Download Determinants of Hospital Mortality After Coronary Artery Bypass

Determinants of Hospital Mortality After
Coronary Artery Bypass Grafting*
Argyris Michalopoulos, MD, FCCP; George Tzelepis, MD, FCCP;
Urania Dafni, PhD; and Stefanos Geroulanos, MD, PhD
Objectives: To examine causes of death and to find predictors of hospital mortality after elective
coronary artery bypass graft (CABG) surgery.
Design: Case-control study.
Setting: Tertiary teaching hospital.
Methods: We prospectively collected various preoperative, operative, and immediate postoperative variables in a cohort of patients undergoing elective CABG surgery.
Results: Of the 2,014 consecutive patients (mean [6 SD] age of 61.3 6 6.7 years old) undergoing
elective CABG over a 2-year period, 27 patients (1.3%) died during their hospitalization. The
main causes of death (either isolated or in combination) were cardiogenic shock (n 5 13), brain
death or stroke (n 5 7), septic shock (n 5 4), ARDS (n 5 2), and pulmonary embolism (n 5 1). A
univariate statistical analysis revealed factors that significantly correlate with outcome: patient
age, preoperative left ventricular ejection fraction, bypass time, aortic cross-clamp time, number
of blood units transfused, number of inotropic agents administered in the operating room during
the first postoperative day (POD), history of arterial hypertension, intra-aortic balloon pump
usage, and perioperative development of shock. A logistic regression analysis showed that the
combination of the number of inotropes and the number of blood units administered in the
operating room during POD 1 was the most important determinant of outcome, with an overall
positive predictive value of 91.7%.
Conclusions: We conclude that the analysis of simple variables enhances our ability to accurately
predict hospital mortality in patients undergoing elective CABG surgery. The number of
inotropic agents and blood transfusions administered during the immediate postoperative period
is the most important independent predictor of hospital mortality.
(CHEST 1999; 115:1598 –1603)
Key words: cardiovascular surgery; coronary artery bypass; heart; risk of mortality
Abbreviations: APACHE 5 acute physiology and chronic health evaluation; CABG 5 coronary artery bypass graft;
IABP 5 intra-aortic balloon pump; LCOS 5 low cardiac output syndrome; LVEF 5 left ventricular ejection fraction;
POD 5 postoperative day
artery bypass graft (CABG) surgery has
C oronary
become a common surgical procedure worldwide. Over the last decade, an increasingly larger
proportion of high-risk patients have been undergoing CABG, resulting in greater morbidity, a longer
ICU stay, and a greater burden on available resources.1,2 The ability to accurately predict patient outcome after cardiac surgery may be particularly useful
when determining a rational allocation of resources
in a given institution.
The scoring systems used to predict patient out*From the Cardiothoracic ICU, Onassis Cardiac Surgery Center,
Athens, Greece.
Manuscript received July 21, 1998; revision accepted January 20,
1999.
Correspondence to: Argyris Michalopoulos, MD, FCCP, Cardiothoracic ICU, Onassis Cardiac Surgery Center, 356 Sygrou Ave,
17674, Athens, Greece; e-mail: [email protected]
come in general ICUs are not reliable when considering postoperative cardiac surgery patients.3 Attempts have been made to develop predictive
models of outcome for this particular group of
patients; however, the majority of the reported models are complex and of modest predictive ability.4,5
These shortcomings, at least in part, may be due to
the fact that the majority of these studies analyzed
preoperative variables and ignored variables related
to the operation itself or to early postoperative care.6
The objectives of this study were to examine
causes of death and to find predictors of hospital
mortality (death occurring after the first postoperative day [POD] in the cardiothoracic ICU) for
patients undergoing elective CABG surgery. We
analyzed preoperative and immediate postoperative
variables in order to develop a simple but accurate
1598
Downloaded From: http://publications.chestnet.org/pdfaccess.ashx?url=/data/journals/chest/21920/ on 05/03/2017
Clinical Investigations
predictive index for patient outcome in the ICU. An
analysis of immediate postoperative variables has
the theoretical advantage of being able to identify
certain aspects of medical care that are amenable to
therapy.7
Materials and Methods
This is a case-control study of patients undergoing elective
CABG surgery over a 2-year period (from July 1993 to June 1995)
at the Onassis Cardiac Surgery Center, in Athens, Greece.
