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Global burden of postoperative death

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  • University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa

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Deaths within 30 days of surgery in low-, middle-, and high-income countries
Dmitri Nepogodiev MBChB, Janet Martin PharmD, Prof Bruce Biccard PhD, Alex
Makupe MMed Surgery, Aneel Bhangu PhD on behalf of the NIHR Global Health
Research Unit on Global Surgery*
*Collaborating co-authors listed in appendix
Corresponding author: Mr Aneel Bhangu, MBChB PhD FRCS, NIHR Global Health
Research Unit on Global Surgery, 2nd Floor, Institute of Translational Medicine,
Heritage Building, University of Birmingham, Mindelsohn Way, Birmingham, B15
2TH. Correspondence to: a.a.bhangu@bham.ac.uk
Category: Correspondence
Word count: 395
Acknowledgements: None.
Declaration of interests: The authors have no competing interests to declare.
Funding: This report was funded by a National Institute for Health Research (NIHR)
Global Health Research Unit Grant (NIHR 17-0799). The funder had no role in study
design, data collection, analysis and interpretation, or writing of this report. The
funder has approved the submission of this report for publication. The views
expressed are those of the authors and not necessarily those of the National Health
Service, the NIHR, or the UK Department of Health and Social Care.
The Lancet Commission on Global Surgery identified that 313 million surgical
procedures are performed each year1. Little is known about the quality of surgery
globally, as robust postoperative death rates are available for only 29 countries2. The
rate of postoperative death captures the success of the system of surgical care and
improvement in this metric is a priority worldwide.
We aimed to estimate how many people around the world die within 30 days of
surgery, based on surgical volume, case-mix, and postoperative death rates adjusted
for country income. England’s Hospital Episode Statistics linked Office of National
Statistics dataset (HES-ONS) is one of the world’s most comprehensive procedure-
specific mortality resources, with national coverage from a universal health care
system. We used this dataset as the baseline for our estimations for high income
settings, adjusting case-mix and mortality to estimate total postoperative deaths in
low- and middle-income countries (LMICs). We estimated likely additional
postoperative deaths if surgical volume was expanded to address the annual unmet
need for 143 million operations in LMICs3 (S1 Appendix).
Overall, our analysis suggests that currently at least 4.2 million people around the
world die within 30 days of surgery each year, with half of these deaths occurring in
LMICs. We project that expanding surgical services to address unmet need would
increase total deaths to 6.1 million annually, with an additional 1.9 million occurring in
LMICs each year. Based on 4.2 million deaths, 7.7% of all deaths globally occur
within 30 days of surgery4. This figure is greater than that attributed to any other
cause of death globally except ischaemic heart disease and stroke (Figure 1). More
people die within 30 days of surgery than the combined 2.97 million deaths attributed
to HIV, malaria, and tuberculosis annually4.
Our approach is limited by a number of necessary assumptions (S2 Appendix). For
example, HES-ONS reports amongst the lowest postoperative death rates in the
world. Basing our calculation on higher baseline postoperative death rates from other
HIC sources substantially increases our projections of total postoperative deaths.
Whilst there is a pressing need to expand surgical services to currently underserved
populations, this must be done in tandem with initiatives to reduce postoperative
deaths. Funders and policy makers should prioritise research that aims to make
surgery safer, particularly in LMICs. Routine measurement of surgical outcomes is
essential to monitoring global progress in addressing the burden of postoperative
death.
Figure 1: Top ten global causes of death in the 2016 Global Burden of Death
study, with addition of postoperative death*
*Percentages give proportion of total global deaths attributable to that cause based
on Global Burden of Disease 2016 data4
IHD: ischaemic heart disease; COPD: chronic obstructive pulmonary disease; LRTI:
lower respiratory tract infections
Authors’ contributions
DN and AB conducted data analysis and interpretation and had access to all data.
DN, JM, BB, AM, AB drafted the manuscript. Collaborators listed in the Appendix
critically revised the manuscript and have approved the final draft and the decision to
submit the manuscript. AB is the guarantor for this report.
Appendix: collaborating authors (PubMed citable)
Dmitri Nepogodiev, Janet Martin, Bruce Biccard, Alex Makupe, Adesoji Ademuyiwa,
Adewale Oluseye Adisa, Maria Lorena Aguilera, Sohini Chakrabortee, J. Edward
Fitzgerald, Dhruva Ghosh, James C. Glasbey, Ewen M. Harrison, Jean C. Allen
Ingabire, Hosni Khairy Salem, Marie Carmela Lapitan, Ismail Lawani, David Lissauer,
Laura Magill, Alex Makupe, Rachel Moore, Daniel C. Osei-Bordom, Thomas D.
Pinkney, Ahmad Uzair Qureshi, Antonio Ramos-De la Medina, Sarah Rayne, Sudha
Sundar, Stephen Tabiri, Azmina Verjee, Raul Yepez, O. James Garden, Richard
Lilford, Peter Brocklehurst, Dion G. Morton, Aneel Bhangu
Table of names for entry to PubMed
First/ middle names
Surname
Dmitri
Nepogodiev
Janet
Martin
Bruce
Biccard
Alex
Makupe
Adesoji
Ademuyiwa
Adewale Oluseye
Adisa
Maria-Lorena
Aguilera
Sohini
Chakrabortee
J. Edward
Fitzgerald
Dhruva
Ghosh
James C.
Glasbey
Ewen M.
Harrison
J.C. Allen
Ingabire
Hosni
Salem
Marie Carmela
Lapitan
Ismail
Lawani
David
Lissauer
Laura
Magill
Rachel
Moore
Daniel C.
Osei-Bordom
Thomas D.
Pinkney
Ahmad Uzair
Qureshi
Antonio
Ramos-De la Medina
Sarah
Rayne
Sudha
Sundar
Stephen
Tabiri
Azmina
Verjee
Raul
Yepez
O. James
Garden
Richard
Lilford
Peter
Brocklehurst
Dion G.
Morton
Aneel
Bhangu
S1 Appendix: Methods and results
Approach to calculating postoperative deaths
The number of postoperative deaths occurring in high income countries (HICs) and
low- and middle-income countries (LMICs) were separately calculated. These two
figures were then summed to calculate a global total for deaths within 30 days of
surgery.
The equation to calculate postoperative deaths in HICs was:
𝑐!"#×𝑥!"# ×𝑦!"#
!"
!
Where:
cHIC = total operations performed annually in HICs
xHIC= proportion of HIC case-mix belonging to particular specialty
yHIC= postoperative death rate in HIC settings for particular specialty
The equation to calculate postoperative deaths in LMICs was:
𝑐!"#$×𝑥!"#$ ×𝑦!"# ×𝑚
!"
!
!
