Factors Predict Perioperative Morbidity pancreatic resection

ORIGINAL ARTICLE – HEALTHCARE POLICY AND OUTCOMES
Preoperative Factors Predict Perioperative Morbidity
and Mortality After Pancreaticoduodenectomy
David Yu Greenblatt, MD, MSPH, Kaitlyn J. Kelly, MD, Victoria Rajamanickam, MS, Yin Wan, MS,
Todd Hanson, BS, Robert Rettammel, MA, Emily R. Winslow, MD, Clifford S. Cho, MD, FACS,
and Sharon M. Weber, MD, FACS
Department of Surgery, University of Wisconsin, Madison, WI
ABSTRACT
Background. Pancreaticoduodenectomy (PD) has long
been associated with high rates of morbidity and mortality.
The objective of this study was to identify preoperative risk
factors for serious complications and mortality after PD
and to construct a prediction tool to facilitate risk stratifi-
cation prior to surgery.
Materials and Methods. Patients who underwent elective
PD from 2005 to 2009 were identified from the American
College of Surgeons National Surgical Quality Improve-
ment Program (ACS NSQIP) database. Multivariate
logistic regression identified predictors of 30-day serious
complications and mortality. A prediction tool was created
and validated in a sample of 1254 patients.
Results. Of 4945 patients who underwent PD, 1342
(27.1%) suffered a serious complication and 127 (2.6%)
died within 30 days. The most frequent complications were
sepsis (15.3%), surgical site infection (13.1%), and respi-
ratory complications (9.5%). After adjusting for potential
confounders, the significant predictors of morbidity inclu-
ded older age, male gender, overweight and obesity,
dependent functional status, chronic obstructive pulmonary
disease (COPD), steroid use, bleeding disorder, leukocy-
tosis, elevated serum creatinine, and hypoalbuminemia.
Significant predictors of 30-day mortality included COPD,
hypertension, neoadjuvant radiation therapy, elevated serum
creatinine, and hypoalbuminemia. Multivariable models
were used to construct a preoperative risk stratification tool.
Conclusions. Preoperative factors are associated with
perioperative outcomes after PD. The prediction tool esti-
mates the probability of early morbidity and mortality for
patients undergoing PD. The tool may be used to provide
information for patient counseling during the informed
consent process and to identify high-risk patients for the
purpose of tailoring perioperative care.
Historically, pancreaticoduodenectomy (PD) has been
associated with high rates of mortality and morbidity. In
Dr. Whipple’s series, the mortality rate was higher than
30%.1
Recently, several high-volume centers have reported
markedly improved mortality rates, as low as 1–2%.2–4
Postoperative morbidity, however, remains common.
Several investigators have attempted to perform risk
stratification for perioperative outcome. Some have included
operative factors such as length of operation in their models,
making them less relevant for use in the preoperative clinical
setting.5,6
Others have used large administrative databases
that do not include important variables such as preoperative
serum albumin, or have focused on in-hospital mortality
rather than 30-day outcomes.7,8
In this study we analyzed the American College of Sur-
geons National Surgical Quality Improvement Program
(ACS NSQIP) database to determine the rate and predictors
of 30-day serious complications and mortality after PD. We
then created and validated a PD-specific prediction tool
based on preoperative factors. The purpose of this prediction
tool is to help estimate the risk of adverse perioperative
outcomes in patients undergoing PD.
The American College of Surgeons National Surgical Quality
Improvement Program and the hospitals participating in it represent
the source of the data used herein; they have not verified and are not
responsible for the statistical validity of the data analysis or for the
conclusions derived by the authors.
Ó Society of Surgical Oncology 2011
First Received: 12 April 2010;
Published Online: 20 February 2011
D. Y. Greenblatt, MD, MSPH
e-mail: greenblatt@surgery.wisc.edu
Ann Surg Oncol (2011) 18:2126–2135
DOI 10.1245/s10434-011-1594-6

METHODS
Data Acquisition and Patient Selection
The ACS NSQIP provides risk-adjusted outcome data to
participating hospitals for the purpose of quality improve-
ment. Descriptions of the qualifications, training, and
auditing of data collection personnel, case inclusion crite-
ria, sampling and data collection strategy, and variable and
outcome definitions are available on the ACS NSQIP
website.9
Patients who underwent elective PD were identified from
the 2005–2009 ACS NSQIP Participant-Use Files (PUFs),
which include data collected from 237 hospitals throughout
the United States. PDs were identified using the Current
Procedural Terminology (CPT) codes 48150, 48152, 48153,
and 48154. We excluded high-risk patients with any of the
following preoperative characteristics: emergency opera-
tion, American Society of Anesthesiologists (ASA) class 5
(moribund), ventilator dependence, severe sepsis or septic
shock (documented organ or circulatory dysfunction in a
patient with signs and symptoms of sepsis), current pneu-
monia, open wound, wound infection, acute renal failure
(increasing azotemia and a rise in creatinine [3 mg/dL in
the 24 h prior to surgery), coma, and disseminated cancer
(cancer that has spread to 1 or more sites in addition to the
primary site, indicating that the cancer is widespread, ful-
minant, or near terminal).
