Predict the 3-month Unfavorable Outcome of Patients with AIS
Ischemic stroke constitutes a dominant cause of disability and mortality globally, and imposes an enormous economic burden especially on developing countries.1,2 Considerable attempts have been made to improve the prognosis of patients with acute ischemic stroke (AIS). In particular, vascular recanalization therapy, such as intravenous thrombolysis (IVT) and endovascular thrombectomy (EVT), has been proven effective in randomized controlled trials and widely adopted in clinical practice.3–7 However, limited benefit of IVT is likely related to the narrow therapeutic time window of 4.5h after stroke and the low recanalization rate of around 20%.8 EVT is mainly applied to large-vessels occlusion stroke patients. And even after successful EVT, more than half of them still die or suffer from a disability.9 Recently, a new therapy concept of multiphase adjuvant neuroprotection was proposed to improve neurologic outcome in patients with AIS after EVT. Nevertheless, current neuroprotective strategies are still limited and need more clinical researches to confirm their efficacy and safety.10,11
Despite receiving standard-of-care treatments, most survivors are still left with long-term disabilities secondary to treatment. Therefore, early prediction of post-stroke functional outcome is of great significance for decision-making in stroke management and aiding patients and their families set realistic expectations. In recent years, based on clinical risk factors confirmed to influence the outcome of patients with ischemic stroke, including age, stroke severity, the pre-stroke functional status, various predictive models have been developed to estimate the prognosis after cerebral infarction in a scoring manner, such as totaled health risks in vascular events (THRIVE) score, stroke prognostication using age and National Institutes of Health Stroke Scale (SPAN)-100, acute stroke registry and analysis of Lausanne (ASTRAL) score and CHA2DS2-VASc score.12–15 The modified Rankin Scale (mRS) scores, as the most frequently used primary outcome measure in clinical stroke trials for its simplicity and ease of interpretation, were commonly applied to evaluate functional outcome of patients with AIS in these scoring systems.16 In addition, a number of blood-based and neuroimaging markers have also been developed to improve the prognostic accuracy, such as copeptin and collateral circulation.17,18 However, a simple and easy-to-use tool for the prognostic prediction of AIS is still lacking.
The advances in computational methods, including machine learning, bring a new avenue for more reliable and accurate prognostic predictions. A nomogram, as a graphical display instrument, is convenient to incorporate all prognostic factors to calculate the precise probability of interest event for each patient, and has been widely applied to prognostic prediction in various diseases, including ischemic stroke. This study was designed to construct a nomogram model to predict the 3-month unfavorable outcome of AIS patients. We conducted a comprehensive evaluation of demographic, clinical, radiological and laboratory parameters that may influence the unfavorable outcome of AIS patients. What is more, the static nomogram was further transformed into a web-based dynamic nomogram owning a user-friendly interface, which became more intelligent and convenient to use.
Materials and Methods
We performed a single-center, prospective observational clinical study of patients with first-ever AIS to create a dynamic nomogram for predicting the individual risk of poor outcome. Between September 2019 and June 2020, we consecutively recruited patients hospitalized for AIS at our stroke center. All enrolled patients were evaluated at baseline and follow-up and totally 93 subjects were involved in the final analyses in this study. Inclusion criteria included: (1) age ≥18 years; (2) patients with focal neurological symptoms related to AIS diagnosed by noncontrast computed tomography (NCCT) or magnetic resonance diffusion-weighted imaging; (3) patients who underwent pre-treatment baseline NCCT and computed tomographic angiography (CTA) within 6 h after onset of symptoms; (4) pre-stroke mRS scores 0–1. Exclusion criteria included: (1) current gestation or lactation; (2) cerebral hemorrhage and brain tumors; (3) severe space-occupying effect (midline shift or even brain herniation).
