Mortality after surgery in Europe: A 7 day cohort study

Article (PDF Available)inThe Lancet 380(9847):1059-1065 · September 2012with78 Reads
DOI: 10.1016/S0140-6736(12)61148-9
Clinical outcomes after major surgery are poorly described at the national level. Evidence of heterogeneity between hospitals and health-care systems suggests potential to improve care for patients but this potential remains unconfirmed. The European Surgical Outcomes Study was an international study designed to assess outcomes after non-cardiac surgery in Europe. METHODS: We did this 7 day cohort study between April 4 and April 11, 2011. We collected data describing consecutive patients aged 16 years and older undergoing inpatient non-cardiac surgery in 498 hospitals across 28 European nations. Patients were followed up for a maximum of 60 days. The primary endpoint was in-hospital mortality. Secondary outcome measures were duration of hospital stay and admission to critical care. We used χ(2) and Fisher's exact tests to compare categorical variables and the t test or the Mann-Whitney U test to compare continuous variables. Significance was set at p

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Available from: Rupert M Pearse Vol 380 September 22, 2012
Lancet 2012; 380: 1059–65
See Comment page 1034
*Members listed in appendix
Barts and The London School of
Medicine and Dentistry, Queen
Mary University of London,
London, UK (R M Pearse MD);
UCINC, Hospital de São José,
Centro Hospitalar de Lisboa
Central, EPE, Lisbon, Portugal
(Prof R P Moreno PhD); Section
of Medical Statistics
(Prof P Bauer PhD), and
Department of Anaesthesia
and General Intensive Care
(Prof P Metnitz PhD), Medical
University of Vienna, Vienna,
Austria; IRCCS AOU San
Martino-IST, Department of
Surgical Sciences and
Integrated Diagnostics,
University of Genoa, Genoa,
Italy (Prof P Pelosi PhD);
Berlin, Germany
(Prof C Spies PhD);
Anaesthesiology and Critical
Care, University Hospital, Lille,
France (Prof B Vallet PhD);
Erasme Hospital, Université
Libre de Bruxelles, Brussels,
Belgium (Prof J-L Vincent PhD);
Department of
Anaesthesiology, University of
Bonn, Bonn, Germany
(Prof A Hoeft PhD); St George’s
Healthcare NHS Trust, London,
UK (A Rhodes FRCP); and St
George’s University of London,
London, UK (A Rhodes)
Correspondence to:
Dr Rupert Pearse, Adult Critical
Care Unit, Royal London
Hospital, London E1 1BB, UK
See Online for appendix
Mortality after surgery in Europe: a 7 day cohort study
Rupert M Pearse, Rui P Moreno, Peter Bauer, Paolo Pelosi, Philipp Metnitz, Claudia Spies, Benoit Vallet, Jean-Louis Vincent, Andreas Hoeft,
Andrew Rhodes, for the European Surgical Outcomes Study (EuSOS) group for the Trials groups of the European Society of Intensive Care Medicine
and the European Society of Anaesthesiology*
Background Clinical outcomes after major surgery are poorly described at the national level. Evidence of heterogeneity
between hospitals and health-care systems suggests potential to improve care for patients but this potential remains
unconfi rmed. The European Surgical Outcomes Study was an international study designed to assess outcomes after
non-cardiac surgery in Europe.
Methods We did this 7 day cohort study between April 4 and April 11, 2011. We collected data describing consecutive
patients aged 16 years and older undergoing inpatient non-cardiac surgery in 498 hospitals across 28 European
nations. Patients were followed up for a maximum of 60 days. The primary endpoint was in-hospital mortality.
Secondary outcome measures were duration of hospital stay and admission to critical care. We used χ² and Fisher’s
exact tests to compare categorical variables and the t test or the Mann-Whitney U test to compare continuous variables.
Signifi cance was set at p<0·05. We constructed multilevel logistic regression models to adjust for the diff erences in
mortality rates between countries.
Findings We included 46 539 patients, of whom 1855 (4%) died before hospital discharge. 3599 (8%) patients were
admitted to critical care after surgery with a median length of stay of 1·2 days (IQR 0·9–3·6). 1358 (73%) patients
who died were not admitted to critical care at any stage after surgery. Crude mortality rates varied widely between
countries (from 1·2% [95% CI 0·0–3·0] for Iceland to 21·5% [16·9–26·2] for Latvia). After adjustment for
confounding variables, important diff erences remained between countries when compared with the UK, the country
with the largest dataset (OR range from 0·44 [95% CI 0·19–1·05; p=0·06] for Finland to 6·92 [2·37–20·27; p=0·0004]
for Poland).
Interpretation The mortality rate for patients undergoing inpatient non-cardiac surgery was higher than anticipated.
Variations in mortality between countries suggest the need for national and international strategies to improve care
for this group of patients.
Funding European Society of Intensive Care Medicine, European Society of Anaesthesiology.