Where:
cLMIC = total operations performed annually in LMICs
xLMIC= proportion of LMIC case-mix belonging to particular specialty
yHIC= postoperative death rate in HIC settings for particular specialty
m = postoperative death rate adjustment constant
Data sources
Surgical volume: The c constants are the estimated number of operations
performed in HICs (cHIC) and LMICs (cLMIC). The total operations performed in HICs
versus LMICs are not readily available, therefore health expenditure per capita was
used as a proxy. Health expenditure per capita $1000 per year was assumed to
indicate HICs, and health expenditure <$1000 per year was assumed to represent
LMICs. On this basis, 187,000,000 (95% confidence interval (CI) 155,800,000 to
224,500,000) operations are performed annually in HICs and 125,900,000 (95% CI
83,900,000 to 202,300,000) are performed in LMICs5.
Case-mix: A key reason for differences in raw postoperative death rates between
HICs and LMICs might be operative case-mix12. We therefore established separate
estimates for case-mix in HICs (variable xHIC) and LMICs (variable xLMIC). Since there
are no readily available LMIC national surgical registries, to estimate LMIC case-mix
we have used data from the African Surgical Outcomes Study (ASOS)6. This
prospective cohort study captured data on 11,422 patients across 247 hospital in 25
countries. ASOS reported case-mix split into 15 surgical specialties; we have merged
‘thoracic (lung)’ and ‘thoracic (gut)’ to produce a list of 14 specialties (S1 Table).
For HICs, case-mix was based on a single national registry of surgical activity from a
universal health system: England’s Hospital Episodes Statistics linked Office of
National Statistics (HES-ONS) dataset. To produce HIC case-mix categories that
would be directly comparable to the LMIC case-mix data, we first cleaned the 2010
HES-ONS to remove non-surgical (e.g. endoscopic and dental) procedures. This
removed 28.8% (374/1,297) procedure codes. The remaining 923 codes were then
categorised into the 14 specialties derived from ASOS to estimate case-mix (S1
Table).
HIC postoperative death rates: 30-day postoperative death rates were separately
derived for each of the 14 specialties (variable y). For our main analysis we used the
HES-ONS dataset. Postoperative death rates were defined as total deaths within 30
days of a procedure as a proportion of finished consultant episodes in that specialty.
The overall postoperative death rate in the HES-ONS dataset was 1.09%
(37,645/3,438,242).
Several studies over the past decade have reported national and international 30-day
postoperative death rates, but their case-mix has varied. As a sensitivity analysis, we
further tested our base model to explore totals for postoperative death using other
recently reported baseline postoperative death rates from high income settings:
§ An analysis of 298,772 non-cardiac surgery cases from the American College
of Surgeons National Surgical Quality Improvement Program (NSQIP)
database from 2005-2007 found the baseline postoperative death rate to be
1.34%7. Since per-specialty estimates were not available, we proportionately
scaled the HES-ONS per-specialty rates by the ratio of the NSQIP (1.34%) to
HES-ONS mortality (1.09%). However, since the NSQIP cohort excluded
cardiac and obstetric surgery, the HES-ONS postoperative death rates were
used for these specialties.
§ The European Surgical Outcomes Study (EUSOS) captured data on patients
undergoing non-obstetric, non-day case surgery across 498 hospitals in 28
European countries8. The overall postoperative death rate was 3.99%
(1855/46539). Since the specialty categories reported in EUSOS did not
match up with the categories established from ASOS and HES-ONS, we
again proportionately scaled the HES-ONS per-specialty postoperative death
rates based on the ratio of EUSOS (3.99%) to HES-ONS reported deaths
(1.09%). Again, as the EUSOS cohort excluded obstetric surgery, the HES-
ONS postoperative death rate for obstetrics was used.
Mortality adjustment: Although many studies have reported higher postoperative
death rates in LMICs compared to HICs9,12, most studies are difficult to interpret as
most LMIC studies are single-centre or single-country and do not allow for a direct
comparison with HIC data. The GlobalSurg Collaborative has published two
prospective multicentre studies (GS1 and GS2) directly comparing postoperative
death rates in LMICs versus HICs. In GS1, in 10,745 patients undergoing emergency
abdominal surgery across 357 centres in 58 countries, the postoperative death rate
was 6.8% (287/4,207) in LMICs versus 4.5% (291/6,538) in HICs10. GS2 included
12,539 patients who underwent either emergency or elective abdominal surgery
across 343 hospitals in 66 countries, and showed that postoperative postoperative
death rates were 2.5% (125/5,098) in LMICs versus 1.5% (110/7,130) in HICs11.
However, since these GlobalSurg studies were limited to abdominal surgery, we
wished to identify comparative data for this analysis that would be more broadly
generalizable across all surgeries. A large systematic review by Bainbridge et al of
global postoperative death across all surgical specialties found postoperative death
rates to be 0.2% (589/309,245) in LMICs compared to 0.1% (5,981/6,738,683) in
HICs12. Therefore, the relative risk for postoperative death for LMICs versus HICs
was 2.14 (95% CI 1.96 to 2.32).
At present, specialty-specific postoperative death rates have not been collected
across a representative range of LMICs. Therefore, we applied Bainbridge et al’s
estimate for increased risk of postoperative death in LMICs to adjust our baseline
HIC postoperative death rates (constant m).
All-cause deaths: The Global Burden of Disease (GBD) study estimated that in
2016 there were 54,698,600 deaths worldwide from all causes4. This figure was used
as the denominator to calculate the proportion of total global deaths that occur within
30 days of surgery.
Main analysis
Our main analysis utilised HIC condition-specific postoperative death rates based on
the HES-ONS dataset (S1 Table). We used the point estimates for surgical volume
(cHIC=187,000,000, cLMIC= 125,900,000) and Bainbridge’s relative risk of death in
LMICs compared to HICs (m=2.14). Based on these parameters, we estimated that
at least 4.2 million people die within 30 days of surgery each year (S2 Table).
To place 4.2 million annual postoperative deaths in context, we compared this figure
to a ranked list of the ten leading global causes of death reported in GBD 2016
(Figure 1).
Sensitivity analysis
To determine the impact of using different baseline rates of postopoerative death to
those provided by HES-ONS, we tested different scenarios based on the estimating
HIC postoperative death rates from NSQIP and EUSOS (S2 Table).
Projection for expanded surgical capacity
At present, around 4.8 billion people worldwide lack timely access to safe and
affordable surgery1. It is estimated that there is an annual unmet need for 143 million
procedures in LMICs3. Increasing access to surgery is therefore a priority. In order to
understand the implications of an increase in surgical volume, we estimated the
number of additional postoperative deaths that might occur if 143 million additional
procedures were performed in LMICs at the current rate of disparity in postoperative
death rates.
S2 Appendix: Strengths and limitations
This analysis has necessarily required a series of assumptions to be made regarding
global surgical volume, surgical case-mix, post-operative death rates in high income
countries (HIC), adjustment for mortality from HICs to low- and middle-income
countries (LMICs), the need for expansion of surgery in LMICs, and comparisons of
postoperative death rates to the leading causes of death reported in the Global
Burden of Disease (GBD) study. In this Appendix the strengths and weaknesses of
each underlying assumption are discussed. Despite these limitations, we believe that
this study represents the best estimate of total postoperative deaths possible using
existing data. Moreover, our modelling is a baseline for future high-quality data to
feed in to.
