Abstract
To estimate National Institutes of Health Stroke Scale (NIHSS) grading of stroke patients with retinal characteristics.
A cross-sectional study was conducted in Shenzhen Traditional Chinese Medicine Hospital. Baseline information and retinal photos were collected within 2 weeks of admission. An NIHSS score was measured for each patient by trained doctors. Patients were classified into 0 to 4 score group and 5 to 42 score group for analysis. Three multivariate logistic models, with traditional clinical characteristics alone, with retinal characteristics alone, and with both, were built.
For clinical characteristics, hypertension duration is statistically significantly associated with higher NIHSS score (P = .014). Elevated total homocysteine levels had an OR of 0.456 (P = .029). For retinal characteristics, the fractal dimension of the arteriolar network had an OR of 0.245 (P < .001) for the left eyes, and an OR of 0.417 (P = .009) for right eyes. The bifurcation coefficient of the arteriole of the left eyes had an OR of 2.931 (95% CI 1.573–5.46, P = .001), the nipping of the right eyes had an OR of 0.092 (P = .003) showed statistical significance in the model.
The area under receiver-operating characteristic curve increased from 0.673, based on the model with clinical characteristics alone, to 0.896 for the model with retinal characteristics alone and increased to 0.931 for the model with both clinical and retinal characteristics combined.
Retinal characteristics provided more information than clinical characteristics in estimating NIHSS grading and can provide us with an objective method for stroke severity estimation.
Keywords: National Institutes of Health Stroke Scale, retinal features, stroke severity estimation
1. Introduction
Stroke is the second leading cause of death and a major cause of disability, worldwide. In China, stroke is the leading cause of death. The incidence of the first stroke in adults aged 40 to 74 has increased from 189 per 100,000 in 2002 to 379 per 100,000 in 2013, with an annual increase of 8.3%.[1–3] The burden of stroke is still increasing due to the aging population.[4] The neurological impairments, emotional and social consequences, and the high risk for recurrence can cause great physical, social, and psychological burdens on stroke patients and their families.[5] Since the mortality and 5-year readmission rates were different for the severity of the different stroke patients, the measurement of stroke severity is critical for stroke management and secondary prevention.[6] Accurate and easy measurement of stroke severity can help with stroke management and secondary prevention since the mortality and 5-year readmission rates of stroke patients differ according to stroke severity.[6]
Originally developed in 1989, the National Institutes of Health Stroke Scale (NIHSS) is a 15-item impairment scale for stroke severity measurement[7] and is now widely used as a standardized evaluation of symptoms and signs for strokes.[8] The scoring range is 0 to 42 points, with higher numbers indicating greater severity. A score of <5 represents no stroke symptoms or a minor stroke, a score of 5 to 15 represents a moderate stroke, a score of 16 to 20 represents a moderate to severe stroke, and a score of 21 to 42 represents a severe stroke. Baseline NIHSS scores have been reported to strongly predict major clinical outcomes after stroke.[9] Stroke patients with a score of no >4 are highly likely to have good clinical outcomes.[10] A 1-point increase in a patient's baseline NIHSS score can lead to an increase in the probability of a poor 2-year postoperative outcome, by 28.5%.[11] It can also help with estimating the probability of each stroke subtype.[12]
Retinal vessels at the back of the eye are the only directly visible vessels in the whole body; they represent a unique site where the microcirculation can be imaged directly,[13] providing an opportunity to study, in vivo, the structure and pathology of the circulation. They can also help to detect changes in microvasculature relating to the development of cardiovascular disease.[14] Retinal vascular changes are a summary marker of a patient's lifetime exposure to risk factors,[15] and many retinal characteristics are related to strokes and show great value in stroke recurrence prediction.[16,17] Currently, no research has investigated the relationship between retinal characteristics and the NIHSS grading.
