Morbidity and mortality after massive transfusion in patients undergoing non-cardiac surgery
Can J Anesth/J Can Anesth
Morbidity and mortality after massive transfusion in patients undergoing non-cardiac surgery Morbidite´ et mortalite´ apre` s une transfusion massive chez des patients subissant une chirurgie non cardiaque
Alparslan Turan 0 1 2 3 4
Dongsheng Yang 0 1 2 3 4
Angela Bonilla 0 1 2 3 4
Ayako Shiba 0 1 2 3 4
Daniel I. Sessler 0 1 2 3 4
Leif Saager 0 1 2 3 4
Andrea Kurz 0 1 2 3 4
0 D. Yang, MS Department of Quantitative Health Sciences, Cleveland Clinic , Cleveland, OH , USA
1 A. Turan, MD (&) D. Yang , MS A. Bonilla, MD A. Shiba, MD D. I. Sessler, MD L. Saager, MD A. Kurz , MD Department of Outcomes Research, Cleveland Clinic , 9500 Euclid Avenue, P-77, Cleveland, OH 44195 , USA
2 Author contributions Alparslan Turan, Dongsheng Yang, Angela Bonilla, Ayako Shiba, Daniel I. Sessler, and Andrea Kurz helped design the study and conduct the study. Alparslan Turan, Dongsheng Yang, Angela Bonilla, Ayako Shiba, Daniel I. Sessler, Andrea Kurz, and Leif Saager helped write the manuscript
3 A. Shiba, MD Anesthesiology Institute, The Jikei University School of Medicine , Tokyo , Japan
4 A. Bonilla, MD Northside Medical Center , Youngstown, OH , USA
Background Massive transfusion is associated with high morbidity and mortality, yet existing reports of massive transfusion are limited. Our primary aim was to determine the incidence of complications and 30-day mortality among patients who received massive transfusions and to explore risk factors associated with 30-day mortality.
Methods We evaluated 971,455 patients from the
American College of Surgeons National Surgical Quality
Improvement Program (NSQIP) database. We assessed the
associations between 30-day mortality and baseline,
intraoperative, and postoperative factors among 5,143
patients who received massive transfusions and for whom
complete data were available.
Results The crude 30-day postoperative mortality of the
non-transfused, low transfusion (1-4 units), and massive
transfusion (C 5 units) patients in the NSQIP was 1.2%,
8.9%, and 21.5%, respectively. Of the 5,143 massive
transfusion patients with non-missing covariable data, 17% (95%
confidence interval [CI] 16% to 18%) died within 30 days of
surgery, while 54% (95% CI 53% to 56%) had at least one
non-fatal major complication. The following baseline and
intraoperative variables were independently associated with
30-day mortality after adjusting for multiple testing: age,
American Society of Anesthesiologists (ASA) physical status,
emergency case, surgical types, coma [ 24 hr before
surgery, systemic sepsis, preoperative international normalized
ratio of prothrombin time, the number of intraoperative
transfusions, and requirement of postoperative transfusion.
Conclusion Massive transfusion is associated with
substantial risk for respiratory and infectious complications and
for mortality. Patients who died within 30 days of a massive
perioperative transfusion were generally older, more likely
to have vascular surgical procedure and abnormal
international normalized ratio of prothrombin time, higher ASA
physical status, preoperative coma and sepsis, and higher
postoperative bleeding requiring transfusion, and they were
likely given more intraoperative red cell units.
Contexte Les transfusions massives sont associe´es a` des
taux e´leve´s de morbidite´ et de mortalite´; toutefois, les
comptes rendus existants portant sur les transfusions
massives sont peu nombreux. Notre objectif principal e´tait
de de´terminer l’incidence de complications et la mortalite´
a` 30 jours parmi les patients ayant rec¸u des transfusions
massives et d’explorer les facteurs de base associe´s a` une
mortalite´ a` 30 jours.
Me´thode Nous avons e´value´ 971 455 patients dont les
dossiers ont e´te´ tire´s de la base de donne´es du Programme
national d’ame´lioration de la qualite´ chirurgicale (National
Surgical Quality Improvement Program – NSQIP) du Colle`ge
ame´ricain des chirurgiens (American College of Surgeons).
