses revealed that patients admitted to the ICUs had lower 30-day mortality (adjusted hazard ratio: 0.89; 95% confidence interval: 0.83–0.96; P = 0.005). Linear regression analyses showed no significant difference in hospital length of stay or ICU or HDU length of stay. Conclusions An association was observed between ICU admission and lower 30-day mortality in patients with septic shock. These findings could provide essential insights for building a more appropriate treatment system.
... In Belgium, there are no criteria for ICU discharge and the decision is often taken by the medical team alone. This result was certainly also influenced by the rather high occupancy rate in the study despite the high number of ICU beds in Belgium (Chrusch et al., 2009;Fergusson et al., 2020;Rhodes et al., 2012). In addition, patients who died quickly could also have influenced this result. ...
Introduction: Hospitals with better nursing resources report more favourable patient outcomes with almost no difference in cost as compared to those with worse nursing resources. The aim of this study was to assess the association between nursing cost per intensive care unit bed and patient outcomes (mortality, readmission, and length of stay). Methodology: This was a retrospective cohort study using data collected from the intensive care units of 17 Belgian hospitals from January 01 to December 31, 2018. Hospitals were dichotomized using median annual nursing cost per bed. A total of 18,235 intensive care unit stays were included in the study with 5,664 stays in the low-cost nursing group and 12,571 in the high-cost nursing group. Results: The rate of high length of stay outliers in the intensive care unit was significantly lower in the high-cost nursing group (9.2% vs 14.4%) compared to the low-cost nursing group. Intensive care unit readmission was not significantly different in the two groups. Mortality was lower in the high-cost nursing group for intensive care unit (9.9% vs 11.3%) and hospital (13.1% vs 14.6%) mortality. The nursing cost per intensive care bed was different in the two groups, with a median [IQR] cost of 159,387€ [140,307–166,690] for the low-cost nursing group and 214,032€ [198,094–230,058] for the high-cost group. In multivariate analysis, intensive care unit mortality (OR = 0.80, 95% CI: 0.69–0.92, p < 0.0001), in-hospital mortality (OR = 0.82, 95% CI: 0.72–0.93, p < 0.0001), and high length of stay outliers (OR = 0.48, 95% CI: 0.42–0.55, p < 0.0001) were lower in the high-cost nursing group. However, there was no significant effect on intensive care readmission between the two groups (OR = 1.24, 95% CI: 0.97–1.51, p > 0.05). Conclusions: This study found that higher-cost nursing per bed was associated with significantly lower intensive care unit and in-hospital mortality rates, as well as fewer high length of stay outliers, but had no significant effect on readmission to the intensive care unit.
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Objectives In critically ill patients requiring mechanical ventilation for at least 21 days, 1-year mortality can be estimated using the ProVent score, calculated from four variables (age, platelet count, vasopressor use and renal replacement therapy). We aimed to externally validate discrimination and calibration of the ProVent score and, if necessary, to update its underlying regression model. Design Retrospective, observational, single-centre study. Setting 11 intensive care units at one tertiary academic hospital. Patients 780 critically ill adult patients receiving invasive mechanical ventilation for at least 21 days. Primary outcome measure 1-year mortality after intensive care unit discharge. Results 380 patients (49%) had died after 1 year. One-year mortality for ProVent scores from 0 to 5 were: 15%, 27%, 57%, 66%, 72% and 76%. Area under the receiver operating characteristic curve of the ProVent probability model was 0.76 (95% CI 0.72 to 0.79), calibration intercept was −0.43 (95% CI −0.59 to −0.27) and calibration slope was 0.76 (95% CI 0.62 to 0.89). Model recalibration and extension by inclusion of three additional predictors (total bilirubin concentration, enteral nutrition and surgical status) improved model discrimination and calibration. Decision curve analysis demonstrated that the original ProVent model had negative net benefit, which was avoided with the extended ProVent model. Conclusions The ProVent probability model had adequate discrimination but was miscalibrated in our patient cohort and, as such, could potentially be harmful. Use of the extended ProVent score developed by us could possibly alleviate this concern.
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Using the exogenous shock of the COVID-19 pandemic, we study how informed market participants incorporate fiscal space into their trading decisions. At the onset of the pandemic, short-selling activity shifted towards companies with low financial flexibility but only in countries with limited fiscal space. Among such companies, short sellers specifically targeted those that generate their revenues mainly in the domestic market. These short sellers entered their positions before the market crash, thereby generating significant abnormal returns. We find no evidence of either herding behavior prior to the market crash or a long-run performance reversal of short sellers. These findings support the notion that short sellers help to promote price efficiency in times of crisis, where governments with budgetary constraints are unable to provide sufficient stimuli to their economies.