For all eligible patients, their baseline demographic, clinical, and laboratory information was acquired, including: age, sex, Body Mass Index (BMI), vascular risk factors, medical history, serum biochemistry data, admission National Institute of Health Stroke Scale (NIHSS), NIHSS at day 14, the Alberta Stroke Program Early Computed Tomography Score (ASPECTS), occlusion site (anterior circulation or posterior circulation), types of reperfusion therapy and Trial of Org 10172 in acute stroke treatment (TOAST) classification. BMI was defined as body weight (kilograms) divided by height (meters) squared. All biochemical tests were performed in the department of laboratory medicine of our hospital, which required all participants to remain fasted for at least 10 hours prior to collecting blood at 7:00 am. ASPECTS or posterior circulation (PC) ASPECTS was visually evaluated on the baseline NCCT by deducting one or two points from the total score of 10 for every area with early ischemic signs. One experienced neurologist with 3 years of experience in clinical evaluation and research took on the task, who was blind to all clinical data.
An adequate sample was necessary to ensure predictive power of risk prediction models. According to Harrell’s guidelines, when primary outcome is a binary variable, the minimum sample size of the two response levels should be 10 times greater than the number of predictors. We totally selected 3 predictors through least absolute shrinkage and selection operator (LASSO) in our study, so a sample size of at least 30 participants was required for positive events.19
The primary outcome was evaluated with mRS scores at 3 months through telephone interview by a professional neurologist at our stroke center, who was blinded to baseline clinical and imaging information. An unfavorable outcome (UFO) was defined as a mRS >2.
R software (R version 3.5.1, R Foundation for Statistical Computing, Vienna, Austria) was used for all statistical analyses. Patients were dichotomized into two groups based on good prognosis (mRS ≤2) and bad prognosis (mRS >2). We displayed categorical variables as frequency (percentage) and continuous variables as means ± standard deviations (SD) or medians (interquartile ranges, IQR), as appropriate. Differences in baseline characteristics were compared between two groups using Pearson chi-square tests for categorical variables and Kruskal–Wallis tests for continuous variables. P < 0.05 was considered statistically significant with two-sided test.
After descriptive statistics, the following procedures were carried out to construct the nomogram. First, we screened the significant variables related to poor prognosis of AIS patients with the use of LASSO regression, which serves as a dimensionality reduction approach as well as takes into account double-standard error by means of a penalty function. Second, we conducted an analysis of missing data by multiple imputation using chained equations (MICE) generated from the “MICE” package in the statistical program “R” version 18.104.22.168 Fifty imputed datasets were created for use in subsequent regression analyses to determine the factors affecting 3-month unfavorable outcome of AIS patients. The pooled effect estimates were presented as ORs and their 95% CIs. Third, a 3-month unfavorable prognostic nomogram model based on identified predictors was established using the R package “rms” and then transformed into a dynamic nomogram on web page by package “DynNom”. Finally, we plotted a calibration curve to evaluate the calibration of the nomogram by comparing the predicted unfavorable outcome with actual outcome. The clinical usefulness of our nomogram was further assessed through decision curve analysis (DCA), which quantified the net benefit across a range of threshold probabilities.
In total, 93 patients who met the inclusion criteria were entered into the study and evaluated (missing values are depicted in Figure 1). There are 28 (30.1%) patients in the UFO group. Baseline data of the two groups are compared in Tables 1–3. Significant differences between two cohorts are observed in age, glycosylated hemoglobin (GHb), Essen score, admission NIHSS, NIHSS at day 14 and ASPECTS. The patients in UFO cohort were older, and had higher levels of GHb (median 6.8% vs 5.9%; P = 0.004), Essen score (median 3.0 vs 2.0; P = 0.005), admission NIHSS (6.0 vs 3.0; P = 0.004), and NIHSS at day 14 (4.0 vs 1.0; P < 0.001), and had lower ASPECTS (9.0 vs 10.0; P < 0.001).
Table 1 Baseline Characteristics of the Two Study Groups
Table 2 Comparison of the Serum Biochemistry Data of the Two Study Groups
Table 3 Therapeutic Characteristics of the Two Study Groups
Figure 1 The aggregation plot of missing values. The first graph describes percentages of missing data, and the second graph shows percentages of different missing combinations. There were no missing values in 79% of participants.
