Leveron&Nexovas
Neurology India
menu-bar5 Open access journal indexed with Index Medicus
  Users online: 1067  
 Home | Login 
About Editorial board Articlesmenu-bullet NSI Publicationsmenu-bullet Search Instructions Online Submission Subscribe Videos Etcetera Contact
  Navigate Here 
 Search
 
  
 Resource Links
  »  Similar in PUBMED
 »  Search Pubmed for
 »  Search in Google Scholar for
 »Related articles
  »  Article in PDF (648 KB)
  »  Citation Manager
  »  Access Statistics
  »  Reader Comments
  »  Email Alert *
  »  Add to My List *
* Registration required (free)  

 
  In this Article
 »  Abstract
 »  Materials and Me...
 » Results
 » Discussion
 » Conclusion
 »  References
 »  Article Tables

 Article Access Statistics
    Viewed282    
    Printed16    
    Emailed0    
    PDF Downloaded9    
    Comments [Add]    

Recommend this journal

 


 
Table of Contents    
ORIGINAL ARTICLE
Year : 2022  |  Volume : 70  |  Issue : 4  |  Page : 1548-1553

Using Multiple Logistic Regression to Determine Factors Affecting Delaying Hospital Arrival of Patients with Acute Ischemic Stroke


1 Department of Community Medicine, Faculty of Medicine, Ardabil University of Medical Science, Ardabil, Iran
2 Department of Neurology, Faculty of Medicine, Ardabil University of Medical Science, Ardabil, Iran
3 Department of Medicine, Ardabil Branch, Islamic Azad University, Ardabil, Iran

Date of Submission28-Sep-2020
Date of Decision04-Oct-2021
Date of Acceptance13-Oct-2021
Date of Web Publication30-Aug-2022

Correspondence Address:
Afshan Sharghi
Department of Community Medicine, Faculty of Medicine, Ardabil University of Medical Science, Ardabil
Iran
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0028-3886.355102

Rights and Permissions

 » Abstract 


Background: Early treatment of ischemic stroke patients who arrive at the hospital ≤4.5 hours after the onset of symptoms with recombinant tissue plasminogen activator is more beneficial and very important.
Objective: This study is aimed to investigate the factors delaying the hospital arrival of patients with acute ischemic stroke by using multiple logistic regression analysis.
Methods and Materials: This descriptive cross-sectional study was done on patients diagnosed with acute ischemic stroke who were referred to Ardabil city Training and Research hospital at 2018. All patients and/or patient relatives were interviewed and data were collected through a checklist including demographic and clinical data of patients to explore the involved factors delaying hospital arrival of patients and then analyzed using multiple logistic regression analysis.
Results: Of all included patients, only 25.3% arrived at the hospital in ≤ 4.5 hours. By using multivariate logistic regression analysis, living in cities (P = 0.007), cigarette consumption (P = 0.032), having valvular heart disease (P = 0.008), and gender (P = 0.049) were factors associated with an early arrival to the hospital.
Conclusions: Results showed that most of the patients had a considerable delay in arriving at the hospital in ≤ 4.5 hours. Thus, providing health promotion strategies to improve society awareness of early symptoms of stroke, training of local physicians about the importance of early arrival of stroke patients, and more extended ambulance services in all cities and rural areas are necessary for better management of acute stroke patients in this area.


Keywords: Ardabil, ischemic stroke, late arrival
Key Message: This article focus on the variables which be involved in the delay of patients with acute ischemic stroke in arrival to the hospital.


How to cite this article:
Amani F, Fattahzadeh-Ardalani G, Sharghi A, Jafarizadeh R. Using Multiple Logistic Regression to Determine Factors Affecting Delaying Hospital Arrival of Patients with Acute Ischemic Stroke. Neurol India 2022;70:1548-53

How to cite this URL:
Amani F, Fattahzadeh-Ardalani G, Sharghi A, Jafarizadeh R. Using Multiple Logistic Regression to Determine Factors Affecting Delaying Hospital Arrival of Patients with Acute Ischemic Stroke. Neurol India [serial online] 2022 [cited 2022 Oct 2];70:1548-53. Available from: https://www.neurologyindia.com/text.asp?2022/70/4/1548/355102




Early start of treatment in patients with acute ischemic stroke by recombinant tissue plasminogen activator (rt-PA) within 4.5 hours of onset of stroke could be important. This early treatment after the onset of stroke symptoms is more effective in the prediction of disability and mortality in stroke cases.[1],[2]

Recombinant tissue plasminogen activator (r-tPA) has been used in many countries within the first 4.5 hours after the onset of symptoms. Due to late admission of patients with stroke, only a small proportion of stroke cases can receive this medication.[3],[4],[5]

Median time from onset to admission to hospital in most studies ranged from 4 hours to more than 24 hours.[6],[7],[8]

Increasing the awareness of patients and their families about the importance of early arrival time of patients to the hospital after the onset of stroke symptoms for early treatment is important.

