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ORIGINAL RESEARCH
published: 23 December 2021
doi: 10.3389/fneur.2021.792678
Frontiers in Neurology | www.frontiersin.org 1December 2021 | Volume 12 | Article 792678
Edited by:
Adriano Pinto,
University of Minho, Portugal
Reviewed by:
Ren Shenghan,
Xidian University, China
Bing Zhang,
Nanjing Drum Tower Hospital, China
*Correspondence:
Minwen Zheng
zhengmw2007@163.com
Weixun Duan
Duanweixun@126.com
†These authors have contributed
equally to this work
Specialty section:
This article was submitted to
Stroke,
a section of the journal
Frontiers in Neurology
Received: 13 October 2021
Accepted: 30 November 2021
Published: 23 December 2021
Citation:
Zhao H, Xu Z, Zhu Y, Xue R, Wang J,
Ren J, Wang W, Duan W and
Zheng M (2021) The Construction of a
Risk Prediction Model Based on
Neural Network for Pre-operative
Acute Ischemic Stroke in Acute Type
A Aortic Dissection Patients.
Front. Neurol. 12:792678.
doi: 10.3389/fneur.2021.792678
The Construction of a Risk Prediction
Model Based on Neural Network for
Pre-operative Acute Ischemic Stroke
in Acute Type A Aortic Dissection
Patients
Hongliang Zhao 1†, Ziliang Xu 1† , Yuanqiang Zhu1, Ruijia Xue 1, Jing Wang 1, Jialiang Ren 2,
Wenjia Wang 2, Weixun Duan3
*and Minwen Zheng 1
*
1Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China, 2GE Healthcare China, Beijing,
China, 3Department of Cardiovascular Surgery, Xijing Hospital, Fourth Military Medical University, Xi’an, China
Objective: To establish a pre-operative acute ischemic stroke risk (AIS) prediction model
using the deep neural network in patients with acute type A aortic dissection (ATAAD).
Methods: Between January 2015 and February 2019, 300 ATAAD patients diagnosed
by aorta CTA were analyzed retrospectively. Patients were divided into two groups
according to the presence or absence of pre-operative AIS. Pre-operative AIS risk
prediction models based on different machine learning algorithm was established with
clinical, transthoracic echocardiography (TTE) and CTA imaging characteristics as input.
The performance of the difference models was evaluated using the receiver operating
characteristic (ROC), precision-recall curve (PRC) and decision curve analysis (DCA).
Results: Pre-operative AIS was detected in 86 of 300 patients with ATAAD (28.7%).
The cohort was split into a training (211, 70% patients) and validation cohort (89, 30%
patients) according to stratified sampling strategy. The constructed deep neural network
model had the best performance on the discrimination of AIS group compare with other
machine learning model, with an accuracy of 0.934 (95% CI: 0.891–0.963), 0.921 (95%
CI: 0.845–0.968), sensitivity of 0.934, 0.960, specificity of 0.933, 0.906, and AUC of
0.982 (95% CI: 0.967–0.997), 0.964 (95% CI: 0.932–0.997) in the training and validation
cohort, respectively.
Conclusion: The established risk prediction model based on the deep neural network
method may have the big potential to evaluate the risk of pre-operative AIS in patients
with ATAAD.
Keywords: aortic dissection, acute ischemic stroke, angiography, risk assessment, deep neural network, acute
type A aortic dissection
Zhao et al. Risk Prediction Model for AIS-ATAAD
INTRODUCTION
Pre-operative acute ischemic stroke (AIS) is one of important
factors that affects the outcomes of surgical treatment and long-
term post-operative survival in patients with acute type A aortic
dissection (ATAAD) (1). Although ATAAD is a serious aortic
disease that requires immediate surgical repair once diagnosed
(1,2), The treatment of ATAAD with pre-operative AIS is more
challenging. Many studies had proven that neurological damage
caused by pre-operative AIS or cerebral malperfusion indicates
a poor prognosis following ATAAD (3,4). In order to avoid the
deterioration of AIS in patients with ATAAD, early preventive
intervention during surgery is needed.