Surgical volume
Global surgical volume was most recently estimated in a modelling study by Weiser
et al5. Surgical volume data were obtained for 66 countries. A model based on total
health expenditure per capita and population was developed, to extrapolate surgical
volume for other countries for which surgical volume data were not available. The
authors provided a breakdown of total surgical volume by total health expenditure per
capita: very low ($100), low ($101-400), middle ($401-1000), and high (>$1000).
A total of 136 of 139 LMICs (as designated in the DAC List of ODA Recipients) and
14 of 55 HICs were recorded by Weiser et al as having very low, low, or middle
levels of total health expenditure per capita. Therefore, within the constraints of the
data available, LMIC surgical volume was taken as the sum of operations performed
in all countries with total health expenditure per capita $1000. HIC surgical volume
was based on total surgical volume in countries with expenditure >$1000 per capita.
The Weiser study was modelled to provide surgical volume for 2012. Given that
global surgical volume expanded from an estimated 226 million cases per year in
2004 to 313 million cases per year in 2012, it is likely that surgical volume has
continued to increase, with significantly more cases performed in 2018 than Weiser’s
2012 estimate. This would lead to an under-estimation of total postoperative deaths.
Surgical case-mix
For HICs, case-mix was based on a national registry of surgical activity from a
universal health system: England’s Hospital Episodes Statistics (HES). Although
more recent operative volume statistics are available, case-mix estimates were
based on the 2010 HES dataset to maintain consistency with use of the Hospital
Episodes Statistics linked Office of National Statistics (HES-ONS) dataset for
estimates of postoperative death rates. The HES dataset captures all procedures
performed within National Health Service (NHS) hospitals, accounting for over 90%
of total surgical volume in England. HES does not capture data for procedures
performed in private hospitals. Since private hospitals in England typically focus on
low to intermediate risk surgery, the HES dataset may overestimate volume in
higher-risk surgical specialties. This would be likely to lead to an overestimation of
total postoperative deaths.
The assumption that surgical case-mix in England is representative of case-mix
across all other HICs represents a significant limitation since differing epidemiological
profiles across HICs are likely to lead to differing surgical case-mix. The effect of this
on the estimate of total postoperative deaths is uncertain.
There are no readily available LMIC national surgical registries. Therefore, the
African Surgical Outcomes Study (ASOS) was used to estimate LMIC case-mix. This
cohort was based on prospective data collection across 247 hospital in 25 African
countries. Inevitably larger hospitals are more likely to participate in international
studies and the ASOS data may not reflect the case-mix across all surgical units in
Africa. Moreover, it is unknown how generalisable case-mix derived in Africa is to
LMICs on other continents.
Postoperative mortality in high income countries
For the primary analysis, baseline HIC postoperative death rates were extracted from
England’s HES-ONS dataset, a national registry from a universal health system. The
most recent publicly available HES-ONS linked dataset dates from 2010.
Postoperative death rates in England have decreased since 201013, therefore using
HES-ONS may either overestimate HIC postoperative death rates in 2018.
There are few robust national estimates of postoperative death rates encompassing
the full scope of surgical activity. Amongst those postoperative death rates that are
reported, the lowest is 0.54%, from the New Zealand the Perioperative Mortality
Review Committee. The highest rate is 3.99%, from the European Surgical
Outcomes Study (EUSOS). The HES-ONS postoperative death rate (1.09%) is
therefore amongst the lowest reported and is similar to the 1.34% rate reported by
the American College of Surgeons National Surgical Quality Improvement Program
(NSQIP). Variation in reported postoperative death rates is partly a reflection of
subtle differences in inclusion criteria, for example whether day-case procedures
contribute to the reported postoperative death rate.
The main analysis utilised postoperative death rates based HES-ONS, as this
dataset offered the greatest granularity, providing speciality-specific rates for the full
scope of surgical activity, including day case surgery and Caesarean section.
Specialty-specific postoperative death rates were important to obtain in order to
account for differences in case-mix between HICs and LMICs.
In order to explore the effect of adopting higher baseline HIC postoperative death
rates than that derived from HES-ONS, we performed a sensitivity analysis. We
tested our base case model to estimate totals for postoperative death using the
NSQIP and EUSOS data (S2 Table).
Postoperative death rate adjustment from high to low/middle income countries
Bainbridge et al study is the most extensive review of postoperative death rates
across all surgical specialties. This systematic review included data from 87 studies
on 21.4 million patients undergoing surgery with general anaesthetic. Although the
baseline postoperative death rates identified by the Bainbridge study were low (0.2%
in LMICs and 0.1% in HICs), the relative risk (2.14) for death following surgery in
LMICs versus HICs was broadly consistent with the findings of the prospective,
international GS110 (RR 1.53), GS211 (1.59), and ASOS/ISOS6,14 (RR 2.20) studies.
Since the GS1 and GS2 studies only included patients undergoing abdominal
surgery, the Bainbridge study offers a more generalizable estimate for postoperative
death rate adjustment, and is similar to the ASOS/ISOS comparison.
Using a single estimate for postoperative death rate adjustment for all surgery types
may lead to underestimation of postoperative death rates for some specific
procedures. For example, the postoperative death rate for caesarean section in the
ASOS study was 0.53% (20/3,792) compared to 0.01% (16/158,229) in HES-ONS.
Therefore, the estimate from the Bainbridge study that we have used to adjust HES-
ONS mortality rates may be conservative, underestimating total postoperative
deaths.
Need for expansion of surgery
Rose et al calculated the global unmet need for surgery by estimating the total
required surgical volume and subtracting the current volume of surgery performed.
Required surgical volume was derived by taking prevalence for 21 disease
categories from the 2010 Global Burden of Disease (GBD) study and multiplying this
by the rates of surgery for each disease category, based on New Zealand registry
data. Whilst the study provides an estimate for the total need for surgery by surgery
type, equivalent figures by surgery type for unmet need are not provided. Therefore it
was not possible to determine the case-mix of the unmet need for surgery.
Consequently, we assumed that in the event of all unmet surgical need being
addressed, LMIC surgical case-mix would remain broadly similar to that recorded in
the ASOS dataset. The effect of this on our estimate for total postoperative deaths
that might occur if there were no unmet need for surgery is uncertain, as this
depends on whether unmet need is predominantly for procedures with low or high
postoperative death rates.
Global Burden of Disease study comparison
The main output from this study was an estimate for the absolute number of
postoperative deaths. In order to put this figure in to context it was compared to the
numbers of deaths associated with the top ten causes of death in GBD. This
comparison should be interpreted with caution. As postoperative death is not a
recognised cause of death in GBD studies, each postoperative death has been
double counted across one of the 249 causes of death reported by GBD, most likely
the underlying condition for which the patient was operated. For example, the IHD
category may include patients who died following complications of cardiac surgery, or
as a result of a postoperative myocardial infarct. Similarly, the lung cancer category
may include patients who die as a result of complications following surgery for lung
cancer, and the stroke category is likely to include some patients who die as a result
of postoperative stroke. However, this risk of overlap is common for many categories
of global mortality, and represents a ubiquitous limitation of estimating global burden
of condition-specific mortality more generally.