2. Materials and methods
2.1. Study population
We conducted a cross-sectional study in Shenzhen Traditional Chinese Medicine Hospital from September 2018 to March 2019. A total of 165 stroke patients were enrolled in our study. Inclusion criteria included the clear diagnosis of stroke with computed tomography or cranial magnetic resonance scan results, an age of 30 to 80 years, and an adequate sitting balance to carry out the retinal photography. Patients who were clinically unstable, required close monitoring, or who had an eye disease that had influenced the retinal vessel structures were excluded from our study.
2.2. Measurement and definition
Upon admission, baseline information including age, sex, medical history, physical examination result, and laboratory test results was collected by trained doctors. All patients underwent detailed radiographic evaluations, including a cranial magnetic resonance (MR) scan and a duplex color Doppler ultrasound or contrast-enhanced MR angiography. The NIHSS score was measured by their doctors in charge within 24 hours of admission.[8] Retinal photography was carried out within 2 weeks of admission.
Body mass index was calculated as weight (kg) divided by the square of height (m). Diabetes mellitus (DM) was defined as a fasting blood glucose concentration >7.0 mmol/L or an glycated hemoglobin value >6.5%; patients with a history of diabetes were also labeled as having DM. Hypertension was defined as a systolic blood pressure >140 mmHg, a diastolic blood pressure >90 mmHg, or patients with a history of hypertension. Dyslipidemia was defined as total cholesterol (TC) ≥240 mg/L, triglyceride (TG) ≥200 mg/dL, high-density lipoprotein cholesterol <40 mg/dL, low-density lipoprotein cholesterol ≥160 mg/dL, or as patients with a history of dyslipidemia. Details regarding measuring the characteristics of retinal imaging analysis have been documented elsewhere.[17] Briefly, characteristics related to arteriole-venous nicking, arteriole occlusion, hemorrhages, exudates, tortuosity, bifurcation coefficients, asymmetry of branches, and bifurcation angles were calculated automatically using the automatic retinal image analysis system. Retinal information for both eyes were used in our analysis.
2.3. Statistical analysis
Quantitative variables were expressed as the mean ± standard deviation, and categorical variables were expressed as counts with percentages. For univariate analysis, we conducted 2 independent samples t tests to compare continuous data and used chi-square tests for categorical data. Three multivariate logistic models were developed (namely: Model 1: including only clinical characteristics, Model 2: including only retinal characteristics, and Model 3: including both clinical and retinal characteristics) to further investigate the associations between retinal characteristics and NIHSS score grading. Classification accuracy and the area under the curve of the receiver operating characteristic (ROC) were measured for each model. The cut-off point for the NIHSS score was 4, and a value of no >4 was defined as being indicative of a minor stroke.[18]
All statistical analyses were performed using SPSS 24.0(SPSS Inc., IBM, Chicago, IL,USA). Two-tailed P values of <.05 were considered statistically significant.
3. Results
The general characteristics of the study participants are summarized in Table 1. The average age was 54.88 standard deviation: ±11.09) years; there were more men (83.03%) than women and more ischemic stroke patients (70.3%) than hemorrhage stroke patients (27.88%). The average NIHSS score was 4.92 (standard deviation: ±2.80), and nearly 53.33% of patients had an NIHSS score >4. A total of 138, 107, and 62 patients had hypertension, diabetes, and dyslipidemia, respectively. The majority of study participants were non-smokers (67.27%) and non-drinkers (76.97%).
Table 1.