Computed tomography angiography (CTA) and transthoracic
echocardiography (TTE) have shown high sensitivity in the
evaluation of high-risk plaque and vascular anatomy (5), and
thus, are commonly used methods for the diagnosis of aortic
dissection (6). Some studies have suggested about 5–10% of
patients with ATAAD had an AIS before surgery, which was
caused by the extension of the dissection into the common
carotid arteries, thromboembolism or cerebral hypoperfusion
(2,4,7).
Neural network has become a popular algorithm for medical
data analysis, especially for classification tasks, because it
allows us to solve quite complex classification tasks (8,9).
Unfortunately, medical classification tasks usually have to be
performed under more or less limited conditions, involving many
cases in the data set, i.e., their size and uneven distribution
between disease categories. One option is to reduce the majority
class to the size of the minority class. This will make the training
set too small and lead to unsuccessful machine learning. Neural
network has many training optimization methods. For example,
using an appropriate loss function can effectively solve the
problem of sample imbalance (10).
Our previous study using univariate and multivariate analysis
found that the true lumen diameter ratio of the ascending aorta
(aAO), the common carotid artery (CCA) dissection, and the
aortic valve insufficiency may be associated with pre-operative
AIS (11). However, traditional univariate and multivariate
analyses do not consider the information between characteristics,
and thus, the corresponding logistic model in did not perform
better in our previous study. Therefore, a risk prediction model
used to accurately predict the pre-operative AIS in patients with
ATAAD using deep neural network, which can deeply fuse the
information among characteristics, will be constructed to help
doctor make informed treatment decisions.
MATERIALS AND METHODS
Study Population and Definitions
Between January 2015 and February 2019, a total of 300 ATAAD
patients, diagnosed by aortic CTA and with no history of ischemic
stroke or cerebrovascular disease, were retrospectively included
in this study. ATAAD patients were divided into two groups
according to the presence (AIS+group) or absence (AIS- group)
of pre-operative AIS. Clinical characteristics, TTE imaging
information and CTA imaging information were collected.
TABLE 1 | The clinical characteristic of patients with ATAAD.
Characteristics AIS +n=86 AIS – n=214 P
Demographics
Sex (male) 65 (75.6) 180 (84.1) 0.118
Age (year) 52.5 ±9.8 48.1 ±10.5 0.001*
Medical history
Hypertension 61 (70.9) 134 (62.6) 0.218
Marfan’s syndrome 1 (1.2) 2 (0.9) 0.638
Diabetes 1 (1.2) 3 (1.4) 0.676
Coronary heart disease 3 (3.5) 14 (6.5) 0.229
Clinical symptoms
Chest pain 18 (20.9) 47 (22.0) 0.967
Back pain 12 (14.0) 30 (14.0) 0.999
Chest and back pain 21 (24.4) 84 (39.3) 0.021*
Abdominal pain 17 (19.8) 45 (21.0) 0.931
Emergency examination
Systolic blood pressure 133 [99.2;146] 136 [116;154] 0.040*
Diastolic blood pressure 66.5 [55.0;77.8] 71.0 [60.0;84.8] 0.034*
Hypotension 12 (14.0) 11 (5.1) 0.019*
Malperfusion 17 (19.8) 27 (12.6) 0.050
Tamponade 10 (11.6) 10 (4.7) 0.029*
Transthoracic echocardiography
AVI (moderate or severe) 22 (25.6) 46 (21.5) 0.268
LVEF 48.5 ±5.8 49.2 ±5.9 0.332
Time interval
From symptoms onset to
MR examination (h)
16.2 (8–50) 10 (6–23) 0.142
ATAAD, acute type A aortic dissection; AIS, acute ischemic stroke; AVI, aortic valve
insufficiency; LVEF, left ventricular ejection fraction. *represents the statistical differences.
Detailed information was shown in Table 1 (clinical and TTE)
and Table 2 (CTA).
ATAAD was defined as any non-traumatic dissection of the
aorta proximal to the left subclavian artery presenting within 14
days of symptom onset (1). All included ATAAD patients in this
study are involved with ascending aorta as well as a primary entry
tear either in the ascending aorta or the aortic arch (prior to
the left subclavian artery). AIS was defined as a cerebrovascular
accident representing a loss of neurological function (loss or
slurring of speech, altered state of consciousness) caused by
an ischemic event and further confirmed by diffusion MRI.