Nous avons e´value´ les associations entre la mortalite´ a`
30 jours et les facteurs pre´ope´ratoires, perope´ratoires
et postope´ratoires parmi 5143 patients ayant rec¸u des
transfusions massives et au sujet desquels des donne´es comple`tes
Re´sultats La mortalite´ postope´ratoire a` 30 jours brute
des patients non transfuse´s, ayant rec¸u peu de transfusions
(1-4 unite´s) et ayant rec¸u des transfusions massives (C 5
unite´s) dans le NSQIP e´tait de 1,2 %, 8,9 %, et 21,5 %,
respectivement. Parmi les 5143 patients ayant rec¸u des
transfusions massives pour lesquels nous disposions de
toutes les donne´es covariables, 17 % (intervalle de
confiance [IC] 95 %: 16 % a` 18 %) sont morts dans les 30
jours suivant la chirurgie, alors que 54 % (IC 95 %: 53 % a`
56 %) ont subi au moins une complication majeure non
fatale. Les variables de base et perope´ratoires suivantes
ont e´te´ associe´es de fac¸on inde´pendante a` la mortalite´
a` 30 jours, apre`s ajustement pour tenir compte des tests
multiples: l’aˆge, le statut physique ASA (American Society of
Anesthesiologists), les cas d’urgence, les types de chirurgie,
un coma [ 24 h avant la chirurgie, un sepsis syste´mique, le
rapport international normalise´ pre´ope´ratoire du temps de
prothrombine, le nombre de transfusions perope´ratoires, et
le besoin de transfusions postope´ratoires.
Conclusion Les transfusions massives sont associe´es a`
des risques importants de complications respiratoires et
infectieuses ainsi qu’a` un risque de mortalite´. Les patients
de´ce´de´s dans les 30 jours suivant une transfusion
pe´riope´ratoire massive e´taient en re`gle ge´ne´rale plus aˆge´s,
plus enclins a` devoir subir une chirurgie vasculaire et
pre´sentaient un rapport international normalise´ anormal du
temps de prothrombine, un statut physique ASA plus e´leve´,
avaient souffert d’un coma pre´ope´ratoire et d’un sepsis,
ainsi que de saignements postope´ratoires plus importants
ne´cessitant des transfusions, et ils ont probablement rec¸u
davantage d’unite´s de globules rouges pendant l’ope´ration.
Since the first reported successful blood transfusion in 1667
by Jean-Baptiste Denys, blood transfusion has evolved
considerably with type matching and improved storage
techniques. Undoubtedly, many lives have been saved.1
But administration of blood products remains associated
with numerous complications and side effects, including
infections, inflammation, and complications related to the
collection, testing, preservation, and storage of blood
products.2,3 The potential for longer-term adverse
outcomes related to transfusions are substantial but less
Massive transfusion is a necessary treatment of
hemorrhagic shock; however, high rates of perioperative
morbidity and mortality are associated with massive
transfusion.4 Available reports of massive transfusion are
from single institutions; they are limited to trauma or
cardiac patients, have inadequate statistical power, or
suffer from incomplete patient follow-up.5-7 These drawbacks
make it difficult to generalize previous results in the
surgical population of large community and teaching hospitals
in United States. Previous studies do not fully evaluate
perioperative risk factors and the association of massive
transfusion with complications and mortality.8-10
Consequently, defining risk factors associated with adverse
longer-term morbidity and mortality remains problematic.
The American College of Surgeons National Surgical
Quality Improvement Program (NSQIP) database includes
surgical outcomes from 200 participating centres that
prospectively collect data using standardized methods, and
it provides researchers with a vast and broad sample. Our
primary objective was to use the NSQIP database to
determine the incidences of any non-fatal major
complications and 30-day mortality among patients who were
given C 5 red blood cell units. Our secondary aim was to
assess associations between 30-day mortality and baseline
risk factors, intraoperative risk factors, and postoperative
complications in patients who received massive
With the approval of the Institutional Review Board, this
retrospective cohort study was based on data acquired from
the American College of Surgeons National Surgical
Quality Improvement Program (ACS-NSQIP) from
January 2006 to December 2009. Data were prospectively
collected in a standardized fashion according to strict
definitions of preoperative characteristics, intraoperative
information, and postoperative outcomes. Dedicated
surgical clinical nurse reviewers collected data from patients’
computerized and paper medical records, physician office
records, and telephone interviews with patients. The
accuracy and reproducibility of these data are well
established.11,12 The ACS provides training for
participating hospitals, ongoing education opportunities, and
auditing to ensure data reliability. Preoperative through
30day postoperative data are collected by a systematic
sampling algorithm called the eight-day cycle and entered
online in a Health Insurance Portability and Accountability
Act (HIPAA)-compliant secure Web-based platform that
can be accessed 24 hr a day.13
We identified non-transfused patients, patients who
received 1-4 units of red blood cells, and patients who
received massive transfusions of at least 5 units of red
blood cells intraoperatively. Components of our composite
outcome are detailed in Table 1. We further defined
superficial surgical site or urinary tract infections as minor
Important data were missing in 21% of patients receiving
massive transfusions; about 92% of the missing data were
laboratory variables among this group of patients.