Baseline characteristics of the 165 patients.
| Baseline characteristics | |
| Age | 54.88 ± 11.09 |
| NIHSS score | 4.92 ± 2.80 |
| NIHSS score group | |
| <5 | 77 (46.67%) |
| ≥5 | 88 (53.33%) |
| Gender | |
| Female | 28 (16.97%) |
| Male | 137 (83.03%) |
| Stroke subtype | |
| Ischemic | 116 (70.3%) |
| Hemorrhage | 46 (27.88%) |
| Both | 3 (1.82%) |
| Hypertension | |
| No | 27 (16.36%) |
| Yes | 138 (83.64%) |
| Diabetes | |
| No | 107 (64.85%) |
| Yes | 58 (35.15%) |
| Dyslipidemia | |
| No | 103 (62.42%) |
| Yes | 62 (37.58%) |
| Smoking | |
| No | 111 (67.27%) |
| Former smoker | 37 (22.42%) |
| Current smoker | 17 (10.3%) |
| Drinking | |
| No | 127 (76.97%) |
| Former drinker | 29 (17.58%) |
| Current drinker | 9 (5.45%) |
In the univariate analysis, clinical characteristics such as age, body mass index, sex, stroke subtype, diabetes, smoking habit, and drinking habit, showed no statistical significance between the 2 groups (P > .05). Hypertension duration was positively associated with increasing NIHSS grade (P = .014), while elevated total homocysteine (tHcy) levels were negatively associated with increasing NIHSS grade (P = .006) (Table 2).
Table 2.
Comparison between 2 NIH stroke score groups.
| Characteristics | NIHSS ≤ 4 | NIHSS > 4 | P |
| Age | 55.23 ± 12.06 | 54.58 ± 10.24 | .710 |
| BMI | 23.8 ± 2.93 | 23.11 ± 3.11 | .146 |
| Gender | .380 | ||
| Female | 11 (39.29%) | 17 (60.71%) | |
| Male | 66 (48.18%) | 71 (51.82%) | |
| Stroke subtype | .403 | ||
| Ischemic | 57 (49.14%) | 59 (50.86%) | |
| Hemorrhage | 18 (39.13%) | 28 (60.87%) | |
| Both | 2 (66.67%) | 1 (33.33%) | |
| Hypertension | 0.063 | ||
| No | 17 (62.96%) | 10 (37.04%) | |
| Yes | 60 (43.48%) | 78 (56.52%) | |
| Years of hypertension | 0.014 | ||
| 0 | 17 (62.96%) | 10 (37.04%) | |
| <5 | 22 (36.07%) | 39 (63.93%) | |
| 5–10 | 7 (30.43%) | 16 (69.57%) | |
| >10 | 31 (57.41%) | 23 (42.59%) | |
| Diabetes | 0.338 | ||
| No | 47 (43.93%) | 60 (56.07%) | |
| Yes | 30 (51.72%) | 28 (48.28%) | |
| Dyslipidemia | 0.533 | ||
| No | 50 (48.54%) | 53 (51.46%) | |
| Yes | 27 (43.55%) | 35 (56.45%) | |
| Elevated tHcy levels | 0.006 | ||
| No | 44 (39.29%) | 68 (60.71%) | |
| Yes | 33 (62.26%) | 20 (37.74%) | |
| Smoking | 0.807 | ||
| No | 50 (45.05%) | 61 (54.95%) | |
| Former smoker | 18 (48.65%) | 19 (51.35%) | |
| Current smoker | 9 (52.94%) | 8 (47.06%) | |
| Drinking | 0.289 | ||
| No | 57 (44.88%) | 70 (55.12%) | |
| Former drinker | 17 (58.62%) | 12 (41.38%) | |
| Current drinker | 3 (33.33%) | 6 (66.67%) | |
| LCRAE | 14.8 ± 0.7 | 14.7 ± 0.63 | 0.334 |
| LCRVE | 21.24 ± 0.64 | 21.17 ± 0.64 | 0.485 |
| Laangle | 71.35 ± 1.81 | 71.55 ± 1.59 | 0.433 |
| LBCA | 1.62 ± 0.07 | 1.63 ± 0.08 | 0.196 |
| LBCV | 2.18 ± 7.75 | 2.07 ± 7.24 | 0.924 |
| Lacclusion | 0.1 ± 0.03 | 0.1 ± 0.04 | 0.908 |
| LNipping | 0.18 ± 1.01 | –0.16 ± 0.97 | 0.026 |
