DOI: https://doi.org/10.21203/rs.3.rs16209/v1
Background The treatment of acute ischemic stroke is heavily timedependent, and even though, with the most efficient treatment, the longterm functional outcome is still highly variable. In this current study, the authors selected acute ischemic stroke patients who were qualified for intravenous thrombolysis with recombinant tissue plasminogen activator and followed by intraarterial thrombectomy. With primary outcome defined by the functional level in a oneyear followup, we hypothesize that patients with older age are at a disadvantage in poststroke recovery. However, an agethreshold should be determined to help clinicians in selection of patients to undergo such therapy.
Methods This is a retrospective chart review study that include 92 stroke patients in Changhua Christian hospital with a total of 68 evaluation indexes recorded. The current study utilized the forward stepwise regression model whose AdjR2 and pvalue in search of important variables for outcome prediction. The chngpt package in R indicated the threshold point of the age factor directing the better future functionality of the stroke patients.
Results Datasets revealed the threshold of the age set at 79 the most appropriate. Admission Barthel Index, Age, Ipsi ICA RI, Ipsi VA PI, Contra MCA stenosis, Contra ECA RI, and inhospital pneumonia are the significant predicting variables. The higher the age, inhospital pneumonia, Contra MCA stenosis, Ipsi ICA RI and Ipsi VA PI, the less likely patient to recover from functional deficits as the result of acute ischemic stroke; the higher the value of Contra ECA RI and Admission Barthel Index, the better chance to recover at oneyear follow up.
Conclusions Parameters of preintervention datasets could provide important information to aid firstline clinicians in decision making. Especially, in patients whose age is above seventynine receives diminish return in the benefit to undergo such intervention and should be considered seriously by both the patients and the physicians.
The treatment of acute ischemic stroke is heavily timedependent, and even though, with the most efficient treatment, the longterm functional outcome is still highly variable. In general, there are three clusters of parameters that influence the treatment outcome; these are the preintervention baseline characteristics, such as age, sex, body weight, comorbidities, nature of the stroke, etc.; second, interventional methods; and lastly, postinterventional care, including method of secondary prevention, rehabilitation program and much more.
In this current study, the authors selected acute ischemic stroke patients who were qualified for intravenous thrombolysis with recombinant tissue plasminogen activator and followed by intraarterial thrombectomy, following the 2018 American Heart and Stroke Association's guidelines^{1}. Additionally, all of the patients who underwent intraarterial thrombectomy in Changhua Christian Hospital also received carotid Doppler examination as part of the protocol. This offers additional data for evaluation in this study.
The primary goal of this study was to investigate whether prethrombectomy treatment parameters and baseline characteristics can offer valuable information in predicting patients’ longterm functional outcomes in acute ischemic stroke patients who underwent intravenous thrombolysis followed by intraarterial thrombectomy. Therefore, allow clinicians to better select the appropriate candidate for such treatment.
In this current study, all patients were selected from Changhua Christian Hospital with a retrospective chart review conducted by the Department of Neurology. Participants were selected based on the confirmed diagnosis of first acute ischemic stroke and underwent intravenous thrombolysis therapy with recombinant tissue plasminogen activator (rtPA), followed by intraarterial thrombectomy. Patients were selected based on the current indication for intravenous thrombolysis of presenting within 4.5 hours of the onset of symptoms^{1} and did not have any contraindications to receive rtPA. CT angiography perfusion scan was followed in these patients to screen for candidates to receive intraarterial thrombectomy.
Additional inclusion criteria are neuroimage confirmed anterior circulation obstruction, above eighteen of age, and were followed for at least one year. Patients with intracerebral hemorrhage, aneurysm rupture, cerebral arteriovenous malformation, and recurrent stroke were excluded. A total of 92 patients was selected fulfilling the above criteria between 2015 and 2017.
Preintervention data include baseline patient demographics such as age, body mass index, systolic and diastolic blood pressure, total cholesterol, HbA1c, and other comorbidities were documented. Additionally, inhospital complication such as pneumonia was also recorded.