Early neurological attention to stroke cases can be associated with better functional outcome of patients and less hospitalization time. Several studies reported that certain factors such as age, gender, underlying diseases, certain risk factors, and transport options played the main role in the late arrival of patients with stroke to the hospital.[9],[10],[11] Analyzing the factors associated with delay in arrival of stroke patients to hospital are more important. This study is aimed to investigate the factors delaying the hospital arrival of patients with acute ischemic stroke by using multiple logistic regression analysis.


 » Materials and Methods Top


This descriptive cross-sectional study was a hospital-based study done on 676 patients with ischemic stroke who were referred to Ardabil city training and research hospital a province in northwest of Iran in 2018 (population: 1,320,000). All stroke patients with neurologic focal symptoms for more than 24 hours and confirmed existence changes in brain CT were entered in the study. Verbal informed consent was obtained from all subjects and relatives.

Data were collected after interviewing patients and relatives and then entered in a checklist including demographic and clinical data of patients such as age, gender, residence place, marital status, history of DM, HTN, hyperlipidemia, heart disease, smoking consumption, type of risk factor, mode of transportation to the hospital, time of arrival to the hospital emergency, and time of onset diagnostic and therapeutically actions.

Data analysis method

Source of data

The study was conducted in the Ardabil city hospital based on the data from the neurology center. Necessary data were collected from the files of the studied patients who were hospitalized in the stroke section of Ardabil city hospital in 2018.

Dependent variable

The response variable of our study is time of arrival to the hospital after the onset of stroke symptoms. Thus, the response variable yi for the time can be explained as 1 for patients entering the hospital emergency ≤4.5 hours after onset of stroke symptoms (early arrival), and as 0 for those who were entered to the hospital >4.5 hours (delay).

Independent variables

In this study, we have a group of variables such as gender, age, place of residence, marital status, smoking, alcohol drinking, used medication, and certain risk factors such as DM, HTN, HLP, valvular heart disease, AF, coronary, and CVA.

Binary logistic regression

In binary logistic regression, the dependent variable was binary (0, 1), and by using this model, we estimated the probability of a dichotomous response (which is also its mean) for various values of explanatory variables proposed by Willy (1996) [14]. We fit a following model of the form:



The term on the right side of the equation is a logistic function in which the relationships between P and x are nonlinear and can be linearized. This transformation is called the logit transformation of the probability P and the ratio is called the odds. Thus, the multiple logistic regression model can be expressed as



+β3x3+β4x4.

The variables x1, x2, x3, and x4 are independent variables (such as gender, place of residence, Cigarette and Valvular Heart disease) that affect the dependent variable (arrival time) and can be included in the final model of logistic regression replace x1, x2, x3, and x4 in above model.

Fitting the logistic regression model

Logistic regression uses a maximum likelihood estimation technique to estimate the parameters required for the regression model. In this model, there are n independent random observations corresponding to the random variables (Y1, Y2,…., Yn). As Yi is a Bernoulli random variable with functional form, the likelihood function can be given by



Statistical data analysis

Data were analyzed using descriptive statistical methods in SPSS version 21 and the P < 0.05 was considered to be statistically significant. In addition, we used the multiple logistic regression analysis by backward stepwise (Wald) method to determine the factors affecting the delay arrival of patients with acute ischemic stroke to the hospital.