Because the MRI examination room was adjacent to CT
examination room in our emergency department and only
cranial DWI sequence was performed, the total examination time
was <5 min.
This study complied with the Helsinki Declaration (2000) and
was approved by the institutional review board of Xijing Hospital
affiliated with the Fourth Military Medical University (20120216-
4). Informed consent was obtained from each patient or their
legal representative.
Aortic CTA
CTA examinations were performed using a second-generation
dual source CT machine (Somatom Definition Flash; Siemens
Frontiers in Neurology | www.frontiersin.org 2December 2021 | Volume 12 | Article 792678
Zhao et al. Risk Prediction Model for AIS-ATAAD
TABLE 2 | The CTA imaging characteristic information of patients with ATAAD.
Characteristics AIS +(n=86) AIS – (n=214) P
The diameter of aAO 48.0 [44.0;52.0] 47.0 [43.0;50.8] 0.373
The true lumen diameter of
aAO
12.0 [6.47;16.0] 17.0 [13.0;22.8] <0.001*
The true lumen diameter
ratio of aAO
0.24 [0.15;0.32] 0.36 [0.26;0.48] <0.001*
The false lumen thrombus of
aAO
23 (26.7) 68 (31.8) 0.473
Retrograde aAO dissection 11 (12.8) 31 (14.5) 0.843
Intimal flap plaque 30 (34.9) 39 (18.2) 0.003*
Entry tear in the aortic arch 41 (47.7) 123 (57.5) 0.157
CCA dissection 67 (77.9) 62 (29.0) <0.001*
Innominate artery or CCA
from false lumen
12 (14.0) 7 (3.3) 0.001*
Low density of unilateral ICA 24 (27.9) 13 (6.1) <0.001*
VA dissection 1 (1.2) 3 (1.4) 0.676
VA from false lumen 5 (5.8) 5 (2.3) 0.125
VA from aortic arch 1 (1.2) 5 (2.3) 0.448
Low density of unilateral VA 8 (9.3) 6 (2.8) 0.021*
SA dissection 32 (37.2) 37 (17.3) <0.001*
SA from false lumen 3 (3.5) 2 (0.9) 0.144
VSACV 0 (0.00) 2 (0.9) 0.508
ATAAD, acute type A aortic dissection; AIS, acute ischemic stroke; aAO, ascending
aorta; CCA, common carotid artery; ICA, internal carotid artery; VA, vertebral artery; SA,
subclacian artery; VSACV, vagal subclavian artery congenital variation. *represents the
statistical differences.
Healthcare, Forchheim, Germany) with a high-pitch spiral scan
mode. Patients underwent combined CTA imaging of the neck
and aorta in the cranio-caudal direction. For all scans, patients
were in a supine position with both arms raised. Each patient
received an injection of 70 ml of iopromide (Ultravist 370,
370 mgI/mL; Bayer Schering Pharma, Berlin, Germany) at a
flow rate of 5 mL/s, followed by 40 mL of saline solution at a
flow rate of. Bolus tracking was performed on the suprarenal
descending aorta with an attenuation threshold of 100 HU.
The scanning parameters were as follows: tube voltage of 100
kV, reference tube current of 300 mAs per rotation, pitch of
3.0 and slice collimation of 2 ×128 ×0.6 mm by means
of a z-flying focal spot. The CTA images were transferred
to an external workstation (syngo MMWP VE 36 A; Siemens
Healthcare, Forchheim, Germany) for further postprocessing.
Finally, CTA characteristics were identified from each patient
CTA imaging by two experts with at least 10 years’ experience on
radiology imaging.
