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World Journal of
Gastrointestinal Surgery
ISSN 1948-9366 (online)
World J Gastrointest Surg 2021 October 27; 13(10): 1110-1292
Published by Baishideng Publishing Group Inc
WJGS https://www.wjgnet.com IOctober 27, 2021 Volume 13 Issue 10
World Journal of
Gastrointestinal Surgery
W J G S
Contents Monthly Volume 13 Number 10 October 27, 2021
FRONTIER
Long-term survival outcome of laparoscopic liver resection for hepatocellular carcinoma
1110
Lam S, Cheng KC
OPINION REVIEW
Review of minimally invasive pancreas surgery and opinion on its incorporation into low volume and
resource poor centres
1122
Cawich SO, Kluger MD, Francis W, Deshpande RR, Mohammed F, Bonadie KO, Thomas DA, Pearce NW, Schrope BA
MINIREVIEWS
Research progress regarding programmed cell death 1/programmed cell death ligand 1 inhibitors
combined with targeted therapy for treating hepatocellular carcinoma
1136
Zheng LL, Tao CC, Tao ZG, Zhang K, Wu AK, Wu JX, Rong WQ
Transanal minimally invasive surgery using laparoscopic instruments of the rectum: A review
1149
Kim MJ, Lee TG
Current surgical management of duodenal gastrointestinal stromal tumors
1166
Lim KT
Gastric endoscopic submucosal dissection in Western countries: Indications, applications, efficacy and
training perspective
1180
De Luca L, Di Berardino M, Mangiavillano B, Repici A
ORIGINAL ARTICLE
Case Control Study
Laparoscopy for Crohn's disease: A comprehensive exploration of minimally invasive surgical techniques
1190
Wan J, Liu C, Yuan XQ, Yang MQ, Wu XC, Gao RY, Yin L, Chen CQ
Retrospective Study
Onodera's Prognostic Nutritional Index is a novel and useful prognostic marker for gastrointestinal
stromal tumors
1202
Wang H, Xu YY, You J, Hu WQ, Wang SF, Chen P, Yang F, Shi L, Zhao W, Zong L
Utility of preoperative systemic inflammatory biomarkers in predicting postoperative complications after
pancreaticoduodenectomy: Literature review and single center experience
1216
Coppola A, La Vaccara V, Caggiati L, Carbone L, Spoto S, Ciccozzi M, Angeletti S, Coppola R, Caputo D
WJGS https://www.wjgnet.com II October 27, 2021 Volume 13 Issue 10
World Journal of Gastrointestinal Surgery
Contents Monthly Volume 13 Number 10 October 27, 2021
Low serum albumin may predict poor efficacy in patients with perforated peptic ulcer treated
nonoperatively
1226
Liang TS, Zhang BL, Zhao BB, Yang DG
Oesophageal adenocarcinoma: In the era of extended lymphadenectomy, is the value of neoadjuvant
therapy being attenuated?
1235
Park JS, Van der Wall H, Kennedy C, Falk GL
Outcomes of reduction hepatectomy combined with postoperative multidisciplinary therapy for advanced
hepatocellular carcinoma
1245
Asahi Y, Kamiyama T, Kakisaka T, Orimo T, Shimada S, Nagatsu A, Aiyama T, Sakamoto Y, Kamachi H, Taketomi A
Development and validation of a prediction model for deep vein thrombosis in older non-mild acute
pancreatitis patients
1258
Yang DJ, Li M, Yue C, Hu WM, Lu HM
SCIENTOMETRICS
Immunotherapy after liver transplantation: Where are we now?
1267
Au KP, Chok KSH
CASE REPORT
Hodgkin lymphoma masquerading as perforated gallbladder adenocarcinoma: A case report
1279
Manesh M, Henry R, Gallagher S, Greas M, Sheikh MR, Zielsdorf S
Whole circumferential endoscopic submucosal dissection of superficial adenocarcinoma in long-segment
Barrett's esophagus: A case report
1285
Abe K, Goda K, Kanamori A, Suzuki T, Yamamiya A, Takimoto Y, Arisaka T, Hoshi K, Sugaya T, Majima Y, Tominaga K,
Iijima M, Hirooka S, Yamagishi H, Irisawa A
WJGS https://www.wjgnet.com III October 27, 2021 Volume 13 Issue 10
World Journal of Gastrointestinal Surgery
Contents Monthly Volume 13 Number 10 October 27, 2021
ABOUT COVER
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Professor, Surgeon, Department of Surgery, La Paz Universitary Hospital, Pozuelo de Alarcon 28223, Madrid,
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Submit a Manuscript: https://www.f6publishing.com World J Gastrointest Surg 2021 October 27; 13(10): 1258-1266
DOI: 10.4240/wjgs.v13.i10.1258 ISSN 1948-9366 (online)
ORIGINAL ARTICLE
Retrospective Study
Development and validation of a prediction model for deep vein
thrombosis in older non-mild acute pancreatitis patients
Du-Jiang Yang, Mao Li, Chao Yue, Wei-Ming Hu, Hui-Min Lu
ORCID number: Du-Jiang Yang
0000-0002-0597-1143; Mao Li 0000-
0003-1728-9026; Chao Yue 0000-
0001-9066-0149; Wei-Ming Hu 0000-
0003-1605-5084; Hui-Min Lu 0000-
0002-5759-1919.