 » Results Top


This study was a descriptive hospital-based study carried out on 676 patients with ischemic stroke who were hospitalized in Ardabil city hospital. Of all patients, 382 (56.5%) were female, and the mean age of patients was 69.3 ± 13.2 (range: 25–98) years. The main stroke risk factors were as follows: high blood pressure (69.2%), heart disease (43.8%), diabetes (31%), smoking (10.8%), alcohol (0.4%), and CVA (29%). It was determined that 171 patients (25.3%) arrived at the hospital ≤ 4.5 hours and 505 patients (74.7%) arrived at the hospital > 4.5 hours from the onset of stoke symptoms [Table 1].
Table 1: Socio-demographic characteristics of stroke patients (n=676)

Click here to view


All necessary factors that contributed to the early and late arrival of patients to the hospital are shown in [Table 2]. Of them, gender, place of residence, cigarette consumption, and having valvular heart disease had a significant influence. Based on classification table results from Chi-square test, our model correctly classified of the total patients into two classes (early/delay) and 74.7% of patients were with delay in arrival time.
Table 2. Comparison of patinets'socio-demographic and clinical characteristics by the time of arrival at the hospital in bivariate analysis

Click here to view


In the multiple logistic regression analysis, effective factors such as residing in cities (P = 0.007), cigarette consumption (P = 0.032), having valvular heart disease (P = 0.008), and gender (P = 0.045) were associated with an early arrival to the hospital. The odds of having an early arrival time to the hospital is 0.69 times lower for females as opposed to males or 1.45 times more for males than females. The odds of having an early arrival time to the hospital is 1.79 times greater for city people as opposed to urban residing people. The odds of having an early arrival time to the hospital is 1.88 times greater for smokers as opposed to non-smokers. The odds of having an early arrival time to the hospital is 3.31 times greater for people having valvular heart disease as opposed to people without any valvular heart disease [Table 3].
Table 3. Factors contributing to an early arrival in multiple logistic regression analysis

Click here to view


The final model for including significant factors by multiple logistic regression is as below:





Based on the above model, we can calculate the probability of classification of patients and decide about the category class for each new patient in the future. Also, we can write this model in the form of

Logit (arrival time) = -0.27-0.38*male + 0.57*city + 0.628*cigarret + 1.2*valvular heart diseases.


 » Discussion Top


In our study, we examined the conditions causing a delay between the time of arrival to the hospital and the onset of stroke symptoms.

We found that 171 patients (25.3%) arrived to the emergency ward of the hospital in the early arrival time (first 4.5 hours) after the onset of the stroke symptoms. In the studies conducted about the hospital arrival time of patients with stroke in other places, these rates were in the range of 29.5%–72.5%, and in comparison to the abovementioned studies, in our study, the rate of delay in arrival time was significantly lower. Ardabil city has a central hospital for stroke that accepts all stroke patients from urban and rural areas in Ardabil province and there was many distance between urban and rural areas from this central hospital in Ardabil city. The higher delay in arrival time to hospital among patients with stroke can be due to the geographical distribution of cases, traffic density of patients, residence place, and distance of cities and rural from the hospital in Ardabil city.[12],[13],[14],[15],[16],[17]

Memis et al.[12] in a study showed that 33.3% of stroke patients were not aware of the importance of seeking immediate medical help, and in their study, differences between two sexes in two groups ≤3 hours and >3 hours was not significant, which was not in line with our study results because the difference between two sexes was significant in our study. Also, in their study, the most defined causes for delay in arrival at the hospital were “waiting for symptoms to go away” and “not realizing the urgency of seeking medical help” jointly with 67.8%.

In more studies similar to our study, distance had a significant relation to a delay in arrival to the hospital because they stated that distances of more than 15 km can be linked with a delay in transportation and late arrival to the hospital in acute ischemic stroke patients.[18],[19]

The average age of patients in the early arrival group was 70.3 ± 12.9 years and in the late arrival group was 68.9 ± 13.2 years, which was not significant. Similar to our study, in studies conducted by Wannarong[20] and Cemile et al.,[21] the difference between ages in two groups was not significant.

In the literature, some studies similar to our study have found that the female gender affects the time of arrival to the hospital adversely. In our study, we found that the rate of late arrival time to hospital among females was significantly different from that among males, which may be due to the absence of a family member during the day for providing transportation service while many women are at home alone or dealing with children, and compared to more studies, the rate of late arrival in female patients was significantly higher. Also, the reason for the higher rate of early arrival in males is due to their activity in work life and society and more access to the hospital and emergency services than females at home.[6],[21],[22],[23],[24],[25],[26]