Outcomes
The 30-day outcomes included serious complications
and mortality. We defined serious complication or mor-
bidity as the diagnosis of any of the following in the
30 days after PD: sepsis (sepsis or septic shock), surgical
site infection (deep surgical site infection, organ/space
infection, or dehiscence), respiratory complication (pneu-
monia, ventilator dependence for greater than 48 h, or
unplanned reintubation), venous thromboembolism (pul-
monary embolism or deep vein thrombosis), cardiac
complication (acute myocardial infarction or cardiac arrest
requiring resuscitation), neurologic complication (stroke or
coma), renal failure (postoperative progressive renal
insufficiency with a rise in serum creatinine [2 mg/dL, or
acute renal failure requiring dialysis), and hemorrhage
(bleeding requiring transfusion of at least 4 units of packed
red blood cells). We did not consider superficial surgical
site infection, urinary tract infection, or peripheral nerve
injury to be serious complications. Additional outcomes
included reoperation within 30 days and length of hospital
stay (LOS) after surgery.
Potential Explanatory Variables
Demographics consisted of age, gender, and race (white,
black, or other). Variables related to preoperative health
included functional status (independent vs totally or par-
tially dependent), body mass index (classified according to
World Health Organization definitions), weight loss (10%
of total body weight in 6 months), current smoking, alco-
hol use (2 drinks per day in the past 2 weeks),
corticosteroid use, and recent blood transfusion or opera-
tion.10
Comorbidities included diabetes mellitus, chronic
obstructive pulmonary disease (COPD), coronary artery
disease (CAD; history of angina, myocardial infarction,
previous percutaneous cardiac intervention, or previous
cardiac surgery), peripheral vascular disease (history of
revascularization or amputation for peripheral vascular
disease, rest pain, or gangrene), neurological disease (his-
tory of stroke with or without residual deficit, transient
ischemic attack, hemiplegia, paraplegia, or quadriplegia,
central nervous system tumor, or impaired sensorium),
dyspnea, pneumonia, congestive heart failure (CHF), and
bleeding disorder. Neoadjuvant therapy variables included
chemotherapy (within 30 days prior to surgery) and radi-
ation therapy (within 90 days prior to surgery). Operative
variables included wound class, ASA class, number of
units of blood transfused during the procedure, and LOS.
Preoperative laboratory values consisted of white blood
cell (WBC) count, hematocrit, platelet count, International
Normalized Ratio (INR), sodium, blood urea nitrogen
(BUN), creatinine, serum glutamic oxaloacetic transaminase
(SGOT), alkaline phosphatase, and albumin. Laboratory
values above the 90th percentile were considered ‘‘abnor-
mally high,’’ and those below the 10th percentile were
deemed ‘‘abnormally low.’’ Each categorical laboratory
variable included an indicator of missingness, an approach
supported by a study on missing data in ACS NSQIP.11
Statistical Analysis
The total population of 6199 patients was randomly
divided into an 80% sample of 4945 patients for analysis
and multivariable model development and a 20% sample of
1254 patients for model validation. The frequencies of the
independent and dependent variables were determined in
the analysis sample. Continuous variables were compared
using the Wilcoxon rank sum test or Mann–Whitney U test,
and categorical variables with chi-square tests. All vari-
ables with P values .1 were eligible for inclusion in the
multivariable models for morbidity and mortality. To avoid
multicollinearity for the purpose of causal modeling, only
one variable was included from each set of intercorrelat-
ed variables. Multivariate logistic regression was used to
Pancreaticoduodenectomy Morbidity and Mortality Prediction Tool 2127

calculate adjusted odds ratios and 90% confidence intervals
(90% CIs) for 30-day morbidity and mortality. Concor-
dance indices (C indices) were calculated to quantify the
predictive accuracy of the final multivariable models of
morbidity and mortality. Analyses were performed using
SAS 9.1.3 for Windows (SAS Institute, Cary, NC). All tests
of significance were at the P .1 level, and all P values
were 2-tailed.
