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The number of risk factors increases the recurrence events in ischemic stroke

European Journal of Medical Research volume 27, Article number: 138 (2022) Cite this article

Abstract

Purpose

Stroke is a significant cause of disability worldwide and is considered a disease caused by long-term exposure to lifestyle-related risk factors. These risk factors influence the first event of stroke and recurrent stroke events, which carry more significant risks for more severe disabilities. This study specifically compared the risk factors and neurological outcome of patients with recurrent ischemic stroke to those who had just experienced their first stroke among patients admitted to the Hospital.

Patients and methods

We observed and analyzed 300 patients’ data who met the inclusion and exclusion criteria. This retrospective observational study was conducted on consecutive acute ischemic stroke patients admitted to the top referral hospital, West Java, Indonesia. The data displayed are epidemiological characteristics, NIHSS score at admission and discharge, and the type and number of risk factors. Data were then analyzed using appropriate statistical tests.

Results

Most patients had more than one risk factor with hypertension as the most frequent (268 subjects or 89.3%). In patients who experienced ischemic stroke for the first time, the average National Institutes of Health Stroke Scale (NIHSS) score was lower (6.52 ± 3.55), and the alteration of NIHSS score was higher (1.22 ± 2.26) than those with recurrent stroke (6.96 ± 3.55) for NIHSS score and 1.21 ± 1.73 for alteration of NIHSS score). We processed the data with statistical analysis and showed a positive correlation between age (P < 0.05) and the number of risk factors (P < 0.001) in the recurrent ischemic stroke group.

Conclusions

Age and the number of risk factors correlate with recurrent ischemic strokes.

Introduction

Stroke has become the main cause of disabilities and mortality throughout the world [1]. Ischemic stroke contributes 85% of the total stroke cases, with thrombotic ischemic stroke as the primary pathology, in which the main underlying process is atherosclerosis [2,3,4]. Stroke is a damaging health problem that leads to an enormous treatment cost, creating additional difficulties considering that a higher number of stroke patients is thought to survive in developing countries [5, 6].

Since stroke is considered a disease caused by long-term exposure to lifestyle-related risk factors, modification of these risk factors will highly influence the event of stroke and stroke-related disability level [7]. It has been acknowledged that the risk factors for ischemic thrombotic stroke consist of modifiable and nonmodifiable risk factors, with gender, age, ethnicity, and race as the nonmodifiable factors and hypertension, dyslipidemia, diabetes mellitus, and smoking as the modifiable risk factors [7, 8].

Risk factors and lifestyles vary from one country to another, making explorations of the stroke risk factors, along with their clinical features, an essential measure to determine the pattern of stroke in specific populations. This is especially paramount in patients with recurrent stroke events. Recurrent strokes are dangerous threats and can be fatal; thus, we should avoid them. Prevention of recurrent strokes is a valuable process, and data need to be available to support efforts to minimize the incident that can avert high treatment and rehabilitation costs [9].

Stroke severity can be clinically assessed based on neurological disorder parameters [7]. Because of the long-term disability effect associated with stroke, making an accurate early prediction of future functional ability is imperative. This will then determine the therapeutic objectives that are adjusted to the needs of the patient as well as provide the prognostic overview of the patient's future condition [10].

The NIHSS is commonly used in the clinical practice of acute stroke to evaluate the neurological outcome. [10, 11]. It is often used as a standardized instrument to evaluate the severity of neurological deficits in patients admitted to the emergency room and stroke unit. NIHSS has predictive validity, because its initial score is a strong predictor for stroke complications during hospitalization and neurological outputs in the 3-month post-stroke. It is responsive to changes and can measure disorders across the stroke severity range [11]. A lower NIHSS score during the initial phase of acute ischemic stroke independently correlates with good neurological outcomes [10].

In this study, we want to compare risk factors and neurologic outcomes in patients with a first ischemic stroke with those with recurrent ischemic stroke.