Article
Background Reported data suggest that 4.2 million deaths will occur within 30 days of surgery worldwide each year, half of which are in low- and middle-income countries. Postoperative complications are a leading cause of long-term morbidity and mortality. Patients who survive and leave the hospital after surgical complications regularly experience reductions in long-term survival and functional independence, resulting in increased costs. With a high volume of surgery performed, there is a growing perception of the substantial impact of even minor enhancements in perioperative care. The Latin American Surgical Outcomes Study (LASOS) is an international, multicentre, prospective cohort study of adults submitted to in-patient surgery in Latin America aiming to provide detailed data describing postoperative complications and surgical mortality. Methods LASOS is a 7 day cohort study of adults undergoing surgery in Latin America. Details of preoperative risk factors, intraoperative care, and postoperative outcomes will be collected. The primary outcome will be in-hospital postoperative complications of any cause. Secondary outcomes include in-hospital all-cause mortality, duration of hospital stay after surgery, and admission to a critical care unit within 30 days after surgery during the index hospitalisation. Results The LASOS results will be published in peer-reviewed journals, reported and presented at international meetings, and widely disseminated to patients and public in participating countries via mainstream and social media. Conclusions The LASOS may augment our understanding of postoperative complications and surgial mortality in Latin America. Clinical trial registration NCT05169164.
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The Winter Meeting of the British Thoracic Society (BTS) is a platform for the latest clinical and scientific research in respiratory medicine. This review summarises the key symposia and presentations from the BTS Winter Meeting 2021 held online due to the COVID-19 pandemic.
Article
Objectives: We aim to describe incidence and outcomes of cardiopulmonary resuscitation (CPR) efforts and their outcomes in ICUs and their changes over time. Design: Retrospective cohort analysis. Setting: Patient data documented in the Austrian Center for Documentation and Quality Assurance in Intensive Care database. Patients: Adult patients (age ≥ 18 yr) admitted to Austrian ICUs between 2005 and 2019. Interventions: None. Measurements andn main results: Information on CPR was deduced from the Therapeutic Intervention Scoring System. End points were overall occurrence rate of CPR in the ICU and CPR for unexpected cardiac arrest after the first day of ICU stay as well as survival to discharge from the ICU and the hospital. Incidence and outcomes of ICU-CPR were compared between 2005 and 2009, 2010 and 2014, and 2015 and 2019 using chi-square test. A total of 525,518 first admissions and readmissions to ICU of 494,555 individual patients were included; of these, 72,585 patients (14.7%) died in hospital. ICU-CPR was performed in 20,668 (3.9%) admissions at least once; first events occurred on the first day of ICU admission in 15,266 cases (73.9%). ICU-CPR was first performed later during ICU stay in 5,402 admissions (1.0%). The incidence of ICU-CPR decreased slightly from 4.4% between 2005 and 2009, 3.9% between 2010 and 2014, and 3.7% between 2015 and 2019 (p < 0.001). A total of 7,078 (34.5%) of 20,499 patients who received ICU-CPR survived until hospital discharge. Survival rates varied slightly over the observation period; 59,164 (12.0%) of all patients died during hospital stay without ever receiving CPR in the ICU. Conclusions: The incidence of ICU-CPR is approximately 40 in 1,000 admissions overall and approximately 10 in 1,000 admissions after the day of ICU admission. Short-term survival is approximately four out of 10 patients who receive ICU-CPR.
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Objective: To provide guidance and recommendations for the planning or renovation of intensive care units (ICUs) with respect to the specific characteristics relevant to organizational and structural aspects of intensive care medicine. Methodology: The Working Group on Quality Improvement (WGQI) of the European Society of Intensive Care Medicine (ESICM) identified the basic requirements for ICUs by a comprehensive literature search and an iterative process with several rounds of consensus finding with the participation of 47 intensive care physicians from 23 countries. The starting point of this process was an ESICM recommendation published in 1997 with the need for an updated version. Results: The document consists of operational guidelines and design recommendations for ICUs. In the first part it covers the definition and objectives of an ICU, functional criteria, activity criteria, and the management of equipment. The second part deals with recommendations with respect to the planning process, floorplan and connections, accommodation, fire safety, central services, and the necessary communication systems. Conclusion: This document provides a detailed framework for the planning or renovation of ICUs based on a multinational consensus within the ESICM.