Author contributions: Yang DJ, Li
M, and Lu HM contributed to
conception and design; Yang DJ, Li
M, Yue C, and Hu WM contributed
to collection and analysis data;
Yang DJ wrote the manuscript; Lu
HM revised the manuscript.
Supported by The Sichuan
Provincial Department of Science
and Technology Supporting
Project, No. 2018SZ0381; and 1.3.5
project for disciplines of excellence,
West China Hospital, Sichuan
University, No. ZYJC18027.
Institutional review board
statement: This study was
reviewed and approved by the
Institutional Ethics Committee of
the West China Hospital.
Informed consent statement: For
retrospective study, informed
consent was waived according to
our institutional guideline.
Conflict-of-interest statement:
There are no conflicts of interest to
disclose.
Data sharing statement: No
Du-Jiang Yang, Department of Gastrointestinal Surgery, West China Hospital, Sichuan
University, Chengdu 610041, Sichuan Province, China
Mao Li, Chao Yue, Wei-Ming Hu, Hui-Min Lu, Department of Pancreatic Surgery, West China
Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Corresponding author: Hui-Min Lu, PhD, Professor, Department of Pancreatic Surgery, West
China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, Sichuan Province,
China. hm.lu@scu.edu.cn
Abstract
BACKGROUND
Deep vein thrombosis (DVT) may cause pulmonary embolus, leading to late
deaths. The systemic inflammatory and hypercoagulable state of moderate and
severe acute pancreatitis (non-mild acute pancreatitis, NMAP) patients may
contribute to the development of venous thromboembolism. Accurate prediction
of DVT is conducive to clinical decisions.
AIM
To develop and validate a potential new prediction nomogram model for the
occurrence of DVT in NMAP.
METHODS
NMAP patient admission between 2013.1.1 and 2018.12.31 at the West China
Hospital of Sichuan University was collected. A total of 220 patients formed the
training set for nomogram development, and a validation set was constructed
using bootstrapping with 100 resamplings. Univariate and multivariate logistic
regression analyses were used to estimate independent risk factors associated
with DVT. The independent risk factors were included in the nomogram. The
accuracy and utility of the nomogram were evaluated by calibration curve and
decision curve analysis, respectively.
RESULTS
A total of 220 NMAP patients over 60 years old were enrolled for this analysis.
DVT was detected in 80 (36.4%) patients. The final nomogram included age, sex,
surgery times, D-dimer, neutrophils, any organ failure, blood culture, and classi-
fication. This model achieved good concordance indexes of 0.827 (95%CI: 0.769-
0.885) and 0.803 (95%CI: 0.743-0.860) in the training and validation sets,
Yang DJ et al. Prediction model for DVT
WJGS https://www.wjgnet.com 1259 October 27, 2021 Volume 13 Issue 10
additional data are available.
Open-Access: This article is an
open-access article that was
selected by an in-house editor and
fully peer-reviewed by external
reviewers. It is distributed in
accordance with the Creative
Commons Attribution
NonCommercial (CC BY-NC 4.0)
license, which permits others to
distribute, remix, adapt, build
upon this work non-commercially,
and license their derivative works
on different terms, provided the
original work is properly cited and
the use is non-commercial. See: htt
p://creativecommons.org/License
s/by-nc/4.0/
Manuscript source: Unsolicited
manuscript
Specialty type: Peripheral vascular
disease
Country/Territory of origin: China
Peer-review report’s scientific
quality classification
Grade A (Excellent): 0
Grade B (Very good): B
Grade C (Good): 0
Grade D (Fair): 0
Grade E (Poor): 0
Received: May 20, 2021
Peer-review started: May 20, 2021
First decision: June 22, 2021
Revised: July 1, 2021
Accepted: September 19, 2021
Article in press: September 19, 2021
Published online: October 27, 2021
P-Reviewer: Byeon H
S-Editor: Gong ZM
L-Editor: A
P-Editor: Wu RR
respectively.
CONCLUSION
We developed and validated a prediction nomogram model for DVT in older
patients with NMAP. This may help guide doctors in making sound decisions
regarding the administration of DVT prophylaxis.
Key Words: Acute pancreatitis; Deep vein thrombosis; Prediction model; Bootstrap;
Nomogram; Discrimination and calibration
©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Core Tip: Deep vein thrombosis (DVT) may cause pulmonary embolus, leading to late
death. Few studies have focused on DVT in moderate and severe acute pancreatitis. We
identified eight predictors and developed and established a prediction nomogram
model for DVT in older patients with moderate and severe acute pancreatitis. This
model achieved good concordance indexes and may help guide doctors in the adminis-
tration of DVT prophylaxis.