Hamilton et al.14 pointed out that missing data in NSQIP are not
missing-at-random data; we therefore included only
patients with complete data.
Pre-specified complications as well as mortality within
30-day follow-up were analyzed. For each of these
outcomes, the proportion of patients experiencing the outcome
and its associated 95% binomial exact confidence interval
(CI) were estimated.
The surgical procedure is a strong predictor of
complications and mortality; thus, it was important to adjust for
this variable in assessing the relationship between massive
transfusion and outcome. We clustered patient procedures
into one of the 244 clinically meaningful categories by
using the clinical classifications software (CCS) procedures
developed by the Agency for Healthcare Research and
svcsproc/ccssvcproc.jsp). We then aggregated the
identified CCS categories into one of the following 12 surgical
categories: general, vascular, thoracic, orthopedic,
neurosurgery, urology, otolaryngology, plastics, podiatry,
ophthalmology, oral surgery, or gynecology. Since some of
12 surgical categories in the dead group had
low-frequencies (n \ 7), we combined them into a single group in the
The bivariate association between 30-day mortality and
candidate baseline and intraoperative risk factors was
performed with univariable logistic regression (Table 2).
The association between the 31 factors listed in Table 2
and postoperative 30-day mortality in massive transfusion
patients was assessed using a multivariable logistic
regression model. A backward variable selection procedure
with an inclusion criterion of P \ 0.10 was used. The
variance inflation factor was computed for each predictor
in the linear regression to examine any possible
multicollinearity. The association between individual major
complications and postoperative 30-day mortality was
assessed using a multivariable logistic regression model
adjusting for significant baseline covariables via a
backward selection procedure (with stay criterion of P \ 0.10)
among the baseline and intraoperative factors listed in
Table 2, including transfusions for postoperative bleeding.
The relationship between the number of red blood cell
units given intraoperatively and time to discharge alive was
assessed with a Cox proportional hazard model adjusting
for baseline and intraoperative variables via a backward
variable selection procedure (with stay criterion of
P \ 0.10). Patients who expired before discharge were
* Data are presented as n (%), mean (standard deviation), or median [25th, 75th percentiles], and P values were from the univariable logistic regression.
CI = confidence interval; ASA = American Society of Anesthesiologists; COPD = chronic obstructive lung disease; CVA = cerebrovascular accident;
PRBC = packed red blood cells; PT = prothrombin time; PTT = partial thromboplastin time; RBC = red blood cell; SIRS = systemic inflammatory response
considered failures and assigned a length of stay one day
longer than any patient who was discharged alive. We also
assessed the proportional hazard assumption for the
number of red blood cell transfusions using visual Schoenfeld
Our sample size is based on using all available patients
from 2006-2009. The primary goal of the study was to
estimate incidences of the primary outcome with good
precision. With 5,143 patients, we were able to form a 95%
CI for any complication with half-widths from 0.5-1.5%
using the observed highest incidence of 54% and the lowest
incidence of 3%, respectively.
SAS statistical software (SAS Institute Inc., Carey, NC,
USA) was used for all analyses. All tests were two-sided.
When assessing the association between risk factors and
30-day mortality, Bonferroni correction was used to adjust
for multiple testing in the multivariable logistic
regression model based on the number of initial 31 candidate
variables (i.e., alpha = 0.05/31 = 0.0016). Bonferroni
correction was also used when assessing the association
between 30-day mortality and eight individual
complications (i.e., alpha = 0.05/39 = 0.0013).