Deep Neural Network
In this study, dataset is stratified split into training (70%) and
validation (30%) cohort. A four layers deep neural network
was performed with all the clinical, TTE and CTA imaging
characteristics as input. The random search method (12) was
used to select the best number of nodes for each hidden layer
and the number of nodes for output layer was set to 1 represent
AIS+probability (Figure 1). In order to overcome the influence
FIGURE 1 | The model architecture of the deep neural network.
of data imbalance focal loss (10) were used as loss function as
shown in equation (1). The Adam optimizer was used and model
was trained with 300 epochs.
loss =−(1 −ˆ
p)γlog(ˆ
p) if y=1
−ˆ
pγlog(1 −ˆ
p) if y=0(1)
Whereγis a adjust factor andγ > 0
Machine Learning Model
In addition, the univariate and multivariate stepwise logistic
regression analysis (Uni +Multi analysis) (11), the 10-fold
CV based Least absolute shrinkage and selection operator
(CV LASSO) risk factor selection method (13), support vector
machine (SVM) model (14), random forest (RF) model (15), and
neural network (NN) were also used for comparison. According
to the number of input characteristics, the number of NN’s nodes
for each hidden layer were set as 16, 8, and 4, respectively. The
number of nodes for output layer was set to 2, the same as the
number of patient’s group. The deep belief network (DBF) (16)
training method was used to initialize the weights of the NN.
Briefly, the first four layers of NN consisted of three restricted
Boltzmann machines (RBMs). When pervious RBM finished the
training, the trained weights would be used as the initialized
weights for the corresponding NN layer, and the output of
this trained RBM would be used as the input for the current
RBM training.
For Uni +Multi analysis model, characteristics with
significant difference between AIS+and AIS- group in univariate
model were included in the construction of multivariate stepwise
logistic regression model. For CV LASSO model, all variables
were selected through CV LASSO algorithm. For SVM, RF, NN
and DNN model, all variables were included in the construction
of the model. The first difference between NN and DNN was
Frontiers in Neurology | www.frontiersin.org 3December 2021 | Volume 12 | Article 792678
Zhao et al. Risk Prediction Model for AIS-ATAAD
that the number of nodes for each hidden layer in NN was
manually set according to number of input characteristics,
while, for DNN, these number was optimized through random
search method, which could make the performance of model
to the best. The second difference was the weight initialization
(DBF training method for NN and random initialization
for DNN).
Statistical Analysis
The training and validate of the deep neural network and DBN
were implemented using TensorFlow 2.0 with GPU support
on a Python 3.8 platform (https://www.python.org/). The other
conventional machine learning model was implemented with R
software (Version 4.1.0, https://www.rproject.com/).
All statistical analysis was performed using R software.
Summary statistics are presented as counts with percentages for
categorical values, as mean ±standard deviation for normally
distributed continuous variables, or as medians with quartiles for
non-normally distributed continuous variables. Data distribution
was checked using the Shapiro-Wilk test. Continuous variables
with normal data distributions were compared using two-sample
ttests. For data with skewed distributions, non-parametric
Mann-Whitney tests were used. Categorical variables were
compared using chi-square statistics, and the Fisher exact test
was used if observed frequencies were <5. Receiver operating
characteristic (ROC) curve as well as precision recall curve (PRC)
analysis and the area under the curve (AUC) was calculated. The
best cutoff point was obtained by the Youden-index of ROC
curve, then accuracy, sensitivity, specificity, positive predictive
rate, and negative predictive rate were calculated. To estimate the
clinical usefulness of the different models, decision curve analysis
(DCA) was conducted by calculating the net benefits at different
threshold probabilities (17). For all statistical analyses, p<0.05
was considered statistically significant.
RESULTS
Patient Clinical and TTE Imaging
Characteristics
Pre-operative AIS was detected in 86 of 300 patients with ATAAD
(28.7%). Among clinical and TTE imaging characteristics, the age
(52.5 ±9.8 years vs. 48.1 ±10.5 years, p=0.001), the incidence
of hypotension (14.0 vs. 5.1%, p=0.019) and tamponade (11.6
vs. 4.7%, p=0.029) were significantly higher in the AIS+group
compared to the AIS- group, the systolic blood pressure (129.3
±34.0 vs. 137.5 ±31.4 ml/Hg, p=0.040) and the incidence of
the chest and back pain (24.4 vs. 39.3%, p=0.021) and were
significantly lower in the AIS+group compared to the AIS-
group. Other clinical characteristics did not differ significantly
between the AIS+group and AIS- group. Detailed clinical and
TTE imaging characteristics are shown in Table 1.
Patient CTA Imaging Characteristics
Among CTA imaging characteristics, the true lumen diameter
of the aAO (11.9 ±5.9 vs. 17.8 ±7.3 mm, p<0.001) and
the true lumen diameter ratio of the aAO (0.25 ±0.11 vs.