More than 230 million major surgical procedures are
undertaken worldwide each year.1 For most patients, risks
of surgery are low and yet evidence increasingly suggests
that complications after surgery are an import ant cause of
death.2–5 About 10% of patients undergoing surgery in the
UK are at high risk of complications, accounting for 80%
of postoperative deaths.2–4 If this rate is applicable
worldwide, up to 25 million patients undergo high-risk
surgical procedures each year, of whom 3 million do not
survive until hospital discharge. Patients who develop
complications but survive to leave hospital often have
reduced functional independence and long-term survival.5–8
Despite obvious diff erences in procedure-related and
patient-related mortality risks, most surgical patients use
one care pathway, sharing standard facilities for pre-
operative assessment, anaesthesia, operating rooms, post-
anaesthetic recovery, and hospital wards. This approach is
adequate for most patients but might not meet the needs
of the small number of patients at high risk of
complications and death. In the USA, evidence of
variations in postoperative mortality within health-care
systems suggest the potential to implement measures that
improve patient outcomes.9 Low rates of admission to
critical care for patients at high risk of complications
undergoing non-cardiac surgery are of particular
concern,2–4 and might be aff ected by international diff er-
ences in the provision of critical care.10,11 With high volumes
of surgery under taken, even a low rate of avoidable harm
will be associated with many preventable deaths.
International comparative data might provide important
insights into delivery of health care for surgical patients.
However, little or no data are available describing provision
of care or outcomes for unselected surgical patients. The
objective of the European Surgical Outcomes Study
(EuSOS) was to describe mortality rates and patterns of
critical care resource use for patients undergoing non-
cardiac surgery across several European nations.
Study design and participants
We did this European cohort study between 0900 h (local
time) on April 4, 2011, and 0859 h on April 11, 2011. All
adult patients (older than 16 years) admitted to
participating centres for elective or non-elective inpatient
surgery commencing during the 7 day cohort period were
1060 Vol 380 September 22, 2012
eligible for inclusion. Patients undergoing planned day-
case surgery, cardiac surgery, neurosurgery, radiological,
or obstetric proced ures were excluded because these
patients receive care within separate, dedicated pathways.
Participating hospitals (appendix pp 11–68) were a
voluntary convenience sample, identifi ed through
membership of the European Society of Intensive Care
Medicine and the European Society of Anaesthesiology
and by direct approach from national study coordinators.
Ethics requirements diff ered by country. In Denmark,
centres were exempt from ethics approval because this
study was deemed to be a clinical audit. In all other
nations formal ethics approval was applied for and given.
In Finland alone we were required to obtain written
informed consent from individual patients.
Local investigators were supported by national coordin-
ators and via a website that provided key documentation,
including the protocol and guidance on study procedures.
We obtained data describing perioperative care facilities
once for each hospital at the beginning of the study. We
collected data describing consecutive patients with paper
case record forms, which we made anonymous before
entering the information onto a secure internet-based
electronic case record form (OpenClinica, Boston, MA,
USA). We completed an operating theatre case report
form for each eligible patient who we then followed up
until hospital discharge for data describing hospital stay,
admission to critical care, and in-hospital mortality. We
completed a critical care case record form to capture data
describing the fi rst admission to critical care for any
individual patient at any time during the follow-up
period. Example case record forms are available from the
study website.
We selected patient-level variables on the basis that they
were objective, routinely collected for clinical reasons,
could be transcribed with a high level of accuracy, and
would be relevant to a risk adjustment model in most
patients. We censored critical care and hospital discharge
data at 60 days after surgery. We assessed data for
completeness and then checked for plausibility and
consistency with prospectively defi ned ranges.12
The primary endpoint was in-hospital mortality.
Secondary outcome measures were duration of hospital
stay and admission to critical care.
Statistical analysis
Our aim was to recruit as many participating hospitals
as possible and to recruit every eligible patient in those
hospitals. We anticipated that a minimum sample size of
20 000 patients would enable a precise estimate of
mortality. This sample size was also expected to provide a
suffi cient number of events (>200) for construction of a
robust logistic regression model for mortality.
We used SPSS (version 19.0) for data analysis.
Categorical variables are presented as number (%) and
continuous variables as mean (SD) when normally
distributed or median (IQR) when not. We used χ² and
Fisher’s exact tests to compare categorical variables and
the t test or the Mann-Whitney U test to compare
continuous variables. Signifi cance was set at p<0·05. We
constructed several binary logistic regression models to
identify factors independently associated with hospital
mortality and to adjust for diff erences in confounding
factors between countries. These included a one-level
model and a hierarchical two-level generalised linear
mixed model, with patients being at the fi rst level and
hospital at the second. Factors were entered into the
model based on their univariate relation to outcome
(p<0·05). All factors were biologically plausible with a
sound scientifi c rationale and a low rate of missing data.
The results of the model are reported as adjusted odds
ratios (OR) with 95% CI. We assessed the models
through sensitivity analyses with three random (disjoint)
subsamples of countries and a fourth sample removing
all patients from the largest country in the dataset (the
UK). We explored all possible interacting factors and
examined how they might have aff ected the fi nal results.