Among 971,455 patients in NSQIP during 2006-2009, 862
were missing information on blood transfusion, 917,651
were not given blood, 45,457 were given 1-4 units, and
7,485 received massive transfusions. The crude 30-day
postoperative mortality of the non-transfusion, low
transfusion (1-4 units), and massive transfusion (C 5 units)
patients was 1.2%, 8.9%, and 21.5%, respectively.
Meanwhile, the crude 30-day postoperative mortality of 862
patients with missing information on blood transfusion was
3.5% (Fig. 1).
Among 7,485 patients receiving massive transfusion,
771 were excluded because of American Society of
Anesthesiologists (ASA) physical status greater than IV,
and 1,571 were excluded because of incomplete baseline or
preoperative laboratory data. The observed incidence of
30-day mortality was 19% in the excluded patients who
had incomplete baseline information (n = 1,571). The
observed incidences of 30-day mortality and individual
complications are summarized in Table 1. Eight hundred
sixty-seven (17%; 95% CI 16% to 18%) of the 5,143
massive transfusion patients expired within 30 days of
Table 2 shows the bivariate association between 30-day
mortality and baseline, intra-operative, and postoperative
factors. Univariably, the number of intraoperative red
blood cell transfusions in patients who expired within
30 days postoperatively (median [25th, 75th percentiles]: 8
]) was greater than in patients who survived (6 [
with univariable P \ 0.001 (Fig. 2). In a multivariable
analysis of the massive transfusion patients (Table 3), nine
variables were independently associated with 30-day
mortality after adjusting for multiple testing: age, ASA
physical status, emergency case, surgical types, coma [ 24
hr before surgery, systemic sepsis, preoperative
international normalized ratio (INR) of prothrombin time
(abnormal vs normal), the number of intraoperative red
blood cell transfusions, and postoperative blood
transfusion. Discriminative ability of the model on the original
data was good, with a c-statistic (SE) of 0.80 (0.008).
Multivariable associations between the individual major
complications and mortality are shown in Table 4. Patients
with systemic infection, urinary tract complications, central
nervous system complications, cardiovascular and other
complications were more likely to expire within 30-days of
surgery (all P \ 0.001).
About half of the massive transfusion patients were
discharged alive within 13 days (95% CI 12 to 13) in a
Kaplan-Meier time-to-event analysis. Patients were an
estimated 5% less likely to be discharged alive at any point
in time for each unit increase in red blood cell transfusion
[multivariable hazard ratio (95% CI): 0.95 (0.942 to 0.955);
P \ 0.001] adjusted for significant baseline and
intraoperative covariables. There was no serious violation of the
proportional hazard assumption for the number of red
blood cell transfusions over time.
Our study showed that 30-day mortality was 17%, and
nonfatal major complications were seen in more than 50% of
patients when C 5 units were transfused. The most
common complications were respiratory events, systemic
infections, and renal complications — and some were
significantly associated with mortality. Mortality was
associated with increased age, vascular surgical
procedures, high preoperative INR values, preoperative coma
and sepsis, higher ASA physical status scores, requirement
of postoperative transfusion, and the number of
intraoperative transfusions (5% less likely to be discharged alive
with each unit transfused).
We confirmed a number of traditional risk factors for
transfusion-associated mortality. For example, risk was
higher in older patients, in those given higher (worse) ASA
physical status scores, in those having complex and
emergency procedures, and in those who had renal failure
or sepsis.9 Then again, we also identified several new
factors. For example, preoperative INR level was
independently associated with elevated 30-day mortality. There
are some reports suggesting that coagulopathy contributes
to mortality in trauma patients, and high initial partial
thromboplastin time was found to be a predictor of
mortality. The mechanism behind these findings is elusive but
presumably associated with an increased risk of bleeding
and thus an increased requirement for transfused red blood
cells.15 Alternatively, transfusions per se provoke
substantial changes in the coagulation system, specifically,
dilutional and consumption coagulopathy16 and decreased
platelet numbers, which worsen the outcomes in patients
with pre-existing coagulation weaknesses.