RESULTS
Frequency of Adverse Outcomes After
Pancreaticoduodenectomy
The analysis sample consisted of 4945 patients who
underwent elective PD. Of these, 1342 (27.1%) had a
serious complication and 127 died (2.6%) within 30 days
of surgery. Table 1 lists the frequencies of each of the
complications. The most common serious complications
were sepsis (15.3%), surgical site infection (13.1%), and
respiratory complications (9.5%). The 30-day reoperation
rate was 7.3%. Patients who had 30-day morbidity or
mortality had a much higher frequency of 30-day reoper-
ation compared with those who did not (22.6% vs 1.6%;
P .001). The 30-day mortality rate in patients who
underwent reoperation was 17.0%. Patients who suffered
morbidity or mortality had a significantly greater mean
length of postsurgery hospital stay (19.8 vs 10.1 days,
P .001).
Of the 3603 patients who did not have a serious com-
plication, only 17 patients (0.5%) died within 30 days of
surgery. The mortality rate in the group who had any
serious complication was 8.2% (P .001). Categories of
morbidity that were associated with high rates of 30-day
mortality included cardiovascular (66.2%), neurologic
(35.5%), renal (41.0%), bleeding (24.0%), and respiratory
(18.9%) complications (Table 1).
Characteristics of Patients Who Had 30-Day Morbidity
Table 2 displays the characteristics of the 4945 patients
who underwent PD and either did or did not have a serious
complication within 30 days. The group that experienced
morbidity was older, had a higher percentage of males, a
higher mean BMI, a lower frequency of recent weight loss,
and a higher frequency of dependent functional status and
each of the following comorbid conditions: dyspnea,
COPD, CAD, hypertension, peripheral vascular disease,
neurologic disease, steroid use, and bleeding disorders. The
morbidity group had a higher frequency of abnormally high
preoperative values of BUN, creatinine, and WBC count,
and abnormally low values of albumin and hematocrit. The
complication group had a lower frequency of pylorus-
sparing resections, higher ASA classification, more intra-
operative blood transfusions, and a longer mean operative
time (390 vs 366 min, P .001). There was no difference
between the 2 groups in terms of wound contamination class
or the frequency of postoperative diagnosis of malignancy.
Characteristics of Patients Who Had 30-Day Mortality
Table 3 shows the results of univariate analysis of fac-
tors associated with 30-day mortality. Compared with those
who survived beyond 30 days of PD, those who died were
on average older and had a higher frequency of dependent
functional status and each of the following comorbidities:
dyspnea, COPD, CAD, CHF, hypertension, and neurologic
disease. The percentage of patients who underwent neo-
adjuvant radiation therapy was also higher in the mortality
group. Preoperative laboratory values that were associated
with early mortality included BUN, creatinine, albumin,
SGOT, platelet count, and INR. Of the operative variables,
ASA class, blood transfusion, and length of operation were
each associated with 30-day mortality.
Multivariable Models of 30-Day Morbidity
and Mortality
Preoperative variables that were significantly associated
with morbidity and mortality in univariate analysis were
used to construct multivariable models of these adverse
outcomes. Table 4 displays the results of the multivariate
analysis. After adjusting for potential confounders, the
factors that were significantly associated with 30-day
morbidity included age 80 years and older, male gender,
overweight, obesity, dependent functional status, COPD,
steroid use, bleeding disorder, leukocytosis, elevated serum
creatinine, and hypoalbuminemia. Significant predictors of
TABLE 1 Frequency of adverse postoperative outcomes in 4945
patients who underwent pancreaticoduodenectomy, and their associ-
ation with 30-day mortality
Outcome Frequency, % Mortality, %
Any serious complication 27.1 8.2
Sepsis 15.3 8.9
Surgical site infection 13.1 5.3
Respiratory complication 9.5 18.9
Thromboembolic complication 3.2 3.9
Renal complication 1.6 41.0
Cardiovascular complication 1.6 66.2
Hemorrhage 1.5 24.0
Neurologic complication 0.6 35.5
Reoperation 7.3 17.0
Mortality 2.6 100
2128 D. Y. Greenblatt et al.

30-day mortality included COPD, hypertension, neoadju-
vant radiation therapy, elevated serum creatinine, and
hypoalbuminemia.