Importantly, we do not seek to imply causality between patients undergoing surgery
and dying within 30 days. Some postoperative deaths may be entirely unrelated to
the patient’s surgery, for example, deaths resulting from trauma. For some patients
postoperative death represents a failure of surgery as a treatment strategy; for
instance, a patient who succumbs despite attempted surgery for a ruptured
abdominal aortic aneurysm. Many postoperative deaths however occur as a result of
postoperative complications. Whilst on a global level we are unable to differentiate
between these different categories of postoperative death, it is important to bring the
overall scale of the postoperative death to policy makers’ attention since many
postoperative deaths could be prevented with improved perioperative care.
S1 Table: Baseline case-mix and postoperative death rates by specialty
Surgical specialty
Case-mix
HIC* (xHIC)
LMIC** (xLMIC)
Breast
2.5%
2.1%
Cardiac
1.0%
0.5%
Gynaecological
6.0%
12.1%
Head and neck
10.2%
4.2%
Hepatobiliary
2.1%
1.6%
Lower gastrointestinal
5.9%
8.7%
Neurosurgery
4.2%
2.3%
Obstetric
4.6%
35.2%
Orthopaedic
21.0%
16.4%
Thoracic
0.9%
1.4%
Upper gastrointestinal
1.6%
2.8%
Urology
6.5%
5.2%
Vascular
3.7%
2.2%
Other
29.7%
5.1%
*High income country (HIC) baseline estimates based on the Hospital Episode
Statistics linked Office of National Statistics (HES-ONS) dataset
**Low- and middle-income country (LMIC) baseline case-mix based on the African
Surgical Outcomes Study (ASOS)
S2 Table: Estimates of total postoperative deaths
Source'for'baseline'
postoperatice'death'rate!
Postoperative'deaths'
Proportion'of'global'
mortality'attributable'to'
postoperative'death'
LMIC'
HIC'
Total'
HES-ONS!
2,180,787!
2,047,446!
4,228,233!
7.7%!
Projection*if*surgical*
provision*expanded*
4,099,878!
2,047,446!
6,147,324!
11.2%!
NSQIP!
2,580,824!
2,421,633!
5,002,457!
9.1%!
Projection*if*surgical*
provision*expanded*
4,851,948!
2,421,633!
7,273,581!
13.3%!
EUSOS!
7,553,262!
7,111,492!
14,664,755!
26.8%!
Projection*if*surgical*
provision*expanded*
14,200,129!
7,111,492!
21,311,621!
39.0%!
EUSOS: European Surgical Outcomes Study; HES-ONS: Hospital Episode Statistics
linked Office of National Statistics dataset; HIC: high income countries; LMIC: low-
and middle-income countries; NSQIP: American College of Surgeons National
Surgical Quality Improvement Program
! !
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14.! Ahmad!T,!Bouwman!RA,!Grigoras!I,!et!al.!Use!of!failure-to-rescue!to!
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... About 312.9 million surgical procedures are undertaken worldwide each year. 1 Postoperative deaths are the third greatest contributor to all deaths, which accounts for 7.7% of all deaths globally. 2,3 Surgical mortality has declined over the last decade, 4 but the number of patients in the need for critical care monitoring is still increasing. 5,6 Intensive care unit (ICU) admission following major surgery is considered a standard of care in many healthcare systems. ...
... (1) We estimated the relative influence provided by GBM and reported the impact of different features on predicting ICU admission. (2) According to the results of the testing cohort, the model that attained the best performance was chosen as the final model and the individual patient's predicted probabilities of outcome was calculated. On the basis of predicted probabilities, we explored the distribution of probabilities across the hospital length of stay (LOS). ...
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Background To develop a highly discriminative machine learning model for the prediction of intensive care unit admission (>24h) using the easily available preoperative information from electronic health records. An accurate prediction model for ICU admission after surgery is of great importance for surgical risk assessment and appropriate utilization of ICU resources. Method Data were collected retrospectively from a large hospital, comprising 135,442 adult patients who underwent surgery except for cardiac surgery between 1 January 2014, and 31 July 2018 in China. Multiple existing predictive machine learning algorithms were explored to construct the prediction model, including logistic regression, random forest, adaptive boosting, and gradient boosting machine. Four secondary analyses were conducted to improve the interpretability of the results. Results A total of 2702 (2.0%) patients were admitted to the intensive care unit postoperatively. The gradient boosting machine model attained the highest area under the receiver operating characteristic curve of 0.90. The machine learning models predicted intensive care unit admission better than the American Society of Anesthesiologists Physical Status (area under the receiver operating characteristic curve: 0.68). The gradient boosting machine recognized several features as highly significant predictors for postoperatively intensive care unit admission. By applying subgroup analysis and secondary analysis, we found that patients with operations on the digestive, respiratory, and vascular systems had higher probabilities for intensive care unit admission. Conclusion Compared with conventional American Society of Anesthesiologists Physical Status and logistic regression model, the gradient boosting machine could improve the performance in the prediction of intensive care unit admission. Machine learning models could be used to improve the discrimination and identify the need for intensive care unit admission after surgery in elective noncardiac surgical patients, which could help manage the surgical risk.
... Another problem that may cover the effect of anesthesia and reduce the enthusiasm for further investigation is surgical curability, which is high today, especially in low grade cancers [3]. On the other hand the main cause of postoperative cancer death is metastasis, which occurs in one third of operated patients [110][111][112]. Only a small number of patients with pancreatic cancer will be operable and, among these, 7% will survive at 5 years, indicating a high recurrence rate and a lack of effective treatments [113]. ...
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Knowledge shows a divergence of results between preclinical and clinical studies regarding anesthesia and postoperative progression of cancer. While laboratory and animal data from then 2000s onwards raised much enthusiasm in this field of research leading to several clinical investigations worldwide, data from randomized trials seem to have killed off hope for many scientists. However several aspects of the actual knowledge should be reevaluated and there is space for new strategies of investigation. In this paper, we perform a critical review of actual knowledge and propose new research strategies with a special focus on anesthetic management and repurposed anesthetic adjuvants for pancreatic cancer.
... worldwide die within 30 days of surgery each year. This number of postoperative deaths accounts for 7.7% of all deaths globally, making it the third greatest contributor to deaths in the world [1]. In a study of the University Hospital of Charleroi in Belgium, post-operative mortality rates within 30 post-operative days were 1.1% [2]. ...