As part of the protocol, all patients underwent carotid doppler examination for evaluation of the status of blood flow in large vessels including the common carotid artery (CCA), internal carotid artery (ICA), external carotid artery (ECA), and vertebral artery (VA), and the presence of artherosclerotic plaque. Multiple parameters were extrapolated, including peak systolic velocity (PSV), enddiastolic velocity (EDV), resistance index (RI), and pulsatility index (PI).
Primary outcome in this study was to identify any significant change in the functionality of patients who underwent treatment, which is done by assessment of NIHSS score^{2}, mRS score^{3}, and Barthel index^{4} both upon admission to the hospital and at 1year follow up. Additionally, using statistical methods, described in the following section, to determine whether any of the preintervention variables have a significant impact on the functional outcome.
Statistical analysis
Data was collected from 92 stroke patients with a total of 68 independent preinterventional indexes and 4 outcome variable (Appendix 1) were recorded. Authors use principal component analysis (PCA) to integrate the prognostic variables into an aggregative index, prognosis, which is an index that takes into account all four outcome variables and their contribution to the overall outcome. Then using the forward stepwise regression model to determine which, if any, of the sixtyeight independent variables had a significant effect on the prognosis. Authors end up with only eleven variables and only seven of which had statistically significance.
Classify the Variables
To facilitate the analysis, the data is first divided into preinterventional and outcome variable, which are classified into categories, orders, and continuations. The outcome variables include the following: Follow up CT cerebral bleeding, MBD NIHSS, MBD mRS, MBD Barthel Index, and the remaining 68 variables are classified as preinterventional independent variables.
Use Principal Component Analysis on the prognostic variables
The authors use principal component analysis (PCA) to integrate the outcome variables into an aggregative index, prognosis. According to equation (1), each principal component recombines the original variables into a new set of several independent variables. The coefficient of X_{i} in the linear combination is its eigenvector which is obtained by maximizing the explanatory variation of the corresponding principal component^{5}. Therefore, authors can determine the relationship between the principal component and the original variable.
PCA = ϕ_{1}X_{1} + ϕ_{ 2}X_{2} +...+ ϕ_{ n}X_{n} (1)
In the current study, prognosis is an outcome variable that is influenced by the need of follow up head CT in case of suspected intracranial bleed, the NIHSS score, mRS score, and Barthel index. The relationship is shown in the below equation (2):
Prognosis = 0.218 Follow.up.CT + 0.566 NIHSS + 0.528mRS  0.595B.I (2)
Exploratory Data Analysis
The exploratory data analysis section was performed with the ggplot2 package in R. Authors describe the statistics of variables in order to understand the simple information of the data.
Model Architecture
(1) Multiple Regression Model
Multiple regression is to explore the correlation between the independent variable and the dependent variable and to build a regression model^{6}. The equation (3) shown below.
y = b_{0} + b_{1}x_{1} + b_{2}x_{2} +...+b_{n}x_{n} (b_{0}...b_{n}: Regression coefficients) (3)
In our research , authors regarded all variables in preintervention as independent variables, and prognosis as a dependent variable, and built a multiple regression model. Authors find that only a few variables are significant, however, with AdjR^{2} being only 0.3106, the model has a relatively low explanatory power. In order to improve the model, forward stepwise regression model is then used.
(2) Forward Stepwise Regression Model
The forward stepwise regression begins with an empty regression, adding variables one by one to select the best performing model according to the Akaike information criterion (AIC). AIC is a standard for assessing the complexity of statistical models and measuring the superiority of statistical model fit the data. The model with the lowest AIC value should be given priority when selecting the model. This is done by adding the independent variables one by one until the additional contribution of any one of the variables does not provide any statistical significance. Finally, the authors identified eleven variables with only seven of them are significant (table 3). This method improved the AdjR^{2} of our model from 0.3106 to 0.4588, with pvalue less than 0.05, and therefore, the model setup by using forward stepwise regression provides more explanatory power.
