|
 
 |
| ORIGINAL ARTICLE |
|
| Year : 2020 | Volume
: 6
| Issue : 2 | Page : 77-83 |
|
Copeptin: Short term prognostic biomarker in ischemic stroke among Indian - A prospective cohort study
Kevin T John1, Ali Hasan Faiz Karnam1, Ravichandran Kandasamy2, Rajalaxmi Sarangi3, Nayyar Iqbal1, Sunil K Nanda4
1 Department of General Medicine, PIMS, Puducherry, India
2 Department of Statistics, PIMS, Puducherry, India
3 Department of Biochemistry, KIMS, Bhubaneshwar, Odisha, India
4 Department of Biochemistry, PIMS, Puducherry, India
| Date of Submission | 04-May-2020 |
| Date of Decision | 29-May-2020 |
| Date of Acceptance | 12-Jun-2020 |
| Date of Web Publication | 21-Dec-2020 |
Correspondence Address:
Ali Hasan Faiz Karnam
Department of General Medicine, PIMS, Puducherry
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jcrsm.jcrsm_31_20
Background: Copeptin a precursor of arginine vasopressin is associated with stroke severity and functional outcome among Chinese and Western population. We aimed to evaluate copeptin as a prognostic marker in patients with acute ischemic stroke (IS) by assessing the functional outcome of patients after 90 days of stroke.
Methodology: Sixty out of 88 adult patients were included in the study who presented with symptoms of acute stroke within 72 h of onset. Patients with intracranial hemorrhage and transient ischemic attack were excluded from the study. Spearman correlation method was applied to see correlation between copeptin and National Institutes of Health Stroke Scale (NIHSS) calculated at the time of admission. Logistic regression analysis was done to identify independent risk variables for the functional outcome (modified Rankin Scale) of IS. Receiver operating characteristic curve was calculated to find the sensitivity and specificity of copeptin.
Results: Out of 60 patients, 62% were male and 38% were female. The mean age was 57.7 ± 14.8 years. Copeptin levels were high among patients with severe stroke (14.9 ± 0.8). Copeptin at the time of admission showed a significant positive correlation with stroke severity (r = 0.702, P < 0.001). The multiple logistic regression analysis showed that copeptin level (odds ratio [OR]: 3.05, 95% confidence interval [CI]: 1.00–9.32, P = 0.05) and NIHSS at day 0 (OR: 7.05, 95% CI: 1.39–35.78, P = 0.02) were independently associated with functional outcome at day 90. The area under the curve was 0.898, with a sensitivity of 77.8% and specificity of 97.6%, P < 0.001.
Conclusion: Copeptin is independently associated with the severity and functional outcome of IS among Indians. A larger cohort study may be required to further establish its association.
Keywords: Copeptin, ischemic stroke, National Institutes of Health Stroke Scale, stroke severity
How to cite this article:
John KT, Faiz Karnam AH, Kandasamy R, Sarangi R, Iqbal N, Nanda SK. Copeptin: Short term prognostic biomarker in ischemic stroke among Indian - A prospective cohort study. J Curr Res Sci Med 2020;6:77-83 |
How to cite this URL:
John KT, Faiz Karnam AH, Kandasamy R, Sarangi R, Iqbal N, Nanda SK. Copeptin: Short term prognostic biomarker in ischemic stroke among Indian - A prospective cohort study. J Curr Res Sci Med [serial online] 2020 [cited 2023 May 30];6:77-83. Available from: https://www.jcrsmed.org/text.asp?2020/6/2/77/304202 |
| Introduction | |  |
Stroke is one of the leading causes of disability and mortality in India. It is one of the causes for chronic disability leading to loss of working force in the country in terms of disease-adjusted life year.[1] Various community-based studies conducted in India showed that the prevalence of stroke showed a huge variation of 147–922/100,000 across the country.[2],[3],[4],[5] The estimated age-adjusted prevalence rate for stroke ranges between 84/100,000 and 262/100,000 in rural and between 334/100,000 and 424/100,000 in urban areas.[6] Among the two major variants of stroke, a vast majority (87%) of cerebrovascular accidents are of ischemic origin.[7]
Management of stroke is time dependent, and any delay in initiation of therapy will lead to devastating effects such as permanent disability and even death. Thrombolysis and mechanical thrombectomy are well-established treatments at the early stage of acute ischemic stroke (IS).[8] As both the treatment modalities are time bound, early risk assessment of the severity of the disease and prognosis would play a pivotal role in optimizing the care for those who would benefit maximum from early intervention.