Concern about the harmful effects of transfusion has
traditionally focused on infectious and hemolytic
transfusion reactions; however, these risks are now rare and
probably declining. In contrast, it is increasingly clear that
transfusions cause non-infectious complications, such as
immunomodulation, lung injury, alloimmunization,
metabolic derangements, and major organ dysfunction.17 For
example, 54% of the massive transfusion patients in our
study had at least one non-fatal major complication. The
most common complications were respiratory
complications, which were observed in 38% of the patients.
Respiratory problems after transfusion are well known,
especially transfusion-related acute lung injury (TRALI).
Transfusion-related acute lung injury is one of the most
severe complications among the non-infectious
complications and may be the leading cause of transfusion-related
deaths.18 It is thought to result from anti-neutrophil antigen
antibodies or anti-human leukocyte antigen antibodies.18-20
Transfusion volume was associated with a risk-adjusted
increase in the likelihood of minor complications,
* Estimated odds ratio (99.84% CI) and P values from multivariable logistic regression model
** Significant if P \ 0.0016 in the multivariable logistic regression using Bonferroni correction (i.e., 0.05/31 tests)
CI = confidence interval; ASA = American Society of Anesthesiologists; COPD = chronic obstructive lung disease; PT = prothrombin time;
PTT = partial thromboplastin time; RBC = red blood cell; SIRS = systemic inflammatory response syndrome
including prolonged postoperative ventilation after cardiac
surgery.8 Although respiratory complications were the
most frequent type of complication, we were unable to
show a specific association between respiratory
complications and increased mortality. Lack of association probably
results because the ACS-NSQIP database lacks clear
definition and diagnoses of TRALI and it includes minor
respiratory complications that are far more common but
have less prognostic importance.
It is reasonably well established that blood transfusions
cause immunosuppression. For example, blood
transfusions might be associated with increased risk of cancer
recurrence and bacterial infections.21 Leukocyte-mediated
immunosuppressive effects of transfusions may contribute
to the increased risk of infection.22 Consistent with this
theory, 40% of the massive transfusion patients in the
NSQIP registry developed infections, and 25% developed
systemic infectious complications, including systemic
inflammatory response syndrome, sepsis, and septic shock.
Our results are also consistent with those of Hill et al.21
who examined the relationship between transfusion and
infections in a meta-analysis and found that transfusions
more than triple the risk of systemic postoperative
infection. Furthermore, results in trauma patients are similar to
our results, with transfusions substantially increasing
systemic infection risk and more than doubling mortality.23
Massive transfusion almost doubled the risk of renal
failure in our study population. How transfusions might
contribute to renal failure remains unclear; however,
leukocytes are widely believed to be an important cause of
acute kidney injury. As might thus be expected, leukocyte
reduction of allogeneic blood products appears to decrease
* Estimated odds ratio (99.87% CI) of 30-day mortality in those with vs without the specified major complication, and P values from the
multivariable logistic regression adjusted for sex (P = 0.01), age (P \ 0.001), ASA classification (P = 0.005), race (P = 0.039), emergency
case (P \ 0.001), surgical type (P \ 0.001), functional health status prior to current illness (P = 0.007), previous cardiac surgery (P = 0.048),
coma [ 24 hr (P = \ 0.001), steroid use for chronic condition (P \ 0.001), preoperative systemic sepsis (P \ 0.001), abnormal preoperative
international normalized ratio of PT (P \ 0.001), number of intraoperative RBC transfusions (P \ 0.001)
** Significant if P \ 0.0013 in the multivariable logistic regression using Bonferroni correction (i.e., 0.05/39 tests)
CI = confidence interval; ASA = American Society of Anesthesiologists; PT = prothrombin time; RBC = red blood cells
acute kidney injury and mortality in massive transfusion
patients.22 We do not know which red blood cell units were
leukocyte-reduced; but virtually all transfused blood in the
United States during the study period was filtered. We do
not know the mechanism of renal failure in the NSQIP
patients, but the most probable mechanism seems to be
kidney hypoperfusion. Although this is generally reversible
when renal perfusion pressure is restored, prolonged
hypoperfusion may lead to irreversible renal failure. As
expected, renal complications were associated with
increased mortality. This finding is consistent with
previous studies showing that perioperative renal injury is
common and substantially increases the risk of mortality.
Transfusions were associated with a subtle increase in
central nervous system and cardiovascular complications.