During this period, a total of 2,014 consecutive adult patients
(1,792 men and 222 women) with a mean age of 61.3 6 6.7 years
old (range, 22 to 83 years old) were admitted to the cardiothoracic ICU. Of these 2,014 patients, 27 patients died (the case
group); a sample of 708 patients who were successfully discharged from the hospital served as the control group. Of the 735
total patients enrolled in the study, 646 patients (87.9%) were
men and 89 patients (12.1%) were women. The majority of these
patients (n 5 523; 71.2%) were smokers. The preoperative left
ventricular ejection fraction (LVEF) was grade 1 (. 50%) in 209
patients, grade 2 (35 to 50%) in 406 patients, grade 3 (20 to 34%)
in 112 patients, and grade 4 (, 20%) in 8 patients. All patients
undergoing repeat, urgent, or emergency CABG surgery during
this period were excluded from the study.
The operations were performed by five surgeons. Myocardial
protection was achieved using combined antegrade and retrograde cold blood cardioplegia or antegrade cold crystalloid
cardioplegia. Anesthetic management was provided by three
teams according to a standard protocol. Ethical approval was
obtained from the Ethics and Research Committee of the Onassis
Cardiac Surgery Center.
The data collected on each patient included demographics,
medical history, and several preoperative, perioperative, and
immediate postoperative variables that were thought to influence
outcome. Also considered were all complications that occurred
during the ICU stay, the length of the ICU stay, the outcome
(death or discharge), and the cause of death. The variables
consisted of age, gender, preoperative LVEF assessed by angiography (grade 1, . 50%; grade 2, 35 to 50%; grade 3, 20 to 34%;
and grade 4, , 20%), and New York Heart Association class.
Other variables were bypass time, aortic cross-clamp time,
number of grafts, type of cardioplegia used, number of inotropic
agents administered during POD 1 (for at least 6 consecutive
hours) in the ICU, use of an intra-aortic balloon pump (IABP),
and number of blood units transfused in the operating room and
during POD 1 in the ICU. A blood transfusion was given when
the patient’s packed cell volume was , 27%.
All patients had one or two central venous catheters. A
Swan-Ganz catheter was used in patients with , 35% preoperative LVEF, as well as in patients with postoperative low cardiac
output syndrome (LCOS) and/or sepsis. Inotropic drugs were
administered to treat LCOS, defined as a cardiac index of , 2
L/min/m2, despite optimal filling pressures (wedge pressure, 12
mm Hg). The inotropes were included only if they were given in
the ICU for $ 6 h during POD 1. Initially, inotropic therapy in
this situation consisted of dobutamine, epinephrine, or dopamine
(not a renal dose) up to the maximum dosages (epinephrine, 2
mg/kg/min; dobutamine, 20 mg/kg/min; or dopamine, 20 mg/kg/
min). If there was a need for additional inotropic support within
this critical period (for persistence of the condition), milrinone, 1
mg/kg/min, or norepinephrine, 0.3 mg/kg/min, was then added.8
Definitions
All definitions were used as stated in the study protocol.
Cardiogenic shock was defined as the failure of the cardiac pump
to maintain a systemic blood flow sufficient to fulfill the patient’s
metabolic requirements.9 Perioperative acute myocardial infarction was defined as the appearance of a new and persistent Q
wave on ECG and/or the elevation of the creatinine phosphokinase/myocardial band enzyme to . 40 U total or a persistent
elevation of . 10% for . 24 h.