In terms of residence place, 71% of patients lived in urban and 29% in rural areas. In this study, we found residing in a city as a significant factor for the early arrival of patients to the hospital, which was not similar to Cemile et al.[21] because they noted that residing in a city did not have a significant effect on early arrival of patients. Arulprakash et al.[19] in a study reported that urban residents were more likely to arrive to the hospital earlier but did not find any significant relation between residing in a city and early arrival. In our study, we found a significant relationship between residing in the city and early arrival and we showed that patients who reside in a city had a 1.79 times lower rate of late arrival to the hospital compared with other patients. This may be due to more information provided to the patients and their relatives in advance and more effort from the emergency service teams throughout the city. Unfortunately, in Ardabil province, due to the inequality in health services provided for the people in rural and urban areas, variations in the distance from cities and rural areas, low awareness among city and rural people about the signs and symptoms of stroke, more distance from home to hospital in some areas, and easy transportation of patients in some areas, this difference could not be confirmed.[19]

Also, within 4.5 hours after onset of stroke symptom, the rate of patients arriving at the hospital's stroke unit has been reported as 50%–70% in previous studies; in many Western countries, at least 50% of patients arrived within 6 hours. This study reports this rate to be at 25.3%, which was lower than more studies.[27],[28],[29],[30]

Also, results from some countries varied from 17.5% to 46%, which was similar to our study results because this rate was 25.3% in our study.[20]

The stroke risk factors, including diabetes mellitus, hypertension, hyperlipidemia, atrial fibrillation, smoking, and family history of stroke, were reported to be associated with hospital arrival time in several studies.[20],[31],[32]

Cemile et al.[21] in a study showed that among all factors, only three factors, namely female gender, private vehicle, and NIHSS, were the main factors that affected early arrival of patients; in our study, similar to Cemile et al.,[21] there was no significant relationship between the main risk factors such as DM. HTN, AF, CVA, and coronary with the arrival time of patients to the hospital, but similar to the Cemile et al., study the relation between having valvular heart disease with arrival time was significant, the relationship between gender with early arrival of patients to the hospital was statistically significant (P = 0.009).

In this study, the relation between valvular heart disease and early arrival to the hospital was also statistically significant. It would be helpful to mention that one of the future lines of research in the study on factors affecting the arrival time to the hospital in ischemic stroke patients would be their relationship with different stroke subtypes. It would be useful to assess the differences between cardioembolic infarction and non-cardioembolic infarction because cardioembolic stroke is the subtype of ischemic infarction with the highest in-hospital mortality. The short-term prognosis of patients with cardioembolic stroke is poor compared to other ischemic stroke subtypes. However, in this study, we did not study the stroke subtypes.[33]

In the study was done by Wannarong et al.,[20] by using multivariate logistic regression analysis, they showed that factors significantly associated with early hospital arrival included previous ischemic stroke/transient ischemic attack, severe stroke (NIHSS > 15), seizure as an initial symptom, and diagnosis of hemorrhagic stroke. Factors associated with late hospital arrival were awakening or unknown-onset stroke and referral from other hospitals or clinics. In our study, the reasons for early and late arrival of stroke patients to the hospital were not in line with Wannarong et al.[20] because we found other factors to be associated with the early and late arrival of patients to the hospital. Also, in this study, 51.4% of patients arrived at the hospital within 4.5 hours and only 6.6% arrived by ambulance.

In contrast to the abovementioned study, in our study, gender was an independent factor associated with arrival time to the hospital. With regard to the early arrival of men than women, we can say that women differ from men in the distribution of risk factors and stroke subtype, stroke severity, and outcome.[34]

Ashraf et al.[6] in a study showed that of all factors, only five factors, namely directly reaching hospital, distance <15 km, coronary artery diseases, hemiplegia, and educational status are the effective factors for the early arrival of stroke patients, which was not in line with our study results.

Limitation

In this study we have not any limitation.


 » Conclusion Top


The study results showed that four factors—place of residence, smoking, gender, and having valvular heart disease—are among all involved factors delaying hospital arrival of acute stroke patients. This study cannot reflect all predicted factors involved in the late arrival of patients; thus, conducting studies with a large sample size is recommended. Also, in this study, we found the need to develop a strategy for improving the inter-hospital transfer process. Further studies on additional factors associated with early hospital arrival time are recommended to support educational efforts for early stroke treatment and prevention and to help us to increase the awareness of the use of emergency medical services.

Ethical approval

The study was supported and approved by the ethical committee of Ardabil University of Medical Science and registered by ethical code IR.REC.ARUMS.1397.016.