While the incidence of these complications was low, they
were the ones most associated with mortality. The
mechanisms by which transfusions might increase the risk of
serious central nervous system and cardiovascular
complications remains unclear, but circulatory overload,
metabolic derangements, and inflammatory and
coagulopathic complications may all contribute. It is also likely
that transfusions increase mortality at least in part by
aggravating pre-existing conditions.
There are many definitions of massive transfusion in the
literature without much consensus. We used 5 units, which
is more appropriate for acute surgical bleeding than for
major trauma. Consistent with our definition, others have
used C 5 units in a four-hour period.24,25 Our surgical time
frame also fits the definition since most of the operations in
the NSQIP dataset lasted around four hours. Currently, our
study could not make an inference about the effect of blood
transfusion on mortality by using only massive transfusion
patients; however, when we look at the independent
association and the association between mortality and the
complications and risk factors, we treated the number of
blood transfusions as an important confounding factor. For
example, if we assess the independent association between
age and mortality in the massive transfusion, the number of
blood transfusions would be a very important confounding
factor for which we have to adjust. For this reason, we kept
the number of blood transfusions in the model as a
confounding variable to adjust but not to assess.
An advantage of using the ACS-NSQIP registry is that it
includes pooled data from numerous academic and
nonacademic institutions throughout the country. The large
sample size and broad representation of the ACS-NSQIP
registry thus provides excellent information about large
community and teaching hospitals; however, the situation
in small and rural non-participating facilities is unknown.
Furthermore, inclusion criteria are uniform and reliability
is enhanced by consistent data collection and auditing.
It would probably be impossible to perform a
randomized trial of massive transfusion, but lack of random
allocation left us to rely on associations, which can result
from bias and confounding, along with (or instead of) the
presumed causal mechanism. We used multivariable
analysis to correct for imbalances and known and available
confounding factors; however, there are known potential
confounding factors that are not included in the NSQIP
database, and surely there are others that remain unknown.
For example, the registry does not include medications
which might include drugs that impair coagulation.
While we identify a clear statistical association between
massive transfusion and adverse outcomes, including
death, it is simply an association and does not necessarily
imply a causal mechanism. There are many convincing
mechanisms by which transfusions might worsen
outcomes, but the association could equally well result, fully
or partially, from confounding. For example, patients who
have more extensive operations (of a given type)
experience more tissue injury and stress and consequently fare
worse. The most conservative interpretation of our results
is as follows: There is a non-causal association between
massive transfusion and adverse outcomes.
An additional limitation is that storage duration of red
blood cell units in our study is unknown. Prolonged storage
of transfused red blood cells appears to provoke numerous
serious postoperative morbidities along with mortality.26
Exclusion of patients with missing information (the
complete-case approach) can also result in substantial bias
and underestimate risks of patients because excluded
patients may have the highest comorbidities and thus be at
highest risk of mortality. To estimate the contribution of this
potential bias, we calculated the mortality rates in excluded
and included patients and found the rates to be similar.
Another concern is that the NSQIP registry contains only
limited detail about anesthetic management; consequently, it
is difficult to estimate the extent to which interventions by
anesthesiologists might have influenced outcomes. Another
important factor is that the current data set reflects the
outcomes of blood bank and surgical practices in the United
States, which may be different in other countries.
In summary, the complications most associated with
mortality are infections and complications of the renal,
cardiovascular, and central nervous systems. While the associations
with adverse outcomes are clear and highly statistically
significant, the relative contributions of transfusions vs underlying
disease and surgical factors remain to be determined. Patients
who expired after massive transfusion within 30 days
postoperatively were generally older, more likely to have vascular
surgical procedure and abnormal preoperative INR values,
higher ASA physical status scores, preoperative coma and
sepsis, postoperative bleeding requiring transfusion, and were
likely given intraoperative red blood cell units.
Acknowledgements The American College of Surgeons National
Surgical Quality Improvement Program and the hospitals
participating in the ACS NSQIP are the source of the data used herein; they
have not verified and are not responsible for the statistical validity of
the data analysis or the conclusions derived by the authors.
Funding The work was supported by internal funds. None of the
authors has any personal financial interest in this research. The work
is from the Department of OUTCOMES RESEARCH, Cleveland Clinic.
Competing interests None declared.
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