The patients were defined as having severe sepsis if they
manifested the following: (1) at least two of the following criteria:
(a) body temperature . 38°C or , 36°C, (b) WBC counts
. 12 3 103/mL or , 4 3 103/mL or immature neutrophils
. 10%, (c) heart rate . 90 beats/min, and (d) respiratory rate
. 20 breaths/min or Paco2 , 32 mm Hg; (2) a documented
infection; and (3) a systemic manifestation of peripheral hypoperfusion, such as lactic acidosis, oliguria (daily urine output
, 400 mL), or acute alteration of mental status.10 Septic shock
was defined as the combination of hypotension (a systolic BP
, 90 mm Hg or a decline of . 40 mm Hg from baseline in the
absence of other causes for hypotension) not responding to
$ 500 mL of IV fluid challenge and signs of tissue hypoperfusion
in a patient with severe sepsis.11
The diagnostic criteria for ARDS included (1) a history of a
precipitating condition, (2) refractory hypoxemia (Pao2 , 60 mm
Hg and fraction of inspired oxygen . 0.6), (3) radiologic evidence
of bilateral pulmonary infiltrates suggesting pulmonary edema,
(4) pulmonary artery occlusion pressure , 15 to 18 mm Hg, and
(5) total thoracic compliance , 30 mL/cm H2O.12 The mortality
rate refers to death occurring in the hospital following open heart
surgery after POD 1.
Statistical Analysis
The data were analyzed using the appropriate software (SAS,
version 6.03; SAS Institute; Cary, NC13; and Stata, version 4.0;
Stata Corporation; College Station, TX).14 The differences were
calculated using Fisher’s Exact Test for dichotomous or categorical variables and the two-sample Wilcoxon test for continuous
variables. A backward stepwise regression analysis was used to
identify the set of independent variables that contributed significantly to the fit of the model (removing criterion, p . 0.20).
When a multilevel categorical variable was found to be significant, tests were conducted to determine whether some levels
could be combined. Gender and cardioplegia were binary determinants; LVEF, aortic cross-clamp time, bypass time, number of
grafts, units of blood transfused, and numbers of inotropic drugs
administered were interval-scaled determinants. The dependent
variable was binary, with 1 denoting death and 0 denoting a live
discharge from the hospital. The predictability of the final logistic
regression model regarding patient mortality was assessed by
comparing the expected and actual number of deaths. The fit of
the model was assessed by the Hosmer-Lemeshow goodness-offit statistic.15 All reported p values are two-sided.
Results
Of the 27 patients who died, 25 were men (92.6%)
and 2 were women (7.4%), with a mean (6 SD) age
of 67.2 6 7.2 years old. The patients who died had a
mean (6 SD) preoperative LVEF of 0.35 6 0.1. The
overall mortality rate was 1.3%. All deaths occurred
in the ICU. The causes of death were cardiogenic
CHEST / 115 / 6 / JUNE, 1999
Downloaded From: http://publications.chestnet.org/pdfaccess.ashx?url=/data/journals/chest/21920/ on 05/03/2017
1599
shock in 13 patients (48.1%), stroke or brain death in
7 patients (25.9%), septic shock in 4 patients
(14.8%), ARDS in 2 patients (7.4%), and pulmonary
embolism in 1 patient (3.7%). The patient characteristics and several perioperative variables of surviving
and nonsurviving patients are shown in Table 1.
Among the determinants of outcome, an age . 65
years old was associated with an odds ratio of 4.3
(p , 0.001); perioperative shock necessitating IABP
usage significantly increased the risk of death (odds
ratio, 21.9; p , 0.001). A bypass time . 95 min
similarly increased the risk of death in the ICU (odds
ratio, 15.3; p , 0.001). However, the most significant determinant of death was the administration of
more than one inotrope in the operating room and/or
the ICU during POD 1 (odds ratio, 71.8; p , 0.001),
as well as the transfusion of . 6 blood units during
the same period (odds ratio, 30.3; p , 0.001).
A multivariate logistic regression analysis was used
to identify the determinants of death. Thirteen potential determinants were entered into the initial
model: age, gender, history of diabetes mellitus,
history of arterial hypertension, preoperative LVEF,
bypass time, aortic cross-clamp time, type of cardioplegia, number of grafts, LCOS, number of inotropes used, IABP usage, and number of blood
transfusions. The backward selection procedure led
to a final model including only age (p 5 0.14),
number of inotropes (p , 0.001), and number of
blood units (p , 0.001). The number and percentage of cases in each outcome group for the four
Table 1—Demographics, Preoperative, and Immediate
Postoperative Variables in Surviving and Nonsurviving
CABG Patients*
Patient Characteristics
Patients, No.