Citation: Yang DJ, Li M, Yue C, Hu WM, Lu HM. Development and validation of a prediction
model for deep vein thrombosis in older non-mild acute pancreatitis patients. World J
Gastrointest Surg 2021; 13(10): 1258-1266
URL: https://www.wjgnet.com/1948-9366/full/v13/i10/1258.htm
DOI: https://dx.doi.org/10.4240/wjgs.v13.i10.1258
INTRODUCTION
Acute pancreatitis (AP) is a common and potentially lethal disease with a rising
incidence. The incidence of AP is 34 cases per 100,000 people in the general population
per year worldwide[1]. Among gastrointestinal diseases, AP is one of the most
common reasons for hospitalization in the United States, and the disease accounts for
$2.6 billion health care dollars per year[2-4]. According to the 2012 Atlanta classi-
fication, most AP patients have mild acute pancreatitis. However, 20% of patients
develop moderate or severe acute pancreatitis (non-mild acute pancreatitis, NMAP).
Furthermore, the mortality of NMAP can reach 35%, which is significantly higher than
that of mild acute pancreatitis[5]. Researchers usually focus on complications such as
organ failure and infection in NMAP[6,7]. However, few of studies have paid attention
to venous thromboembolism in NMAP. A previous study showed that the incidence of
venous thromboembolism in hospitalized patients was approximately 0.4% to 1.3%[8].
NMAP usually requires a long hospital stay. The systemic inflammatory and hyperco-
agulable state of NMAP patients may contribute to the development of venous
thromboembolism[9-11]. Deep vein thrombosis (DVT), a kind of venous thromboem-
bolism, commonly develops in the lower extremities. It can cause acute pulmonary
embolism (PE) when it falls and flows to the lung[12,13]. A recent study showed that
the rate of DVT in AP patients could reach 38%[14]. Older patients more easily
develop venous thromboembolism. This may increase the difficulty of treatment in
older NMAP patients. However, there is a lack of a scoring model for predicting
develop of DVT in NMAP patients. The existing scores for DVT are not suitable for
critically ill patients[15-17]. In the past, nomograms were used as a graphical
calculation to help solve engineering problems. As a statistical tool, nomograms have a
unique advantage in visualizing the relationships of involved parameters. This
approach enables users to calculate the overall probability of clinical outcome for an
individual patient[18,19]. Recently, it has been widely used in clinical prediction
models[20,21]. Thus, the aim of this study was to develop a prediction model for DVT
in older NMAP patients.
Yang DJ et al. Prediction model for DVT
WJGS https://www.wjgnet.com 1260 October 27, 2021 Volume 13 Issue 10
MATERIALS AND METHODS
Study design and participants
Medical records of older NMAP patients admitted to West China Hospital from
2013.1.1 to 2018.12.31 were retrospectively collected. Included criteria were as follows:
1. AP was diagnosed in West China Hospital and classified as moderate or severe; 2.
More than 60 years old. Pancreatic tumors are one of the causes of AP and are also a
risk factor for DVT development[22]. Thrombosis development in other places may be
a confounding factor in this study. Thus, patients who had the following diagnoses
were excluded from this study: (1) Pancreatic tumor; and (2) Thromboses in other
locations. This study followed the Transparent Reporting of a Multivariable Prediction
Model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines.
Data collection and definition
AP was diagnosed through a combination of clinical manifestations and signs (i.e.,
sudden onset of upper abdominal pain), laboratory tests (amylase or lipase levels were
three times higher than normal limits), and imaging examinations (abdominal
ultrasound, abdominal computed tomography (CT), and magnetic resonance imaging)
[23]. AP was classified according to the revised Atlanta Classification[23]. The
definition of non-mild acute pancreatitis (NMAP) is acute pancreatitis classified as
moderate or severe. Acute pancreatitis patients aged over 60 years old were defined as
older acute pancreatitis patients. The diagnosis of DVT was based on the results of
color Doppler ultrasonography when patients presented swelling or pitting edema,
redness, and leg tenderness. Organ failure (OF) was defined as a patient who had at
least one failure of respiratory function, cardiovascular function, or renal function.
Respiratory failure was defined as PaO2 < 60 mmHg, despite FiO2 of 0.30, or a need for
mechanical ventilation. Cardiovascular failure was defined based on circulatory
systolic blood pressure < 90 mmHg, despite adequate fluid resuscitation, or a need for
inotropic catecholamine support. Renal failure was defined as creatinine level > 177
μmol/L after rehydration or the need for hemofiltration or hemodialysis.
The following variables were recorded for the study population: Age, sex, etiology,
smoking, drinking, surgery times, any organ failure, respiratory failure, renal failure,
cardiovascular failure, severity classification, onset time to diagnosis of DVT, and
blood index. In DVT patients, all variables were collected until the time of DVT
diagnosis. In NDVT patients, all variables were collected throughout the whole
hospital stay. Repeated measurements of continuous variables are shown on average.