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Clinicians in intensive care units (ICUs) who perceive the care they provide as inappropriate experience moral distress and are at risk for burnout. This situation may jeopardize patient quality of care and increase staff turnover. To determine the prevalence of perceived inappropriateness of care among ICU clinicians and to identify patient-related situations, personal characteristics, and work-related characteristics associated with perceived inappropriateness of care. Cross-sectional evaluation on May 11, 2010, of 82 adult ICUs in 9 European countries and Israel. Participants were 1953 ICU nurses and physicians providing bedside care. Perceived inappropriateness of care, defined as a specific patient-care situation in which the clinician acts in a manner contrary to his or her personal and professional beliefs, as assessed using a questionnaire designed for the study. Of 1651 respondents (median response rate, 93% overall; interquartile range, 82%-100% [medians 93% among nurses and 100% among physicians]), perceived inappropriateness of care in at least 1 patient was reported by 439 clinicians overall (27%; 95% CI, 24%-29%), 300 of 1218 were nurses (25%), 132 of 407 were physicians (32%), and 26 had missing answers describing job title. Of these 439 individuals, 397 reported 445 situations associated with perceived inappropriateness of care. The most common reports were perceived disproportionate care (290 situations [65%; 95% CI, 58%-73%], of which "too much care" was reported in 89% of situations, followed by "other patients would benefit more" (168 situations [38%; 95% CI, 32%-43%]). Independently associated with perceived inappropriateness of care rates both among nurses and physicians were symptom control decisions directed by physicians only (odds ratio [OR], 1.73; 95% CI, 1.17-2.56; P = .006); involvement of nurses in end-of-life decision making (OR, 0.76; 95% CI, 0.60-0.96; P = .02); good collaboration between nurses and physicians (OR, 0.72; 95% CI, 0.56-0.92; P = .009); and freedom to decide how to perform work-related tasks (OR, 0.72; 95% CI, 0.59-0.89; P = .002); while a high perceived workload was significantly associated among nurses only (OR, 1.49; 95% CI, 1.07-2.06; P = .02). Perceived inappropriateness of care was independently associated with higher intent to leave a job (OR, 1.65; 95% CI, 1.04-2.63; P = .03). In the subset of 69 ICUs for which patient data could be linked, clinicians reported received inappropriateness of care in 207 patients, representing 23% (95% CI, 20%-27%) of 883 ICU beds. Among a group of European and Israeli ICU clinicians, perceptions of inappropriate care were frequently reported and were inversely associated with factors indicating good teamwork.
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To test the use of a human resources-based classification of levels of care of ICUs; to evaluate the match between planned vs operative levels of care on a large sample of European ICUs. Analysis of the database of a multicentric, multinational, prospective cohort study, involving 89 ICUs from 13 European areas. Database of EURICUS-I. Provision of resources was measured as the number of nurses per ICU bed. Use of resources was measured by the daily use of a therapeutic index (nine equivalents of nursing manpower use score, NEMS) at patient level. Work utilization ratio (WUR) indicated the total number of NEMS points actually scored divided by the total possible NEMS score on each ICU. The planned level of care (LOC) or the mean number of patients to be assisted by one nurse (P/N ratio) made available to the unit was derived from the number of nurses and the number of beds in the ICU. The operative LOC or the actual mean number of patients who were assisted by one nurse (P/N ratio) was computed by dividing the number of NEMS points equivalent to the work of three nursing-shifts (46 points) by the mean daily NEMS score at ICU level. Severity of illness was evaluated by the new simplified acute physiology score. Kappa statistics, intraclass correlation coefficients, and interrater percentage of agreement were used to evaluate the reliability of the data collected for total NEMS score. Chi2 statistics and one-way analysis of variance were used when appropriate. Data of 16,047 patients (74,383 patient-days) admitted to the ICUs were analyzed. With an overall value of 26.5+/-9.3, the mean NEMS score at ICU level varied significantly among European areas. These differences were not explained by the severity of illness of the patients. The mean WUR was 0.73+/-0.29, presenting also significant differences among ICUs and European areas that were not explained by severity of illness. There was a mismatch between planned vs operative LOCs on 68 ICUs (76%); on 65 (73%), the operative LOC was lower than the planned LOC. This loss of resources concerned particularly the 61 ICUs planned to operate at LOC 3. The use of human resources-based classification of LOCs is an objective method for evaluation of the match between provision and use of resources in the ICU. This study has shown a large mismatch between planned and utilized LOC in a sample of 89 European ICUs. This mismatch, suggesting an important loss of invested resources, was more apparent in the ICUs that were planned to operate at a higher level of care.