Acknowledgements

The author would like to thank all the patients and their relatives for participating in the study.

Declaration of patient consent

The consent form were taken and completed for all patients.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
 » References Top

1.
Frey James L. Recombinant tissue plasminogen activator (rtPA) for stroke. The perspective at 8 years. Neurologist 2005;11:123-33.  Back to cited text no. 1
    
2.
Weintraub MI. Thrombolysis (tissue plasminogen activator) in stroke a medicollegal quagmire. Stroke 2006;37:1917-22.  Back to cited text no. 2
    
3.
Marler JR, Tilley BC, Lu M, Brott TG, Lyden PC, Grotta JC, et al. Early stroke treatment associated with better outcome: the NINDS rt-PA study. Neurology 2000;55:1649-55.  Back to cited text no. 3
    
4.
Stolz E, Hamann GF, Kaps M, Misselwitz B. Regional differences in acute stroke admission and thrombolysis rates in the German federal State of Hesse. Dtsch Arztebl Int 2011;108:607-9.  Back to cited text no. 4
    
5.
Al Khathaami AM, Mohammad YO, Alibrahim FS, Jradi HA. Factors associated with late arrival of acute stroke patients to emergency department in Saudi Arabia. SAGE Open Med 2018;6:2050312118776719.  Back to cited text no. 5
    
6.
Ashraf VV, Maneesh M, Praveenkumar R, Saifudheen K, Girija AS. Factors delaying hospital arrival of patients with acute stroke. Ann Indian Acad Neurol 2015;18:162-6.  Back to cited text no. 6
[PUBMED]  [Full text]  
7.
Chen CH, Huang P, Yang YH, Liu CK, Lin TJ, Lin RT. Pre-hospital and in-hospital delays after onset of acute ischemic stroke: A hospital-based study in southern Taiwan. Kaohsiung J Med Sci 2007;23:552-9.  Back to cited text no. 7
    
8.
Hong ES, Kim SH, Kim WY, Ahn R, Hong JS. Factors associated with prehospital delay in acute stroke. Emerg Med J 2011;28:790-3.  Back to cited text no. 8
    
9.
Moser DK, Kimble LP, Alberts MJ, Alonzo A, Croft JB, Dracup K, et al. Reducing delay in seeking treatment by patients with acute coronary syndrome and stroke: A scientific statement from the American Heart Association Council on cardiovascular nursing and stroke council. Circulation 2006;114:168-82.  Back to cited text no. 9
    
10.
Kleindorfer DO, Lindsell CJ, Broderick JP, Flaherty ML, Woo D, Ewing I, et al. Community socioeconomic status and prehospital times in acute stroke and transient ischemic attack. Do poorer patients have longer delays from 911 call to the emergency department?. Stroke 2006;37:1508-13.  Back to cited text no. 10
    
11.
Rajajee V, Saver J. Prehospital care of the acute stroke patient. Tech Vasc Interv Radiol 2005;8:74-80.  Back to cited text no. 11
    
12.
Memis S, Tugrul E, Evci ED, Ergin F. Multiple causes for delay in arrival at hospital in acute stroke patients in Aydin, Turkey. BMC Neurol 2008;8:15.  Back to cited text no. 12
    
13.
Tekin Guveli B, Yatmazoglu M, Acar E, Senadæm S, Coban E, Dayan C, et al. Pre-hospital obstacles in thrombolytic therapy and suggested solutions. Turkish Journal of Cerebrovascular Diseases 2015;21:194-7.  Back to cited text no. 13
    
14.
Kocak S, Dogan E, Kokcam M, Girisgin AS, Bodur S. Limitations in thrombolytic therapy in acute ischemic stroke. Pak J Med Sci 2012;28:586-90.  Back to cited text no. 14
    
15.
Dimitriou P, Tziomalos K, Christou K, Kostaki S, Angelopoulou SM, Papagianni M, et al. Factors associated with delayed presentation at the emergency department in patients with acute ischemic stroke. Brain Inj 2019;33:1257-61.  Back to cited text no. 15
    
16.
Atay V. Acute stroke through the perspective of a county hospital: Problems and opportunities. Turk J Neurol 2016;22:13-8.  Back to cited text no. 16
    
17.
Fladt J, Meier N, Thilemann S, Polymeris A, Traenka C, Seiffge DJ, et al. Reasons for prehospital delay in acute ischemic stroke. J Am Heart Assoc 2019;8:e013101.  Back to cited text no. 17
    