Statistical analysis
Continuous variables are described as the mean (SD) or median and binary variables
are expressed as counts (%). Statistical analysis was performed using R software.
(Version 3.6.1)
Prediction model development
Relevant predictors included age, sex, surgery times, any organ failure, respiratory
failure, cardiovascular failure, renal failure, blood culture, C-reactive protein,
neutrophils, serum albumin, D-dimer, severity classification of DVT in patients with
AP identified from a previous study[24,25] and advice of pancreatologists. Patients
with more than 30% of the preselected predictors missing were excluded from model
development.
In this study, 80 patients were identified with DVT, and more than ten times
patients with NDVT were identified. Due to the imbalance between the DVT and
NDVT groups, undersampling was performed to adjust the number between the two
groups. A total of 10% NDVT patients were randomly selected compared with DVT
patients. Finally, 140 NDVT patients were selected. Thus, training data included 80
DVT and 140 NDVT patients.
Logistic regression was used to identify the variables that were significantly
correlated with DVT in the training group. Variables with a P-value less than 0.05 and
more than 0.05 but suggested by pancreatologists were fed to a multivariate logistic
regression model. Stepwise selection was used to further eliminate redundant
variables. The resulting multivariate logistic regression model was used to build the
prediction model.
Prediction model validation
The bootstrap method was used to evaluate the performance of the prediction model.
In the bootstrap method, 100 random samples were drawn with replacement from the
Yang DJ et al. Prediction model for DVT
WJGS https://www.wjgnet.com 1261 October 27, 2021 Volume 13 Issue 10
original data set and the coefficients were recalculated.
To validate the prediction model, two criteria were used to evaluate the prediction
performance. On the one hand, the concordance index (c-index) was calculated to
estimate the discrimination of the prediction model. On the other hand, calibration
curves were plotted to evaluate the consistency between predicted DVT probability
and actual DVT proportion. Values of 1 and 0.5 indicate perfect discrimination and no
discrimination, respectively. The C-index and calibration results presented are an
average of the bootstrapped samples.
RESULTS
Baseline clinical characteristics
Medical records of NMAP patients over 60 years admitted to West China Hospital
from 2013.1.1 to 2018.12.31 were collected. DVT was diagnosed in 80 patients. Due to
the imbalance of the data, undersampling was performed on selected NDVT patients.
Finally, 140 NDVT patients were randomly selected for analysis. The baseline charac-
teristics of the patients, including demographics, clinical indexes, and blood indexes,
in the two groups are shown in Table 1. There are 81 and 49 females in the NDVT
group and DVT group, respectively. The DVT group included patients aged between
60 and 88 years (mean age: 70.16 years), and the NDVT group included patients aged
69.81 years. Biliary was the most common etiology in both groups. There were 37
(26.4%) and 33 (23.6%) NDVT patients who smoked and drank, respectively.
Seventeen (21.2%) and 15 (18.8%) DVT patients smoked and drank, respectively. All
organ failure in the DVT group was significantly higher than that in the NDVT group.
Respiratory failure accounted for the largest proportion in OF. In total, 65% of patients
were classified as severe. However, 67% of patients in the NDVT group were classified
as moderate. Blood culture, D-dimer, and serum albumin in the DVT group were
significantly different between the two groups. Table 2 shows the thrombus location of
the DVT patients. The most common location of vein thrombosis was both lower
limbs, which were detected in 31 (38.8%) patients. Only 3 (3.7%) patients were found
to have vein thrombosis in the left upper limb, and 12 (15%) patients had vein
thrombosis detected in more than two locations.
Prediction model development
Univariate and multivariate analyses were performed to select potential predictors. A
nomogram model was constructed based on the results of the multivariate logistics
regression analysis and the suggestions of pancreatologists. Finally, 8 potential
predictors based on 220 patients were selected. These features included sex, age,
surgery times, renal failure, classification, D-dimer, blood culture, and neutrophils.
Figure 1 shows the nomogram in which sex, age, surgery times, renal failure, classi-
fication, D-dimer, blood culture, and neutrophils defined the individual risk of DVT in
NMAP patients. In this nomogram, D-dimer is a continuous variable and every 5 unit
increase in D-dimer results in an approximately 0.8-point increase in risk points. The
nomogram maps the predicted probability of DVT on a scale of 0 to 220. For each
covariate, a vertical line is drawn upwards, and the corresponding points are noted.
This is repeated for each covariate ending with a total score that corresponds to a
predicted probability of morbidity at the bottom of the nomogram. The odds ratios of
the nomogram variables are summarized in Table 3.
Validation prediction model
The C-index for the prediction nomogram was 0.827 (95%CI: 0.769-0.885). It was
confirmed to be 0.803 (95%CI: 0.743-0.860) through bootstrapping validation, which
suggested the model’s good discrimination. The calibration curve in Figure 2 shows
good concordance between the estimated risk of DVT and the actual presence of DVT.