This study is registered with, number
Role of the funding source
The study was funded by the European Society of
Intensive Care Medicine and the European Society of
Anaesthesiology who appointed an independent steering
committee (appendix p 11), who were responsible for
study design, conduct, and data analysis. Members of the
steering committee had full access to the study data and
were solely responsible for interpretation of the data,
drafting and critical revision of the report, and the
decision to submit for publication.
For the EuSoS study protocol
Figure 1: Study profi le
(A) All patients. (B) Patients admitted to critical care. CRF=case report form.
985 patients with operating room CRF
236 duplicates
749 with data available for inclusion
206 with inconsistent data
543 available for analysis
4 with missing hospital
outcome data
539 included in analysis
3635 patients with critical care CRF
9 duplicates
3626 with data available for inclusion
23 with missing operating room data
3603 available for analysis
4 with missing hospital
outcome data
3599 included in analysis
Articles Vol 380 September 22, 2012
We collected data describing patients undergoing in-
patient surgery in 498 hospitals across 28 European
nations. Median number of operating theatres in each
hospital was 15 (IQR 10–22) and median number of
critical care beds was 19 (9–40). Data were returned for
46 985 cases of which 446 were removed having been
identifi ed as duplicates or having missing critical care or
mortality data, leaving 46 539 for analysis (fi gure 1). A
median number of 83 (39–125) patients were included
per hospital and 1045 (455–1732) per coun try. 281 (56%)
hospitals were affi liated to a university, recruiting
31 132 patients (68% of total, appendix p 2).
Table 1 shows baseline data for all patients. Overall
crude mortality was 4·0% and the median duration of
hospital stay was 3·0 days (IQR 1·0–7·0). Prevalence of
comorbid disease, grade of surgery, crude mortality rates,
duration of hospital stay, and number of critical care
admissions diff
ered substantially between countries
(table 2, appendix p 2). Table 2 shows unadjusted OR for
hospital mortality by country. 3599 patients (8%) were
admitted to critical care at some point during hospital
stay, of whom 2555 (71%) had planned admissions
(fi gure 2). Median stay in critical care was 1·2 days
(0·9–3·6). 1358 patients who died were not admitted to
critical care at any stage after surgery (73% of all deaths).
506 patients (14%) admitted to critical care died before
hospital discharge, of whom 218 (43%) died after the fi rst
admission to critical care was complete.
We explored variables associated with hospital mortality
in a univariate analysis, the fi ndings of which were much
the same as for a sensitivity analysis of diff erent subsets
of the database (table 1, appendix pp 3–4). We then
constructed several binary logistic regression models to
adjust for baseline diff erences that might explain the
unadjusted OR for individual countries (table 2). We
developed both single-level and multilevel models
(appendix pp 5–8) with variables that were signifi cant in
the univariate analysis. The point estimates for the OR
did not diff er greatly between the one-level and two-level
models, but the hierarchical model consistently provided
a more conservative estimate of country eff ects across the
sensitivity tests (appendix p 9).
We constructed a further model including all signifi cant
interacting factors (appendix p 10). Since this increased
model complexity did not substantially change the
country-level estimates, we report results of the more
parsimonious two-level model without interactions
(fi gure 3). Factors that were independently associated with
mortality and that we therefore used to adjust for baseline
confounders were: country where surgery was done,
urgency of surgery, grade of surgery, surgical procedure
category, age, American Society of Anesthesi ologists
(ASA) score, metastatic disease, and cirrhosis (appendix
pp 7–8). We entered ASA score rather than the Lee Revised
Cardiac Index because, although the two were highly
correlated, less data describing ASA score were missing.