Materials and methods

Subject selection and study design

This study was a cross-sectional analytical observational using retrospective data from 300 medical records admitted to the Neurological Department of Dr. Hasan Sadikin General Hospital, the top referral hospital in West Java, Indonesia, from November 2018 to April 2020. The type of stroke was classified according to the TOAST classification, whereas the subtypes within the inclusion criteria were extensive artery atherosclerosis and small artery occlusion. Stroke severity was stratified based on NIHSS score as follows: 1–4 mild, 5–15 moderate, and more than 15 was a severe stroke [10, 11]. All patients did not treat with thrombolysis or thrombectomy [3]. The inclusion criteria included acute ischemic stroke patients with infarction that matched the thrombotic features (atherothrombotic, thromboembolic, and lacunar) on a head CT scan without contrast. Meanwhile, the exclusion criteria were the presence of etiology of cardioembolic strokes, such as atrial fibrillation and myocardial infarction, and also features of cardioembolic infarction on CT scan [12,13,14]. Data was then analyzed to know the relationship of a risk factor with recurrent ischemic stroke events and compare their neurological outcomes using an appropriate statistical test.

Statistical analysis

Categorical data are presented in number, frequency, and percentage, while numerical data are presented in mean, standard deviation, and range. Data analysis was started with the numerical data normality testing using the Kolmogorov–Smirnov test. The unpaired T test was used to compare the means between numerical data with normal distribution, while the Mann–Whitney test was used for data that were not normally distributed. For categorical data, the Chi-square test, with Kolmogorov–Smirnov dan exact Fisher tests, were used as alternatives if the requirements for the Chi-square test were not met. Significance was assessed using the P value with p ≤ 0.05 considered statistically significant.

Results

During the study period, data from 300 patients with ischemic stroke were obtained. Slightly more males (51.7%) were included in the study, with most subjects were  < 60 years (64%). The average NIHSS score on admission was 6.71±3.540, while the score at discharge was 5.50 ± 3.135. The majority of patients were admitted (62.0%) and discharged (53%) with moderate NIHSS scores (Table 1). Hypertension was identified as the most frequent risk factor among these patients (89.3%), and most patients had three risk factors (35.3%) (Table 2). During the acute phase of ischemic stroke, patients were given antiplatelet aggregation, antihypertensive and diabetes therapy, cholesterol, and uric acid lowering drugs, depending on the risk factors. Patients are also advised to quit smoking. Management is following the guidelines widely used in the field of neurology [15, 16].

Table 1 Ischemic stroke patient characteristics
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Table 2 Risk factors in ischemic stroke patients
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The relationship between the number of risk factors and age with the recurrence of ischemic stroke was considered to be statistically significant (P < 0.05) (Table 3).

Table 3 Comparison of risk factors between recurrent stroke patients and first-ever stroke patients
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We showed that the most infarction located in basal ganglia. The statistical analysis of the relationship between infarction location and recurrent ischemic stroke and first-ever ischemic stroke showed that P was more than 0.05 (P > 0.05), reflecting a statistically insignificant difference (Table 4).

Table 4 Comparison of infarction location between recurrent stroke patients and first-ever stroke patients
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The relationship between the average NIHSS score and NIHSS degree at admission and discharge in recurrent ischemic stroke and first-ever ischemic stroke groups was statistically insignificant (P > 0.05) (Table 5).

Table 5 Comparison between NIHSS and the occurence of recurrent stroke and first-ever stroke
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The comparison of NIHSS alteration between recurrent ischemic stroke and first-ever stroke patients resulted in P > 0.05. This means that no statistically significant association was found between variables (Table 6).

Table 6 Comparison of NIHSS alteration between recurrent stroke and first-ever stroke patients
Full size table

Discussion

Along with the increase in life expectancy, ischemic stroke with high morbidity has become an important public health problem. Although various studies are available, including studies on clinical characteristics and topography of ischemic stroke with different results, only a few studies have evaluated the risk factors and their relationship with neurological outcome in patients by comparing patients with recurrent ischemic stroke to patients with first-ever ischemic stroke [17,18,19,20].

In our study, age is an essential nonmodifiable risk factor for all patients with ischemic stroke and significant independent predictors of recurrent strokes. Stroke is an ageing-related disease with an incidence that increases dramatically with age [17]. The incidence increases twice for every decade after 55 [18, 19]. About three-quarters of all stroke events occur in people aged  ≥ 65 years. With ageing, brain microcirculation and macrocirculation undergo structural and functional changes. The microcirculation changes associated with increasing age are allegedly mediated by endothelial dysfunction and impaired brain autoregulation, and neurovascular coupling. Endothelial dysfunction increases nerve inflammation, disturbance of cerebral autoregulation leading to microvascular injuries, and impaired neurovascular coupling that decreases the cortical function [20].