(3) Segmented linear regression
Segmented linear regression is a method when the independent variables clustered into different groups^{7}. That is, there are different relationships between the variables in these regions. The following equation (4) is a threshold equation model:
η = α_{1} + α_{2}^{T}z + β_{1} (x  e)_{+} + yx (4)
In this equation ‘e’ is the threshold parameter, and ‘x’ is the predictor with threshold effect, ‘z’ denotes additional predictors.
Authors estimate a segmented linear regression model to determine if there is a threshold for independent variable age, that would mark as a boundary for poor prognosis with statistical significance. The package authors utilized is chngpt package in R and uses the formula built in forward stepwise regression.
Exploratory Data Analysis
Table 1 shows the significantly preinterventional predictive variables in acute ischemic stroke patients who underwent intravenous thrombolysis followed by intraarterial thrombectomy.
The patients’ Barthel Index on admission is averaged to a value of 13.42 (range 0 to 80, lower the score means higher disability). This suggested that patients who presented with acute ischemic stroke were already disabled upon admission. The patients’ average age is 65, the youngest patient is 25, and the oldest patient is 88. It showed the majority of patients who suffered from stroke are elderly patients. The average value of Ipsi ICA RI is 0.7259, with a minimum value of 0.5100, and a maximum value of 1.7700. The average value of Ipsi VA RI is 0.7815, with a minimum value of 0.5300, and a maximum value of 1.0000. The average value of Contra ECA RI is 0.9466, with a minimum value of 0.7400, and a maximum value of 2.3100. This indicates that stroke was more likely to occur in a person with high arterial stagnation. The average value of Contralateral MCA stenosis CTA is 0.3216, with a minimum value of 0.0000, and a maximum value of 1.0000. The average value of Inhospital pneumonia is 8.413, with a minimum value of 5.0000, and a maximum value of 10.0000.
Furthermore, admission Barthel Index, age, Ipsi ICA RI, Ipsi VA PI, Contra ECA RI, Contra MCA stenosis on CTA, and Inhospital pneumonia are the most significant variables based on the dataset analysis. (Table 1).
Multiple Linear Regression and Forward Stepwise Regression
Table 2 showed the difference between the original model and forward stepwise regression model. The AdjR^{2 } of original model is 0.3106 and the pvalue of original model is 0.07819. The AdjR^{2 } of forward stepwise model is 0.4588 and the pvalue of forward stepwise model is 3.137e09. The result showed that the forward stepwise model is superior to the original model.
In Table 3, it showed that Admission Barthel Index (pvalue = 0.00212), Age (pvalue = 0.03030), Ipsi ICA RI (pvalue = 0.00155), Ipsi VA PI (pvalue = 0.01274), Contra MCA stenosis CTA (pvalue = 0.03008), Contra ECA RI (pvalue = 0.02184) and Inhospital pneumonia (pvalue = 0.02126) are the significant variables.
Using the aforementioned equations (2) and (3), shown once again below. If the age is increased by one year, the prognosis value will increase by 0.017807, from equation (3). Then setting equation (2) is equal to equation (3), authors inferred that patients with older age were more likely to receive a follow up CT of the head due to intracranial bleed, a higher NIHSS and mRS scores, and lower Barthel Index score upon discharge. Using the same logic, the authors arrive at the same conclusion with other variables such as Ipsi ICA RI, Ipsi VA PI, Contra MCA stenosis CTA, and Inhospital pneumonia.
Prognosis = 0.218(Follow.up.CT) + 0.566(NIHSS) + 0.528(mRS)  0.595(B.I forward) (2)
Prognosis = 0.026867(Admission Barthel Index) + 0.017807(Age) + 2.493430(Ipsi ICA RI) + 0.214155(Ipsi VA PI) + 0.546799(Contralateral MCA stenosis CTA)  1.148134(Contra ECA RI)  0.437726(Stroke right left) + 0.561732(Inhospital pneumonia)  0.449998(MRA ipsilateral Collateral flow) + 0.006712(CTP mismatch) + 0.140513(Admission CT ASPECTS) (3)
On the other hand, if the value of Contra ECA RI rises by value of one, the prognosis will decrease by 1.148134. That is to say, the higher the value of Contra ECA RI, the less chance to have follow up CT cerebral bleeding, and the better chance to recover. Other variables like Admission Barthel Index and MRA ipsilateral collateral flow showed similar trends.