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Background: An effective use of surgical antibiotic prophylaxis (SAP) appears essential to prevent the development of infections linked to surgery while inappropriate and excessive prescriptions of prophylactic antibiotics increase the risk of adverse effects, bacterial resistance and Clostridium difficile infections. In this study, we aimed to analyze SAP practices in an acute secondary hospital in Belgium during the years 2016-2021 in order to evaluate the impacts of combined stewardship interventions, implemented thanks to a physician-pharmacist collaboration. Methods: A quasi-experimental study on SAP practices was conducted during 5 years (2016-2021) in a Belgian University Hospital. We first performed a retrospective observational transversal study on a baseline group (2016.1-2016.4). Then, we constituted a group of patients (2017.1-2017.4) to test a combined intervention strategy of stewardship which integrated the central role of a pharmacist in antibiotic stewardship team and in the pre-operative delivery of nominative kits of antibiotics adapted to patient factors. After this test, we collected patient data (2018.1-2018.4) to evaluate the sustained effects of stewardship interventions. Furthermore, we evaluated SAP practices (2019.1-2019.4) after the diffusion of a computerized decision support system. Finally, we analyzed SAP practices in the context of the COVID-19 pandemic (2020.1-2020.4 and 2021.1-2021.4). The groups were compared from year to year in terms of compliance to institutional guidelines, as evaluated from seven criteria (χ2 test). Results: In total, 760 surgical interventions were recorded. The observational study within the baseline group showed that true penicillin allergy, certain types of surgery and certain practitioners were associated with non-compliance (p < 0.05). Compared with the baseline group, the compliance was significantly increased in the test group for all seven criteria assessed (p < 0.05). However, the effects were not fully sustained after discontinuation of the active interventions. Following the diffusion of the computerized decision support system, the compliance to guidelines was not significantly improved. Finally, the COVID-19 pandemic did not appear to affect the practices in terms of compliance to guidelines. Conclusions: This study shows that optimization of SAP practices is achievable within a proactive multidisciplinary approach including real-time pharmaceutical interventions in the operating area and in the care units practicing SAP.
... [3][4][5][6] It is estimated that at least 4 million deaths occur in the first 30 days after surgery globally, each year. 7 Previous registries report that patients may be unintentionally harmed in almost 4% of hospital admissions, of which nearly 50% are associated with a surgical intervention, emphasizing the need to focus on safety and standardizing protocols for healthcare improvement in surgical care. 8 Improvement in postoperative results is a team effort, focusing on prevention, confirmation of patient data and treatment goals, recognizing possible complications, applying standardized operating procedures (SOPs), including pre-skin closure protocols that guarantee surgical material withdrawal and adequate haemostasis, and postoperative standardized incident reporting systems, which must be ensured by all members of the surgical team. ...
Article
Background Cardiac surgery is associated with a significant risk of potential postoperative complications. We describe a case of a patient with an unusual late cardiac perforation caused by a needle used to fix temporary epicardial pacing wires to the skin, which slowly migrated across subcutaneous tissues for 2 years following postoperative period. Case summary We report a case of middle-aged woman admitted to the cardiac intensive care unit due to suspected acute myocardial infarction. Multimodality imaging revealed the presence of an unusual intracardiac foreign body, located inside the interventricular septum and perforating towards the left atria, complicated by a small intracardiac fistula between septal coronary branches and the right ventricle. Analysis of previous exams revealed that a needle used to fix temporary epicardial pacing wires to the skin had been left inside the patient, beneath the level of the diaphragm, after cardiac surgery in 2018. This foreign body slowly migrated across the diaphragm, towards the mediastinum, finally lodging inside the heart, after a period of 3 years. The patient was referred to cardiac surgery for foreign body retrieval. Discussion We describe an unusual case of cardiac perforation caused by a needle used to fix these wires to the skin, which migrated across subcutaneous tissues and finally lodged inside the basal interventricular septum and left atria. Full compliance with standardized surgical care bundles, as well as the implementation of a structured incident reporting system, is of upmost importance to prevent postoperative complications and improve surgical care.
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Aim Postoperative pulmonary complications (PPCs) can increase the risk of postoperative mortality, and the geriatric population has high incidence of PPCs. Early identification of high-risk geriatric patients is of great value for clinical decision making and prognosis improvement. Existing prediction models are based purely on structured data, and they lack predictive accuracy in geriatric patients. We aimed to develop and validate a deep neural network model based on combined natural language data and structured data for improving the prediction of PPCs in geriatric patients. Methods We consecutively enrolled patients aged ≥65 years who underwent surgery under general anesthesia at seven hospitals in China. Data from the West China Hospital of Sichuan University were used as the derivation dataset, and a deep neural network model was developed based on combined natural language data and structured data. Data from the six other hospitals were combined for external validation. Results The derivation dataset included 12,240 geriatric patients, and 1949(15.9%) patients developed PPCs. Our deep neural network model outperformed other machine learning models with an area under the precision-recall curve (AUPRC) of 0.657(95% confidence interval [CI], 0.655–0.658) and an area under the receiver operating characteristic curve (AUROC) of 0.884(95% CI, 0.883–0.885). The external dataset included 7579 patients, and 776(10.2%) patients developed PPCs. In external validation, the AUPRC was 0.632(95%CI, 0.632–0.633) and the AUROC was 0.889(95%CI, 0.888–0.889). Conclusions This study indicated that the deep neural network model based on combined natural language data and structured data could improve the prediction of PPCs in geriatric patients.
Article
Background People with kidney failure have high risk of postoperative morbidity and mortality. Although the Revised Cardiac Risk Index (RCRI) is used to estimate the risk of major postoperative events, it has not been validated in this population. We aimed to externally validate the RCRI and determine whether updating the model improved predictions for people with kidney failure. Methods We derived a retrospective, population-based cohort of adults with kidney failure (maintenance dialysis or sustained estimated glomerular filtration rate [eGFR] <15 mL/min/1.73 m²) who had surgery in Alberta, Canada between 2005 and 2019. We categorized participants based on RCRI variables and assigned risk estimates of death or major cardiac events, and then estimated predictive performance. We re-estimated the coefficients for each RCRI variable and internally validated the updated model. Net benefit was estimated with decision curve analysis. Results After 38,541 surgeries, 1,204 (3.1%) events occurred. The estimated C-statistic for the original RCRI was 0.64 (95% confidence interval [CI]: 0.62, 0.65). Examination of calibration revealed significant risk overestimation. In the re-estimated RCRI model, discrimination was marginally different (C-statistic 0.67 [95%CI 0.66, 0.69]), though calibration was improved. There was net benefit when examined with decision curve analysis, while the original RCRI was associated with harm. Conclusions The RCRI performed poorly in a Canadian kidney failure cohort and significantly overestimated risk, suggesting RCRI use in similar kidney failure populations should be limited. A re-estimated kidney failure specific RCRI may be promising, though needs external validation. Novel perioperative models for this population are urgently needed.
Article
Purpose of review: Postoperative mortality in the 30 days after surgery remains disturbingly high. Inadequate, intermittent and incomplete monitoring of vital signs in the nonoperating room environment is common practice. The rise of nonoperating room anaesthesia and sedation outside the operating room has highlighted the need to develop new and robust methods of portable continuous respiratory monitoring. This review provides a summary of old and new technologies in this environment. Recent findings: Technical advances have made possible the utilization of established monitoring to extrapolate respiratory rate, the increased availability and user friendliness of side stream capnography and the advent of other innovative systems. The use of aggregate signals wherein different modalities compensate for individual shortcomings seem to provide a reliable and artefact-free system. Summary: Respiratory monitoring is required in several situations and patient categories outside the operating room. The chosen modality must be able to detect respiratory compromise in a timely and accurate manner. Combing several modalities in a nonobtrusive, nontethered system and having an integrated output seems to give a reliable and responsive signal.