More male patients were included in our study, both in the recurrent ischemic stroke group and the first-ever ischemic stroke group. Estrogen is protective in many tissues, including the brain, adipose tissue, heart, and blood vessels. In the context of ischemic stroke, estrogen has a highly neuroprotective nature in various levels, including suppressing the risk factor for stroke pathology through the anti-atherogenic effect in blood vessels and adipogenesis regulation. Estrogen also improves the stroke pathology through vasodilation of the coronary arteries and directing the nerve protection of the brain and glial cells during ischemia [21].

Gender difference in the epidemiology of ischemic stroke is age-dependent because of its influence on stroke risk and neurological outcome changes with age. In childhood and early adulthood, men have a higher incidence of ischemic stroke and worse functional outcomes than women. At middle age, the rate of ischemic stroke begins to rise among women, with the onset of menopause and the diminishing female sex hormones. After median age, the rate of stroke increases in women, with some reports showing higher stroke events in older women (age > 85 years) than older men [21].

In recurrent and first-ever ischemic strokes, hypertension is the most prominent modifiable risk factor, followed by dyslipidemia, diabetes, smoking, and hyperuricemia. The results of this study are the same as our previous research [22]. The mechanisms can explain the harmful effects of hypertension, dyslipidemia, DM, hyperuricemia, and smoking on the incidence of ischemic stroke. Hypertension influences cerebrovascular autoregulation by changing the mechanical characteristics of the brain blood vessels through remodeling and hardening and affecting the myogenic tone. Its changes in autoregulation specifically damage the periventricular white matter located in the border area between different arterial regions [4, 9, 23].

Dyslipidemia plays a complex role in cerebrovascular diseases [16]. There is a solid and direct relationship between total cholesterol, LDL, and ischemic stroke. Oxidized cholesterol, especially LDL, triggers inflammation and forms plaque on the blood vessel wall, blocking the arterial blood flow [24]. DM is associated with increased coagulation factors and hyperinsulinemia, which are essential in developing microangiopathy strokes. Macroangiopathy infarction occurs, because DM accelerates the atherosclerotic process of the large cerebral arteries[9]. Elevated uric acid levels are associated with the incidence and development of atherosclerosis. Uric acid can activate inflammasome proteins causing cell damage [25]. Most smokers experience stroke since smoking affects thrombosis and facilitates platelet aggregation by causing an imbalance between cerebral blood vessel coagulation and abnormal fibrinolysis. It changes the function of the blood–brain barrier and interferes with normal endothelial cell function [26].

We found that the variable differentiating the recurrent ischemic stroke from the first-ever ischemic stroke is the number of risk factors present. Recurrent ischemic stroke patients have more risk factors than patients who experience stroke for the first time. This is following previous research. A global population-based study has identified ten risk factors contributing to 88% of stroke risks. Since most individuals have several risk factors, they significantly increase the incidence of stroke [20, 27]. One explanation is that patients with multiple risk factors are difficult to control their risk factors; thus, they would be susceptible to recurring strokes. Stroke is stated to be a result of long-term exposure to co-existing risk factors [28]. Another factor is related to the pathological process underlying ischemic stroke caused by atherosclerosis. Thrombus formation in ruptured atherosclerotic plaques is the pathological basis of acute arterial thrombotic events [29, 30]. Several risk factors play an essential role in developing atherosclerosis and plaque instability [31,32,33].

No significant difference is found in the location of infarction between recurrent ischemic stroke patients and first-ever ischemic stroke patients. Infarction of thrombotic ischemic stroke is most numerous in the subcortical area, mainly in the basal ganglia [12]. In contrast to cardioembolic infarcts, which often cause large-sized infarcts in the cortical area, including malignant infarcts in the middle cerebral artery [14, 34, 35]. Lipohyalinosis and medial hypertrophy secondary to atherosclerosis mainly occur in the lenticulostriate arteries of the media cerebral arteries and the perforating arteries of the anterior cerebral arteries (known as recurrent Heubner arteries). These two arteries transport blood to the basal ganglia [12].

