Original ArticleJ Gastric Cancer 2015;15(4):262-269 http://dx.doi.org/10.5230/jgc.2015.15.4.262

Copyrights © 2015 by The Korean Gastric Cancer Association www.jgc-online.org

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Introduction

Radical gastrectomy with lymph node dissection in patients

with gastric cancer has been associated with a high rate of post-

operative complications, ranging from 20% to 46%.1-4 Accumu-

lated surgical experience and recent advances in surgical instru-

ments and perioperative management have led to a reduction in

postoperative morbidity and mortality.5-9 However, despite these

advances, major complications, particularly in high-risk patients,

remain problematic.

Intraabdominal abscess is one of the most commonly re-

ported post-gastrectomy complications. The incidence of in-

traabdominal abscess, manifesting as complex fluid collection

on computed tomography (CT), abdominal pain, fever, and

leukocytosis, ranges from 0.6% to 17%.1,3,7,8,10 The abscess is

usually detected within several days of surgery, and immediate

intervention is possible if the patient is still hospitalized. How-

ever, there are patients who present to the emergency depart-

ment with delayed intraabdominal abscess after an uneventful

discharge. With the recent introduction of the enhanced recov-

ery after surgery pathway hospital stays are shorter, and fewer

pISSN : 2093-582X, eISSN : 2093-5641

Correspondence to: Young-Woo Kim

Gastric Cancer Branch, Research Institute and Hospital, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang 10408, KoreaTel: +82-31-920-1635, Fax: +82-31-920-0696E-mail: gskim@ncc.re.krReceived November 11, 2015Revised December 11, 2015Accepted December 12, 2015*These authors contributed equally to this paper as co-first authors.

Nomogram Estimating the Probability of Intraabdominal Abscesses after Gastrectomy in Patients with Gastric Cancer

Bang Wool Eom*, Jungnam Joo1,*, Young-Woo Kim, Boram Park1, Hong Man Yoon, Keun Won Ryu, and Soo Jin Kim

Gastric Cancer Branch and 1Biometric Research Branch, Research Institute and Hospital, National Cancer Center, Goyang, Korea

Purpose: Intraabdominal abscess is one of the most common reasons for re-hospitalization after gastrectomy. This study aimed to de-velop a model for estimating the probability of intraabdominal abscesses that can be used during the postoperative period.Materials and Methods: We retrospectively reviewed the clinicopathological data of 1,564 patients who underwent gastrectomy for gas-tric cancer between 2010 and 2012. Twenty-six related markers were analyzed, and multivariate logistic regression analysis was used to develop the probability estimation model for intraabdominal abscess. Internal validation using a bootstrap approach was employed to correct for bias, and the model was then validated using an independent dataset comprising of patients who underwent gastrectomy between January 2008 and March 2010. Discrimination and calibration abilities were checked in both datasets. Results: The incidence of intraabdominal abscess in the development set was 7.80% (122/1,564). The surgical approach, operating time, pathologic N classification, body temperature, white blood cell count, C-reactive protein level, glucose level, and change in the hemoglobin level were significant predictors of intraabdominal abscess in the multivariate analysis. The probability estimation model that was developed on the basis of these results showed good discrimination and calibration abilities (concordance index=0.828, Hosmer-Lemeshow chi-statistic P=0.274). Finally, we combined both datasets to produce a nomogram that estimates the probability of intraab-dominal abscess. Conclusions: This nomogram can be useful for identifying patients at a high risk of intraabdominal abscess. Patients at a high risk may benefit from further evaluation or treatment before discharge.

Key Words: Stomach neoplasms; Postoperative complications; Abdominal abscess; Nomograms

Nomogram for Intra-Abdominal Abscesses

263

intraabdominal abscesses after discharge has been reported.11-14

Therefore, accurate identification of clinical findings indicative

of a higher probability of intraabdominal abscess, which could

prompt further evaluation and treatment before discharge, would

be helpful.