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The surgical workforce, like the rest of the population, is ageing. This has raised concerns about the association between the age of the surgeon and their surgical outcomes. We performed a systematic review and meta-analysis of cohort studies on postoperative mortality and major morbidity according to the surgeons’ age. The search was performed on February 2021 using the Embase, Medline and CENTRAL databases. Postoperative mortality and major morbidity were evaluated as clinical outcomes. We categorized the surgeons’ age into young-, middle-, and old-aged surgeons. We compared the differences in clinical outcomes for younger and older surgeons compared to middle-aged surgeons. Subgroup analyses were performed for major and minor surgery. Ten retrospective cohort studies on 29 various surgeries with 1,666,108 patients were considered. The mortality in patients undergoing surgery by old-aged surgeons was 1.14 (1.02–1.28, p = 0.02) (I² = 80%) compared to those by middle-aged surgeon. No significant differences were observed according to the surgeon’s age in the major morbidity and subgroup analyses. This meta-analysis indicated that surgeries performed by old-aged surgeons had a higher risk of postoperative mortality than those by middle-aged surgeons. Thus, it necessitates the introduction of a multidisciplinary approach to evaluate the performance of senior surgeons.
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Background: There is a need to increase access to surgical treatments in African countries, but perioperative complications represent a major global health-care burden. There are few studies describing surgical outcomes in Africa. Methods: We did a 7-day, international, prospective, observational cohort study of patients aged 18 years and older undergoing any inpatient surgery in 25 countries in Africa (the African Surgical Outcomes Study). We aimed to recruit as many hospitals as possible using a convenience sampling survey, and required data from at least ten hospitals per country (or half the surgical centres if there were fewer than ten hospitals) and data for at least 90% of eligible patients from each site. Each country selected one recruitment week between February and May, 2016. The primary outcome was in-hospital postoperative complications, assessed according to predefined criteria and graded as mild, moderate, or severe. Data were presented as median (IQR), mean (SD), or n (%), and compared using t tests. This study is registered on the South African National Health Research Database (KZ_2015RP7_22) and ClinicalTrials.gov (NCT03044899). Findings: We recruited 11 422 patients (median 29 [IQR 10-70]) from 247 hospitals during the national cohort weeks. Hospitals served a median population of 810 000 people (IQR 200 000-2 000 000), with a combined number of specialist surgeons, obstetricians, and anaesthetists totalling 0·7 (0·2-1·9) per 100 000 population. Hospitals did a median of 212 (IQR 65-578) surgical procedures per 100 000 population each year. Patients were younger (mean age 38·5 years [SD 16·1]), with a lower risk profile (American Society of Anesthesiologists median score 1 [IQR 1-2]) than reported in high-income countries. 1253 (11%) patients were infected with HIV, 6504 procedures (57%) were urgent or emergent, and the most common procedure was caesarean delivery (3792 patients, 33%). Postoperative complications occurred in 1977 (18·2%, 95% CI 17·4-18·9]) of 10 885 patients. 239 (2·1%) of 11 193 patients died, 225 (94·1%) after the day of surgery. Infection was the most common complication (1156 [10·2%] of 10 970 patients), of whom 112 (9·7%) died. Interpretation: Despite a low-risk profile and few postoperative complications, patients in Africa were twice as likely to die after surgery when compared with the global average for postoperative deaths. Initiatives to increase access to surgical treatments in Africa therefore should be coupled with improved surveillance for deteriorating physiology in patients who develop postoperative complications, and the resources necessary to achieve this objective. Funding: Medical Research Council of South Africa.
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Background: Monitoring levels and trends in premature mortality is crucial to understanding how societies can address prominent sources of early death. The Global Burden of Disease 2016 Study (GBD 2016) provides a comprehensive assessment of cause-specific mortality for 264 causes in 195 locations from 1980 to 2016. This assessment includes evaluation of the expected epidemiological transition with changes in development and where local patterns deviate from these trends. Methods: We estimated cause-specific deaths and years of life lost (YLLs) by age, sex, geography, and year. YLLs were calculated from the sum of each death multiplied by the standard life expectancy at each age. We used the GBD cause of death database composed of: vital registration (VR) data corrected for under-registration and garbage coding; national and subnational verbal autopsy (VA) studies corrected for garbage coding; and other sources including surveys and surveillance systems for specific causes such as maternal mortality. To facilitate assessment of quality, we reported on the fraction of deaths assigned to GBD Level 1 or Level 2 causes that cannot be underlying causes of death (major garbage codes) by location and year. Based on completeness, garbage coding, cause list detail, and time periods covered, we provided an overall data quality rating for each location with scores ranging from 0 stars (worst) to 5 stars (best). We used robust statistical methods including the Cause of Death Ensemble model (CODEm) to generate estimates for each location, year, age, and sex. We assessed observed and expected levels and trends of cause-specific deaths in relation to the Socio-demographic Index (SDI), a summary indicator derived from measures of average income per capita, educational attainment, and total fertility, with locations grouped into quintiles by SDI. Relative to GBD 2015, we expanded the GBD cause hierarchy by 18 causes of death for GBD 2016. Findings: The quality of available data varied by location. Data quality in 25 countries rated in the highest category (5 stars), while 48, 30, 21, and 44 countries were rated at each of the succeeding data quality levels. Vital registration or verbal autopsy data were not available in 27 countries, resulting in the assignment of a zero value for data quality. Deaths from non-communicable diseases (NCDs) represented 72·3% (95% uncertainty interval [UI] 71·2–73·2) of deaths in 2016 with 19·3% (18·5–20·4) of deaths in that year occurring from communicable, maternal, neonatal, and nutritional (CMNN) diseases and a further 8·43% (8·00–8·67) from injuries. Although age-standardised rates of death from NCDs decreased globally between 2006 and 2016, total numbers of these deaths increased; both numbers and age-standardised rates of death from CMNN causes decreased in the decade 2006–16—age-standardised rates of deaths from injuries decreased but total numbers varied little. In 2016, the three leading global causes of death in children under-5 were lower respiratory infections, neonatal preterm birth complications, and neonatal encephalopathy due to birth asphyxia and trauma, combined resulting in 1·80 million deaths (95% UI 1·59 million to 1·89 million). Between 1990 and 2016, a profound shift toward deaths at older ages occurred with a 178% (95% UI 176–181) increase in deaths in ages 90–94 years and a 210% (208–212) increase in deaths older than age 95 years. The ten leading causes by rates of age-standardised YLL significantly decreased from 2006 to 2016 (median annualised rate of change was a decrease of 2·89%); the median annualised rate of change for all other causes was lower (a decrease of 1·59%) during the same interval. Globally, the five leading causes of total YLLs in 2016 were cardiovascular diseases; diarrhoea, lower respiratory infections, and other common infectious diseases; neoplasms; neonatal disorders; and HIV/AIDS and tuberculosis. At a finer level of disaggregation within cause groupings, the ten leading causes of total YLLs in 2016 were ischaemic heart disease, cerebrovascular disease, lower respiratory infections, diarrhoeal diseases, road injuries, malaria, neonatal preterm birth complications, HIV/AIDS, chronic obstructive pulmonary disease, and neonatal encephalopathy due to birth asphyxia and trauma. Ischaemic heart disease was the leading cause of total YLLs in 113 countries for men and 97 countries for women. Comparisons of observed levels of YLLs by countries, relative to the level of YLLs expected on the basis of SDI alone, highlighted distinct regional patterns including the greater than expected level of YLLs from malaria and from HIV/AIDS across sub-Saharan Africa; diabetes mellitus, especially in Oceania; interpersonal violence, notably within Latin America and the Caribbean; and cardiomyopathy and myocarditis, particularly in eastern and central Europe. The level of YLLs from ischaemic heart disease was less than expected in 117 of 195 locations. Other leading causes of YLLs for which YLLs were notably lower than expected included neonatal preterm birth complications in many locations in both south Asia and southeast Asia, and cerebrovascular disease in western Europe. Interpretation: The past 37 years have featured declining rates of communicable, maternal, neonatal, and nutritional diseases across all quintiles of SDI, with faster than expected gains for many locations relative to their SDI. A global shift towards deaths at older ages suggests success in reducing many causes of early death. YLLs have increased globally for causes such as diabetes mellitus or some neoplasms, and in some locations for causes such as drug use disorders, and conflict and terrorism. Increasing levels of YLLs might reflect outcomes from conditions that required high levels of care but for which effective treatments remain elusive, potentially increasing costs to health systems.