Over the past decade, radiological findings have been extensively studied to predict recurrent strokes. However, most imaging studies investigate different study populations and outcome definitions, making it difficult to draw conclusions that can be generalized. When considering imaging features as predictors, moderate evidence was found for the relationship between recurrent ischemic stroke and old ischemic lesions. Although white matter lesions are associated with stroke recurrence, the role of imaging predictors is less established and has only been investigated for MRI. Further research is needed on imaging modalities to visualize the characteristics and causes of recurrent ischemic stroke [34].

A High NIHSS value during acute ischemic stroke is one factor that plays a role in the incidence of recurrent stroke [36]. NIHSS values are associated with thrombogenic plaque levels and a reflection of reperfusion, which may result in a higher risk of recurrence of acute ischemic stroke [37, 38]. In our study, although the mean and alteration of NIHSS values in first-ever stroke patients were better than the values in recurrent ischemic stroke patients , the difference is not statistically significant. Many factors affect the neurological outcome. In the acute phase of stroke, the strongest predictors of neurological outcome are stroke severity and age. Stroke severity can be evaluated by looking at nerve damages and observing the size and location of infarction on MRI or head CT. Other important factors that affect neurological outputs are epidemiological factors, comorbid conditions, stroke complications, and ischemic stroke mechanisms [7].

Recovery from an acute ischemic stroke is a multifaceted process, ranging from hours to months. In the acute phase of stroke, it is difficult to predict the final recovery rate due to the magnitude of heterogeneity during recovery in the first 3 months after a stroke. The longer the physiological assessment time from the stroke event, the better and more specific the prediction. For example, neurological conditions on day 4 predict better long-term neurological outcomes than those on the first day of ischemic stroke [39].

Our study has limitations, because it is a single-site, hospital-based study. Therefore, a more extensive multicenter study is needed. Based on the results of this study, we recommend further research in screening for risk factors to prevent or minimize the risk of stroke recurrence. The prospective ongoing studies are also necessary to assess the neurological outcome with existing modalities.

Conclusions

Our study showed that age and the number of risk factors correlate with recurrent ischemic strokes.

Availability of data and materials

The authors confirm that the data supporting the findings of this study are available within the article.

References

  1. Deb P, Sharma S, Hassan KM. Pathophysiologic mechanisms of acute ischemic stroke: an overview with emphasis on therapeutic significance beyond thrombolysis. Pathophysiology. 2010;17(3):197–218.

    Article  CAS  PubMed  Google Scholar 

  2. Liu R, Pan M-X, Tang J-C, Zhang Y, Liao H-B, Zhuang Y, Zhao D, Wan Q. Role of neuroinflammation in ischemic stroke. Neuroimmunol Neuroinflammation. 2017;4:158–66.

    Article  CAS  Google Scholar 

  3. Adams HP Jr, Biller J. Classification of subtypes of ischemic stroke: history of the trial of org 10172 in acute stroke treatment classification. Stroke. 2015;46(5):e114-117.

    Article  PubMed  Google Scholar 

  4. Nam KW, Kwon HM, Jeong HY, Park JH, Kim SH, Jeong SM. High neutrophil to lymphocyte ratios predict intracranial atherosclerosis in a healthy population. Atherosclerosis. 2018;269:117–21.

    Article  CAS  PubMed  Google Scholar 

  5. Feigin VL, Forouzanfar MH, Krishnamurthi R, Mensah GA. Global burden of stroke: an underestimate—Authors’ reply. Lancet. 2014;383(9924):1205–6.

    Article  PubMed  Google Scholar 

  6. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, Abraham J, Adair T, Aggarwal R, Ahn SY, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380(9859):2095–128.

    Article  PubMed  Google Scholar 

  7. Soliman RH, Oraby MI, Fathy M, Essam AM. Risk factors of acute ischemic stroke in patients presented to Beni-Suef University Hospital: prevalence and relation to stroke severity at presentation. Egypt J Neurol Psychiatr Neurosurg. 2018;54(1):8.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Kim SY, Guevara JP, Kim KM, Choi HK, Heitjan DF, Albert DA. Hyperuricemia and risk of stroke: a systematic review and meta-analysis. Arthritis Rheum. 2009;61(7):885–92.

    Article  PubMed  PubMed Central  Google Scholar 

  9. El-Gohary TM, Alshenqiti AM, Ibrahim SR, Khaled OA, Ali ARH, Ahmed MS. Risk factors and types of recurrent stroke: a Saudi hospital based study. J Phys Ther Sci. 2019;31(10):743–6.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Kwakkel G, Veerbeek JM, van Wegen EE, Nijland R, der Harmeling-vanWelDippel BCDW. Predictive value of the NIHSS for ADL outcome after ischemic hemispheric stroke: does timing of early assessment matter? J Neurol Sci. 2010;294(1–2):57–61.