This study aimed to identify clinical and laboratory markers

associated with intraabdominal abscess in patients who have un-

dergone gastrectomy for gastric cancer and to develop a model

for estimating the probability of intraabdominal abscess based on

multivariate analysis.

Materials and Methods

1. Study cohort

The study cohort (development set) consisted of 1,564 pa-

tients who underwent curative gastrectomy for gastric cancer

in the National Cancer Center, Korea, from April 2010 to June

2012. We adopted all surgical approaches: open gastrectomy and

laparoscopic or robot-assisted gastrectomy, regardless of the

extent of the surgery (subtotal or total gastrectomy). In patients

with early gastric cancer, more than D1+ lymph node dissection

was performed, and in those with more advanced tumors, more

than D2 lymph node dissection was performed, in accordance

with the Japanese Gastric Cancer Association guideline.15

An independent cohort comprising of 1,508 patients who

underwent gastrectomy for gastric cancer from January 2008 to

March 2010 was used as a validation set to evaluate the perfor-

mance of the development model. The inclusion and exclusion

criteria for the validation set were the same as those for the de-

velopment set.

Patients generally received in-hospital postoperative care for

5 to 7 days after surgery, and laboratory tests were conducted

on postoperative days 1, 3, 5, and 7. After discharge, the pa-

tients visited the outpatient clinic within 1 month for short-term

evaluation. Regular follow-up was then performed for 5 years.

The last follow-up days were August 31, 2012, and December

31, 2013, in the development and validation sets, respectively.

This study was approved by the Institutional Review Board at

the National Cancer Center (No. NCCNCS-13-786).

2. Definition of intraabdominal abscess

An intraabdominal abscess was defined by the following

three conditions: 1) Appearance of an extra-luminal fluid col-

lection on a postoperative abdominal CT scan; 2) Absence of

any other infection focus on the abdominal CT scan; 3) Record

of treatments such as antibiotics and/or surgical, endoscopic, and

radiological intervention provided because of moderate or se-

vere complications, according to the Accordion Severity Grad-

ing System of Surgical Complications.16 Clinically insignificant

fluid collections not requiring treatment were excluded from the

analysis.

3. Clinicopathological variables

All clinical variables suspected to be associated with intraab-

dominal abscess, including age, sex, body mass index (BMI),

comorbidities, surgical approach, extent of gastrectomy, extent

of lymph node dissection, combined operation, operating time,

pathological stage, number of antibiotics used, and transfusion,

were analyzed in both cohorts, along with postoperative vital

signs, inflammation-related laboratory values (i.e., white blood

cell [WBC] count, segmented neutrophils, C-reactive protein

[CRP], total bilirubin [T. bil], glucose, serum amylase), and

change in hemoglobin level (HbD) from the preoperative period

to postoperative day 5 or 7. The highest values recorded during

the hospital period were used, and the tumor stage was defined

according to the American Joint Committee on Cancer staging

system, 7th edition.17

4. Statistical analysis

All continuous variables are presented as means±standard

deviation, and categorical variables are shown as proportions.

Distribution differences were tested using the chi-square test for

categorical variables and the t-test for continuous variables.

Logistic regression with intraabdominal abscess as an out-

come was carried out for both the univariate and multivariate

analyses. Univariate analyses were separately conducted for 26

potentially related factors, and all variables were then included

in the multivariate model. The final risk estimation model was

formulated on the basis of backward variable selection, with an

elimination criterion of P＞0.05. Adjacent categories of certain

categorical variables, such as serum amylase, were grouped to-

gether when decreasing odds ratios were observed for increasing

values of related factors. The performance of the model was

then evaluated in terms of discrimination and calibration. For

discrimination, we examined the receiver operating character-

istics curve (ROC) and associated area under the ROC curve

(AUC), and to test calibration, we used the Hosmer-Lemeshow

(HL) chi-square statistic.

Eom BW, et al.