The disease severity is assessed by a validated tool like the National Institutes of Health Stroke Scale (NIHSS) developed by the National Institute of Neurological Disorders and Stroke, and functional outcome at 90 days is assessed by the modified Rankin Scale (mRS 90). NIHSS uses 11 neurological parameters to assess the severity. The severity is divided into minor, moderate, moderate to severe, and severe according to the score 1–4, 5–15, 16–20, and 21–42, respectively. mRS uses the score from 0 to 6, good or favorable outcome is considered if score is between 0 and 2, unfavorable outcome is 3–5, and death is 6.
Copeptin is a 39-amino acid glycopeptide that comprises the C-terminal part of the preprovasopressin. It is secreted from the hypothalamus in healthy as well as in critically ill individuals. It has been used as a diagnostic and prognostic marker in various disease conditions such as acute myocardial infarction, heart failure, sepsis, septic shock, and ischemic and hemorrhagic stroke.[9] The normal value of copeptin in healthy individuals ranges from 0.76 to 5.0 ng/ml (1.7–11.25 pmol/L).[10],[11]
Copeptin is a stable peptide and is released in equimolar ratio to arginine vasopressin (AVP). It is stable at room temperature and hence can be easily measured with automated assays.[12] In contrast to other neuronal hormones, copeptin directly reflects the intracerebral process and is released in systemic circulation bypassing the blood–brain barrier. AVP level is found to rise in patients during IS and is found to have shown correlations with the level of severity of the stroke.[13],[14] AVP, being an unstable peptide with a short half-life, is difficult to estimate. Copeptin can be used as a prognostic marker since it is more stable and can be easily measured as compared to AVP.[15]
Copeptin has found to be useful as a prognostic marker for functional outcome and death in patients with stroke.[16] The prognostic accuracy of copeptin in stroke patients was superior to that of other commonly measured laboratory parameters, such as blood glucose, white blood cell count and C-reactive protein, as well as clinical measures (e.g., blood pressure and temperature). Several studies done in China have shown the positive role of copeptin in prognosis of stroke.[17] There is no study done in India to the best of our knowledge to show the association of copeptin with severity and outcome in IS. Hence, the objective of this study was to evaluate copeptin as a prognostic marker in patients with acute IS by assessing the functional outcome of patients after 90 days of stroke.
| Methodology | | |
It is a cohort study, conducted at Pondicherry Institute of Medical Sciences, Puducherry, a tertiary care hospital in South India during a period of 1½ years from December 2014 till June 2016. All patients above 18 years of age presenting with acute IS within 7 days of onset were included in the study. Patients with onset of more than 7 days, hemorrhagic stroke, transient ischemic attack, seizure disorder, cerebral tumor, migraine, coronary artery disease, chronic kidney disease, and sepsis were excluded from the study.
The detailed history was collected from the patients satisfying inclusion and exclusion criteria. The demographic details, presenting complaint, time of onset of stroke, past history of comorbidities such as hypertension and type 2 diabetes mellitus, personal history, and family history were recorded. All patients were examined in detail, and their pulse, blood pressure, body mass index, and neurological deficit were recorded. NIHSS was calculated on the day of admission and again on the 5th day of admission by independent medical personnel trained for recording these details. mRS (90) was calculated after 90 days of follow-up period either on patient's follow-up visit to the outpatient department of our hospital or by contacting them over telephone. Computed tomography of the brain or magnetic resonance imaging of the brain was done to confirm the ischemic origin of stroke. Electrocardiogram along with blood investigations for copeptin, complete blood count, blood urea, creatinine, and lipid profile was done at the time of presentation to the hospital.
Hypertension among the participants was defined if the patient was a known hypertensive or was on any antihypertensive medication. Type 2 diabetes mellitus among the participants were defined if the participants were either already on treatment for diabetes or in hospital fasting blood glucose was > 126 mg/dL or postprandial blood glucose was > 200mg/dL or HbA1c was > 6.5%. Dyslipidemia was considered if participants were already on statins or if their total cholesterol was >250 mg/dL or low-density lipid profile was >100 mg/dL or triglyceride was >200 mg/dL.