Segmented Linear Regression
Figure 1 showed the three plots of the threshold of this data. The first plot shows the scatter plot between Age and Prognosis, and it represents that the samples scattered around 60 to 80 years old. The second plot shows the threshold of the Age variables is 79. The third plot shows the threshold which is estimated by bootstrap in order to know the frequency. As we find that the frequency around 80 is the highest, thus we can presume the threshold for the variable 'age' is 79.
According to the result of the segmented linear regression, the authors made the Age beyond 79 as 1, and below 79 as 0, and conducted a simple linear regression to determine if there is a statistical difference between the two samples. The result is shown in Table 4, and it confirmed our presumption in the early paragraph that patients with age greater than 79 indeed have a worse prognosis than those who are younger than the age of 79 (pvalue = 0.008757).
The current study demonstrated the higher the age, the less likely a stroke patient might recover from receiving treatment with intravenous thrombolysis followed by intraarterial thrombectomy. This is especially resourceful for firstline clinicians in selecting whom to receive such treatment and as a tool to weigh out the benefit and risk. We believe this is the first paper that pinpointed the age threshold as an important indicator of functional outcome in acute ischemic stroke patients underwent such treatment.
As far as preintervention carotid doppler parameters, the higher the Ipsi VA RI, which indicates compromised blood flow starting at the inlet of posterior circulation, therefore in the event of acute ischemic stroke of the anterior circulation, there are less available blood flow to rescue the ischemic brain cells and leading to higher risk of functional disability^{7} in such patients.
Interestingly, the current study showed that the higher the value of Contra ECA RI leads to a better functional outcome in ischemic stroke patients. We hypothesize that impaired contralateral blood flow redirects blood flow toward the opposite side, which is the ischemic hemisphere, that is sufficient to prevent the progression of the disease and is meaningful in predicting the longterm functional outcome in patients.
The authors used different statistical analysis methods to identify specific variables and whether each had a significant impact on the overall prognosis. However, the AdjR^{2} of the forward stepwise regression is only 0.4588, and the AdjR^{2} of the simple linear regression is only 0.06361, which both can be attributed to the relatively small sample size(N = 92), therefore, the model had low explanatory power. It would increase our confidence in our observation if future studies with larger sample size and still showed similar trends.
The statistical analysis method we used was all supposed that there were linear relation between outcome variables and preinterventional variables, therefore authors use multiple linear regression and segmented regression as study models. However, it might be nonlinear relationship between two variables, and this should be considered in future studies to tailor consistency between the model and the realworld situation.
A major strength of this study is low heterogeneity among participants, given that most patients came from the surrounding local communities and shared the same ethnic group. Other strengths were technical consistency, as the same technician performed all of the carotid duplex scans, and the ability to compare each patient’s functional scores one year after the treatment allowed adequate time to also observe functional improvement. However, this could also potentially introduce confounding factors, such as different types of rehabilitation programs that participants may have attended within the year. The major shortcoming of the current study is of relatively small sample size (N = 92) as well as the fact there was no placebo group for comparison. Besides, the homogeneity in the ethnic group might not be able to represent other ethnic groups and therefore the general application can be limited in such regard.
The higher the age, inhospital pneumonia, Contra MCA stenosis on CTA, Ipsi ICA RI and Ipsi VA PI, are correlated with higher risk of functional disability, whereas higher the value of Contra ECA RI and Barthel Index on admission showed relative better likelihood in improving functional outcome in the longterm in acute ischemic stroke patients who underwent ITT and IAT.