264

Internal validation using a bootstrap approach was then em-

ployed to correct biases.18 In this approach, random samples

of the same sample size were drawn from the original dataset

with replacement to form a bootstrap sample. The same model-

building technique was used on the bootstrap samples. This

bootstrap resampling procedure was repeated 2,000 times to ob-

tain the average bias, which provided a bias-corrected estimate

for performance measures. The risk estimation model was then

applied to the validation set to examine the performance of the

model on independent data. When the performance was accept-

able, the same procedure was performed for the entire dataset

(development and validation sets).19

Data analyses were performed using SAS ver. 9 (SAS Institute

Inc., Cary, NC, USA), and R software (ver. 2.12.1). All reported

P-values are two-sided.

Results

1. Clinicopathological characteristics in the

development and validation datasets

The incidence of intraabdominal abscess was 7.80%

(122/1,564) in the development set and 7.76% (117/1,508) in the

validation set. Among patients in the abscess group of the devel-

opment set, 65 patients (53.3%) were diagnosed with the abscess

during hospitalization and 43 patients (35.2%) presented to the

emergency department with abscess after discharge. Intraab-

dominal abscess was detected incidentally during prechemo-

therapy CT scans at 3 to 5 weeks postoperatively in 14 patients

(11.5%).

Patient characteristics and clinicopathological features were

compared between abscess and non-abscess groups in both

datasets (Supplement 1). In the development dataset, patients in

the abscess group were older and had higher BMI and prolonged

operating times, compared with the patients in the non-abscess

group. The abscess group had a greater number of male patients,

and the numbers of open surgeries, total gastrectomies, com-

bined operations, transfusions, and advanced pT and pN stages

were higher. Similar results were noted in the validation set, and

significant differences were observed for sex, incidence of hy-

pertension, extent of gastrectomy, combined operation, operat-

ing time, numbers of antibiotics used, transfusion, and pT.

In terms of postoperative vital signs and inflammation-relat-

ed laboratory data (Supplement 1), in the development set, fe-

ver; elevated heart rate, WBC count, segment neutrophil count,

and CRP; T. bil; glucose; and amylase levels; along with greater

values of HbD were associated with abscess, and the validation

set showed comparable results with the development set.

2. Development of the risk estimation model

Univariate and multivariate logistic regression analyses were

first performed using the development dataset (Table 1). The

apparent discrimination and calibration performances were good,

with AUC=0.828 (95% confidence interval [CI], 0.788~0.868)

and HL chi-square test statistic=3.888 with P=0.274 (Fig. 1).

After bias correction with 2,000 cycles of bootstrap resampling,

the bias-corrected AUC was 0.801, and the HL chi-square test

statistic was 5.511 with P=0.138, which implied a good value.

The risk estimation model for probability of intraabdominal

abscess formulated using the development set was then applied

to the independent validation set. The AUC was 0.791 (95%

Table 1. Multivariate logistic regression analysis using the development set

Characteristic Subgroup Odds ratio (95% CI) P-value

Surgical approach Laparoscopy/robot

1

Open 1.76 (1.11~2.81) 0.017

Operating time (min) 1.01 (1.00~1.01) <0.001

pN 0 1

1 1.70 (0.94~3.09) 0.079

2, 3 1.75 (1.07~2.86) 0.026

Body temperature (oC)* <37.8 1

≥37.8 2.22 (1.44~3.42) <0.001

WBC (/μl)* <12 1

12~14.9 1.66 (1.04~2.63) 0.033

≥15 2.10 (1.18~3.74) 0.012

CRP (mg/dl)* <10 1

10~14.9 1.10 (0.50~2.41) 0.817

15~19.9 1.78 (0.83~3.82) 0.139

≥20 4.52 (2.18~9.36) <0.001

Glucose (mg/dl)* <150 1

150~199 1.18 (0.74~1.88) 0.493

≥200 2.34 (1.36~4.00) 0.002

Hemoglobin difference (g/dl)

<3 1

≥3 1.89 (1.23~2.90) 0.004

CI = confidence interval; WBC = white blood cell count; CRP = C-reactive protein. *The highest values for the vital signs and inflammatory markers were used in the analysis.