| LFDa | 0.31 ± 1.02 | –0.28 ± 0.9 | <0.001 |
| LFDv | 0.12 ± 1.07 | –0.11 ± 0.93 | 0.135 |
| RCRAE | 13.95 ± 0.62 | 13.91 ± 1.57 | 0.860 |
| RCRVE | 20.75 ± 0.73 | 20.76 ± 0.91 | 0.960 |
| Raangle | 70.39 ± 2.15 | 70.07 ± 2.02 | 0.334 |
| RBCA | 1.64 ± 0.06 | 2.44 ± 7.41 | 0.350 |
| RBCV | 1.28 ± 0.03 | 2.07 ± 7.38 | 0.349 |
| Racclusion | 0.11 ± 0.05 | 0.11 ± 0.06 | 0.837 |
| RNipping | –0.09 ± 0.6 | 0.07 ± 1.25 | 0.307 |
| RFDa | 0.09 ± 0.09 | –0.07 ± 1.36 | 0.304 |
| RFDv | 0.2 ± 1.09 | –0.18 ± 0.87 | 0.016 |
Three multivariate logistic models were built for stroke severity estimation. Model 1 included only clinical information, Model 2 included only retinal information, and Model 3 included both (Table 3). Both main effects and interaction effects were included in models. Both Model 1 and Model 3 showed that a hypertension duration was positively associated with NIHSS score. Compared with non-hypertension patients, patients with hypertension duration <5 years had an OR of 2.599 (95% CI 0.995–6.788, P = .051), patients with hypertension duration >5 years but <10 years had an OR of 3.747 (95% CI 1.127–12.458, P = .031), and patients with hypertension duration of >10 years had an OR of 1.309 (95% CI 0.498–3.442, P = .583). The elevated tHcy levels with an OR of 0.456 (95% CI 0.226–0.922, P = .029) showed a negative association with NIHSS score, which was consistent with the univariate results.
Table 3.
Multivariate logistic model of NIHSS grading.
| Model 1 | Model 2 | Model 3 | ||||
| Characteristics | OR (95% CI) | P | OR (95% CI) | P | OR (95% CI) | P |
| No Hypertension_ | Ref | .054 | Ref | / | Ref | .083 |
| Hypertension_Years<5 | 2.599 (0.995,6.788) | .051 | / | / | 4.67 (1.013,21.529) | .048 |
| Hypertension_Years<10 | 3.747 (1.127,12.458) | .031 | / | / | 5.468 (0.857,34.875) | .072 |
| Hypertension_Years>=10 | 1.309 (0.498,3.442) | .583 | / | / | 1.361 (0.316,5.848) | .679 |
| Elevated tHcy levels | 0.456 (0.226,0.922) | .029 | / | / | 0.178 (0.055,0.577) | .004 |
| LFDa | / | / | 0.245 (0.13,0.459) | <.001 | 0.253 (0.129,0.496) | <.001 |
| LBCA | / | / | 2.931 (1.573,5.46) | .001 | 3.849 (1.834,8.076) | <.001 |
| RNipping | / | / | 0.092 (0.02,0.434) | .003 | 0.061 (0.009,0.418) | .004 |
| RFDa | / | / | 0.417 (0.216,0.807) | .009 | 0.437 (0.214,0.895) | .024 |
| RAVR∗RNipping | / | / | 0.558 (0.414,0.754) | <.001 | 0.549 (0.392,0.769) | <.001 |
| LCRVE∗RNipping | / | / | 0.485 (0.333,0.706) | <.001 | 0.476 (0.313,0.724) | .001 |
| RBCV∗RNipping | / | / | 0.036 (0.005,0.277) | .001 | 0.02 (0.002,0.249) | .002 |
| LCRAE∗RNipping | / | / | 2.945 (1.678,5.167) | <.001 | 3.034 (1.615,5.699) | .001 |
| RCRAE∗RNipping | / | / | 0.012 (0.001,0.19) | .002 | 0.008 (0,0.229) | .005 |
| LAocclusion∗RNipping | / | / | 0.338 (0.124,0.927) | .035 | 0.315 (0.098,1.02) | .054 |
| LAangle∗LNipping | / | / | 0.199 (0.096,0.413) | <.001 | 0.144 (0.061,0.342) | <.001 |