    Article  PubMed  Google Scholar 

  11. Kogan E, Twyman K, Heap J, Milentijevic D, Lin JH, Alberts M. Assessing stroke severity using electronic health record data: a machine learning approach. BMC Med Inform Decis Mak. 2020;20(1):8.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Caplan LR, Caplan LR. Caplan’s stroke: a clinical approach. Boston: Butterworth Heinemann; 2017.

    Google Scholar 

  13. Gąsiorek P, Banach M, Maciejewski M, Głąbiński A, Paduszyńska A, Bielecka-Dąbrowa A. Stroke as a result of cardioembolism—characteristic features in the context of diagnostic methods and secondary prevention. Folia Cardiol. 2018;13(1):21–8.

    Google Scholar 

  14. Arboix A, Alio J. Cardioembolic stroke: clinical features, specific cardiac disorders and prognosis. Curr Cardiol Rev. 2010;6(3):150–61.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Powers WJ, Rabinstein AA, Ackerson T, Adeoye OM, Bambakidis NC, Becker K, Biller J, Brown M, Demaerschalk BM, Hoh B, et al. 2018 Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2018;49(3):e46–110.

    Article  PubMed  Google Scholar 

  16. Yaghi S, Elkind MS. Lipids and cerebrovascular disease: research and practice. Stroke. 2015;46(11):3322–8.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Cui Q, Naikoo NA. Modifiable and non-modifiable risk factors in ischemic stroke: a meta-analysis. Afr Health Sci. 2019;19(2):2121–9.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Kocaman G, Duruyen H, Kocer A, Asil T. Recurrent ischemic stroke characteristics and assessment of sufficiency of secondary stroke prevention. Noro Psikiyatr Ars. 2015;52(2):139–44.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Boehme AK, Esenwa C, Elkind MS. Stroke risk factors, genetics, and prevention. Circ Res. 2017;120(3):472–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Yousufuddin M, Young N. Aging and ischemic stroke. Aging. 2019;11(9):2542–4.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Roy-O’Reilly M, McCullough LD. Age and sex are critical factors in ischemic stroke pathology. Endocrinology. 2018;159(8):3120–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Juli C, Heryaman H, Nazir A, Ang ET, Defi IR, Gamayani U, Atik N. The lymphocyte depletion in patients with acute ischemic stroke associated with poor neurologic outcome. Int J Gen Med. 2021;14:1843–51.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Mir MA, Al-Baradie SR, Malik D, Alhussainawi DM. Pathophysiology of stroke. In: Mir MA, editor. Recent Advances in Stroke Therapeutics. 1st ed. Nova Science Publishers, Inc; 2014, p. 25–80.

    Google Scholar 

  24. Xu T, Zhang JT, Yang M, Zhang H, Liu WQ, Kong Y, Xu T, Zhang YH. Dyslipidemia and outcome in patients with acute ischemic stroke. Biomed Environ Sci. 2014;27(2):106–10.

    CAS  PubMed  Google Scholar 

  25. Yu W, Cheng JD. Uric acid and cardiovascular disease: an update from molecular mechanism to clinical perspective. Front Pharmacol. 2020;11:582680.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Fekadu G, Chelkeba L, Kebede A. Risk factors, clinical presentations and predictors of stroke among adult patients admitted to stroke unit of Jimma university medical center, south west Ethiopia: prospective observational study. BMC Neurol. 2019;19(1):187.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Dufouil C, Beiser A, McLure LA, Wolf PA, Tzourio C, Howard VJ, Westwood AJ, Himali JJ, Sullivan L, Aparicio HJ, et al. Revised framingham stroke risk profile to reflect temporal trends. Circulation. 2017;135(12):1145–59.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Kim YD, Jung YH, Saposnik G. Traditional risk factors for stroke in East Asia. J Stroke. 2016;18(3):273–85.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Margetic S. Inflammation and haemostasis. Biochem Med. 2012;22(1):49–62.

    Article  CAS  Google Scholar 

  30. Hartman J, Frishman WH. Inflammation and atherosclerosis: a review of the role of interleukin-6 in the development of atherosclerosis and the potential for targeted drug therapy. Cardiol Rev. 2014;22(3):147–51.