NIHSS has a set of 11 items, and for each item, the score was given from 0 to 4 depending on the severity. The minimum score is 0 and maximum score is 42. The stroke can be classified into minor, moderate, moderate to severe, and severe according to score of 1–4, 5–15, 16–20, and 21–42, respectively. mRS was used to measure the clinical outcome of stroke. The scale is 0–6, 0 being no symptom and 6 being death. According to scale, outcome was classified as good if at 90-day follow-up the scale was between 0 and 2 and poor if the scale was between 3 and 6.
Copeptin was measured with an enzyme-linked immunosorbent assay (ELISA) kit at the time of admission, and the value was compared with NIHSS score of 0 and 5th day of admission and also with mRS at 90-day follow-up. The kit used for measuring copeptin level was developed by Shanghai Yehua Biological Technology Co. Ltd., Shanghai, China (Cat. No.: YHB0830Hu). It uses ELISA based on biotin double-antibody sandwich technology, and the value measured was expressed in nanogram/milliliter.
Statistical analysis
The sample size of 60 was estimated assuming 15 patients for each of the four independent risk variables: age, gender, hypertension, and type 2 diabetes mellitus. Data entry was done in Excel 2010, and analysis was done in SPSS version 20.1 (IBM SPSS Inc., Honk Kong). Descriptive data were presented as frequencies and percentages for categorical variables and mean and standard deviation for continuous variables. Fisher's exact test was used to test the association between variables. One-way ANOVA followed by post hoc tests of Tukey's honestly significant difference (HSD) method was applied to compare copeptin values according to NIHSS at day 0. Mann–Whitney U-test was applied to compare copeptin values according to mRS as the copeptin values were not normally distributed. Receiver operating characteristic (ROC) analysis was carried out to determine the cutoff values of copeptin as a biomarker in the prognosis of acute IS, and corresponding sensitivity and specificity were obtained. Simple and multiple logistic regression analyses were done to identify the independent risk variables of functional outcome of stroke. Factors which are significant at ≤0.1 in simple logistic regression were considered for multiple logistic regressions. Spearman correlation method was applied to see the correlation between copeptin and NIHSS at day 0. For all tests, P = 0.05 or less was considered for statistical significance.
Ethical consideration
The study was conducted with prior approval of the Institute Ethical committee (RC/14/67). The written consent was taken from the participants or from the next of kin before inclusion in the study.
| Results | | |
A total of 88 patients presented with symptoms of stroke during the study period. Twenty-three out of 88 had hemorrhagic stroke. Two patients had transient ischemic attack and three patients were lost to follow-up as their contact details were incorrect. Hence, a total of 60 participants were included in the study, out of which 37 (62%) were male and 23 (38%) were female. The mean age of the participants was 57.7 ± 14.8 years. Systemic hypertension (78%) and type 2 diabetes mellitus (57%) were common modifiable risk factors associated with stroke in our study population. Other modifiable risk factors associated with stroke were smoking and dyslipidemia, 53% and 48%, respectively. Copeptin levels were found to be high in males as compared to females. Patients with age more than or equal to 70 years had increased copeptin level. It was also found to be high among patients with atrial fibrillation as compared to other risk factors [Table 1].