Nomogram for Intra-Abdominal Abscesses

265

CI, 0.751~0.832), and the HL chi-square result was 18.286 with

P=0.001. Although the calibration was slightly off, it showed

good discrimination, and the AUC was close to the value ex-

pected by internal validation.

3. Final model using the total dataset

Because the model development procedure was validated, we

used the total dataset (development and validation sets) to gen-

erate the final risk estimation model. The multivariate analysis

identified the extent of gastrectomy, operating time, transfusion,

pT, temperature, WBC count, segmented neutrophils, CRP and

amylase levels, and HbD as independent risk factors for intraab-

dominal abscess (Table 2).

4. Development of the nomogram

We created a nomogram that estimates the risk of intraab-

dominal abscess on the basis of the final risk estimation model

(Fig. 2). Points are assigned for each factor, and the sum of the

points for all factors included in the model is obtained and cor-

responds to the estimated probability of the development of an

intraabdominal abscess after gastrectomy. Considering that the

incidence of intraabdominal abscess was approximately 7.8%

in this study, we calculated the sensitivity, specificity, positive

predictive value (PPV), and negative predictive value (NPV) for

each cut-off value between 6 and 9% in 0.5% increments (Table

3). For example, if a patient has an estimated probability of in-

traabdominal abscess of ＞7.5%, we can expect approximately

75.3% sensitivity, 74.3% specificity, 19.8% PPV, and 97.3% NPV

for postoperative intraabdominal abscess.

Discussion

Intraabdominal abscess is one of the most common reasons

for an early visit to the emergency department after gastrectomy.

A

0.0 0.2 0.4 0.6 0.8 1.0

1.0

0.8

0.6

0.4

0.2

Sensitiv

ity

1-Specificity

0.0

ActualPredicted

AUC=0.828 (95% CI, 0.788~0.868)Bias corrected AUC=0.801

Development data

1 2 3 4 5 6 7 8 9 10

45

40

35

30

25

20

15

10

5

Pro

babili

tyofdevelo

pin

gabscess

(%)

Decile of predicted risk based on model

0

Development data

�2=3.888 (P-value=0.274)

Bias corrected =5.511 (2

P-value=0.138)�

B

C

0.0 0.2 0.4 0.6 0.8 1.0

1.0

0.8

0.6

0.4

0.2

Sensitiv

ity

1-Specificity

0.0

AUC=0.791 (95% CI, 0.751~0.832)

Validation data

1 2 3 4 5 6 7 8 9 10

45

40

35

30

25

20

15

10

5

Pro

babili

tyofdevelo

pin

gabscess

(%)

Decile of predicted risk based on model

0

Validation dataD

�2=18.286 (P-value=0.001)

Fig. 1. Receiever-operator characteristic curve and calibration plots of the prediction model for the development dataset and the entire (development and validation) dataset. AUC = area under the receiver operating characteristics curve; CI = confidence interval.

Eom BW, et al.

266

Some patients who are symptom-free at the time of discharge

develop abdominal pain or fever within several days after dis-

charge, and only then is an intraabdominal abscess detected. In

this study, we developed a nomogram to estimate the probability

of intraabdominal abscess on the basis of postoperative clinical

findings. A physician can use the nomogram to check the prob-

ability of intraabdominal abscess before discharging the patient,

and may choose to perform further evaluation or treatment if

the patient has a high probability of developing abscess.