Preintervention parameters can serve as important indicators to aid firstline clinicians in decision making. The higher the age, inhospital pneumonia, Contra MCA stenosis on CTA, Ipsi ICA RI and Ipsi VA PI, are correlated with higher risk of functional disability, whereas higher the value of Contra ECA RI and Barthel Index on admission showed relative better likelihood in improving functional outcome in the longterm in acute ischemic stroke patients who underwent ITT and IAT. Especially, in patients whose age is above 79 receives diminish return in the benefit to undergo such intervention and should be considered seriously by both the patients and the physicians.
Ethics approval and consent to participate
The study procedures were approved by the Institutional Review Board of the Changhua Christian Hospital in Changhua, Taiwan (CCH IRB number: 180409). Informed consent was obtained in the usual way as the procedures were the usual standard of care. As the study is retrospective in nature, the informed consent was waived and approved by the committee.
Consent to publish
Not applicable.
Availability of data and materials
The patient datasets are stored after the study is done and sent to the committee for security reasons. It is our hospital policy to guarantee no breach of personal information.
Funding
This project received no support or funding from any organization.
Acknowledgements
Not applicable.
Authors’ contributions:
Conceptualization and study design by Chih Ming Lin, ShuWei Chang, and Henry HorngShing Lu; Methodology by Chih Ming Lin, and Henry HorngShing Lu; Data curation by Chih Ming Lin, YangHao Ou, and ChiKuang Liu; Formal analysis by ChiLing Kao, and Henry HorngShing Lu; Writing  Original draft preparation by YangHao Ou, ChiLing Kao, and Chih Ming Lin. Writing  Review & Editing by YangHao Ou, and Chih Ming Lin.
Conflicts of Interest
The authors have no competing interests to declare
Table1. Preinterventional independent variables with statistical significance
Admission. Barthel.Index 
Age 
Ipsi.ICA.RI 
Ipsi.VA.RI 
Min. : 0.00 1st Qu.: 5.00 Median :10.00 Mean :13.42 3rd Qu.:20.00 Max. :80.00 
Min. :25.00 1st Qu.:55.75 Median :68.00 Mean :65.01 3rd Qu.:77.00 Max. :88.00 
Min. :0.5100 1st Qu.:0.6400 Median :0.7000 Mean :0.7259 3rd Qu.:0.7625 Max. :1.7700 
Min. :0.5300 1st Qu.:0.7000 Median :0.7850 Mean :0.7815 3rd Qu.:0.8500 Max. :1.0000 
Contra. MCA stenosis CTA 
Contra.ECA.RI 
Inhospital pneumonia 

Min. :0.0000 1st Qu. :0.0000 Median :0.0000 Mean :0.3261 3rd Qu.:1.0000 Max. :1.0000 
Min. :0.7400 1st Qu. :0.8175 Median :0.9100 Mean :0.9466 3rd Qu.:1.0000 Max. :2.3100 
Min. : 5.000 1st Qu. : 8.000 Median : 9.000 Mean : 8.413 3rd Qu. : 9.000 Max. :10.000 
These variables were identified using the forward stepwise regression model. Indicating that these seven out of the total of sixtyeight preinterventional variables showed significant influence on the overall prognosis. Additionally, showing the minimum, maximum, median and quartile values of each variable.
Table 2. Model Architecture

Original Model 
Forward Stepwise Regression 
Formula 
Prognosis ~ all variables 
Prognosis ~ Admission Barthel Index + Age + Ipsi ICA RI +Ipsi VA PI + Contra MCA stenosis CTA + Contra ECA RI +Stroke right left + Inhospital pneumonia + MRA ipsilateral Collateral flow +CTP mismatch + Admission CT ASPECTS 
Residual standard error 
1.189 
1.054 
Multiple Rsquared 
0.7954 
0.5243 
Adjusted Rsquared 
0.3106 
0.4588 
Fstatistic 
1.641 
8.015 
pvalue 
0.07819 
3.137e09 *** 
This table showed the difference between the original model using multiple regression and forward stepwise regression model. The AdjR^{2 } of original model is 0.3106 and the pvalue of original model is 0.07819. The AdjR^{2 } of forward stepwise model is 0.4588 and the pvalue of forward stepwise model is 3.137e09.