| LNipping∗RFDv | / | / | 0.293 (0.151,0.568) | <.001 | 0.231 (0.109,0.49) | <.001 |
| LCRAE∗RFDv | / | / | 6.133 (2.863,13.138) | <.001 | 7.34 (3.106,17.347) | <.001 |
| LHemorrhage∗RFDv | / | / | 3.865 (1.793,8.331) | .001 | 3.798 (1.653,8.727) | .002 |
| RBCA∗RVangle | / | / | 0.617 (0.483,0.787) | <.001 | 0.545 (0.409,0.726) | <.001 |
| sLFDa∗sRFDv | / | / | 0.35 (0.192,0.638) | .001 | 0.347 (0.181,0.664) | .001 |
| Constant | .585 | <.001 | .1 | |||
In Model 2, retinal characteristics including the fractal dimension of the arteriolar network (FDa) of both eyes (odds ratio [OR] = 0.245 [95% CI 0.13–0.459, P < .001]) for left eyes and 0.417 (95% CI 0.216–0.807, P = .009) for right eyes, and nipping of the right eyes (OR = 0.092 [95% CI 0.02–0.434, P = .003]) were negatively associated with NIHSS score. Several interactions (RAVR∗RNipping, LCRVE∗RNipping, RBCV∗RNipping, RCRAE∗RNipping, LAocclusion∗RNipping) strengthened such negative association, while LCRAE∗RNipping attenuated the negative association. The bifurcation coefficient of the arteriole of the left eyes (LBCA) with an OR of 2.931 (95% CI 1.573–5.46, P = .001) was positively associated with NIHSS score grading. Several other interactions between retinal characteristics (LAangle∗LNipping, LNipping∗RFDv, LCRAE∗RFDv, LHemorrhage∗RFDv, RBCA∗RVangle, sLFDa∗sRFDv) also showed statistical significance in Model 2.
Model 3 included both clinical and retinal characteristics. The specific OR and P values for each characteristic varied slightly, but the main results were consistent with the first 2 models.
Further diagnostic testing was performed to investigate the prediction accuracy of the 3 models. The area under the ROC (AUC) curve for the multivariate logistic regression with traditional characteristics alone was 0.673 (95% CI 0.591–0.755), with a sensitivity of 49.4% and specificity of 72.7%; the AUC for the multivariate logistic regression with retinal characteristics alone was 0.896 (95% CI 0.850–0.942), with a sensitivity of 76.6% and specificity of 86.2%; and the AUC curve for the multivariate logistic regression with both clinical and retinal characteristics was 0.931 (95% CI 0.894–0.967) with a sensitivity of 80.5% and specificity of 88.5% (Fig. 1).
Figure 1.
The ROC curve of the 3 multivariate logistic models. ROC = the receiver operating characteristic.
4. Discussion
Since acute stroke severity diagnosis remains problematic due to frequently inaccurate clinical estimates,[19] and because required diagnostic ancillary tests may be time-consuming or unavailable on an emergent basis,[20] objective novel measurement methods that are easy and accurate should be developed to help with clinical diagnosis. To our knowledge, this is the first study considering the value of retinal characteristics in estimating stroke severity with an NIHSS score.