DISCUSSION
Using data from a retrospective study including older NMAP patients with DVT, we
developed and internally validated a potential new prediction model for DVT. This is
the first model for predicting DVT in older NMAP patients. The performance of the
prediction model was adequate. This nomogram, based on routinely available
demographic and blood indexes, predicts the probability of DVT in NMAP patients.
Yang DJ et al. Prediction model for DVT
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Table 1 Demographic characteristics
Variables NDVT DVT P value
N 140 80
Age 69.81 (7.52) 70.16 (7.84) 0.745
Gender: Female 81 (57.9) 49 (61.3) 0.726
Etiology
Biliary 65 (47.4) 39 (48.8)
Alcohol 3 (0.21) 5 (0.62)
Hyperlipidemia 23 (16.4) 10 (12.5)
Others 49 (35.0) 26 (32.5)
Smoking 37 (26.4) 17 (21.2) 0.507
Drink 33 (23.6) 15 (18.8) 0.487
Surgery times < 0.001
0 114 (81.4) 47 (58.8)
1 24 (17.1) 23 (28.7)
2 2 (1.4) 6 (7.5)
3 0 (0.0) 4 (5.0)
Any organ failure 72 (51.4) 67 (83.8) < 0.001
Respiratory failure 61 (43.6) 58 (72.5) < 0.001
Renal failure 15 (10.7) 31 (38.8) < 0.001
Cardiovascular failure 23 (16.4) 37 (46.2) < 0.001
Classification < 0.001
Moderate to severe 95 (67.9) 28 (35.0)
Severe 45 (32.1) 52 (65.0)
Blood index
Blood culture positive 8 (5.7) 20 (25.0) < 0.001
D-dimer 5.87 (5.48) 8.78 (6.47) 0.002
CRP 120.77 (84.66) 124.51 (86.79) 0.796
WBC 11.09 (4.37) 11.74 (4.63) 0.312
Neutrophils 9.05 (4.08) 9.87 (4.48) 0.177
Serum albumin 32.90 (4.13) 31.25 (4.31) 0.006
NDVT: None deep vein thrombosis; DVT: Deep vein thrombosis; CRP: C-reactive protein; WBC: White blood cell.
The recent criteria of AP classification were put forward in 2012. Non-mild acute
pancreatitis patients have a poorer prognosis, and they stay in the hospital for a long
time. Hospitalization has been considered a significant risk factor for VTE[26].
Furthermore, older patients usually have slow blood flow. These factors all contribute
to DVT development. However, doctors usually pay attention to DVT when patients
have clinical manifestations, such as calf swelling. In trauma patients, occult DVT may
cause pulmonary embolus, leading to late deaths due to fatality[27]. Early detection of
DVT results in decreased rates of pulmonary embolus and mortality[28]. Therefore,
accurate prediction of DVT is invaluable to provide treatment for each NMAP patient.
Our findings are essentially in line with previous venous thromboembolism studies.
In the present study, we found that DVT mostly develops in both lower limbs at the
same time. However, isolated left upper limbs only accounted for 3.7% of patients. A
previous study showed that upper limb DVT is less than 10% of all DVT[22]. In this
study, more than 16.2% of patients had upper limb DVT.
Yang DJ et al. Prediction model for DVT
WJGS https://www.wjgnet.com 1263 October 27, 2021 Volume 13 Issue 10
Table 2 The location of deep vein thrombosis
Location of the thrombosis Number of patients (n = 80)
Left upper limb isolated 3 (3.7%)
Right upper limb isolated 6 (7.5%)
Both upper limbs 4 (5.0%)
Left lower limb isolated 14 (17.5%)
Right lower limb isolated 10 (12.5%)
Both lower limbs 31 (38.8%)
More than two locations 12 (15.0%)
Table 3 Odds ratio and 95% confidence interval of nomogram parameters
Variables OR (95%CI) P value
Age 1.02 (0.983-1.066) 0.257
Gender 1.23 (0.625-2.431) 0.546
Surgery times 2.70 (1.566-4.651) 0.000
Renal failure 0.35 (0.166-0.728) 0.005
Classification 2.17 (1.133-4.164) 0.020
D-dimer 1.02 (0.971-1.081) 0.382
Blood culture 0.53 (0.218-1.267) 0.152
Neutrophils 1.01 (0.930-1.087) 0.887
Figure 1 Nomogram for predicting deep vein thrombosis in non-mild acute pancreatitis patients. The nomogram maps the predicted probability of
deep vein thrombosis (DVT) on a scale of 0 to 220. For each covariate, draw a vertical line upwards and record corresponding points. Repeated it and added the
points. A total score corresponds to a predicted probability of DVT at the bottom of the nomogram.