Stroke severity defined by NIHSS score calculated on admission showed that 50% (30) had minor stroke, 43% (26) had moderate stroke, and 7% (4) had severe stroke. None of our patients had moderate-to-severe stroke. The copeptin levels were found to be high in severe stroke (14.9 ± 0.8) compared to minor (6.4 ± 2.2) and moderate stroke (8.7 ± 1.8), and these were statistically significant (P < 0.001) by one-way ANOVA [Figure 1]. Post hoc tests by Tukey's HSD method showed a significant difference between severe and minor stroke (P < 0.001), severe and moderate stroke (P < 0.001), and minor and moderate stroke (P < 0.001). | Figure 1: National Institutes of Health Stroke Scale score and mean copeptin level
Click here to view |
Deaths were recorded in all the patients who had severe stroke at admission. Four patients who had moderate stroke initially showed clinical improvement and were reclassified as minor stroke on the 5th day of admission [Table 2]. Functional outcome at 90 days calculated by mRS 90 showed that 42 (70%) participants had good outcome and 18 (30%) had poor outcome including 4 (7%) deaths. Seventy-one percent of poor outcome and four deaths were recorded among males [Table 3]. | Table 2: Stroke severity according to the National Institutes of Health Stroke Scale at time of admission (day 0) and on 5th day of admission
Click here to view |
 | Table 3: Modified Rankin Scale at day 90 of stroke among males and females
Click here to view |
The outcome of stroke measured as mRS at day 90 of stroke significantly (P < 0.001) depended on the stroke severity measured as NIHSS at day 0 [Table 4]. | Table 4: Stroke severity (National Institutes of Health Stroke Scale at day 0) and modified Rankin Scale at day 90 of stroke
Click here to view |
There is a statistically significant difference (P < 0.001) in copeptin levels between good and poor outcomes as classified by mRS 90 (6.7 ± 2.0 vs. 10.8 ± 2.7). Participants who had higher copeptin level at the time of admission had poor outcome at day 90. Copeptin done at the time of admission showed a significant positive correlation with stroke severity (r = 0.702, P < 0.001) [Figure 2]. | Figure 2: Scatter plot showing correlation between copeptin levels (ng/ml) and stroke severity (r = 0.702, P < 0.001)
Click here to view |
The multiple logistic regression analysis showed that copeptin level (odds ratio [OR] = 3.05, 95% confidence interval [CI]: 1.00–9.32, P = 0.05) and NIHSS at day 0 (OR = 7.05, 95% CI: 1.39–35.78, P = 0.02) were independently associated with functional outcome at day 90 [Table 5]. | Table 5: Results of logistic regression analysis of various variables with functional outcome at day 90
Click here to view |
Receiver Operating characteristic curve for Copeptin at 9.7 ng/ml has area under the curve (AUC) of 0.898 (95% CI: 0.799, 0.997) P < 0.001. Corresponding to that cutoff value, the sensitivity is 77.8% and specificity is 97.6%. A similar analysis based on stroke severity (NIHSS at day 0) showed that at score of 7.0, the AUC is 0.954 (95% CI: 0.897–1.000; P < 0.001) with sensitivity of 83.3% and specificity of 95.2% [Figure 3]. | Figure 3: Receiver operating characteristic curve for the National Institutes of Health Stroke Scale score at day 0 and copeptin (ng/ml)
Click here to view |
| Discussion | | |
Stroke is one of the leading causes of noncommunicable disease in India and perhaps one of the leading causes for morbidity and mortality in urban population. The prevalence of IS is much higher as compared to hemorrhagic stroke. The increased prevalence of IS among urban population is contributed to demographic shift and various risk factors such as sedentary lifestyle, smoking, alcohol, and diet. These risk factors, in turn, increase the risk of atherosclerosis and cause IS.[1],[2]
Stroke is more common among middle and older age groups. In our study, we also observed that 70% of the participants were from these age groups similar to the study done by Pandian et al.,[18] although a study by Kissela et al.[19] showed that the incidence of stroke has increased among younger people between the ages of 20 and 54 years. The study by Kissela et al. contributed this to obesity and increase in the incidence of diabetes mellitus along with other risk factors among Western population.
Stroke rates among males are higher and are age specific as compared to females, but females have poor functional outcomes and quality of life.[20] However, a study by Pu et al. done in China showed no major difference among the gender with respect to incidence and outcome.[21] A study done in India also showed no difference in incidence and outcome of stroke among gender.[22] This is may be due to different lifestyles of Asians as compared to Western population.
Several studies have shown that copeptin level was higher in patients with severe stroke (NIHSS >21) and high level was also associated with poor outcome (mRS >3).[17],[23] In our study, there was a significant difference (P < 0.001) in copeptin levels measured in minor stroke (6.4 ± 2.2 ng/ml), moderate stroke (8.7 ± 1.8), and severe stroke (14.9 ± 0.8 ng/ml). The outcome of stroke also depended significantly on stroke severity (P < 0.001). Mortality was 100% in patients with severe stroke (NIHSS 21–42), and those with minor stroke (NIHSS 1–4) recovered well. In multivariate analysis, the copeptin and NIHSS were found to be independent prognostic factors in IS (OR: 3.05, 95% CI: 1.00–9.32 and OR: 7.05, 95% CI: 1.39–35.78, respectively), with NIHSS being slightly superior to copeptin (P = 0.02 vs. 0.05). A study by Urwyler et al.[24] and Zweifel et al.[16] showed a similar association.