All patients
(n=46 539)
Died in
Survived to
(n=44 657)
Odds ratio (95% CI) p value
Age (years) 56·7 (18·5) 61·0 (18·7) 56·6 (18·5) 1·01 (1·01–1·02) <0·0001
Men 22 607 968 21 629 1·15 (1·05–1·26) 0·003
Present smoker 9872 363 9503 0·90 (0·80–-1·01) 0·07
ASA score
1 11 642 362 11 280 Reference ··
2 21 582 633 20 944 0·94 (0·83–1·07) 0·36
3 11 574 539 11 025 1·51 (1·32–1·73) <0·0001
4 1559 279 1277 6·75 (5·71–7·97) <0·0001
5 90 49 41 35·61 (23·23–54·59) <0·0001
Grade of surgery
Minor 12 041 431 11 608 Reference ··
Intermediate 22 231 741 21 483 0·93 (0·82–1·05) 0·22
Major 12 170 685 11 476 1·59 (1·40–1·80) <0·0001
Urgency of surgery
Elective 35 049 1129 33 908 Reference ··
Urgent 8923 483 8436 1·71 (1·52–1·91) <0·0001
Emergency 2557 249 2303 3·20 (2·77–3·70) <0·0001
Surgical specialty
Orthopaedics 12 214 468 11 744 1·02 (0·84–1·24) 0·85
Breast 1500 43 1456 0·76 (0·53–1·07) 0·12
Gynaecology 3972 115 3857 0·76 (0·59–0·99) 0·04
Vascular 2376 140 2233 1·61 (1·26–2·05) 0·0001
2228 155 2071 1·88 (1·48–2·39) 0·0001
4972 284 4683 1·54 (1·25–1·91) 0·0001
Hepato-biliary 2247 113 2134 1·35 (1·04–1·74) 0·025
Plastic or
2432 73 2356 0·79 (0·59–1·06) 0·12
Urology 4881 144 4737 0·78 (0·61–0·99) 0·042
Kidney 463 9 454 0·51 (0·26–1·01) 0·05
Head and neck 5640 174 5466 0·82 (0·65–1·03) 0·09
Other 3463 132 3329 Reference
Laparoscopic surgery 5510 160 5350 0·69 (0·59–0·82) <0·0001
Comorbid disorder
Cirrhosis 498 65 433 3·64 (2·79–4·76) <0·0001
Congestive heart
2154 166 1985 2·10 (1·78–2·48) <0·0001
COPD 5162 244 4912 1·21 (1·05–2·48) 0·008
Coronary artery
6274 387 5881 1·73 (1·54–1·94) <0·0001
Diabetes (taking
2081 135 1939 1·73 (1·44–2·07) <0·0001
Diabetes (not
taking insulin)
3495 147 3348 1·05 (0·88–1·24) 0·61
Metastatic cancer 2173 155 2017 1·91 (1·61–2·27) <0·0001
Stroke 2006 120 1884 1·57 (1·30–1·90) <0·0001
Data are mean (SD) or n unless otherwise specifi ed. Odds ratios were constructed for in-hospital mortality with
univariate binary logistic regression analysis. ASA=American Society of Anesthesiologists. COPD=chronic obstructive
pulmonary disease.
Table 1: Description of cohort
1062 Vol 380 September 22, 2012
With the UK study population as the reference category,
we identifi ed higher unexplained rates of mortality in
Poland, Romania, Latvia, and Ireland (table 2, fi gure 3).
This international prospective study has provided data
for a population of more than 46 000 unselected patients
undergoing inpatient surgery from 28 European coun-
tries. 4% of included patients died before hospital dis-
charge, which was a higher mortality rate than
expected.2,3,6,13–16 We identifi ed substantial diff erences in
crude and risk adjusted mortality rates between
countries. When compared with the UK, the recorded
mortality rates for Poland, Latvia, Romania, and Ireland
were higher even after adjustment for all identifi ed
confounding variables. This pattern could relate to
cultural, demographic, socio economic, and political
diff erences between nations, which might aff ect
population health and health-care outcomes.
A major strength of our study was the large number of
consecutive unselected patients enrolled in a multicentre
and multinational setting. A vigorous approach to follow-
up for missing and incomplete data provided a high-
quality dataset for analysis. The dataset allowed us to
explore probable prognostic factors and to adjust crude
mortality rates to describe diff erences in outcomes
between countries. Our analysis identifi ed several factors
associated with increased mortality. These fi ndings
suggest that surgery-related and patient-related factors
interact to increase mortality risk. Only two comorbid
disease categories were identifi ed as independent
variables. This fi nding probably arose because the ASA
score was designed to describe the severity of coexisting
medical disease.
Evidence suggests that critical-care-based cardio respir-