Some predictors were already confirmed in other studies. D-dimer is the most well
validated and widely used biomarker of venous thromboembolism excluded[25]. It is
usually combined with the Wells score in practice. In this study, D-dimer was an
important predictor of DVT. OF is regarded as one of the most important parameters
of AP patients in the course of the early phase[23]. The main causes of OF are cytokine
cascades resulting in systemic inflammatory response syndrome (SIRS)[29].
Yang DJ et al. Prediction model for DVT
WJGS https://www.wjgnet.com 1264 October 27, 2021 Volume 13 Issue 10
Figure 2 Calibration curve for deep vein thrombosis on the nomogram. The ideal line represents a perfect match between predicted and observed
occurrence of deep vein thrombosis; Apparent line, prediction capability of the model obtained after data analysis; Bias-corrected line, prediction capability of the
model obtained after bootstrap correction.
Respiratory failure, renal failure, and cardiovascular failure commonly take place in
the clinical. OF can lead to long term bed rest and immobilization. Both of these
contribute to DVT development[14]. However, in this study, only renal failure was in
the final prediction model for DVT development. This factor was validated in a
previous study[30]. Vascular endothelium is activated by proinflammatory cytokines
in severe acute pancreatitis. This promotes the activation of coagulation cascades and
circulating neutrophils[31]. Furthermore, neutrophils promote coagulation by
inhibiting anticoagulant factors and releasing neutrophil extracellular traps[32]. These
further promote thrombogenesis. Currently, mechanistic research shows that
neutrophil extracellular traps hold promise for novel clinical treatment of DVT[32].
Patients with positive blood cultures have more severe inflammation than others.
Additionally, infection has been thought to be a risk factor for venous thromboem-
bolism[24]. Surgery is also a risk factor for venous thromboembolism[24]. Some
NMAP patients need reoperation several times. More surgery times mean patients
experience more frequent and longer bed rest.
Overall, our study first focused on the development of DVT in older NMAP
patients, which has never been studied before. We analyzed the risk factors for DVT
and built a nomogram model to predict the probability of developing DVT for NMAP
patients. Proper use of this model can help physicians identify patients with a high
risk of developing DVT.
There are several limitations in this study. First, this was a retrospective study, and
the examination of DVT was not performed routinely. Thus, the diagnosis of DVT may
have been missed in some patients. In addition, this was a single-center study, and
validation was only performed in internal data. The results could be more convincing
if external validation is performed. Moreover, due to the limitation of the sample size,
potential bias may exist in the present study.
CONCLUSION
In this study, a nomogram model was built by combining eight independent risk
factors for DVT. This nomogram score is a reliable and effective tool that can predict
DVT in older patients with NMAP. This may help guide doctors in making sound
decisions regarding the administration of DVT prophylaxis.
ARTICLE HIGHLIGHTS
Research background
Deep vein thrombosis (DVT) may cause pulmonary embolus leading to late deaths.
The systemic inflammatory and hypercoagulable state of moderate and severe acute
Yang DJ et al. Prediction model for DVT
WJGS https://www.wjgnet.com 1265 October 27, 2021 Volume 13 Issue 10
pancreatitis (non-mild acute pancreatitis, NMAP) patients may contribute to the
development of venous thromboembolism. Accurate prediction of DVT is conducive
to clinical decisions.
Research motivation
There is a lack of a scoring model for predicting the development of DVT in NMAP
patients.
Research objectives
We aimed to develop a prediction model for DVT in old NMAP patients.
Research methods
Univariate and multivariate logistic regression analyses were used to select
independent risk factors associated with DVT. The selected risk factors were included
in the nomogram. A validation set was constructed using bootstrapping with 100
resamplings. The accuracy and utility of the nomogram were evaluated by calibration
curve and decision curve analysis, respectively.
Research results
Eighty DVT patients and 140 non-DVT patients were included in this study. Eight
factors including age, sex, surgery times, D-dimer, neutrophils, any organ failure,
blood culture, and classification constitute the prediction model. This model achieved
good concordance indexes of 0.827 (95%CI: 0.769-0.885) and 0.803 (95%CI: 0.743-0.860)
in the training and validation set, respectively.
Research conclusions
A reliable and effective nomogram model that can predict DVT in old patients with
NMAP was constructed.
Research perspectives
The usability of the new model needs further validation by other center data.
REFERENCES
Petrov MS, Yadav D. Global epidemiology and holistic prevention of pancreatitis. Nat Rev
Gastroenterol Hepatol 2019; 16: 175-184 [PMID: 30482911 DOI: 10.1038/s41575-018-0087-5]
1
Gapp J, Hall AG, Walters RW, Jahann D, Kassim T, Reddymasu S. Trends and Outcomes of
Hospitalizations Related to Acute Pancreatitis: Epidemiology From 2001 to 2014 in the United States.
Pancreas 2019; 48: 548-554 [PMID: 30946239 DOI: 10.1097/MPA.0000000000001275]
2
Peery AF, Crockett SD, Murphy CC, Lund JL, Dellon ES, Williams JL, Jensen ET, Shaheen NJ,
Barritt AS, Lieber SR, Kochar B, Barnes EL, Fan YC, Pate V, Galanko J, Baron TH, Sandler RS.