Table3. Preinterventional variables in the forward stepwise regression analyses
Categories 
Coefficients 
Pvalue 
Admission Barthel Index 
0.026867 
0.00212 ** 
Age 
0.017807 
0.03030 * 
Ipsi ICA RI 
2.493430 
0.00155 ** 
Ipsi VA PI 
0.214155 
0.01274 * 
Contralateral MCA stenosis CTA 
0.546799 
0.03008 * 
Contra ECA RI 
1.148134 
0.02184 * 
Stroke right left 
0.437726 
0.06663 . 
Inhospital pneumonia 
0.561732 
0.02126 * 
MRA ipsilateral Collateral flow 
0.449998 
0.06348 . 
CTP mismatch 
0.006712 
0.11008 
Admission CT ASPECTS 
0.140513 
0.14005 
(Significant Value: ***: pvalue < 0.001; **: pvalue < 0.01; *: pvalue < 0.05;
. : pvalue < 0.01)
This table showed total of 11 variables identified using forward stepwise regression model, and 7 of the variables showed statistical significance. Admission Barthel Index (pvalue = 0.00212), Age (pvalue = 0.03030), Ipsi ICA RI (pvalue = 0.00155), Ipsi VA PI (pvalue = 0.01274), Contra MCA stenosis CTA (pvalue = 0.03008), Contra ECA RI (pvalue = 0.02184) and Inhospital pneumonia (pvalue = 0.02126) are the significant variables.
Table 4. The linear regression for age threshold

Model 
Formula 
Prognosis ~ Age 
Residual standard error 
1.386 
Multiple Rsquared 
0.0739 
Adjusted Rsquared 
0.06361 
Fstatistic 
7.182 
pvalue 
0.008757 
To verify the presumption derived from the scatter plot that age of 79 may be a threshold for poor prognosis, the authors divided age variables into two groups; the first group with age greater than 79, and the second group with age below 79, conducted a simple linear regression. The result is shown in this table and confirmed that there was a significant difference in the prognosis between these two age groups.
Appendix 1. List of all 68 preinterventional variables and 4 outcome variables
Stroke right/left 
Intra posterior stenosis CTA 
Ipisilateral MCA stenosis CTA 
Contralateral MCA stenosis CTA 
Stroke location MRI 
MRA ipsilateral Collateral flow 
It belongs to 2a/2b 
Occlusion site endovas 
Gender 
DM 
HTN 
mixed hyperlipidemia 
Smoking 
Drinking 
Previous stroke 
Af history 
Gout 
CAD 
CHF 
CKD 
Inhospital pneumonia 
Inhospital UTI 
IMT ipsilateral 
Duplecx plaque type 
Endovascular mTICI grading 
Admission mRS 
Admission CT ASPECTS 
CTP ischemic core 
CTP mismatch 
CTP perfusion Tmax 
Age 
Admission NIHSS 
Admission Barthel Index 
SBP 
DBP 
BMI 
Admission LDL 
Admission HDL 
Triglyceride 
Total Cholesterol,hSCRP 
BUN 
Cr 
Uric acid 
HbA1c 
Ac Sugar 
Ipsi CCA RI 
Contra CCA RI 
Ipsi ICA RI 
Contra ICA RI 
Ipsi ECA RI 
Contra ECA RI, 
Ipsi VA RI 
Contra VA RI 
Plaque index ipsi 
Plaque index contra 
Ipsi CCA PI 
Contra CCA PI 
Ipsi ICA PI 
Contra ICA PI 
Ipsi ECA PI 
Contra ECA PI 
Ipsi VA PI 
Contra VA PI 
IMT ipsilateral 


Follow up CT cerebral bleeding 
MBD mRS 
MBD Barthel Index 
MBD NIHSS 