These results showed that using clinical variables alone would not have enough of a discriminate capability to separate patients with high NIHSS scores from patients with low NIHSS scores. Only hypertension duration and elevated tHcy levels showed statistical significance in both the univariate and multivariate analyses; no other traditional clinical characteristics showed statistical significance. Patients with a hypertension duration >5 years had a greater probability of higher NIHSS score grading compared with those with a hypertension duration ≤5 years, which is consistent with previous studies.[21–26] It was surprising to find that elevated tHcy levels were negatively associated with NIHSS score grading. The effect of elevated tHcy levels on stroke has been debated in previous studies. Several studies showed that elevated tHcy levels are an independent risk factor for ischemic stroke, while others reached a contrary conclusion.[27–32] The reason that no other traditional clinical characteristics were statistically significant may be due to the high prevalence of these risk factors in both groups. As a result, although these common risk factors are of great importance in developing stroke, they provide little information in estimating the severity of the disease.
For the models involving retinal characteristics, we included not only the main effects of retinal characteristics but also several interaction effects to improve the ability of the models. The model with only retinal characteristics performed better than the model with only clinical characteristics, and the model with both clinical and retinal characteristics performed best. This implies that retinal characteristics provided more information than clinical characteristics in the classification of NIHSS score grading. Thus, it is essential to incorporate retinal image information in the risk assessment models to increase their discrimination power for specific events.
Our research had several limitations. First, due to the relatively small sample size, we did not separate data for the model validation, which needs to be addressed in future studies. Second, the NIHSS scores were classified into only 2 groups in our analysis because few patients had a score >16. Finally, this is a cross-sectional study, and the results can only reveal the potential relationship; therefore, a prospective cohort is needed to confirm the relationship in the future.
Acknowledgments
The authors thank all the staff of the Shenzhen TCM Hospital for supporting our study.
Author contributions
All authors participated in the study design. Yuanyuan Zhuo and Weiqu Yuan acquired the data. Jack Lee and Benny Zee calculated the retinal characteristics using ARIA system. Benny Zee revised the manuscript. All authors read and approved the final manuscript.
Conceptualization: Xingxian Huang.
Data curation: Jiaman Wu.
Formal analysis: Jack Lee.
Funding acquisition: Yuanyuan Zhuo, Zhuoxin Yang, Benny Zee.
Investigation: Xingxian Huang, Weiqu Yuan.
Methodology: Jack Lee, Benny Zee.
Project administration: Zhuoxin Yang.
Resources: Weiqu Yuan, Zhuoxin Yang.
Supervision: Zhuoxin Yang, Benny Zee.
Validation: Jack Lee.
Writing – original draft: Yuanyuan Zhuo, Yimin Qu.
Writing – review & editing: Jiaman Wu.
Footnotes
Abbreviations: AUC = area under receiver-operating characteristic curve, CI = confidence interval, OR = odds ratio, ROC = the receiver operating characteristic, TC = total cholesterol, TG = triglyceride.
How to cite this article: Zhuo Y, Qu Y, Wu J, Huang X, Yuan W, Lee J, Yang Z, Zee B. Estimation of stroke severity with National Institutes of Health Stroke Scale grading and retinal features: a cross-sectional study. Medicine. 2021;100:31(e26846).
YZ, YQ, and JW contributed equally to this work.
Supported by the National Natural Science Fund of China (81803952), the Shenzhen Municipal Science and Technology Bureau (JCYJ20170412174025934), Scientific Research Projects of Guangdong Traditional Chinese Medicine Bureau (20173013), Sanming Project of Medicine in Shenzhen (SZSM201612001) and the Technology, and Business Development Fund of the Chinese University of Hong Kong (TBF17MED004).
Statement of ethics: This study was approved by the Ethics Committee of the Shenzhen Traditional Chinese Medicine Hospital (Approval Number: 2018-75). All patients provided written informed consents.
Data sharing statement: Datasets available upon request to corresponding author.
The authors have no conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.