Burden and Cost of Gastrointestinal, Liver, and Pancreatic Diseases in the United States: Update
2018. Gastroenterology 2019; 156: 254-272.e11 [PMID: 30315778 DOI:
10.1053/j.gastro.2018.08.063]
3
Peery AF, Dellon ES, Lund J, Crockett SD, McGowan CE, Bulsiewicz WJ, Gangarosa LM, Thiny
MT, Stizenberg K, Morgan DR, Ringel Y, Kim HP, DiBonaventura MD, Carroll CF, Allen JK, Cook
SF, Sandler RS, Kappelman MD, Shaheen NJ. Burden of gastrointestinal disease in the United States:
2012 update. Gastroenterology 2012; 143: 1179-1187.e3 [PMID: 22885331 DOI:
10.1053/j.gastro.2012.08.002]
4
van Dijk SM, Hallensleben NDL, van Santvoort HC, Fockens P, van Goor H, Bruno MJ, Besselink
MG; Dutch Pancreatitis Study Group. Acute pancreatitis: recent advances through randomised trials.
Gut 2017; 66: 2024-2032 [PMID: 28838972 DOI: 10.1136/gutjnl-2016-313595]
5
Xu F, Chen X, Li C, Liu J, Qiu Q, He M, Xiao J, Liu Z, Ji B, Chen D, Liu K. Prediction of Multiple
Organ Failure Complicated by Moderately Severe or Severe Acute Pancreatitis Based on Machine
Learning: A Multicenter Cohort Study. Mediators Inflamm 2021; 2021: 5525118 [PMID: 34054342
DOI: 10.1155/2021/5525118]
6
Husu HL, Valkonen MM, Leppäniemi AK, Mentula PJ. Occurrence and Risk Factors of Infected
Pancreatic Necrosis in Intensive Care Unit-Treated Patients with Necrotizing Severe Acute
Pancreatitis. J Gastrointest Surg 2021 [PMID: 33987740 DOI: 10.1007/s11605-021-05033-x]
7
Stein PD, Beemath A, Olson RE. Trends in the incidence of pulmonary embolism and deep venous
thrombosis in hospitalized patients. Am J Cardiol 2005; 95: 1525-1526 [PMID: 15950590 DOI:
10.1016/j.amjcard.2005.02.030]
8
Di Nisio M, van Es N, Büller HR. Deep vein thrombosis and pulmonary embolism. Lancet 2016; 388:
3060-3073 [PMID: 27375038 DOI: 10.1016/S0140-6736(16)30514-1]
9
Mendelson RM, Anderson J, Marshall M, Ramsay D. Vascular complications of pancreatitis. ANZ J 10
Yang DJ et al. Prediction model for DVT
WJGS https://www.wjgnet.com 1266 October 27, 2021 Volume 13 Issue 10
Surg 2005; 75: 1073-1079 [PMID: 16398814 DOI: 10.1111/j.1445-2197.2005.03607.x]
Deiss R, Young P, Yeh J, Reicher S. Pulmonary embolism and acute pancreatitis: case series and
review. Turk J Gastroenterol 2014; 25: 575-577 [PMID: 25417623 DOI: 10.5152/tjg.2014.6089]
11
Geerts W, Selby R. Prevention of venous thromboembolism in the ICU. Chest 2003; 124: 357S-363S
[PMID: 14668418 DOI: 10.1378/chest.124.6_suppl.357s]
12
McLeod AG, Geerts W. Venous thromboembolism prophylaxis in critically ill patients. Crit Care
Clin 2011; 27: 765-780, v [PMID: 22082513 DOI: 10.1016/j.ccc.2011.07.001]
13
Maatman TK, McGuire SP, Lewellen KA, McGreevy KA, Ceppa EP, House MG, Nakeeb A,
Nguyen TK, Schmidt CM, Zyromski NJ. Prospective Analysis of the Mechanisms Underlying
Ineffective Deep Vein Thrombosis Prophylaxis in Necrotizing Pancreatitis. J Am Coll Surg 2021;
232: 91-100 [PMID: 33039510 DOI: 10.1016/j.jamcollsurg.2020.08.774]
14
Barbar S, Noventa F, Rossetto V, Ferrari A, Brandolin B, Perlati M, De Bon E, Tormene D, Pagnan
A, Prandoni P. A risk assessment model for the identification of hospitalized medical patients at risk
for venous thromboembolism: the Padua Prediction Score. J Thromb Haemost 2010; 8: 2450-2457
[PMID: 20738765 DOI: 10.1111/j.1538-7836.2010.04044.x]
15
Caprini JA, Arcelus JI, Reyna JJ. Effective risk stratification of surgical and nonsurgical patients for
venous thromboembolic disease. Semin Hematol 2001; 38: 12-19 [PMID: 11449339 DOI:
10.1016/s0037-1963(01)90094-0]
16
Wells PS, Owen C, Doucette S, Fergusson D, Tran H. Does this patient have deep vein thrombosis?