Creation and Validation of a Prediction Tool
The multivariable models of morbidity and mortality
were used to create a preoperative risk-stratification tool.
The predictive accuracy of the tool, which calculates the
TABLE 2 Characteristics of patients who underwent pancreatico-
duodenectomy (n = 4945), stratified by 30-day morbidity
Characteristic No
morbidity
(n = 3603)
Morbidity
(n = 1342)
P value
Demographics
Age, mean (SD) 63.6 (12) 65.3 (12) .01*
Female gender, % 50.5 44.3 .01*
Race/ethnicity .424
White, % 79.1 78.5
Black, % 7.9 7.2
Other, % 13.0 14.2
Preoperative health and
comorbidities
BMI (kg/m2
), mean (SD) 26.7 (6) 27.4 (6) .01*
Recent weight loss, % 20.7 17.0 .01*
Diabetes mellitus, % 22.2 24.1 .14
Current smoker within last
year, %
22.5 22.5 .97
Alcohol use ([2 drinks per
day), %
3.5 3.8 .61
Functional status: partially
or totally dependent, %
1.7 3.8 .01*
Dyspnea, % 7.1 12.2 .01*
COPD, % 3.4 6.3 .01*
CAD, % 10.0 14.4 .01*
CHF, % 0.2 0.5 .100
Hypertension, % 50.6 57.1 .01*
Peripheral vascular disease, % 1.3 2.5 .01*
Neurologic disease, % 4.5 6.1 .03*
Steroids, % 1.4 2.8 .01*
Bleeding disorder, % 1.8 3.7 .01*
Preoperative chemotherapy, % 1.5 1.5 1.00
Preoperative radiation
therapy, %
2.6 1.9 .14
Preoperative laboratory values
Sodium, mmol/L, mean (SD) 139 (3) 139 (3) .32
BUN, mg/dL, mean (SD) 14.4 (7) 15.5 (8) .01*
Creatinine, mg/dL, mean (SD) 0.90 (0.4) 0.96 (0.5) .01*
Albumin, mg/dL, mean (SD) 3.76 (0.6) 3.68 (0.7) .01*
Total bilirubin, mg/dL, mean
(SD)
2.07 (3) 1.96 (3) .13
SGOT, U/L (mean [SD]) 62.8 (81) 57.6 (74) .20
Alkaline phosphatase, IU/L,
mean (SD)
203 (183) 193 (179) .23
WBC count, thousand cells/
mm3
, mean (SD)
7.3 (3) 7.6 (3) .01*
Hematocrit, %, mean (SD) 37.9 (5) 37.6 (5) .04*
Platelet count, thousand cells/
mm3
, mean (SD)
279 (95) 280 (103) .47
INR, mean (SD) 1.05 (0.2) 1.06 (0.3) .77
TABLE 2 continued
Characteristic No
morbidity
(n = 3603)
Morbidity
(n = 1342)
P value
Operative variables
Type of
pancreaticoduodenectomy
.04*
Standard, % 56.6 59.9
Pylorus-sparing, % 43.4 40.1
Wound class .27
Clean or clean-
contaminated, %
91.0 89.6
Contaminated, % 7.6 8.8
Dirty or infected, % 1.4 1.6
ASA class .01*
No or mild disturbance, % 33.5 27.1
Severe disturbance, % 62.7 66.0
Life-threatening
disturbance, %
3.8 6.7
Blood transfusions .01*
None, % 72.9 61.7
1–2 units, % 19.0 20.9
[2 units, % 7.9 17.4
Length of operation, mean
(SD)
366 (120) 390 (136) .01*
Postoperative diagnosis .37
Malignant, % 72.9 71.6
Benign, % 27.1 28.4
NSQIP risk score
Probability of morbidity,
mean (SD)
0.405 (0.10) 0.431 (0.11) .01*
Probability of mortality,
mean (SD)
0.033 (0.03) 0.041 (0.04) .01*
SD standard deviation, % column percentage, BMI body mass index,
COPD chronic obstructive pulmonary disease, CAD coronary artery
disease, CHF congestive heart failure, WBC white blood cell, BUN
blood urea nitrogen, SGOT serum glutamic oxaloacetic transaminase,
INR International Normalized Ratio, ASA American Society of
Anesthesiologists
* Denotes statistical significance at the P .10 level

predicted probability of 30-day morbidity and mortality
after PD, was evaluated in the both the 80% development
sample of 4945 patients and the 20% validation sample of
1254 patients. The morbidity calculator had C indices of
0.63 and 0.60 in the development and validation samples,
respectively. The mortality calculator C indices were 0.69
in both samples. The C indices of the ACS NSQIP Proba-
bility of Morbidity (MORBPROB) and Probability of
Mortality (MORTPROB) scores, which include operative
variables, were comparable to those of our PD-specific tool,
TABLE 3 Characteristics of patients who underwent pancreatico-