Gender
Male
Female
Age, yr
Smokers
Ejection fraction, %
Grade 3 or 4 (, 34%)
Diabetes mellitus
Arterial hypertension
Aortic cross-clamp time,
min
Cardioplegia
Antegrade and retrograde
Antegrade
Grafts
Blood units transfused
Inotropes
IABP
LCOS
Survivors
708
621 (87.7)
87 (12.3)
60.5 6 10.5
503 (71.0)
0.43 6 0.1
108 (15.2)
223 (31.5)
278 (39.3)
58 6 17
Nonsurvivors
p Value
27
25 (92.6)
2 (7.4)
67.2 6 7.2
20 (74.1)
0.35 6 0.1
12 (44.4)
9 (33.3)
18 (66.7)
89 6 61
, 0.76
, 0.76
, 0.001
, 0.5
, 0.001
, 0.001
, 0.84
, 0.001
, 0.001
levels of inotropes observed are presented in Table
2. In the group of patients receiving less than two
inotropes, only two patients (0.3%) died. On the
contrary, in the group of patients who died, 25
patients (92.6%) received at least two inotropes.
Table 3 shows the number of patients in the
outcome groups as a function of blood transfusions.
Patients who were given , 7 blood units during
POD 1 had a mortality rate of 0.5%, and patients
receiving $ 7 blood units had a mortality rate of 14%
(p , 0.001).
Using inotropes as a binary predictor (with values
of 0 and 1 indicating a good outcome and values of
$ 2 indicating death) in combination with blood
units transfused (with values of , 7 indicating a good
outcome and values of $ 7 indicating death), the
overall positive predictive value was 91.7% and
the overall negative predictive value was 99.3%
(Table 4).
In nonsurviving patients, the mean (6 SD) for
both mechanical ventilatory support and length of
ICU stay was 20.3 6 16.1 days (range, 1 to 62 days).
The nonsurviving patients were more likely to have
prolonged ventilatory support (. 1 day) than the
control group, respectively: 100% vs 5.8%
(p 5 0.001). The nonsurviving patients were also
more likely to have a longer ICU stay (. 2 days),
respectively: 85.2% vs 10.2% (p 5 0.001).
Discussion
Our study included both preoperative and immediate postoperative variables into a logistic regression
model and developed an index to predict outcome
after elective CABG surgery. Of all the parameters
that were analyzed, we found that the number of
inotropic drugs and the number of blood transfusions that were administered in the immediate postoperative period were the best reliable predictors of
poor outcome in this group of patients.
Predicting outcome in postoperative cardiac surgery patients has proved to be an extremely difficult
task. The severity-of-illness scoring systems that are
commonly used in medical ICUs have not been very
Table 2—Outcome Groups as a Function of Number of
Inotropes Used*
434 (61.3)
274 (38.7)
2.5 6 0.8
5.2 6 1.8
0.6 6 0.8
44 (6.2)
105 (14.8)
18 (66.7)
9 (33.3)
2.6 6 0.9
11.1 6 6.5
3.1 6 1.0
16 (59.3)
22 (81.5)
*Data are presented as mean 6 SD or No. (%).
, 0.69
, 0.69
, 0.65
, 0.001
, 0.001
, 0.001
, 0.001
Inotropes
Outcome Group
Good outcome
Death
Total
0–1
$2
603 (99.7)
2 (0.3)
605 (100)
105 (80.8)
25 (19.2)
130 (100)
*Data are presented as No. of patients (%).
1600
Downloaded From: http://publications.chestnet.org/pdfaccess.ashx?url=/data/journals/chest/21920/ on 05/03/2017
Clinical Investigations
Table 3—Outcome Groups as a Function of Blood
Unit Groups*
Blood Units, No.
Outcome Group
Good outcome
Death
Total
,7
$7
560 (99.5)
3 (0.5)
563 (100)
148 (86.0)
24 (14.0)
172 (100)
*Data are presented as No. of patients (%).
useful in predicting death in these patients, as high
scores are often not associated with poor outcome.