18.
Nepal G, Yadav JK, Basnet B, Shrestha TM, Kharel G, Ojha R. Status of prehospital delay and intravenous thrombolysis in the management of acute ischemic stroke in Nepal. BMC Neurol 2019;19:155.  Back to cited text no. 18
    
19.
Arulprakash N, Umaiorubahan M. Causes of delayed arrival with acute ischemic stroke beyond the window period of thrombolysis. J Family Med Prim Care 2018;7:1248-52.  Back to cited text no. 19
[PUBMED]  [Full text]  
20.
Wannarong T, Chotik-anuchit S, Nilanont Y. Factors associated with hospital arrival time in acute stroke. J Med Assoc Thai 2019;102:1-7.  Back to cited text no. 20
    
21.
Cemile H, Mustafa C, Halil K. Factors affecting the arrival time to hospital of patients with acute ischemic stroke. Sanamed 2020;15:145-51.  Back to cited text no. 21
    
22.
Saver JL, Smith EE, Fonarow GC, Reeves MJ, Zhao X, Olson D, et al. The “golden hour” and acute brain ischemia: Presenting features and lytic therapy in >30,000 patients arriving within 60 minutes of stroke onset. Stroke 2010;41:1431-9.  Back to cited text no. 22
    
23.
Tong D, Reeves MJ, Hernandez AF, Zhao X, Olson DM, Fonarow GC, et al. Times from symptom onset to hospital arrival in the Get with the Guidelines – Stroke Program 2002 to 2009: Temporal trends and implications. Stroke 2012;43:1912-17.  Back to cited text no. 23
    
24.
Mandelzweig L, Goldbourt U, Boyko V, Tanne D. Perceptual, social, and behavioral factors associated with delays in seeking medical care in patients with symptoms of acute stroke. Stroke 2006;37:1248-53.  Back to cited text no. 24
    
25.
Smith MA, Lisabeth LD, Bonikowski F, Morgenstern LB. The role of ethnicity, sex, and language on delay to hospital arrival for acute ischemic stroke. Stroke 2010;41:905-9.  Back to cited text no. 25
    
26.
Lichtman JH, Watanabe E, Allen NB, Jones SB, Dostal J, Goldstein LB. Hospital arrival time and intravenous t-PA use in US Academic Medical Centers, 2001–2004. Stroke 2009;40:3845-50.  Back to cited text no. 26
    
27.
Maze LM, Bakas T. Factors associated with hospital arrival time for stroke patients. J Neurosci Nurs 2004;36:136-55.  Back to cited text no. 27
    
28.
Derex L, Adeleine P, Nighoghossian N, Honnorat J, Trouillas P. Factor's influencing early admission in a French stroke unit. Stroke 2002;33:153-9.  Back to cited text no. 28
    
29.
Broadley SA, Thompson PD. Time to hospital admission for acute stroke: An observational study. Med J Aust 2003;178:329-31.  Back to cited text no. 29
    
30.
Agyeman O, Nedeltchev K, Arnold M, Fischer U, Remonda L, Isenegger J, et al. Time to admission in acute ischemic stroke and transient ischemic attack. Stroke 2006;37:963-6.  Back to cited text no. 30
    
31.
Song D, Tanaka E, Lee K, Sato S, Koga M, Kim YD, et al. Factors associated with early hospital arrival in patients with acute ischemic stroke. Stroke 2015;17:159-67.  Back to cited text no. 31
    
32.
Jin H, Zhu S, Wei JW, Wang J, Liu M, Wu Y, et al. Factors associated with prehospital delays in the presentation of acute stroke in urban China. Stroke 2012;43:362-70.  Back to cited text no. 32
    
33.
Arboix A, Josefina A. Acute cardioembolic stroke: An update. Expert Rev Cardiovasc Ther 2011;9:367-79.  Back to cited text no. 33
    
34.
Arboixa A, Cartanyà A, Lowak M, García-Eroles L, Parra O, Oliveres M, et al. Gender differences and woman-specific trends in acute stroke: Results from a hospital-based registry (1986–2009). Clin Neurol Neurosurg 2014;127:19-24.  Back to cited text no. 34
    



 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

Top
Print this article  Email this article
   
Online since 20th March '04
Published by Wolters Kluwer - Medknow