duodenectomy (n = 4945), stratified by 30-day mortality
Characteristic No
mortality
(n = 4818)
Mortality
(n = 127)
P value
Demographics
Age, mean (SD) 63.9 (12) 68.7 (12) .01*
Female gender, % 49.0 44.1 .28
Race/ethnicity .73
White, % 79.0 78.0
Black, % 7.7 9.5
Other, % 13.4 12.6
Preoperative health and
comorbidities
BMI (kg/m2
), mean (SD) 26.9 (6) 27.6 (6) .10
Recent weight loss, % 19.6 22.8 .37
Diabetes mellitus, % 22.5 28.4 .13
Current smoker within last
year, %
22.7 14.2 .02*
Alcohol use ([2 drinks per
day), %
3.5 5.5 .22
Functional status: partially or
totally dependent, %
2.2 5.5 .02*
Dyspnea, % 8.3 16.5 .01*
COPD, % 4.1 9.5 .01*
CAD, % 10.9 21.3 .01*
CHF, % 0.2 1.6 .04*
Hypertension, % 51.9 70.1 .01*
Peripheral vascular disease, % 1.6 2.4 .46
Neurologic disease, % 4.9 8.7 .06*
Steroids, % 1.7 3.2 .29
Bleeding disorder, % 2.3 2.4 .77
Preoperative chemotherapy, % 1.5 1.6 .72
Preoperative radiation
therapy, %
2.3 5.5 .03*
Sodium, mmol/L, mean (SD) 139 (3) 138 (4) .53
BUN, mg/dL, mean (SD) 14.7 (7) 17.2 (10) .01*
Creatinine, mg/dL, mean (SD) 0.91 (0.5) 1.01 (0.4) .01*
Albumin, mg/dL, mean (SD) 3.7 (0.6) 3.5 (0.7) .01*
Total bilirubin, mg/dL, mean
(SD)
2.0 (2.9) 2.1 (2.9) .23
SGOT, U/L (mean [SD]) 61.3 (79) 64.7 (69) .08*
Alkaline phosphatase, IU/L,
mean (SD)
200 (182) 207 (178) .72
WBC count, thousand cells/
mm3, mean (SD)
7.4 (2.5) 7.1 (2.2) .58
Hematocrit, %, mean (SD) 37.8 (4.9) 37.2 (5.8) .17
Platelet count, thousand cells/
mm3, mean (SD)
280 (97) 268 (101) .10*
INR, mean (SD) 1.05 (0.2) 1.09 (0.2) .02*
TABLE 3 continued
Characteristic No
mortality
(n = 4818)
Mortality
(n = 127)
P value
Operative variables
Type of
.86
Standard, % 57.5 56.7
Pylorus-sparing, % 42.5 43.3
Wound class .64
Clean or clean-
contaminated, %
90.6 92.9
Contaminated, % 8.0 5.5
Dirty or infected, % 1.5 1.6
ASA class .01*
No or mild disturbance, % 32.2 15.0
Severe disturbance, % 63.2 77.2
Life threatening
disturbance, %
4.5 7.1
Blood transfusions .01*
None, % 70.5 46.5
1–2 units, % 19.4 24.4
[2 units, % 10.1 28.4
Length of operation, mean
(SD)
371 (125) 413 (144) .01*
Postoperative diagnosis .27
Malignant, % 72.4 77.2
Benign, % 27.6 22.8
NSQIP risk score
Probability of morbidity,
mean (SD)
0.410 (0.10) 0.464 (0.11) .01*
Probability of mortality,
mean (SD)
0.027 (0.03) 0.048 (0.05) .01*
SD standard deviation, % column percentage, BMI body mass index,
COPD chronic obstructive pulmonary disease, CAD coronary artery
disease, CHF congestive heart failure, WBC white blood cell, BUN
blood urea nitrogen, SGOT serum glutamic oxaloacetic transaminase,
INR International Normalized Ratio, ASA American Society of
Anesthesiologists
* Denotes statistical significance at the P .10 level

TABLE 4 Multivariable
models of 30-day morbidity and
mortality after
Characteristic AOR (90% CI)
for morbidity
AOR (90% CI)
for mortality
Demographics
Age
Younger than 50 Reference Reference
50–59 1.05 (0.86–1.28) 1.29 (0.64–2.61)
60–69 0.98 (0.81–1.18) 1.07 (0.54–2.10)
70–79 1.13 (0.94–1.37) 1.54 (0.79–3.01)
80 and older 1.39 (1.10–1.75)* 1.93 (0.93–4.01)
Gender
Female Reference Reference
Male 1.15 (1.02–1.29)* n/a
Preoperative health and comorbidities
BMI (kg/m2
)
18.5 (underweight) 0.69 (0.45–1.07) n/a
18.5–24.9 (normal weight) Reference n/a
25–29.9 (overweight) 1.27 (1.10–1.47)* n/a
30.0–34.9 (obese I) 1.33 (1.11–1.59)* n/a
35.0–39.9 (obese II) 1.40 (1.09–1.80)* n/a
C40.00 (obese III) 1.86 (1.36–2.56)* n/a
Weight loss
No Reference n/a
Yes 0.84 (0.67–1.04) n/a
Functional status
Independent Reference Reference
Partially or totally dependent 1.95 (1.40–2.73)* 1.64 (0.82–3.26)
Dyspnea
No n/a Reference
Yes n/a 1.53 (0.99–2.37)
COPD
No Reference Reference
Yes 1.67 (1.30–2.15)* 1.97 (1.12–3.45)*