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The United States has seven times as many intensive care unit (ICU) beds per capita as the United Kingdom; the effect on care of critically ill patients is unknown. To compare medical ICU admissions in the United States and United Kingdom. Retrospective (2002-2004) cohort study of 172,785 ICU admissions (137 United States ICUs, Project IMPACT database; 160 United Kingdom ICUs, Case Mix Program) with patients followed until initial hospital discharge. United Kingdom (vs. United States) admissions were less likely to be admitted directly from the emergency room (ER) (33.4% vs. 58%); had longer hospital stays before ICU admission (mean days 2.6 ± 8.2 vs. 1 ± 3.6); and fewer were greater than or equal to 85 years (3.2% vs. 7.8%). United Kingdom patients were more frequently mechanically ventilated within 24 hours after ICU admission (68% vs. 27.4%); were sicker (mean Acute Physiology Score 16.7 ± 7.6 vs. 10.6 ± 6.8); and had higher primary hospital mortality (38% vs. 15.9%; adjusted odds ratio, 1.73; 95% confidence interval, 1.50-1.99). There was no mortality difference for mechanically ventilated patients admitted from the ER (adjusted odds ratio, 1.09; 95% confidence interval, 0.89-1.33). Comparisons of hospital mortality were confounded by differences in case mix; hospital length of stay (United Kingdom median 10 d [interquartile range {IQR}, 3-24] vs. United States 6 d [IQR, 3-11]; and discharge practices (more United States patients were discharged to skilled care facilities [29% of survivors vs. 6% in the United Kingdom]). Lower United Kingdom ICU bed availability is associated with fewer direct admissions from the ER, longer hospital stays before ICU admission, and higher severity of illness. Interpretation of between-country hospital outcomes is confounded by differences in case mix, processes of care, and discharge practices.
Article
Objective: Critical care represents a large percentage of healthcare spending in developed countries. Yet, little is known regarding international variation in critical care services. We sought to understand differences in critical care delivery by comparing data on the distribution of services in eight countries. Design: Retrospective review of existing national administrative data. We identified sources of data in each country to provide information on acute care hospitals and beds, intensive care units and beds, intensive care admissions, and definitions of intensive care beds. Data were all referenced and from as close to 2005 as possible. Setting: United States, France, United Kingdom, Canada, Belgium, Germany, The Netherlands, and Spain. Patients: Not available. Interventions: None. Measurements and Main Results: No standard definition existed for acute care hospital or intensive care unit beds across countries. Hospital beds varied three-fold from 221/100,000 population in the United States to 593/100,000 in Germany. Adult intensive care unit beds also ranged seven-fold from 3.3/100,000 population in the United Kingdom to 24.0/100,000 in Germany. Volume of intensive care unit admissions per year varied ten-fold from 216/100,000 population in the United Kingdom to 2353/100,000 in Germany. The ratio of intensive care unit beds to hospital beds was highly correlated across all countries except the United States (r = .90). There was minimal correlation between the number of intensive care unit beds per capita and health care spending per capita (r = .45), but high inverse correlation between intensive care unit beds and hospital mortality for intensive care unit patients across countries (r = −.82). Conclusions: Absolute critical care services vary dramatically between countries with wide differences in both numbers of beds and volume of admissions. The number of intensive care unit beds per capita is not strongly correlated with overall health expenditure, but does correlate strongly with mortality. These findings demonstrate the need for critical care data from all countries, as they are essential for interpretation of studies, and policy decisions regarding critical care services.
Article
Critical care medicine is a global specialty and epidemiologic research among countries provides important data on availability of critical care resources, best practices, and alternative options for delivery of care. Understanding the diversity across healthcare systems allows us to explore that rich variability and understand better the nature of delivery systems and their impact on outcomes. However, because the delivery of ICU services is complex (for example, interplay of bed availability, cultural norms and population case-mix), the diversity among countries also creates challenges when interpreting and applying data. This complexity has profound influences on reported outcomes, often obscuring true differences. Future research should emphasize determination of resource data worldwide in order to understand current practices in different countries; this will permit rational pandemic and disaster planning, allow comparisons of in-ICU processes of care, and facilitate addition of pre- and post-ICU patient data to better interpret outcomes.
Article
Interest in the global burden of critical illness is growing, but comprehensive data to describe this burden and the resources available to provide care for critically ill patients are lacking. Challenges to obtaining population-based global estimates of critical illness and resources to treat it include the syndrome-based definitions of critical illness, incorrect equating of 'critical illness' with 'admission to an intensive care unit', lack of reliable case ascertainment in administrative data, and short prodrome and high mortality of critical illness, limiting the number of prevalent cases. Modeling techniques will be required to estimate the burden of critical illness and disparities in access to critical care using existing data sources. Demand for critical care is likely to increase, related to urbanization, an aging demographic, and the ongoing wars, disasters, and pandemics, whereas economic crises will likely decrease the ability to pay for it. Major unexplored research and public health questions remain unanswered regarding the worldwide burden of critical illness, variation in resources available for treatment, and strategies to prevent and treat critical illness that are broadly effective and feasible.