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Objective To estimate global surgical volume in 2012 and compare it with estimates from 2004. Methods For the 194 Member States of the World Health Organization, we searched PubMed for studies and contacted key informants for reports on surgical volumes between 2005 and 2012. We obtained data on population and total health expenditure per capita for 2012 and categorized Member States as very-low, low, middle and high expenditure. Data on caesarean delivery were obtained from validated statistical reports. For Member States without recorded surgical data, we estimated volumes by multiple imputation using data on total health expenditure. We estimated caesarean deliveries as a proportion of all surgery. Findings We identified 66 Member States reporting surgical data. We estimated that 312.9 million operations (95% confidence interval, CI: 266.2–359.5) took place in 2012, an increase from the 2004 estimate of 226.4 million operations. Only 6.3% (95% CI: 1.7–22.9) and 23.1% (95% CI: 14.8–36.7) of operations took place in very-low- and low-expenditure Member States representing 36.8% (2573 million people) and 34.2% (2393 million people) of the global population of 7001 million people, respectively. Caesarean deliveries comprised 29.6% (5.8/19.6 million operations; 95% CI: 9.7–91.7) of the total surgical volume in very-low-expenditure Member States, but only 2.7% (5.1/187.0 million operations; 95% CI: 2.2–3.4) in high-expenditure Member States. Conclusion Surgical volume is large and growing, with caesarean delivery comprising nearly a third of operations in most resource-poor settings. Nonetheless, there remains disparity in the provision of surgical services globally.
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Background: Surgical interventions occur at lower rates in resource-poor settings, and complication and death rates following surgery are probably substantial but have not been well quantified. A deeper understanding of outcomes is a crucial step to ensure that high quality accompanies increased global access to surgical care. We aimed to assess surgical mortality following three common surgical procedures—caesarean delivery, appendectomy, and groin (inguinal and femoral) hernia repair—to quantify the potential risks of expanding access without simultaneously addressing issues of quality and safety. Methods: We collected demographic, health, and economic data for 113 countries classified as low income or lower-middle income by the World Bank in 2005. We did a systematic review of Ovid, MEDLINE, PubMed, and Scopus from Jan 1, 2000, to Jan 15, 2015, to identify studies in these countries reporting all-cause mortality following the three commonly undertaken operations. Reports from governmental and other agencies were also identified and included. We modelled surgical mortality rates for countries without reported data using a two-step multiple imputation method. We first used a fully conditional specification (FCS) multiple imputation method to establish complete datasets for all missing variables that we considered potentially predictive of surgical mortality. We then used regression-based predictive mean matching imputation methods, specified within the multiple imputation FCS method, for selected predictors for each operation using the completed dataset to predict mortality rates along with confidence intervals for countries without reported mortality data. To account for variability in data availability, we aggregated results by subregion and estimated surgical mortality rates. Findings: From an initial 1302 articles and reports identified, 247 full-text articles met our inclusion criteria, and 124 provided data for surgical mortality for at least one of the three selected operations. We identified 42 countries with mortality data for at least one of the three procedures. Median reported mortality was 7·9 per 1000 operations for caesarean delivery (IQR 2·8–19·9), 2·2 per 1000 operations for appendectomy (0·0–17·2), and 4·9 per 1000 operations for groin hernia (0·0–11·7). Perioperative mortality estimates by subregion ranged from 2·8 (South Asia) to 50·2 (East Asia) per 1000 caesarean deliveries, 2·4 (South Asia) to 54·0 (Central sub-Saharan Africa) per 1000 appendectomies, and 0·3 (Andean Latin America) to 25·5 (Southern sub-Saharan Africa) per 1000 hernia repairs. Interpretation: All-cause postoperative mortality rates are exceedingly variable within resource-constrained environments. Efforts to expand surgical access and provision of services must include a strong commitment to improve the safety and quality of care. Funding: None.
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Surgery is a foundational component of health-care systems. However, previous efforts to integrate surgical services into global health initiatives do not reflect the scope of surgical need and many health systems do not provide essential interventions. We estimate the minimum global volume of surgical need to address prevalent diseases in 21 epidemiological regions from the Global Burden of Disease Study 2010 (GBD). Prevalence data were obtained from GBD 2010 and organised into 119 disease states according to the WHO's Global Health Estimate (GHE). These data, representing 187 countries, were then apportioned into the 21 GBD epidemiological regions. Using previously defined values for the incident need for surgery for each of the 119 GHE disease states, we calculate minimum global need for surgery based on the prevalence of each condition in each region. We estimate that at least 321·5 million surgical procedures would be needed to address the burden of disease for a global population of 6·9 billion in 2010. Minimum rates of surgical need vary across regions, ranging from 3383 operations per 100 000 in central Latin America to 6495 operations per 100 000 in western sub-Saharan Africa. Global surgical need also varied across subcategories of disease, ranging from 131 412 procedures for nutritional deficiencies to 45·8 million procedures for unintentional injuries. The estimated need for surgical procedures worldwide is large and addresses a broad spectrum of disease states. Surgical need varies between regions of the world according to disease prevalence and many countries do not meet the basic needs of their populations. These estimates could be useful for policy makers, funders, and ministries of health as they consider how to incorporate surgical capacity into health systems. US National Institutes of Health. Copyright © 2015 Rose et al. Open access article published under the terms of CC BY-NC-ND. Published by Elsevier Ltd.. All rights reserved.