JAMA 2006; 295: 199-207 [PMID: 16403932 DOI: 10.1001/jama.295.2.199]
17
Eastham JA, Kattan MW, Scardino PT. Nomograms as predictive models. Semin Urol Oncol 2002;
20: 108-115 [PMID: 12012296 DOI: 10.1053/suro.2002.32936]
18
Kattan MW, Giri D, Panageas KS, Hummer A, Cranor M, Van Zee KJ, Hudis CA, Norton L, Borgen
PI, Tan LK. A tool for predicting breast carcinoma mortality in women who do not receive adjuvant
therapy. Cancer 2004; 101: 2509-2515 [PMID: 15495180 DOI: 10.1002/cncr.20635]
19
Park SY. Nomogram: An analogue tool to deliver digital knowledge. J Thorac Cardiovasc Surg
2018; 155: 1793 [PMID: 29370910 DOI: 10.1016/j.jtcvs.2017.12.107]
20
Balachandran VP, Gonen M, Smith JJ, DeMatteo RP. Nomograms in oncology: more than meets the
eye. Lancet Oncol 2015; 16: e173-e180 [PMID: 25846097 DOI: 10.1016/S1470-2045(14)71116-7]
21
Stubbs MJ, Mouyis M, Thomas M. Deep vein thrombosis. BMJ 2018; 360: k351 [PMID: 29472180
DOI: 10.1136/bmj.k351]
22
Banks PA, Bollen TL, Dervenis C, Gooszen HG, Johnson CD, Sarr MG, Tsiotos GG, Vege SS;
Acute Pancreatitis Classification Working Group. Classification of acute pancreatitis--2012: revision
of the Atlanta classification and definitions by international consensus. Gut 2013; 62: 102-111
[PMID: 23100216 DOI: 10.1136/gutjnl-2012-302779]
23
Streiff MB, Agnelli G, Connors JM, Crowther M, Eichinger S, Lopes R, McBane RD, Moll S, Ansell
J. Guidance for the treatment of deep vein thrombosis and pulmonary embolism. J Thromb
Thrombolysis 2016; 41: 32-67 [PMID: 26780738 DOI: 10.1007/s11239-015-1317-0]
24
Audu CO, Gordon AE, Obi AT, Wakefield TW, Henke PK. Inflammatory biomarkers in deep venous
thrombosis organization, resolution, and post-thrombotic syndrome. J Vasc Surg Venous Lymphat
Disord 2020; 8: 299-305 [PMID: 32067731 DOI: 10.1016/j.jvsv.2019.09.008]
25
Heit JA, Silverstein MD, Mohr DN, Petterson TM, O'Fallon WM, Melton LJ 3rd. Risk factors for
deep vein thrombosis and pulmonary embolism: a population-based case-control study. Arch Intern
Med 2000; 160: 809-815 [PMID: 10737280 DOI: 10.1001/archinte.160.6.809]
26
Rogers FB. Venous thromboembolism in trauma patients. Surg Clin North Am 1995; 75: 279-291
[PMID: 7899998 DOI: 10.1016/s0039-6109(16)46588-6]
27
Allen CJ, Murray CR, Meizoso JP, Ginzburg E, Schulman CI, Lineen EB, Namias N, Proctor KG.
Surveillance and Early Management of Deep Vein Thrombosis Decreases Rate of Pulmonary
Embolism in High-Risk Trauma Patients. J Am Coll Surg 2016; 222: 65-72 [PMID: 26616034 DOI:
10.1016/j.jamcollsurg.2015.10.014]
28
Johnson CD, Abu-Hilal M. Persistent organ failure during the first week as a marker of fatal outcome
in acute pancreatitis. Gut 2004; 53: 1340-1344 [PMID: 15306596 DOI: 10.1136/gut.2004.039883]
29
Roch AM, Maatman TK, Carr RA, Colgate CL, Ceppa EP, House MG, Lopes J, Nakeeb A, Schmidt
CM, Zyromski NJ. Venous Thromboembolism in Necrotizing Pancreatitis: an Underappreciated Risk.
J Gastrointest Surg 2019; 23: 2430-2438 [PMID: 30734182 DOI: 10.1007/s11605-019-04124-0]
30
Kylänpää ML, Repo H, Puolakkainen PA. Inflammation and immunosuppression in severe acute
pancreatitis. World J Gastroenterol 2010; 16: 2867-2872 [PMID: 20556831 DOI:
10.3748/wjg.v16.i23.2867]
31
Metz AK, Diaz JA, Obi AT, Wakefield TW, Myers DD, Henke PK. Venous Thrombosis and Post-
Thrombotic Syndrome: From Novel Biomarkers to Biology. Methodist Debakey Cardiovasc J 2018;
14: 173-181 [PMID: 30410646 DOI: 10.14797/mdcj-14-3-173]
32