0.37 ±0.15, p<0.001) were lower in the AIS+group than
in the AIS- group. The intimal flap plaque (34.9 vs. 39.0%, p
=0.003), the CCA dissection (77.9 vs. 29.0%, p<0.001), the
innominate artery or CCA from false lumen (14.0 vs. 3.3%, p
=0.001), the low density of the unilateral ICA (27.9 vs. 6.1%,
p<0.001), the low density of vertebral artery (9.3 vs. 2.8%, p<
0.021), and the subclavian artery dissection (37.2 vs. 17.3%, p<
0.001) were higher in the AIS+group than in the AIS- group.
Other CTA imaging characteristics did not differ significantly
between the AIS+group and AIS- group. Detailed CTA imaging
characteristics are shown in Table 2.
Model Performance
Among those methods, the model constructed by the deep neural
network had the best performance than the other methods,
TABLE 3 | The performance of different prediction models.
Method Training Validation
AUC ACC SEN SPE PPV NPV AUC ACC SEN SPE PPV NPV
Uni +Multi
analysis
0.867
(0.814–0.920)
0.782
(0.720–0.836)
0.836 0.760 0.586 0.919 0.864
(0.768–0.960)
0.798
(0.699–0.876)
0.840 0.781 0.600 0.926
CV LASSO 0.877
(0.826–0.928)
0.787
(0.725–0.840)
0.885 0.747 0.587 0.941 0.874
(0.788–0.960)
0.798
(0.699–0.876)
0.880 0.766 0.595 0.942
RF 0.898
(0.851–0.944)
0.834
(0.777–0.882)
0.852 0.827 0.667 0.932 0.869
(0.774–0.964)
0.843
(0.750–0.911)
0.840 0.844 0.677 0.931
SVM 0.883
(0.832–0.933)
0.834
(0.777–0.882)
0.836 0.833 0.671 0.926 0.868
(0.779–0.956)
0.753
(0.650–0.838)
0.800 0.734 0.541 0.904
DBN 0.909
(0.862–0.955)
0.877
(0.825–0.918)
0.869 0.880 0.746 0.943 0.863
(0.774–0.952)
0.809
(0.712–0.885)
0.720 0.844 0.64 0.885
DNN 0.982
(0.967–0.997)
0.934
(0.891–0.963)
0.934 0.933 0.851 0.972 0.964
(0.932–0.997)
0.921
(0.845–0.968)
0.960 0.906 0.800 0.983
Uni +Multi analysis, univariate and multivariate analysis; CV LASSO, cross validation based least absolute shrinkage and selection operator; RF, random forest; SVM, support vector
machine; DBN, deep belief network; DNN, deep neural network; AUC, the area under the curve; ACC, accuracy; SEN, sensitivity; SPE, specificity; PPV, positive predictive rate; NPV,
negative predictive rate.
Frontiers in Neurology | www.frontiersin.org 4December 2021 | Volume 12 | Article 792678
Zhao et al. Risk Prediction Model for AIS-ATAAD
with an accuracy of 0.934 (95% CI: 0.891–0.963), 0.921 (95%
CI: 0.845–0.968), sensitivity of 0.934, 0.960, specificity of 0.933,
0.906, and AUC of 0.983 (95% CI: 0.967–0.997), 0.964 (95% CI:
0.932–0.997) in the training and validation cohort, respectively
(Table 3). The ROC curves were shown in Figure 2 and the
best cutoff point of deep neural network was 0.402. The PRC
as illustrated in Figure 3, which demonstrated that deep neural
network has the best AUC with highest precision (positive predict
rate) and recall (sensitivity). The DCA curve for the different
models were presented in Figure 4. The DCA showed that if
the threshold probability between 0.05 and 0.694, using the deep
neural network to predict AIS status adds more net benefit
FIGURE 2 | The receiver operating characteristic (ROC) curves for each model in (A) training cohort and (B) validation cohort.
FIGURE 3 | The precision-recall curves (PRC) for each model in (A) training cohort and (B) validation cohort.