Our ability to normalize physiology by means of
pharmacologic and/or mechanical support may in
part account for this limited use of physiology-based
scoring systems.5 Indeed, the most recent simplified
acute physiology score II, mortality probability
model II, and APACHE (acute physiology and
chronic health evaluation) III scoring systems have
excluded cardiac surgery patients.16 –18
Over the last two decades, several studies have
attempted to predict outcome by using preoperative
risk factors in CABG surgery patients, but their
success rate usually has likewise been limited.1,6 In
some of these studies, outcome prediction involved a
large number of preoperative variables19 or exponential equations20 that made their application impractical. A common characteristic of these studies is that
they have focused exclusively on preoperative variables and, in general, have not taken into account
events (ie, surgical complications) that can affect
patient outcome during the operative or immediate
postoperative period. Because cardiac surgery now
involves a greater proportion of high-risk patients
(eg, more repeat operations), operative complications occur more frequently now than a decade ago2
and they are unlikely to be predicted by any preoperative variable.
In a recent study, Turner et al21 took into account
the intraoperative course and the complications that
occurred during the first 24 h in the ICU, and
attempted to develop a predictive model for CABG
surgery patients. In this study,21 APACHE II and III
criteria were not associated with mortality or survival, and the Parsonnet score overestimated hospital
mortality (particularly in the lower ranges), just as it
did in another study.22 This led the authors to
conclude that cardiac surgery remains a difficult area
for outcome prediction, although the inclusion of
intraoperative and postoperative variables can improve predictive ability.
The number of inotropes administered during the
immediate postoperative period (a gross index of
myocardial function) has also been found by previous
investigators21,23 to be an independent determinant
of outcome in these patients. Unlike preoperative
LVEF, this index is more likely to include patients
with intraoperative complications (eg, myocardial
ischemia or infarction), inadequate revascularization,
LCOS related to systemic inflammatory response
syndrome, stunned myocardium, or inadequate myocardial protection during bypass. Apparently, none of
these conditions can be accurately predicted using
any preoperative variable. Furthermore, in some
high-risk cases (eg, low LVEF), there may be a great
improvement in the left ventricular function postoperatively as a result of successful revascularization.24
It follows that, although low preoperative LVEF is a
known predictor of poor immediate postoperative
outcome following cardiac surgery, not all patients
with low preoperative LVEF require inotropic support.24
According to our study, in the presence of
perioperative parameters (eg, blood units and
inotropes), the preoperative LVEF proved to be a
nonsignificant determinant of outcome. In this
respect, our data are in agreement with those of
Turner et al21 and Thompson et al,23 who similarly
showed that when both preoperative and postoperative variables are analyzed, the preoperative
variables usually have little or no contribution to
the final predictive model.
The independent association of blood transfusions
with poor outcome deserves special comment. In our
study, the transfusion requirements were relatively high, even for the control group. This was
due to the fact that blood conservation techniques
were not used during the study period and also to
our excessive zeal to correct low hematocrit values
in the ICU. Nevertheless, this finding raises the
possibility that blood transfusions may indeed lead
to increased mortality, presumably by their immunosuppressive effects.25 The relation of transfusion-induced immunosuppression and increased
mortality rates in surgical patients has been highly
debated over the last decade.26 There is still a
controversy as to whether a cause-and-effect relationship exists between transfusion-induced immunosuppression and increased mortality or if
excessive blood transfusions are simply a marker of
disease severity.27 In postoperative cardiac surgery
patients, recent studies showed that excessive
blood transfusion was an important independent
risk factor for sternal wound infection,28 early
bloodstream infection,29 severe sepsis,30 or increased morbidity and mortality.31
This study illustrates the importance of preventing
the development of LCOS by providing adequate
myocardial protection during cardiopulmonary bypass. In addition, any strategy targeting the avoidCHEST / 115 / 6 / JUNE, 1999
Downloaded From: http://publications.chestnet.org/pdfaccess.ashx?url=/data/journals/chest/21920/ on 05/03/2017
1601
Table 4 —Outcome Prediction Based on the Logistic Regression Model (Death if Predictive Probability > 0.5)*
Inotropes
,2
,2
$2
$2
$7
$7
,7
Total
Sample
132
2
0.01
0.09
50
100
100
99.20
40
22
0.51
0.42
86.4
88.9
90.5
84.2
90
3
0.06
0.15
66.7
100
100
98.9
735
27
0.04
0.16
81.5
99.7
91.7
99.3
Blood Units
,7
Variables
Patients, No.