Abbreviations: CRP, c-reactive protein; GHb, glycosylated hemoglobin; HCY, homocysteine; NIHSS, National Institutes of Health Stroke Scale; PDW, platelet distribution width; PCT, procalcitonin; HDL, high-density lipoprotein; TC, total cholesterol; TG, triglycerides; LDL, low-density lipoprotein; ASPECTS, Alberta Stroke Program Early CT Score.
Variable Selection and Prognostic Model Establishment
Three valuable variables incorporating GHb, ASPECTS, and NIHSS at day 14 were initially selected by LASSO Cox-regression (Figure 2). With the help of multiple imputation by chained equation, we generated 50 imputed datasets to handle the missing data. Then, we further verified the relationship between the three variables and poor prognosis by applying multiple logistics regression analysis. The pooled results with R package (mice, with, pool) were overall analyzed as following: GHb (OR, 1.541; 95% CI, 1.051–2.261), ASPECTS (OR, 0.635; 95% CI, 0.463–0.871), and NIHSS at day 14 (OR 1.484; 95% CI, 1.155–1.907).
Figure 2 Selection of the optimal prognostic factors by LASSO regression analysis. (A) LASSO coefficient profiles of potential predictors. (B) Screening of the optimal penalization coefficient in the LASSO regression.
Model Verification and Clinical Utility
To evaluate the clinical validity of the model, DCA was employed (Figure 3A and B). We can see a wide and practical range of threshold probabilities in validation cohort, which meant the model performed well to predict the poor outcome of AIS patients in clinic. As shown in Figure 3C, the calibration plot, which compared the predicted effective rate and observed effective rate, revealed accurate predictive ability of the model. What is more, the C-index indicated good discriminative ability with a value of 0.891 (95% CI, 0.854–0.928).
Figure 3 Clinical utility and model verification. (A) Decision curve analysis of the nomogram. The y-axis and x-axis indicate the net benefit and the threshold probability, separately. (B) Clinical impact curve. (C) Calibration plot of the nomogram with a 40 repetition bootstrap and mean absolute error of 0.068. The 45° line in the plot represents an ideal nomogram. The dotted line represents predictive capabilities of the current nomogram, whereas the solid line corrects for any bias in the nomogram.
The nomogram was established based on the three significant predictive factors from multivariate logistic regression analysis. Total scores were calculated by adding the corresponding score of each variable to estimate the probability of poor prognosis (Figure 4A). Higher total scores in the nomogram indicated a higher possibility of poor outcome, while lower total scores indicated a lower possibility of poor outcome. In addition to the ordinary nomogram, we also built a dynamic nomogram to facilitate the use for clinicians with an intuitive web-based interface (Figure 4B). People can easily input values of the three predictors followed by a click of the “Predict” button, then the 3-month probability of poor prognosis and 95% confidence interval are exported on the right side of the interface. The probabilities from the website (Dynamic Nomograms: https://odywong.shinyapps.io/DBT_21/) are the same as the Nomograms.
Figure 4 The nomogram incorporates GHb, ASPECTS, NIHSS score at day 14 to predict the probability of 3-month unfavorable outcome in patients with AIS. (A) The traditional nomogram. Points were assigned to each prognostic factor by drawing a vertical line from the certain values to the “points” line. The total points were calculated as the sum of them. Corresponding probability of unfavorable outcome can be easily obtained according to the final total points (significance codes: 0 ‘***’ .001 ‘**’ .01 ‘*’ .05 ‘.’ .1 ‘ ’ 1). (B) The intuitive interface of online dynamic nomogram (https://odywong.shinyapps.io/DBT_21/).