In a meta-analysis[14] including more than 2000 patients showed that plasma copeptin level was elevated in patients with unfavorable outcome among both hemorrhagic and IS, although the OR was higher in IS as compared to hemorrhagic stroke (adjusted OR [aOR] 2.55; 95% CI: 1.97–3.31; I2 = 0% vs. aOR 1.36; 95% CI 1.13–1.64; I2 = 77%, respectively). Copeptin level and mortality were highly associated among IS (hazard ratio: 3.50; 95% CI: 1.45–8.46; I2 = 72%).[25],[26] In our study, AUC of copeptin was 0.898 (95% CI: 0.799–0.997; P < 0.001), with sensitivity of 77.8% and specificity of 97.6% with cutoff value of copeptin at 9.7 ng/ml, when combined with NIHSS at score of 7, the AUC improved to 0.954 (95% CI: 0.897–1.000; P < 0.001), with sensitivity of 83.3% and specificity of 95.2%. This was similar to the study done by Tu et al.[23] and Urwyler et al.[24] In a study by Urwyler et al., the combination of NIHSS and copeptin improved the AUC to 0.79 (95% CI, 0.71–0.87; P < 0.05).
Stroke being one of the most common noncommunicable diseases in India, no study has been done to look for copeptin as a prognostic marker among IS among Indian population. Kumar et al. are conducting a prospective cohort study among patients with intracranial hemorrhage, for which result is awaited.[27] Our study is one of the first studies done from India on role of copeptin as a prognostic marker among IS.
The limitation of this study was short duration follow-up of 90 days. Prognostic value of copeptin cannot be established for long-term follow-up from this study. The sample size taken in this study was small as it was estimated on the basis of prevalence of IS in our region and common modifiable and nonmodifiable risk factor; hence, the generalizability of the result is limited. A larger clinical trial is needed to suggest its use in routine clinical practice. Outcome at day 90 (mRS 90) was not done by an independent observer; hence, there is a possibility of observer bias in the study.
| Conclusion | | |
Copeptin can be an important independent biomarker for prognosis of outcome in IS. Patients with high level of copeptin at the time of admission may have poor outcome and even death. The high sensitivity and specificity of copeptin make it an important prognostic tool for the patient with IS. A larger clinical trial is needed to suggest its use in routine clinical practice. In the absence of copeptin, NIHSS can still be used for the prognosis of IS.
Acknowledgment
We acknowledge Dr. Georgi Abraham and Dr. Jayanthi Arulneyam for their scientific advice for this study.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Pandian JD, Sudhan P. Stroke epidemiology and stroke care services in India. J Stroke 2013;15:128-34.  |
| 2. | Prasad K, Vibha D, Meenakshi. Cerebrovascular disease in South Asia - Part I: A burning problem. JRSM Cardiovasc Dis. 2012;1. doi:10.1258/cvd.2012.012025. |
| 3. | Bansal BC, Prakash C, Jain AL, Brahmanandam KR. Cerebro-vascular disease in young individuals below the age of 40 years. Neurol India 1973;21:11-8. |
| 4. | Banerjee TK, Mukherjee CS, Sarkhel A. Stroke in the urban population of Calcutta-an epidemiological study. Neuroepidemiology 2001;20:201-7. |
| 5. | Bharucha NE, Bharucha EP, Bharucha AE, Bhise AV, Schoenberg BS. Prevalence of stroke in the Parsi community of Bombay. Stroke 1988;19:60-2. |
| 6. | Banerjee TK, Das SK. Fifty years of stroke researches in India. Ann Indian Acad Neurol 2016;19:1-8. [ PUBMED] [Full text] |
| 7. | Lloyd-Jones D, Adams RJ, Brown TM, Carnethon M, Dai S, De Simone G, et al. Heart disease and stroke statistics – 2010 Update: A report from the American Heart Association. Circulation 2010;121:e46-e215. |
| 8. | Emberson J, Lees KR, Lyden P, Blackwell L, Albers G, Bluhmki E, et al. Effect of treatment delay, age, and stroke severity on the effects of intravenous thrombolysis with alteplase for acute ischaemic stroke: A meta-analysis of individual patient data from randomised trials. Lancet 2014;384:1929-35. |