Smoker
No n/a Reference
Yes n/a 0.66 (0.42–1.03)
Diabetes mellitus
No n/a Reference
Yes n/a 1.03 (0.73–1.45)
CAD
No Reference n/a
Yes 1.16 (0.97–1.37) n/a
CHF
No n/a Reference
Yes n/a 3.22 (0.82–12.60)
Hypertension
No n/a Reference
Yes n/a 1.60 (1.13–2.27)*
Peripheral vascular disease
No Reference n/a
Yes 1.41 (0.96–2.09) n/a

which is based solely on preoperative factors (Table 5).
The online prediction tool may be accessed at the follow-
ing web address: https://www.surgery.wisc.edu/research/clinical-
research-program/whipple_outcome_predictor.
DISCUSSION
In this study we analyzed the ACS NSQIP database to
determine the rate and predictors of 30-day morbidity and
TABLE 4 continued
AOR adjusted odds ratio, 90%
CI 90% confidence interval,
BMI body mass index, COPD
chronic obstructive pulmonary
disease, CAD coronary artery
disease, CHF congestive heart
failure, WBC white blood cell,
BUN blood urea nitrogen, SGOT
serum glutamic oxaloacetic
transaminase, ASA American
Society of Anesthesiologists
The morbidity model includes a
BMI*weight loss interaction
term, not shown in the table
* Denotes statistical
significance at the P .10 level
Characteristic AOR (90% CI)
for morbidity
AOR (90% CI)
for mortality
Neurologic disease
No Reference Reference
Yes 1.15 (0.90–1.46) 1.24 (0.72–2.16)
Steroids
No Reference n/a
Yes 1.67 (1.15–2.43)* n/a
Bleeding disorder
No Reference n/a
Yes 1.83 (1.32–2.54)* n/a
Neoadjuvant radiation therapy
No n/a Reference
Yes n/a 2.78 (1.41–5.49)*
WBC count, thousand cells/mm3
B8.6 Reference n/a
8.61–10.4 1.40 (1.21–1.62)* n/a
[10.4 1.31 (1.09–1.57)* n/a
Missing 1.14 (0.73–1.76) n/a
Platelet count, thousand cells/mm3
C214 n/a Reference
174–213 n/a 1.29 (0.88–1.90)
174 n/a 1.28 (0.80–2.05)
Missing n/a 1.79 (0.53–5.99)
Creatinine, mg/dL
B1.0 Reference Reference
1.01–1.2 1.39 (1.22–1.59)* 1.42 (1.00–2.03)
[1.20 1.49 (1.24–1.79)* 1.71 (1.12–2.63)*
Missing 0.86 (0.55–1.34) 0.71 (0.16–3.10)
Albumin, g/dL
C3.4 Reference Reference
2.8–3.39 1.33 (1.15–1.52)* 1.52 (1.06–2.19)*
2.8 1.13 (0.91–1.39) 1.67 (1.01–2.75)*
Missing 1.01 (0.84–1.21) 0.97 (0.56–1.68)
TABLE 5 Comparison of the predictive accuracy of pancreaticoduodenectomy (PD)-specific prediction tools to ACS NSQIP risk scores for
predicting 30-day morbidity and mortality in 2 samples of patients who underwent PD
C index in 80% development
sample (n = 4945)
C index in 20% validation
sample (n = 1254)
NSQIP Probability of Morbidity (MORBPROB) score 0.57 0.59
PD-specific prediction tool for morbidity 0.63 0.60
NSQIP Probability of Mortality (MORTPROB) score 0.66 0.69
PD-specific prediction tool for mortality 0.69 0.69
C index indicates concordance index. The range of possible C index values is 0.5–1, with 1 indicating a perfect prediction tool

mortality after pancreaticoduodenectomy and developed
and validated a prediction tool based exclusively on pre-
operatively determined factors. ACS NSQIP includes
laboratory values as well as information on smoking,
alcohol use, and functional status. These are not available
in administrative databases such as the Healthcare Cost and
Utilization Project Nationwide Inpatient Sample (NIS) and
the Surveillance, Epidemiology, and End Results (SEER)-
Medicare database. ACS NSQIP is based on medical
chart extraction and reliably captures postoperative com-
plications, which are precisely defined. In contrast,
administrative databases typically can only reliably capture
complications that result in reoperation or other procedural
interventions.12
The ability of ACS NSQIP to measure
30-day outcomes regardless of hospital admission status is
a major strength compared with the NIS, which does not
monitor events that occur after hospital discharge. In 2009,