    Article  PubMed  Google Scholar 

  31. Raggi P, Genest J, Giles JT, Rayner KJ, Dwivedi G, Beanlands RS, Gupta M. Role of inflammation in the pathogenesis of atherosclerosis and therapeutic interventions. Atherosclerosis. 2018;276:98–108.

    Article  CAS  PubMed  Google Scholar 

  32. Li Q, Zhou Y, Dong K, Wang A, Yang X, Zhang C, Zhu Y, Wu S, Zhao X. The association between serum uric acid levels and the prevalence of vulnerable atherosclerotic carotid plaque: a cross-sectional study. Sci Rep. 2015;5:10003.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Savoia C, Schiffrin EL. Vascular inflammation in hypertension and diabetes: molecular mechanisms and therapeutic interventions. Clin Sci. 2007;112(7):375–84.

    Article  CAS  Google Scholar 

  34. Kauw F, van Ommen F, Bennink E, Cramer MJ, Kappelle LJ, Takx RA, Velthuis BK, Viergever MA, Wouter van Es H, Schonewille WJ, et al. Early detection of small volume stroke and thromboembolic sources with computed tomography: rationale and design of the ENCLOSE study. Eur Stroke J. 2020;5(4):432–40.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Harrison JK, McArthur KS, Quinn TJ. Assessment scales in stroke: clinimetric and clinical considerations. Clin Interv Aging. 2013;8:201–11.

    PubMed  PubMed Central  Google Scholar 

  36. Zhuo Y, Wu J, Qu Y, Yu H, Huang X, Zee B, Lee J, Yang Z. Clinical risk factors associated with recurrence of ischemic stroke within two years: a cohort study. Medicine. 2020;99(26): e20830.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Zhang C, Zhao X, Wang C, Liu L, Ding Y, Akbary F, Pu Y, Zou X, Du W, Jing J, et al. Prediction factors of recurrent ischemic events in one year after minor stroke. PLoS ONE. 2015;10(3): e0120105.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Wouters A, Nysten C, Thijs V, Lemmens R. Prediction of outcome in patients with acute ischemic stroke based on initial severity and improvement in the first 24 h. Front Neurol. 2018;9:308.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Seitz RJ, Donnan GA. Recovery potential after acute stroke. Front Neurol. 2015;6:238.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We would like to thank all the staff members of Hasan Sadikin Hospital for their contribution during data collection.

Funding

This work was supported by grant from Universitas Padjadjaran and the Ministry of research, and Technology/BRIN of the Republic of Indonesia to NA. Universitas Padjadjaran, 1427/UN6.3.1/LT/2020

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Authors and Affiliations

  1. Doctoral Program, Faculty of Medicine, Padjadjaran University, Bandung, Indonesia

    Cep Juli, Henhen Heryaman &  Arnengsih

  2. Department of Neurology Dr. Hasan Sadikin General Hospital/Faculty of Medicine, Padjadjaran University, Bandung, Indonesia

    Cep Juli & Uni Gamayani

  3. Department of Anatomy, Yong Loo Lin School of Medicine, National University of Singapore, Bandung, Singapore

    Eng-Tat Ang

  4. Department of Physical Medicine and Rehabilitation, Faculty of Medicine, Dr. Hasan Sadikin General Hospital, Padjadjaran University, Bandung, Indonesia

    Irma Ruslina Defi

  5. Department of Biomedical Sciences, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia

    Nur Atik

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Contributions

CJ: planned the study, collected and analyzed the data. HH and A: equally participated in collection of the data. CJ, ETA, IRD, UG and NA: partook in draft and revision of manuscript. NA: planned and designed the whole study, read and approved the final manuscript. All authors read and approved the final manuscript.

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Correspondence to Nur Atik.

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The protocol study was approved by the Padjadjaran University Ethics Research Committee (No. 440/UN6.KEP/EC/2020) and conducted in compliance with the Declaration of Helsinki.

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Juli, C., Heryaman, H., Arnengsih et al. The number of risk factors increases the recurrence events in ischemic stroke. Eur J Med Res 27, 138 (2022). https://doi.org/10.1186/s40001-022-00768-y

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European Journal of Medical Research

ISSN: 2047-783X

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