atory interventions can improve outcomes among high-
risk surgical patients.17–21 However, in our study, only 5% of
patients underwent a planned admission to critical care
with a median stay of about 1 day. Unplanned admissions
to critical care were associated with higher mortality rates
than were planned admissions. Remark ably, most patients
who died (73%) were not admitted to critical care at any
Number of
Median days in
hospital (IQR)
Number admitted
to critical care
Percentage admitted
to critical care (95% CI)
Number died
in hospital
Percentage died in
hospital (95% CI)
Unadjusted OR
(95% CI)
Adjusted OR
(95% CI)
p value
Belgium 1486 3·0 (1·0–6·0) 136 9·2% (7·7–10·6) 47 3·2% (2·3–4·1) 0·89 (0·65–1·21) 1·65 (0·81–3·40) 0·17
Croatia 1767 4·0 (2·0–7·0) 166 9·4% (8·0–10·8) 131 7·4% (6·2–8·6) 2·17 (1·77–2·67) 1·89 (0·94–3·80) 0·07
Cyprus 45 1·0 (1·0–3·0) 0 0 1 2·2% (0·0–6·7) 0·62 (0·09–4·48) 0·82 (0·04–16·70) 0·90
Czech Republic 434 4·0 (2·0–9·0) 21 4·8% (2·8–6·9) 10 2·3% (0·9–3·7) 0·64 (0·34–1·21) 1·30 (0·23–7·46) 0·77
Denmark 1000 2·0 (1·0–5·0) 36 3·6% (2·4–4·8) 32 3·2% (2·1–4·3) 0·90 (0·62–1·29) 1·16 (0·52–2·61) 0·72
Estonia 727 3·0 (1·0–6·0) 51 7·0% (5·2–8·9) 11 1·5% (0·6–2·4) 0·42 (0·23–0·76) 0·60 (0·16–2·28) 0·45
Finland 1071 2·0 (1·0–5·0) 43 4·0% (2·8–5·6) 21 2·0% (1·1–2·8) 0·54 (0·35–0·85) 0·44 (0·19–1·05) 0·06
France 2278 3·0 (1·0–6·0) 132 5·8% (4·8–6·8) 73 3·2% (2·5–3·9) 0·90 (0·70–1·16) 1·36 (0·72–2·56) 0·34
Germany 5284 4·0 (2·0–9·0) 611 11·6% (10·7–12·4) 133 2·5% (2·1–2·9) 0·70 (0·57–0·86) 0·85 (0·50–1·43) 0·54
Greece 1803 3·0 (2·0–7·0) 63 3·5% (2·7–4·3) 65 3·6% (2·7–4·5) 1·01 (0·78–1·33) 1·20 (0·66–2·16) 0·55
Hungary 621 4·0 (2·0–7·0) 44 7·1% (5·1–9·1) 20 3·2% (1·8–4·6) 0·90 (0·57–1·43) 1·23 (0·43–3·50) 0·69
Iceland 162 2·0 (1·0–4·0) 15 9·3% (4·8–13·8) 2 1·2% (0·0–3·0) 0·34 (0·08–1·37) 0·47 (0·07–3·41) 0·46
Ireland 856 3·0 (1·0–6·0) 66 7·7% (5·9–9·5) 55 6·4% (4·8–8·1) 1·86 (1·39–2·49) 2·61 (1·30–5·27) 0·007
Italy 2673 3·0 (2·0–7·0) 200 7·5% (6·5–8·5) 141 5·3% (4·4–6·1) 1·51 (1·24–1·84) 1·70 (0·97–2·97) 0·06
Latvia 302 4·0 (2·0–8·0) 19 6·3% (3·5–9·1) 65 21·5% (16·9–26·2) 7·44 (5·55–9·97) 4·98 (1·22–20·29) 0·025
Lithuania 375 3·0 (2·0–5·0) 14 3·7% (1·8–5·7) 10 2·7% (1·0–4·3) 0·74 (0·39–1·40) 1·21 (0·21–6·95) 0·83
Netherlands 1627 3·0 (1·0–6·0) 126 7·7% (6·4–9·0) 32 2·0% (1·3–2·7) 0·55 (0·38–0·78) 0·63 (0·28–1·41) 0·26
Norway 689 3·0 (1·0–6·0) 31 4·5% (3·0–6·1) 10 1·5% (0·6–2·4) 0·40 (0·21–0·75) 0·51 (0·17–1·49) 0·22
Poland 397 5·0 (2·0–7·5) 8 2·0% (0·6–3·4) 71 17·9% (14·1–21·7) 5·91 (4·48–7·79) 6·92 (2·37–20·27) 0·0004
Portugal 1489 3·0 (1·0–7·0) 103 6·9% (5·6–8·2) 61 4·1% (3·1–5·1) 1·16 (0·88–1·53) 1·43 (0·72–2·83) 0·31
Romania 1298 5·0 (3·0–8·0) 209 16·1% (14·1–18·1) 88 6·8% (5·4–8·2) 1·97 (1·55–2·51) 3·19 (1·61–6·29) 0·001
Serbia 85 5·0 (3·0–7·0) 1 1·2% (0·0–3·5) 2 2·4% (0·0–5·6) 0·65 (0·16–2·67) 1·06 (0·11–10·04) 0·96
Slovakia 1156 3·0 (2·0–7·0) 22 1·9% (1·1–2·7) 129 11·2% (9·3–13·0) 3·41 (2·76–4·20) 2·15 (0·91–5·07) 0·08
Slovenia 518 3·0 (1·0–7·0) 13 2·5% (1·2–3·9) 15 2·9% (1·5–4·3) 0·81 (0·48–1·37) 1·12 (0·30–4·22) 0·86
Spain 5433 3·0 (1·0–7·0) 677 12·5% (11·6–13·3) 208 3·8% (3·3–4·3) 1·08 (0·91–1·28) 1·39 (0·89–2·18) 0·15
Sweden 1314 2·0 (1·0–6·0) 42 3·2% (2·2–4·2) 24 1·8% (1·1–2·6) 0·50 (0·33–0·77) 0·58 (0·23–1·49) 0·26
Switzerland 1019 4·0 (2·0–8·0) 79 7·8% (6·1–9·4) 20 2·0% (1·1–2·8) 0·54 (0·35–0·86) 0·86 (0·25–2·97) 0·81
UK 10 630 2·0 (1·0–6·0) 671 6·3% (5·9–6·8) 378 3·6% (3·2–3·9) 1·00 ·· ··
Odds ratios (OR) referenced against the UK and adjusted for age, American Society of Anesthesiologists’ score, urgency of surgery, grade of surgery (minor, intermediate, major), surgical specialty, and the
presence of either metastatic disease or cirrhosis in a two-level binary logistic regression model (with patient at the fi rst level and hospital at the second).