The first letter “L,” “R” indicates the characteristics of left eyes or right eyes. Aangle = bifurcation angles of arterioles; AVR = arteriolar-to-venular diameter ratio; BCA = bifurcation coefficient of arteriole, BMI = body mass index; CRVE = central retinal venular equivalent; CRAE = central retinal artery equivalent; FDa = fractal dimension of arteriolar network; FDv = fractal dimension of venular network.
Aangle = bifurcation angles of arterioles; AVR = arteriolar-to-venular diameter ratio; BCA = bifurcation coefficient of arteriole; CRAE = central retinal artery equivalent; CRVE = central retinal venular equivalent; FDa = fractal dimension of arteriolar network; FDv = fractal dimension of venular network.
References
- [1].Global, regional, and national age–sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2015;385:117–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [2].Towfighi A, Saver JL. Stroke declines from third to fourth leading cause of death in the United States: historical perspective and challenges ahead. Stroke 2011;42:2351–5. [DOI] [PubMed] [Google Scholar]
- [3].Feigin VL, Nguyen G, Cercy K, et al. Global, regional, and country-specific lifetime risks of stroke, 1990 and 2016. N Engl J Med 2018;379:2429–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [4].Katan M, Luft A. Global burden of stroke. Semin Neurol 2018;38:208–11. [DOI] [PubMed] [Google Scholar]
- [5].Kernan WN, Ovbiagele B, Black HR, et al. Guidelines for the prevention of stroke in patients with stroke and transient ischemic attack. Stroke 2014;45:2160–236. [DOI] [PubMed] [Google Scholar]
- [6].Bjerkreim AT, Khanevski AN, Thomassen L, et al. Five-year readmission and mortality differ by ischemic stroke subtype. J Neurol Sci 2019;403:31–7. [DOI] [PubMed] [Google Scholar]
- [7].Kwah LK, Diong J. National Institutes of Health Stroke Scale (NIHSS). J Physiother 2014;60:61. [DOI] [PubMed] [Google Scholar]
- [8].Brott T, Adams HP, Jr, Olinger CP, et al. Measurements of acute cerebral infarction: a clinical examination scale. Stroke 1989;20:864–70. [DOI] [PubMed] [Google Scholar]
- [9].Adams HP, Davis PH, Leira EC, et al. Baseline NIH Stroke Scale score strongly predicts outcome after stroke: a report of the Trial of Org 10172 in Acute Stroke Treatment (TOAST). Neurology 1999;53:126–31. [DOI] [PubMed] [Google Scholar]
- [10].Appelros P, Terent A. Characteristics of the National Institute of Health Stroke Scale: results from a population-based stroke cohort at baseline and after one year. Cerebrovasc Dis 2004;17:21–7. [DOI] [PubMed] [Google Scholar]
- [11].Lee TH, Huang YH, Su TM, et al. Predictive factors of 2-year postoperative outcomes in patients with spontaneous cerebellar hemorrhage. J Clin Med 2019;8:818. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [12].Leira EC, Adams HP, Rosenthal GE, et al. Baseline NIH stroke scale responses estimate the probability of each particular stroke subtype. Cerebrovasc Dis 2008;26:573–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [13].MacGillivray TJ, Trucco E, Cameron JR, Dhillon B, Houston JG, van Beek EJR. Retinal imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions. Br J Radiol 2014;87:20130832. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Liew G, Wang JJ. [Retinal vascular signs: a window to the heart?]. Rev Esp Cardiol 2011;64:515–21. [DOI] [PubMed] [Google Scholar]
- [15].Moss HE. Retinal vascular changes are a marker for cerebral vascular diseases. Curr Neurol Neurosci Rep 2015;15:40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [16].Bettermann K, Slocomb J, Shivkumar V, Quillen D, Gardner TW, Lott ME. Impaired retinal vasoreactivity: an early marker of stroke risk in diabetes. J Neuroimaging 2017;27:78–84. [DOI] [PubMed] [Google Scholar]