There are several useful scoring systems for predicting clinical

outcomes, including the Sequential Organ Failure Assessment,

the Acute Physiology and Chronic Health Evaluation II scor-

ing system, the Simplified Acute Physiology Score II, and the

Physiological and Operative Severity Score for the enUmeration

of Mortality and Morbidity (POSSUM).20-23 Among these scoring

systems, POSSUM has been validated in various surgical fields,

and a nomogram based on POSSUM has been recently devel-

oped.24-27 However, these scoring systems are used to predict

general morbidity or mortality, whereas our nomogram is solely

focused on the risk of intraabdominal abscess after gastrectomy

in patients with gastric cancer.

Risk factors for post-gastrectomy complications have been

well reported, and include older age, male sex, presence of

comorbidities, advanced tumor stage, open surgical approach,

extended lymph node dissection, combined resection, and pro-

longed operating time.28-32 In terms of intraabdominal abscess

in particular, Lo et al.33 reported that the predisposing factors

include age, prolonged operating time, and combined organ

resection. While previous studies have identified risk factors ac-

cording to baseline or preoperative findings to predict complica-

tions in the early postoperative period, our study was performed

to estimate the probability of intraabdominal abscess in patients

who are ready for discharge. Therefore, we included not only

preoperative baseline factors but also postoperative vital signs

and laboratory findings in our analyses.

One of the potential benefits of our nomogram is the preven-

tion of delayed sepsis. Intraabdominal abscesses that are detected

in a timely fashion are readily treated with antibiotics, percuta-

neous drainage, or both.33 By contrast, delayed detection of an

abscess can result in sepsis or associated complications including

pseudoaneurysm, and the long-term sequelae may be irrevers-

ible. Because our nomogram predicts the probability of intraab-

dominal abscess, the physician can perform additional tests and

render necessary treatment before discharging the patient, and

delayed sepsis might be prevented. This could also contribute to

reduced hospital costs, which are high when patients are read-

mitted with postoperative infections.34-36 In one Swedish study,

the cost of readmission for small bowel obstruction was ap-

proximately equal to that of gastric cancer treatment.37 Another

potential benefit is the limitation of abdominal CT scanning to

only those patients with a high probability of abscess, which

would further contribute to the reduction of hospital costs.

Larger datasets provide better estimates of the effect of each

factor once a procedure for developing a risk estimation model

is validated by using acceptable performance. Therefore, we

evaluated models from both the development dataset and the to-

tal dataset, and found that there was some variation in significant

factors. In the model using the total dataset, surgical approach,

Table 2. Multivariate logistic regression using the total dataset

Characteristic Subgroup Odds ratio (95% CI) P-value

Extent of gastrectomy Subtotal 1

Total 1.60 (1.18~2.18) 0.003

Operating time (min) 1.00 (1.00~1.01) 0.005

Postoperative transfusion Absent 1

Present 2.25 (1.36~3.75) 0.002

pT 1 1

2, 3 1.53 (1.10~2.13) 0.011

4 1.93 (1.29~2.91) 0.002

Body temperature (oC)* <37.8 1

≥37.8 2.32 (1.72~3.13) <0.001

WBC (/μl)* <12 1

12~14.9 1.53 (1.06~2.21) 0.021

≥15 1.54 (1.07~2.22) 0.012

Seg neutrophil (%)* <75 1

75~84.9 1.79 (1.13~2.83) 0.013

≥85 2.72 (1.62~4.56) <0.001

CRP (mg/dl)* <10 1

10~14.9 1.57 (0.93~2.65) 0.095

15~19.9 2.85 (1.73~4.71) <0.001

≥20 5.40 (3.35~8.70) <0.001

Amylase (U/L)* 1~200 1

≥200 1.80 (1.31~2.47) <0.001

Hemoglobin difference (g/dl) <3 1

≥3 1.65 (1.22~2.23) 0.001

CI = confidence interval; WBC = white blood cell count; CRP = C-reactive protein. *The hightest values for the vital signs and inflam-matory markers were used in the analysis.