Article
Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication.
Article
Background: Surgical mortality data are collected routinely in high-income countries, yet virtually no low-or middle-income countries have outcome surveillance in place. The aim was prospectively to collect worldwide mortality data following emergency abdominal surgery, comparing findings across countries with a low, middle or high Human Development Index (HDI). Methods: This was a prospective, multicentre, cohort study. Self-selected hospitals performing emergency surgery submitted prespecified data for consecutive patients from at least one 2-week interval during July to December 2014. Postoperative mortality was analysed by hierarchical multivariable logistic regression. Results: Data were obtained for 10 745 patients from 357 centres in 58 countries; 6538 were from high-, 2889 from middle-and 1318 from low-HDI settings. The overall mortality rate was 1⋅6 per cent at 24 h (high 1⋅1 per cent, middle 1⋅9 per cent, low 3⋅4 per cent; P < 0⋅001), increasing to 5⋅4 per cent by 30 days (high 4⋅5 per cent, middle 6⋅0 per cent, low 8⋅6 per cent; P < 0⋅001). Of the 578 patients who died, 404 (69⋅9 per cent) did so between 24 h and 30 days following surgery (high 74⋅2 per cent, middle 68⋅8 per cent, low 60⋅5 per cent). After adjustment, 30-day mortality remained higher in middle-income (odds ratio (OR) 2⋅78, 95 per cent c.i. 1⋅84 to 4⋅20) and low-income (OR 2⋅97, 1⋅84 to 4⋅81) countries. Surgical safety checklist use was less frequent in low-and middle-income countries, but when used was associated with reduced mortality at 30 days. Conclusion: Mortality is three times higher in low-compared with high-HDI countries even when adjusted for prognostic factors. Patient safety factors may have an important role. Registration number: NCT02179112 (http://www.clinicaltrials.gov).
Article
Background: Despite evidence of high activity, the number of surgical procedures performed in UK hospitals, their cost and subsequent mortality remain unclear. Methods: Time-trend ecological study using hospital episode data from England, Scotland, Wales and Northern Ireland. The primary outcome was the number of in-hospital procedures, grouped using three increasingly specific categories of surgery. Secondary outcomes were all-cause mortality, length of hospital stay and healthcare costs according to standard National Health Service tariffs. Results: Between April 1, 2009 and March 31, 2014, 39 631 801 surgical patient episodes were recorded. There was an annual average of 7 926 360 procedures (inclusive category), 5 104 165 procedures (intermediate category) and 1 526 421 procedures (restrictive category). This equates to 12 537, 8073 and 2414 procedures per 100 000 population per year, respectively. On average there were 85 181 deaths (1.1%) within 30 days of a procedure each year, rising to 178 040 deaths (2.3%) after 90 days. Approximately 62.8% of all procedures were day cases. Median length of stay for in-patient procedures was 1.7 (1.3-2.0) days. The total cost of surgery over the 5 yr period was £54.6 billion ($104.4 billion), representing an average annual cost of £10.9 billion (inclusive), £9.5 billion (intermediate) and £5.6 billion (restrictive). For each category, the number of procedures increased each year, while mortality decreased. One-third of all mortalities in national death registers occurred within 90 days of a procedure (inclusive category). Conclusions: The number of surgical procedures in the UK varies widely according to definition. The number of procedures is slowly increasing whilst the number of deaths is decreasing.
Article
Background: The incidence and impact of postoperative complications are poorly described. Failure-to-rescue, the rate of death following complications, is an important quality measure for perioperative care but has not been investigated across multiple health care systems. Methods: We analysed data collected during the International Surgical Outcomes Study, an international 7-day cohort study of adults undergoing elective inpatient surgery. Hospitals were ranked by quintiles according to surgical procedural volume (Q1 lowest to Q5 highest). For each quintile we assessed in-hospital complications rates, mortality, and failure-to-rescue. We repeated this analysis ranking hospitals by risk-adjusted complication rates (Q1 lowest to Q5 highest). Results: A total of 44 814 patients from 474 hospitals in 27 low-, middle-, and high-income countries were available for analysis. Of these, 7508 (17%) developed one or more postoperative complication, with 207 deaths in hospital (0.5%), giving an overall failure-to-rescue rate of 2.8%. When hospitals were ranked in quintiles by procedural volume, we identified a three-fold variation in mortality (Q1: 0.6% vs Q5: 0.2%) and a two-fold variation in failure-to-rescue (Q1: 3.6% vs Q5: 1.7%). Ranking hospitals in quintiles by risk-adjusted complication rate further confirmed the presence of important variations in failure-to-rescue, indicating differences between hospitals in the risk of death among patients after they develop complications. Conclusions: Comparison of failure-to-rescue rates across health care systems suggests the presence of preventable postoperative deaths. Using such metrics, developing nations could benefit from a data-driven approach to quality improvement, which has proved effective in high-income countries.
Article
Remarkable gains have been made in global health in the past 25 years, but progress has not been uniform. Mortality and morbidity from common conditions needing surgery have grown in the world’s poorest regions, both in real terms and relative to other health gains. At the same time, development of safe, essential, life-saving surgical and anaesthesia care in low-income and middle-income countries (LMICs) has stagnated or regressed. In the absence of surgical care, case-fatality rates are high for common, easily treatable conditions including appendicitis, hernia, fractures, obstructed labour, congenital anomalies, and breast and cervical cancer. In 2015, many LMICs are facing a multifaceted burden of infectious disease, maternal disease, neonatal disease, non-communicable diseases, and injuries. Surgical and anaesthesia care are essential for the treatment of many of these conditions and represent an integral component of a functional, responsive, and resilient health system. In view of the large projected increase in the incidence of cancer, road traffic injuries, and cardiovascular and metabolic diseases in LMICs, the need for surgical services in these regions will continue to rise substantially from now until 2030. Reduction of death and disability hinges on access to surgical and anaesthesia care, which should be available, affordable, timely, and safe to ensure good coverage, uptake, and outcomes. Despite growing need, the development and delivery of surgical and anaesthesia care in LMICs has been nearly absent from the global health discourse. Little has been written about the human and economic effect of surgical conditions, the state of surgical care, or the potential strategies for scale-up of surgical services in LMICs. To begin to address these crucial gaps in knowledge, policy, and action, the Lancet Commission on Global Surgery was launched in January, 2014. The Commission brought together an international, multi- disciplinary team of 25 commissioners, supported by advisors and collaborators in more than 110 countries and six continents. We formed four working groups that focused on thedomains of health-care delivery and management; work-force, training, and education; economics and finance; and information management. Our Commission has five key messages, a set of indicators and recommendations to improve access to safe, affordable surgical and anaesthesia care in LMICs, and a template for a national surgical plan.