- [17].Zhuo Y, Yu H, Yang Z, Zee B, Lee J, Kuang L. Prediction factors of recurrent stroke among Chinese adults using retinal vasculature characteristics. J Stroke Cerebrovasc Dis 2017;26:679–85. [DOI] [PubMed] [Google Scholar]
- [18].Yakhkind A, McTaggart RA, Jayaraman MV, Siket MS, Silver B, Yaghi S. Minor stroke and transient ischemic attack: research and practice. Front Neurol 2016;7:86–186. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].Madden KP, Karanjia PN, Adams HP, Jr, Clarke WR. Accuracy of initial stroke subtype diagnosis in the TOAST study. Trial of ORG 10172 in Acute Stroke Treatment. Neurology 1995;45:1975–9. [DOI] [PubMed] [Google Scholar]
- [20].Hand PJ, Wardlaw JM, Rowat AM, Haisma JA, Lindley RI, Dennis MS. Magnetic resonance brain imaging in patients with acute stroke: feasibility and patient related difficulties. J Neurol Neurosurg Psychiatry 2005;76:1525–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [21].Valentine D, Lord AS, Torres J, et al. How does preexisting hypertension affect patients with intracerebral hemorrhage? J Stroke Cerebrovasc Dis 2019;28:782–8. [DOI] [PubMed] [Google Scholar]
- [22].Turin TC, Okamura T, Afzal AR, et al. Hypertension and lifetime risk of stroke. J Hypertens 2016;34:116–22. [DOI] [PubMed] [Google Scholar]
- [23].Cipolla MJ, Liebeskind DS, Chan S-L. The importance of comorbidities in ischemic stroke: Impact of hypertension on the cerebral circulation. J Cereb Blood Flow Metab 2018;38:2129–49. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [24].Han TS, Wang HH-X, Wei L, et al. Impacts of undetected and inadequately treated hypertension on incident stroke in China. BMJ Open 2017;7:e016581–116581. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [25].Adil MM, Beslow LA, Qureshi AI, Malik AA, Jordan LC. Hypertension is associated with increased mortality in children hospitalized with arterial ischemic stroke. Pediatr Neurol 2016;56:25–9. [DOI] [PubMed] [Google Scholar]
- [26].Katsanos AH, Filippatou A, Manios E, et al. Blood pressure reduction and secondary stroke prevention: a systematic review and metaregression analysis of randomized clinical trials. Hypertension 2017;69:171–9. [DOI] [PubMed] [Google Scholar]
- [27].Wang W, Gao C, Yu C, et al. No association between elevated total homocysteine levels and functional outcome in elderly patients with acute cerebral infarction. Front Aging Neurosci 2017;9:70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [28].Finsterer J. Low risk of ischaemic stroke in hyperhomocysteinaemia. Lancet Neurol 2017;16:682–3. [DOI] [PubMed] [Google Scholar]
- [29].Pang H, Han B, Fu Q, Zong Z. Association of high homocysteine levels with the risk stratification in hypertensive patients at risk of stroke. Clin Ther 2016;38:1184–92. [DOI] [PubMed] [Google Scholar]
- [30].Toole JF, Malinow MR, Chambless LE, et al. Lowering homocysteine in patients with ischemic stroke to prevent recurrent stroke, myocardial infarction, and death: the Vitamin Intervention for Stroke Prevention (VISP) randomized controlled trial. JAMA 2004;291:565–75. [DOI] [PubMed] [Google Scholar]
- [31].Bonaa KH, Njolstad I, Ueland PM, et al. Homocysteine lowering and cardiovascular events after acute myocardial infarction. N Engl J Med 2006;354:1578–88. [DOI] [PubMed] [Google Scholar]
- [32].Lonn E, Yusuf S, Arnold MJ, et al. Homocysteine lowering with folic acid and B vitamins in vascular disease. N Engl J Med 2006;354:1567–77. [DOI] [PubMed] [Google Scholar]