Nomogram for Intra-Abdominal Abscesses

267

pN, and glucose were replaced with the extent of gastrectomy,

pT, neutrophil count, amylase level, and transfusion. When we

performed internal validation by bootstrapping, we found that

significant risk factors were frequently matched with those in

the total dataset, and we therefore decided to develop our nomo-

gram on the basis of the total dataset. This method provided better

statistical accuracy for estimating the effects of each factor.

Once we generated an acceptable risk prediction model, we

checked its discrimination and calibration abilities. Discrimina-

tion accounts for the probability of a model producing higher

risk estimates for patients who develop intraabdominal abscess

than it does for those who do not, and calibration determines

how closely the estimated probabilities match the observed

probabilities. We used the bootstrap approach for internal vali-

dation because bias occurs when discrimination and calibration

are calculated on the basis of a development dataset. The differ-

ence between performance measures in the model for the boot-

strap samples and the original dataset represents the bias indicating

over-fitting, and the bias-corrected measure indicates how well

the model will perform on an external independent dataset.

In producing our model, we defined all cases of fluid col-

lection as intraabdominal abscess, and there were some patients

who had both fluid collection (abscess) and anastomotic leakage.

In the development dataset, among 122 patients in the abscess

group, 17 (13.9%) had CT findings of probable anastomotic

leakage and 14 underwent further evaluations such as an en-

doscopy and upper gastrointestinal series, 3 (2.5%) of whom

had confirmed anastomotic leakage. Further, there were 8 of 16

patients without any suggestion of leakage on CT scan who were

diagnosed with leakage by other means. In total, there were 11

patients (11/122, 9.0%) in the abscess group of the development

dataset who had anastomotic leaks, suggesting that a more thor-

ough evaluation for this complication may be necessary.

The incidence of intraabdominal abscess after gastrectomy

Table 3. Sensitivity, specificity, PPV, and NPV of predicted proba­bility using the nomogram for cut off points from 6% to 9% (increments of 0.5%)

Predicted probability (%)

Sensitivity (%)

Specificity (%)

PPV (%)

NPV (%)

6.0 78.7 69.5 17.9 97.5

6.5 77.0 71.3 18.5 97.3

7.0 76.2 72.8 19.1 97.3

7.5 75.3 74.3 19.8 97.3

8.0 74.1 75.9 20.6 97.2

8.5 73.2 77.4 21.4 97.2

9.0 72.4 78.5 22.1 97.1

PPV = positive predictive value; NPV = negative predictive value.

Points

Gastrectomy

OP time (min)

pT

Bodytemperature ( C)

WBC ( )�l

Seg neutrophil (%)

CRP (mg/dl)

Amylase (mg/dl)

Hb difference

Postoperativetransfuson

Total points

Linear predictor

Risk of abscess (%)

Subtotal Total

1 2 or 3 4

0 10 20 30 40 50 60 70 80 90 100

0900100 200 300 400 500 600 700 800

<37.8 >37.8

>12,000<12,000

<75 75~84.9 >85

<10 10~14.9 15~19.9 >20

<200 >200

<3 >3

No Yes

0 50 100 150 200 250 300 350

6 5 4 3 2 1 0 1 2

1 5 10 20 30 40 50 60 70 80

Fig. 2. A nomogram estimating the probability of developing intraab-dominal abscess after gastrectomy. OP = operative; WBC = white blood cell; CRP = C-reactive protein; Hb = hemoglobin.

Eom BW, et al.

268

in gastric cancer patients may be affected by various additional

factors, including surgeon, institution and tumor location. There-

fore, evaluating our nomogram by using patient data from other

institutions will be necessary. Additionally, the present study was

performed using retrospectively collected data, and there may be

selection, information, and measurement biases. However, we be-

lieve these biases had a weak influence on the results because all

variables were objective values, and there was little missing data.

In conclusion, we have developed a nomogram for estimating

the risk of intraabdominal abscess after gastrectomy in gastric

cancer patients. The availability of a calculated probability for

the development of intraabdominal abscess may help a physi-

cian decide whether further evaluation or treatment should be

ordered before a patient is discharged. Additional external vali-

dation using a multicenter dataset will be useful to generalize the

utility of the nomogram.