Deaths, No.
Predictive probability, mean
SD
Model sensitivity, %
Model specificity, %
Positive predictive value, %
Negative predictive value, %
473
0
0.0001
0.0003
—
100
—
100
*Categories are determined by the number of inotropes administered and blood units transfused.
ance of allogenic transfusion may significantly reduce the possibility of an unfavorable outcome after
CABG surgery.
The limitations of the present study should be
pointed out. First, the index was derived from data
obtained following an ICU stay of about 24 h;
therefore, these data cannot be used as a preoperative indicator of outcome. Nevertheless, despite this
delay in predicting outcome, we believe that there is
still considerable value in predicting outcome during
the early postoperative period. In comparison with
previous studies22,32 using only preoperative variables, the accuracy of our prediction is much higher.
Second, the surgery was performed by one group of
surgeons; therefore, one may argue that the results
of the present study are institution-specific and not
pertinent to all patient groups. Thus, our predictive
index of outcome requires validation in a different
setting.
In conclusion, we found that the inclusion of
certain postoperative variables into the analysis of
risk factors significantly increases our ability to predict hospital mortality in postoperative CABG surgery patients. Of all the variables analyzed, the
combination of inotropic drug therapy and excessive
blood transfusions is significantly associated with
hospital death following CABG.
References
1 Quaini E, Colombo T, Russo C, et al. Hospital morbidity and
mortality after myocardial revascularisation surgery: current
changes in risk factors. Eur J Cardiothorac Surg 1995;
9:275–282
2 Naunheim KS, Fiore AC, Wadley JJ, et al. The changing
mortality of myocardial revascularization: coronary artery
bypass and angioplasty. Ann Thorac Surg 1988; 46:666 – 674
3 Green J, Wintfeld N, Sharkey P, et al. The importance of
severity of illness in assessing hospital mortality. JAMA 1990;
263:241–246
4 Geraci JM, Rosen AK, Ash AS, et al. Predicting the occurrence of adverse events after coronary artery bypass surgery.
Ann Intern Med 1993; 118:18 –24
5 Tuman K, McCarthy R, March R, et al. Morbidity and
duration of ICU stay after cardiac surgery: a model for
preoperative risk assessment. Chest 1992; 102:36 – 44
6 Higgins TL, Estafanous FG, Loop FD, et al. Stratification of
morbidity and mortality outcome by preoperative risk factors
in coronary artery bypass patients: a clinical severity score.
JAMA 1992; 267:2344 –2348
7 Kollef MH, Wragge T, Pasque C. Determinants of mortality
and multiorgan dysfunction in cardiac surgery patients requiring prolonged mechanical ventilation. Chest 1995; 107:1395–
1401
8 Trujillo M, Arai K, Bellorin-Font E. Practical guide for drug
administration by intravenous infusion in intensive care units.
Crit Care Med 1994; 22:1049 –1063
9 Weil MH, von Planta M, Rackow EC. Acute circulatory
failure (shock). In: Braunwald E, ed. Heart disease: a textbook of cardiovascular medicine–international edition. 4th
ed. Philadelphia, PA: WB Saunders, 1992; 569 –587
10 American College of Chest Physicians/Society of Critical Care
Medicine Consensus Conference. Definitions for sepsis and
organ failure and guidelines for the use of innovative therapies in sepsis [review]. Crit Care Med 1992; 20:864 – 874