Table 2: Relation between country and in-hospital mortality
Articles Vol 380 September 22, 2012
stage after surgery. Of patients who died after admission
to critical care, 43% did so after the initial episode was
complete and the patient had been discharged to a
standard ward. These fi ndings suggest a systematic failure
in the process of allocation of critical care resources. This
notion is consistent with previous reports of a failure to
rescue deteriorating surgical patients with a detrimental
eff ect on patient outcomes22 and the high incidence of
myocardial injury in the days after surgery.23 For some
patients with a poor prognosis, postoperative admission to
critical care might have been deemed inappropriate—eg,
after palliative surgery for disseminated malignancy.
However, our data suggest these cases are few in number
(<5% of patients had malignancy, table 1). Meanwhile
other investigators have challenged the suggestion that
patients should be off ered surgery when the standard of
postoperative care is unlikely to be adequate for their
needs.2 The low rate of admission to critical care prevents
any detailed comparison of this resource between nations.
Further research is needed to better understand whether
early admission to critical care can improve survival after
major surgery.
Despite the large sample size, our study might not be
truly representative of current practice across Europe
because only a small proportion of European hospitals
took part. Although in some countries the patient sample
was large enough to show national practice, the high
proportion of patients enrolled in university hospitals in
other countries suggests a degree of selection bias. In
particular, our data might not show the true surgical
Figure 2: Planned and unplanned admission to a critical-care unit according to urgency of surgery
Data are n (%) or median (IQR). We collected data describing the fi rst critical care admission for any individual patient. The data presented do not describe readmission to critical care. Because of
incomplete data for admission planning, 19 admissions to critical care are not presented in this fi gure. EuSOS=European Surgical Outcomes Study. Elective=not immediately life saving; planned within
months or weeks. Urgent=planned surgery within hours or days of the decision to operate. Emergency=as soon as possible; no delay to plan care; ideally within 24 h.
EuSOS cohort
539 (100%); 1864 deaths (4%)
Elective surgery
040 (75%); 1132 deaths (3%)
Urgent surgery
8919 (19%); 483 deaths (5%)
Emergency surgery
2557 (5%); 249 deaths (10%)
admission to
critical care
1864 (5%);
32 (2%) deaths
Stay in critical
care 1 day (1–2)
Hospital stay
9 days (6–15)
Discharged to
ward alive
1832 (98%);
88 (5%) deaths
after discharge
from critical care
critical care
278 (1%);
22 deaths (8%)
Stay in critical
care 2 days (1–3)
Hospital stay
10 days (6–19)
Discharged to
ward alive
256 (92%);
16 (6%) deaths
after discharge
from critical care
No admission to
critical care
32 895 (94%);
973 deaths (3·0%)
Hospital stay
3 days (1–5)
admission to
critical care
490 (5%);
54 deaths (11%)
Stay in critical
care 2 days (1–7)
Hospital stay
14 days (8–28)
Discharged to
ward alive
436 (89%);
30 (7%) deaths
after discharge
from critical care
admission to
critical care
391 (4%);
63 deaths (16%)
Stay in critical
care 3 days (1–6)
Hospital stay
14 days (8–26)
Discharged to
ward alive
328 (84%);
33 (10%) deaths
after discharge
from critical care
No admission to
critical care
8033 (90%);
301 deaths (4%)
Hospital stay
4 days (2–8)
admisison to
critical care
201 (8%);
37 deaths (18%)
Stay in critical
care 3 days (1–8)
Hospital stay
13 days (7–27)
admission to
critical care
356 (14%);
79 deaths (22%)
Stay in critical
care 3 days (1–8)
Hospital stay
15 days (7–28)
No admission to
critical care
1999 (78%);
84 deaths (4%)
Hospital stay
4 days (1–8)
Discharged to
ward alive
164 (82%);
23 (14%) deaths
after discharge
from critical care
Discharged to
ward alive
277 (78%);
26 (9%) deaths
after discharge
from critical care
Figure 3: Adjusted odds ratio for death in hospital after surgery for each country
Czech Republic
UK (reference)
0·01 0·1 1
Adjusted odds ratio (95% CI)
10 100
1064 Vol 380 September 22, 2012
case-mix and standards of care in countries with a small
number of participating hospitals. Although we planned
to enrol every eligible patient undergoing surgery during
the study period, we cannot be sure of the exact
proportion of eligible patients included. Nonetheless,
assuming the volume of surgery during the cohort week
is typical of the participating hospitals, these centres
undertake more than 2·3 million inpatient surgical
procedures each year, which is 1% of the estimated
volume of surgery taking place worldwide.1 Whether
truly repre sentative or not, our fi ndings clearly describe a
large cross-section of health care in Europe.