Acknowledgments

The authors thank Kyoung Rae Kim, Youngsook Kim, Eunju

Yoo, Soosie Kim, Suhee Kim, Hyun Jung Park, and Deok Hee

Kim for data collection and management.

This work was supported by a grant from the National Can-

cer Center (No. NCC-1410130-1).

Conflicts of Interest

No potential conflict of interest relevant to this article was

reported.

Electronic Supplementary Material

The online version of this article (doi: 10.5230/jgc.2015.15.

4.262) contains supplementary materials.

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Supplement 1. Patient characteristics, clinicopathological features, postoperative vital signs, and inflammation-related laboratory findings in the development and validation datasets

Characteristic Subgroup

Development set (n = 1564) Validation set (n = 1508) No abscess

(%) Abscess (%)

P Non-abscess

(%) Abscess

(%) P ( n = 1442 ) (n = 122) (n = 1391) (n = 117)

Age (mean ± SD)

58.5±12.0 61.5±11.3 0.009 58.2±11.9 60.4±12.0 0.055

Sex Male 942 (65.3) 99 (81.1) <0.001 906 (65.1) 94 (80.3) 0.001

Female 500 (34.7) 23 (18.8) 485 (34.9) 23 (19.7)

BMI (kg/m2) (mean ± SD)

23.7±3.1 24.4±3.5 0.013 23.6±3.2 23.8±3.0 0.462

Diabetes mellitus Absent 1234 (85.6) 101 (82.8) 0.403 1226 (88.1) 101 (86.3) 0.562

Present 208 (14.4) 21 (17.2) 165 (11.9) 16 (13.7)

Hypertension Absent 974 (67.5) 75 (61.5) 0.171 1011 (72.7) 73 (62.4) 0.017

Present 468 (32.5) 47 (38.5) 380 (27.3) 44 (37.6)

Heart disease Absent 1332 (92.3) 109 (89.3) 0.233 1347 (96.8) 113 (96.6) 0.784

Present 110 (7.6) 13 (10.7) 44 (3.2) 4 (3.4)

Liver disease Absent 1364 (94.6) 118 (96.7) 0.311 1242 (89.3) 104 (88.9) 0.893

Present 78 (5.4) 4 (3.2) 149 (10.7) 13 (11.1)

Pulmonary disease

Absent 1407 (97.6) 116 (95.1) 0.128 1370 (98.5) 115 (98.3) 0.698

Present 35 (2.4) 6 (4.9) 21 (1.5) 2 (1.7)

Surgical approach Open 661 (45.8) 84 (68.9) <0.001 802 (57.7) 81 (69.2) 0.015

Laparoscopy 781 (54.2) 38 (31.1)

589 (42.3) 36 (30.8)

/Robot

Extent of gastrectomy

Subtotal 1065 (73.9) 76 (62.3) 0.006 1029 (74.0) 50 (42.7) <0.00

1

Total 377 (26.1) 46 (37.7) 362 (26.0) 67 (57.3)

LN dissection D1+ 127 (8.8) 6 (4.9) 0.139 88 (6.3) 4 (3.4) 0.207

D2 1315 (91.2) 116 (95.1) 1303 (93.7) 113 (96.6)

Combined operation

Absent 1305 (90.5) 95 (77.9) <0.001 1344 (96.6) 99 (84.6) <0.00

1

Present 137 (9.5) 27 (22.1) 47 (3.4) 18 (15.4)

Operating time (mean ± SD) 177.3±64.1 214.4±84.8 <0.001 187.1±71.8 212.9±102.