the ACS NSQIP Participant Use Data File contained data
from 237 academic and community hospitals nationwide.
Findings based on these multi-institutional data have
greater external validity than those based on the experience
of individual specialized academic centers.
The 30-day mortality rate of 2.6% that we observed is
considerably lower than the mortality rates reported in
earlier studies based on other large multi-institutional
databases, including Medicare (10.4%), the Department of
Veterans Affairs Healthcare System (VA, 9.3%), and NIS
(6.6%).13–15
This may be due to the fact that high volume
centers are well represented in ACS NSQIP. The rela-
tionship between hospital volume and perioperative
outcomes is well established for PD, and the lower peri-
operative mortality in ACS NSQIP hospitals may be a
reflection of this.16,17
In our study the most frequent serious complications were
sepsis, surgical site infection, and respiratory complications.
Studies from high-volume centers have demonstrated that 2
common complications after PD are pancreatic fistula and
delayed gastric emptying (DGE).4,18–22
A limitation of the
ACS NSQIP database is that these clinical entities are not
included in the list of prospectively recorded postoperative
complications. The International Study Group on Pancreatic
Surgery (ISGPS) has published classification schemes for
grading severity for pancreatic fistula and DGE.23,24
Although pancreatic fistula is not specifically coded for in
ACS NSQIP, ISGPS Grade B and C pancreatic leaks are
likely to be recorded as organ/space surgical site infection or
sepsis. DGE would not be captured by any current ACS
NSQIP complication, but would result in prolonged LOS. As
ACS NSQIP is currently planning a hepatopancreatobiliary
(HPB) module, pancreatic fistula and DGE should be
included, using ISGPS criteria.23–25
Several authors have reported risk factors for early
morbidity and mortality after pancreatic resection. In an
analysis of the VA-NSQIP database, the rates of 30-day
morbidity and mortality after PD were 45.9% and 9.3%,
respectively.14
Significant predictors of mortality included
preoperative hypoalbuminemia, preoperative hyperbiliru-
binemia ([20 mg/dL), ASA class, and operative time. In a
single-institution study, biochemical risk factors for 30-day
morbidity and mortality included preoperative hypoalbu-
minemia and elevated preoperative BUN and postoperative
serum amylase.26
Two studies have been published on predictors of in-
hospital mortality based on NIS data. In 1 study analyzing
5481 patients who underwent pancreatic resection for
malignancy, the overall in-hospital mortality rate was
6.1%.7
Multivariate analysis revealed the following vari-
ables were associated with inpatient mortality: nonteaching
hospital type, small or medium hospital size, nonelective
admission, patient age [70, renal failure, neurological
disease, hypothyroidism, congestive heart failure, liver
disease, and hypertension. To improve the predictive
accuracy of the model, several additional variables that
were not statistically significant were included: pancrea-
tectomy type, sex, COPD, and diabetes. In another study
that used the NIS database, 5715 patients who underwent
pancreatic resection for cancer had an in-hospital mortality
rate of 5.8%.8
Variables that were statistically significant
and clinically relevant were included in a multivariable
logistic regression model, and a weighted scoring system
was developed for stratifying inpatient mortality risk. The
final list of covariates included age, sex, Charlson comor-
bidity score, type of pancreatectomy, and hospital volume.