Some of our fi ndings might be indicative of limitations
of commonly used risk-adjustment variables with un-
expected patterns of survival across categories for both
ASA score and grade of surgery. This fi nding could result
from the poor ability of clinicians to discriminate between
the less severe categories of these variables. Random
partitioning of the countries into three equal groups and
repetition of the modelling exercise showed much the
same results with regards to the OR of the relevant eff ect
factors, showing some stability of the risk adjustment in
subsets of countries. This stability was further confi rmed
in more complex models that included interactions
between variables for which none of the interactions with
the country factor contributed signifi cantly to prediction.
We identifi ed other interactions that did signifi cantly
contribute to prediction but we did not record a substantial
change in country eff ects when estimated from the
extended model including these interactions. We
therefore decided to use the simpler of the hierarchical
models for the fi nal analysis because our aim had been to
construct a parsimonious model that practising clinicians
would easily understand.
As far as we are aware, this was the fi rst large,
prospective, international assessment of surgical out-
comes (panel). In some countries, data are available that
describe survival after specifi c procedures such as
vascular, joint replacement, or bowel cancer surgery.24–26
However, these audits are poorly representative of overall
national surgical populations because high-risk patients
are under-represented. The few previous estimates
suggest an overall mortality for unselected inpatient
surgery of between 1% and 2%,2,3,6,13–16 but these values
are representative of only a few health-care systems. In a
previous study13 of national registry data from the
Netherlands, 30 day mortality was reported as 1·85%,
which is much the same as the crude hospital mortality
of 2% for this country in the EuSOS study. In the UK, a
prospective investigation2 with a very similar methods to
EuSOS identifi ed a postoperative critical care admission
rate of 6·7%, which is much the same as to the value of
6% for EuSOS in the UK.2 However, 30 day mortality
was 1·6% compared with 3·6% for 60 day in-hospital
mortality for UK patients in EuSOS. Reports from
nations outside Europe describe 30 day mortality rates
from 1·3% to 2·0%.6,14,15
Previous investigators have described the diff erences
in provision of health services across Europe, in
particular numbers of critical care beds.10,11 The reported
seven-times greater provision of critical care beds for
Germany than for the UK is likely to aff ect rates of
admission to critical care and postoperative out-
comes.10,11,27 This fi nding is in keeping with our present
data that show a greater rate of admission to critical care
after surgery in Germany than in the UK. Other studies
have shown that fewer than a third of high-risk non-
cardiac surgical patients are admitted to critical care
after surgery in the UK despite high mortality rates,2–4
which is consistent with the results of our study; across
Europe 73% of surgical patients who died were never
admitted to critical care. This situation contrasts with
perioperative care for cardiac surgical patients who by
defi nition have severe comorbid disease and undergo
major body cavity surgery followed by routine admission
to critical care with mortality rates of less than 2%.28
Several reasons could explain why outcomes for cardiac
and non-cardiac surgical patients diff er but the quality of
perioperative care is likely to be among the most
important. The heath-care community increasingly
recognises the importance of the entire perioperative
care pathway including pre operative assessment,
optimisation of coexisting medical disease, integrated
care pathways relevant to the surgical procedure, WHO
surgical checklists, advanced haemo dynamic monitoring
during surgery, early admission to critical care, acute
pain management and critical-care outreach services,
and hospital discharge planning together with the
Panel: Research in context
Systematic review
We searched Medline for original research from the past 10 years describing mortality
rates in large unselected national and international populations of patients undergoing
non-cardiac surgery. We used the search terms “surgery”, “mortality, and “complications”
and widened our search to include retrospective analyses of health-care registries and
prospective epidemiological studies. Publications were screened by title and then by
abstract for relevance to the objectives of our study. Additionally, coinvestigators in
various European nations searched for publicly available registry analyses reporting
mortality rates for unselected populations of surgical patients. We identifi ed seven large
national studies2,3,6,13-16 describing mortality rates for the population of interest, three of
which involved prospective data collection. No studies were identifi ed that provided
international comparative data. The last search was done on June 15, 2012.
As far as we are aware, this was the fi rst large prospective international epidemiological
study of unselected non-cardiac surgical patients and as such it provides a new
perspective on mortality after surgery. A few national reports describe mortality rates
from 1·3% to 2·0%.2,3,6,13-16 In our study, the overall crude mortality rate of 4% was higher
than anticipated. We identifi ed important variations in risk-adjusted mortality rates
between nations, and critical care resources did not seem to be allocated to patients at
greatest risk of death. Our fi ndings raise important public health concerns about the
provision of care for patients undergoing surgery in Europe.
Articles Vol 380 September 22, 2012
primary care physician.20,21 Routine audit and reporting
of data for clinical outcomes has also proved a highly
eff ective instru ment for improvement of the quality of
perioperative care.29
Our fi ndings suggest both the need and potential to
imple ment measures to improve postoperative outcomes.