4 0.009

Number of Antibiotics

1–2 1434 (99.4) 120 (98.4) 0.18 145 (10.4) 20 (17.1) 0.026

usage ≥3 8 (0.6) 2 (1.6) 1246 (89.6) 97 (82.9)

Postoperative transfusion

Absent 1402 (97.2) 111 (91.0) 0.001 1332 (95.8) 97 (82.9) <0.00

1

Present 40 (2.8) 11 (9.0) 59 (4.2) 20 (17.1)

pT 1 915 (63.5) 52 (42.6) <0.001 742 (53.3) 45 (38.5) 0.001

2,3 381 (26.4) 43 (35.2) 469 (33.7) 45 (38.5)

4 146 (10.1) 27 (22.1) 180 (12.9) 27 (23.1)

pN 0 994 (68.9) 61 (50.0) <0.001 893 (64.2) 67 (57.3) 0.084

1 167 (11.6) 20 (16.4) 173 (12.4) 12 (10.3)

2,3 281 (19.5) 41 (33.6) 325 (23.4) 38 (32.5) *Body temperature (°C)

<37.8 1106 (76.7) 51 (41.8) <0.001 1042 (74.9) 49 (41.9) <0.00

1

≥37.8 336 (23.3) 71 (58.2) 349 (25.1) 68 (58.1)

*Pulse rate <100 1035 (71.8) 62 (50.8) <0.001 1044 (75.1) 48 (41.0) <0.00

1

100–119 338 (23.4) 41 (33.6) 299 (21.5) 55 (47.0)

≥120 69 (4.8) 19 (15.6) 48 (3.5) 14 (12.0)

*WBC (/㎕) <12 990 (68.7) 51 (41.8) <0.001 385 (27.7) 16 (13.7) 0.002

12–14.9 334 (23.2) 43 (35.2) 430 (30.9) 36 (30.8)

≥15 118 (8.2) 28 (23.0) 576 (41.4) 65 (55.6)

*Seg neutrophil (%)

<75 393 (27.3) 15 (12.3) <0.001 351 (25.2) 10 (8.5) <0.00

1

75–84.9 884 (61.3) 68 (55.7) 879 (63.2) 67 (57.3)

≥85 165 (11.4) 39 (32.0) 161 (11.6) 40 (34.2)

*CRP (mg/dL) <10 479 (33.2) 11 (9.0) <0.001 576 (41.4) 14 (12.0) <0.00

1

10–14.9 430 (29.8) 20 (16.4) 357 (25.7) 19 (16.2)

15–19.9 300 (20.8) 26 (21.3) 258 (18.5) 34 (29.1)

≥20 233 (16.2) 65 (53.3) 200 (14.4) 50 (42.7)

*T. bil (mg/dL) <1.2 834 (57.8) 51 (41.8) <0.001 813 (58.4) 60 (51.3) 0.196

1.2–2.9 584 (40.5) 64 (52.5) 545 (39.2) 52 (44.4)

≥3 24 (1.7) 7 (5.7) 33 (2.4) 5 (4.2)

*Glucose (mg/dL) <150 834 (57.8) 49 (40.2) <0.001 413 (29.7) 23 (19.7) 0.003

150–199 450 (31.2) 41 (33.6) 755 (54.3) 62 (53.0)

≥200 158 (11.0) 32 (26.2) 223 (16.0) 32 (27.4)

*Amylase (U/) <100 800 (55.5) 52 (42.6) <0.001 629 (45.2) 31 (26.5) <0.00

1

101–200 440 (30.5) 36 (29.5) 461 (33.1) 34 (29.1)

>201 202 (14.0) 34 (27.9) 301 (21.6) 52 (44.4)

Hemoglobin difference

<3 1135 (87.7) 61 (50.0) <0.001 884 (63.6) 54 (46.2) <0.00

1

(g/dL) ≥3 307 (21.3) 61 (50.0) 507 (36.5) 63 (53.8)

SD, standard deviation; BMI, body mass index; LN, lymph node; WBC, white blood cell count; CRP, C-reactive protein; T. bil, total bilirubin. * The highest values for the vital signs and inflammatory markers were used in the analysis.