| 9. | Dobsa L, Edozien KC. Copeptin and its potential role in diagnosis and prognosis of various diseases. Biochem Med (Zagreb) 2013;23:172-90. |
| 10. | Struck J, Morgenthaler NG, Bergmann A. Copeptin, a stable peptide derived from the vasopressin precursor, is elevated in serum of sepsis patients. Peptides 2005;26:2500-4. |
| 11. | Battista S, Audisio U, Galluzzo C, Maggiorotto M, Masoero M, Forno D, et al. Assessment of diagnostic and prognostic role of copeptin in the clinical setting of sepsis. Biomed Res Int 2016;2016:1-7. doi.org/10.1155/2016/3624730. |
| 12. | Nickel CH, Bingisser R, Morgenthaler NG. The role of copeptin as a diagnostic and prognostic biomarker for risk stratification in the emergency department. BMC Med 2012;10:7. |
| 13. | Katan M, Morgenthaler NG, Dixit KC, Rutishauser J, Brabant GE, Müller B, et al. Anterior and posterior pituitary function testing with simultaneous insulin tolerance test and a novel copeptin assay. J Clin Endocrinol Metab 2007;92:2640-3. |
| 14. | Choi KS, Kim HJ, Chun HJ, Kim JM, Yi HJ, Cheong JH, et al. Prognostic role of copeptin after stroke: A systematic review and meta-analysis of observational studies. Sci Rep 2015;5:11665. |
| 15. | Katan M, Morgenthaler N, Widmer I, Puder JJ, König C, Müller B, et al. Copeptin, a stable peptide derived from the vasopressin precursor, correlates with the individual stress level. Neuro Endocrinol Lett 2008;29:341-6. |
| 16. | Zweifel C, Katan M, Schuetz P, Siegemund M, Morgenthaler NG, Merlo A, et al. Copeptin is associated with mortality and outcome in patients with acute intracerebral hemorrhage. BMC Neurol 2010;10:34. |
| 17. | Xu Q, Tian Y, Peng H, Li H. Copeptin as a biomarker for prediction of prognosis of acute ischemic stroke and transient ischemic attack: A meta-analysis. Hypertens Res 2017;40:465-71. |
| 18. | Pandian JD, Singh G, Kaur P, Bansal R, Paul BS, Singla M, et al. Incidence, short-term outcome, and spatial distribution of stroke patients in Ludhiana, India. Neurology 2016;86:425-33. |
| 19. | Kissela BM, Khoury JC, Alwell K, Moomaw CJ, Woo D, Adeoye O, et al. Age at stroke: Temporal trends in stroke incidence in a large, biracial population. Neurology 2012;79:1781-7. |
| 20. | Reeves MJ, Bushnell CD, Howard G, Gargano JW, Duncan PW, Lynch G, et al. Sex differences in stroke: Epidemiology, clinical presentation, medical care, and outcomes. Lancet Neurol 2008;7:915-26. |
| 21. | Pu Y, Wei N, Yu D, Wang Y, Zou X, Soo YOY, et al. Sex differences do not exist in outcomes among stroke patients with intracranial atherosclerosis in China: Subgroup analysis from the chinese intracranial atherosclerosis study. Neuroepidemiology 2017;48:48-54. |
| 22. | Kaur P, Verma SJ, Singh G, Bansal R, Paul BS, Singla M, et al. Stroke profile and outcome between urban and rural regions of Northwest India: Data from Ludhiana population-based stroke registry. Eur Stroke J 2017;2:377-84. |
| 23. | Tu WJ, Chen H, Cui LH. Prognostic value of copeptin in patients with acute ischemic stroke. Iran J Neurol 2011;10:58. |
| 24. | Urwyler SA, Schuetz P, Fluri F, Morgenthaler NG, Zweifel C, Bergmann A, et al. Prognostic value of copeptin: One-year outcome in patients with acute stroke. Stroke 2010;41:1564-7. |
| 25. | Zhang JL, Yin CH, Zhang Y, Zhao LB, Fu HJ, Feng JC. Plasma copeptin and long-term outcomes in acute ischemic stroke. Acta Neurol Scand 2013;128:372-80. |
| 26. | De Marchis GM, Katan M, Weck A, Fluri F, Foerch C, Findling O, et al. Copeptin adds prognostic information after ischemic stroke: Results from the CoRisk study. Neurology 2013;80:1278-86. |
| 27. | Kumar A, Kumar P, Misra S, Sagar R, Kathuria P, Vibha D, et al. Biomarkers to enhance accuracy and precision of prediction of short-term and long-term outcome after spontaneous intracerebral haemorrhage: A study protocol for a prospective cohort study. BMC Neurol 2015;15:136. |
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]
|
Search Pubmed for