In addition to further research in this discipline, the root
causes of this problem could be better understood through
increased use of high-quality registries designed to
capture robust data describing quality of care and clinical
outcomes for surgical patients. This step would require
increased funding for this specifi c area of he alth services
research. The high mortality rate after surgery might be
modifi ed by changes in the organisation of care.20
All authors were involved in the design and conduct of the study. Data
collection and collation was done by the members of the EuSOS study
group. AR, RM, and PB did the data analysis with input from RP. The
report was drafted by RP and AR and revised following critical review by
all authors.
Confl icts of interest
We declare that we have no confl icts of interest.
This study was funded by the European Society of Intensive Care
Medicine and the European Society of Anaesthesiology. RP is a National
Institute for Health Research (UK) Clinician Scientist.
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    • "This emphasis applies to health care services in every country in the world regardless of how their healthcare system is administered or who the buyer is. Indications are that health care in Iceland, one of the Nordic countries with nationalized healthcare is good as it ranks among the best in international comparisons on patient outcomes (OECD 2013, Pearse et al. 2012). These indicators however primarily focus on mortality and morbidity outcomes statistics and nursing sensitive outcomes are not identified. "
    [Show abstract] [Hide abstract] ABSTRACT: Aims and objectives: To identify the contribution of hospital, unit, staff characteristics, staffing adequacy and teamwork to missed nursing care in Iceland hospitals. Background: A recently identified quality indicator for nursing care and patient safety is missed nursing care defined as any standard, required nursing care omitted, or significantly delayed, indicating an error of omission. Former studies point to contributing factors to missed nursing care regarding hospital, unit and staff characteristics, perceptions of staffing adequacy as well as nursing teamwork, displayed in the Missed Nursing Care Model. Design: This was a quantitative cross-sectional survey study. Methods: The sample were all registered nurses and practical nurses (N=864) working on 27 medical, surgical and intensive care inpatient units in 8 hospitals throughout Iceland. Response rate was 69.3%. Data were collected in March-April 2012 using the combined MISSCARE Survey-Icelandic and the Nursing Teamwork Survey-Icelandic. Descriptive, correlational and regression statistics were used for data analysis. Results: Missed nursing care was significantly related to hospital and unit type, participants' age and role, and their perception of adequate staffing and level of teamwork. The multiple regression testing of Model 1 indicated unit type, role, age and staffing adequacy to predict 16% of the variance in missed nursing care. Controlling for unit type, role, age and perceptions of staffing adequacy, the multiple regression testing of Model 2 showed that nursing teamwork predicted an additional 14% of the variance in missed nursing care. Conclusions: The results shed light on the correlates and predictors of missed nursing care in hospitals. This study gives direction as to the development of strategies for decreasing missed nursing care, including ensuring appropriate staffing levels and enhanced teamwork. Relevance to clinical practice: By identifying contributing factors to missed nursing care, appropriate interventions can be developed and tested. This article is protected by copyright. All rights reserved.
    Full-text · Article · Jun 2016
    • "In recent years, public reporting of hospital quality indicators in general, and outcome indicators in particular, have been established in several health care systems and in international comparative studies [1][2][3][4]. In particular, mortality within a fixed time period after admission, for certain conditions, is in widespread use as a quality indicator. "
    [Show abstract] [Hide abstract] ABSTRACT: Background The purpose of this study was to assess the validity of patient administrative data (PAS) for calculating 30-day mortality after hip fracture as a quality indicator, by a retrospective study of medical records. Methods We used PAS data from all Norwegian hospitals (2005–2009), merged with vital status from the National Registry, to calculate 30-day case-mix adjusted mortality for each hospital (n = 51). We used stratified sampling to establish a representative sample of both hospitals and cases. The hospitals were stratified according to high, low and medium mortality of which 4, 3, and 5 hospitals were sampled, respectively. Within hospitals, cases were sampled stratified according to year of admission, age, length of stay, and vital 30-day status (alive/dead). The final study sample included 1043 cases from 11 hospitals. Clinical information was abstracted from the medical records. Diagnostic and clinical information from the medical records and PAS were used to define definite and probable hip fracture. We used logistic regression analysis in order to estimate systematic between-hospital variation in unmeasured confounding. Finally, to study the consequences of unmeasured confounding for identifying mortality outlier hospitals, a sensitivity analysis was performed. Results The estimated overall positive predictive value was 95.9% for definite and 99.7% for definite or probable hip fracture, with no statistically significant differences between hospitals. The standard deviation of the additional, systematic hospital bias in mortality estimates was 0.044 on the logistic scale. The effect of unmeasured confounding on outlier detection was small to moderate, noticeable only for large hospital volumes. Conclusions This study showed that PAS data are adequate for identifying cases of hip fracture, and the effect of unmeasured case mix variation was small. In conclusion, PAS data are adequate for calculating 30-day mortality after hip-fracture as a quality indicator in Norway.
    Full-text · Article · May 2016
    • "Patients requiring high-risk surgery were not selected for inclusion in study population, because PVI hemodynamic monitoring is considered for intermediate surgical risk [6]. This population of patients is the more frequently observed in practice [2]. Inclusion criteria were large for the study population, reinforcing the external validity of the study. "
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