In the current study, preoperative factors that were
associated with 30-day morbidity in multivariate analysis
included older age, male sex, overweight, obesity, depen-
dent functional status, COPD, steroid use, bleeding disorder,
leukocytosis, elevated serum creatinine, and hypoalbumi-
nemia. Significant predictors of 30-day mortality included
COPD, hypertension, neoadjuvant radiation therapy, ele-
vated serum creatinine, and hypoalbuminemia. The majority
of these risk factors has been shown to be associated with
perioperative morbidity or mortality in previous studies on
outcomes after pancreatic resection.4,6–8,14,15,21,26–29
We used the results of the multivariate logistic regres-
sion analysis to construct a prediction tool. The predictive
accuracy of the tool may improve in future versions as
more data accrue, and HPB-specific variables are added to
ACS NSQIP. The factors included in the prediction tool are
all measurable in the preoperative setting, in contrast to
other models such as the Physiologic and Operative
Severity Score for the Enumeration of Mortality and
Morbidity (POSSUM), the Surgical Apgar score, and the
NSQIP Probability of Morbidity and Mortality risk scores,
which all include operative variables.5,6
The online pre-
diction tool is easy to use, does not require calculation of a

separate comorbidity score or use of a printed nomogram,
and is adaptable as new data become available.
We envision 2 main uses of the PD-specific prediction
tool for 30-day morbidity and mortality. First, the tool may
be used to help estimate the probability of adverse out-
comes for preoperative counseling. In this large series of
patients who underwent PD drawn from more than 200
academic and private hospitals nationwide, the incidence
of 30-day mortality was approximately 1 in 35, and the
frequency of 30-day serious complications was approxi-
mately 1 in 4. Our analysis indicates that patients with
certain preoperative characteristics, such as advanced age,
morbid obesity, and hypoalbuminemia, are at significantly
increased risk for adverse outcomes after PD. The predic-
tion tool may be used to convey to patients their estimated
relative probability of major morbidity and mortality. The
predicted probabilities generated by the calculator should
be interpreted by an experienced surgeon in the context of
the patient’s unique clinical situation. Referral to a high-
volume center should be considered for high-risk patients.
The second main potential use of the prediction tool is to
aid in the identification of high-risk patients for the purpose
of tailoring perioperative care. Optimization of specific
medical comorbidities and nutritional status may decrease
the frequency of adverse postoperative events. Identifying
high-risk patients is an important first step in the devel-
opment of targeted interventions aimed at improving
outcomes. Future iterations of the prediction tool may
estimate the probability of specific complications, which
would have clinical utility. For example, the tool could be
used to screen for patients who are at high risk for devel-
oping postoperative pneumonia, who then could be treated
with a protocol of aggressive respiratory therapy.
The main limitations of our study are related to the
source of the data. The ACS NSQIP is a voluntary pro-
gram, and the 237 participating sites do not represent a
statistically valid sample of all hospitals in the United
States. The database contains many clinical variables, but
essentially no information on patient socioeconomic status.
Hospital identifiers are not included in the PUF. Therefore,
it is not possible to determine the association of hospital
procedure volume on surgical outcomes using this data-
base. There is a similar paucity of surgeon-level variables,
and the database does not include information on surgeon
experience or procedure volume. Besides PD-specific
complications such as pancreatic fistula and DGE, ACS
NSQIP could be improved for the purpose of studying PD
outcomes by including factors such as biliary stenting,
pancreatic duct diameter, gland texture, and the use of
drains.25
Despite these limitations, the prediction tool described
in this study is the first of its kind to use preoperative
factors alone to estimate the probability of 30-day
morbidity and mortality after PD. The tool may serve as an
adjunct to the surgeon’s clinical judgment and experience
in estimating the risk of adverse outcomes for individual
patients. Potential uses of the prediction tool include
helping convey information in patient counseling and the
informed consent process, and identifying high-risk
patients for the purpose of tailoring perioperative care to
improve outcomes.
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