11 Pittet D, Rangel-Frausto S, Li N, et al. Systemic inflammatory response syndrome, sepsis, severe sepsis and septic
shock: incidence, morbidities and outcomes in surgical ICU
patients. Intensive Care Med 1995; 21:302–309
12 Goldsberry DT, Hurst JM. Adult respiratory distress syndrome and sepsis. New Horizons 1993; 2:342–347
13 SAS procedures guide: release 6.03 edition. Cary, NC: SAS
Institute, 1988
14 Stata Statistical Software: release 4.0. College Station, TX:
Stata Corporation, 1995
15 Hosmer DW, Lemeshow S. In: Applied logistic regression.
New York, NY: John Wiley, 1989; 25– 81
16 Le Gall FR, Lemeshow S, Saulnier F. A new simplified acute
physiology score (SAPS) based on a European/North American multicenter study. JAMA 1993; 270:2957–2963
17 Lemeshow S, Teres D, Klar J, et al. Mortality probability
models (MPM II) based on an international cohort of intensive care unit patients. JAMA 1993; 270:2478 –2486
18 Knaus WA, Draper EA, Wagner DP. The APACHE III
prognostic system. Chest 1991; 100:1619 –1636
19 Magovern JA, Sakert T, Magovern GJ, et al. A model that
1602
Downloaded From: http://publications.chestnet.org/pdfaccess.ashx?url=/data/journals/chest/21920/ on 05/03/2017
Clinical Investigations
20
21
22
23
24
25
predicts morbidity and mortality after coronary artery bypass
graft surgery. J Am Coll Cardiol 1996; 28:1147–1153
Kennedy JW, Kaiser GC, Fisher LD, et al. Multivariate
discriminant analysis of the clinical and angiographic predictors of operative mortality from the Collaborative Study in
Coronary Artery Surgery (CASS). J Thorac Cardiovasc Surg
1980; 80:876 – 887
Turner JS, Morgan CJ, Thakrar B, et al. Difficulties in
predicting outcome in cardiac surgery patients. Crit Care
Med 1995; 23:1843–1850
Nashef SAM, Carey F, Silcock M, et al. Risk stratification for
open heart surgery: trial of the Parsonnet system in a British
hospital. BMJ 1992; 305:1066 –1067
Thompson MJ, Elton RA, Sturgeon KR, et al. The Edinburgh
cardiac surgery score survival prediction in the long-stay ICU
cardiac surgical patient. Eur J Cardiothorac Surg 1995;
9:419 – 425
Zaroff J, Aronson S, Lee BK, et al. The relationship between
immediate outcome after cardiac surgery, homogeneous cardioplegia delivery, and ejection fraction. Chest 1994; 106:
38 – 45
Blumberg N, Heal JM. Effects of transfusion on immune
26
27
28
29
30
31
32
function: cancer recurrence and infection. Arch Pathol Lab
Med 1994; 118:371–379
Klein HG. Immunologic aspects of blood transfusion [review]. Semin Oncol 1994; 21(suppl 3):16 –20
Vamvakas EC, Carven JH, Hibberd PL. Blood transfusion
and infection after colorectal cancer surgery. Transfusion
1996; 36:1000 –1008
Zacharias A, Habib RH. Factors predisposing to median
sternotomy complications: deep vs superficial infection.
Chest 1996; 110:1173–1178
Ryan T, Mc Carthy JF, Rady MY, et al. Early bloodstream
infection after cardiopulmonary bypass: frequency rate,
risk factors, and implications. Crit Care Med 1997; 25:
2009 –2014
Michalopoulos A, Stavridis G, Geroulanos S. Severe sepsis in
cardiac surgical patients. Eur J Surg 1998; 164:217–222
Rady MY, Ryan T, Starr NJ. Perioperative determinants of
morbidity and mortality in elderly patients undergoing cardiac surgery. Crit Care Med 1998; 26:225–235
Ryan TA, Rady MY, Bashour AC, et al. Predictors of outcome
in cardiac surgical patients with prolonged intensive care stay.
Chest 1997; 112:1035–1042
CHEST / 115 / 6 / JUNE, 1999
Downloaded From: http://publications.chestnet.org/pdfaccess.ashx?url=/data/journals/chest/21920/ on 05/03/2017
1603