Frontiers in Neurology | www.frontiersin.org 5December 2021 | Volume 12 | Article 792678
Zhao et al. Risk Prediction Model for AIS-ATAAD
FIGURE 4 | The decision curve analysis (DCA) for each model in (A) training cohort and (B) validation cohort.
than either the other models, “treat all patients” or the “treat
none” strategies.
DISCUSSION
In this study, four potential risk factors related to pre-operative
AIS in patients with ATAAD were found using the neural
network method. After CV LASSO regression, we found the age,
the true lumen diameter ratio of the aAO, the CCA dissection, the
low density of unilateral ICA exhibited the highest importance
to the four risk factors, which might suggest that these four
characteristics were more important to the assessment of pre-
operative AIS. The corresponding results were discussed in
detail below.
Dumfarth et al. pointed out that the pre-operative infractions
in ATAAD patients were more likely to occur in the right cerebral
hemisphere, which was consistent with our observations (18).
They also suggested that pre-operative neurologic dysfunction
was an independent risk factor of post-operative neurologic
injury (16). Thus, objective assessment of pre-operative stroke
is very important. Many studies had proven that pre-operative
cerebral malperfusion and AIS are serious complications of
ATAAD, which indicate poor prognosis (3,7,19,20).
In the construction of classifications or a prediction model,
the univariate and multivariate analysis only takes those
characteristics that have significant difference between AIS+
and AIS−group into account and do not consider the
information between characteristics. Although the traditional
machine learning based feature selection method, such as LASSO
regression, SVM and RF, can consider the information between
characteristics, this kind of method remains those characteristics
that have relatively big contribution to the model. The NN
method can make the most use of all the input characteristics,
deeply take all the information between them into account,
select and merge them into several factors that important to
the classifications or prediction model (Table 3), and thus, had
better performance than univariate and multivariate analysis and
traditional machine learning based feature selection methods.
For DNN method, it not only had the advantages of NN, but
also achieved the optimization for the number of nodes in each
hidden layer and considered the data imbalance problem, and
thus, had the best performance than other methods.
This study had some limitations. Firstly, the data used in
this study was from a single hospital. Future studies are needed
that consider multi-center data to validate the reliability of our
constructed risk model. Secondly, this study was a retrospective
study, so dataset may contain some bias. Future studies are
needed to collect larger amounts of perspective data to the
validate results of this study and provide a more objective and
scientific theoretical basis for the prevention and treatment of
pre-operative AIS in patients with ATAAD. Thirdly, this study
only looked at pre-operative data. Future study will use both pre-
and post-operative data to further improve the risk model.
In summary, this study provided a risk prediction model using
deep neural network method to predict the occurrence of pre-
operative AIS in patients with ATAAD. This risk model can
provide information that is useful for identifying which ATAAD
patients are at high risk of AIS before surgery, and therefore, help
doctors decide whether patients need follow-up examinations or
surgery, and decide the best timing for it.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
Frontiers in Neurology | www.frontiersin.org 6December 2021 | Volume 12 | Article 792678
Zhao et al. Risk Prediction Model for AIS-ATAAD
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by Xijing Hospital affiliated with the Fourth Military
Medical University (20120216-4). The patients/participants
provided their written informed consent to participate in
this study.
AUTHOR CONTRIBUTIONS
HZ, WD, and MZ contributed to the conception and design.
RX and JW contributed to the acquisition of data. ZX,
JR, and WW contributed to the and analysis of data.
HZ, ZX, and MZ contributed to the interpretation of the
results. HZ and ZX contributed to the manuscript writing.
YZ, WD, and MZ contributed to the manuscript reviewing.
All authors contributed to the article and approved the
submitted version.
FUNDING
This study has received funding by the National Natural
Science Foundation of China under Grant No. 81870218, the
Subject Boosting Project of Xijing Hospital under Grant No.
XJZT18ML20, and the Key Research and Development Plan of
Shaanxi Province under Grant No. 2020SF-151.
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Conflict of Interest: JR and WW were employed by the company GE Healthcare
China.
The remaining authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a potential
conflict of interest.
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Copyright © 2021 Zhao, Xu, Zhu, Xue, Wang, Ren, Wang, Duan and Zheng.
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Frontiers in Neurology | www.frontiersin.org 7December 2021 | Volume 12 | Article 792678
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