This study established a dynamic nomogram to predict the 3-month unfavorable outcome for AIS patients based on GHb, ASPECTS, and NIHSS score at day 14. It is well known that hyperglycemia is correlated with poor outcomes and increased mortality in AIS patients.21–24 The underlying mechanisms include tissue acidosis, blood–brain barrier disruption, reduced fibrinolytic activity and increased production of reactive oxygen species.25 Elevated blood glucose always occurs among patients with poorly controlled diabetes; however, it can also be observed in nondiabetic patients with acute ischemic stroke. The latter is called stress-induced hyperglycemia (SIH) mainly resulting from the activation of the hypothalamic–pituitary–adrenal axis.26 In contrast to SIH, GHb (the production of glucose binding to hemoglobin) reflects the average blood glucose levels over the last two to three months before the onset of ischemic stroke. Our results are consistent with previous prospective studies identifying high GHb to be associated with three-month worse prognosis in AIS patients and as a short-term predictor of prognosis, independent of the transient elevated blood glucose levels induced by SIH.27,28 Therefore, strict pre-stroke glycemic control is important for decreasing the risk of adverse post-stroke outcomes.
ASPECTS is a widely used screening tool to analyze early ischemic changes in AIS patients on NCCT,29 which plays a vital role not only for guiding clinical practice but also for research purposes. In the original trial, ASPECTS value of 7 or below was a cutoff to differentiate whether patients achieved functional dependence after thrombolytic therapy within 3 hours of symptom onset. Subsequent studies recognized baseline ASPECTS ≥6 as a key selection criterion of patients who would benefit from EVT,30,31 which is recommended in updated guidelines of the American Heart Association on acute stroke management.32 In line with previous reports, ASPECTS on baseline NCCT in our nomograms is a significant predictor of unfavorable outcome.33–35
NIHSS score is widely applied to measure the severity and predict the unfavorable outcome of patients with ischemic stroke.36,37 High NIHSS score is associated with a larger infarct size and cerebral edema,38,39 which ultimately lead to poor outcomes. A significant number of predictive models incorporate NIHSS score as a predictor,40 and our model is no exception. Unlike most studies, unfavorable outcome had no significant correlation with the baseline NIHSS score in the present study, but with NIHSS score at day 14. This may be related to the differences in sample size and population characteristics. Our results confirm that baseline severity of ischemic stroke cannot determine actual prognosis of patients.
In our study, acute treatments, such as IVT and EVT, were not found to be associated with the 3-month outcome of patients with AIS. This may be because proportions of the therapeutic modalities (IVT and EVT) were not statistically significantly different in the 2 groups (favorable and unfavorable outcomes) (Table 3).
Our dynamic nomogram, which includes the three easy-to-obtain variables mentioned above, can calculate the probability of an individual’s unfavorable outcome. For example, the probability of unfavorable 3-month outcome of a patient with a GHb of 7.0%, baseline ASPECTS score of 6 and NHISS score at day 14 of 4 is 76.4%. Prevention is better than treatment, and the dynamic nomogram helps clinicians timely identify high-risk patients with AIS and provides an opportunity for early intervention and close monitoring. Also, it can be used for risk group stratification in clinical researches.
There are some strengths in our dynamic nomogram compared with the current prognostic scores to predict unfavorable outcomes of patients with AIS. In terms of processing variables, some prognostic scores categorize the discrete and continuous predictors like age, NIHSS score and blood glucose level into two or more groups based on artificial cut-off values, which reduces the accuracy of models due to neglect of within-category information. However, our nomogram fully analyzes the effect of specific value of a certain variable, and therefore may serve as a more precisely individualized predictive tool for prognosis. Moreover, because of the result visualization, it is more intuitionistic and convenient in practical application.
Meanwhile, there are several limitations in this study. First, our sample size was relatively limited, and more samples might be needed to increase the accuracy of the research results. Second, this was a single-center study, which led to selection bias. External validation in other stroke centers is necessary due to the differences of race, patient’s wealth, hospital type and ischemic stroke treatments. Third, risk factors excluded in our study, such as collateral circulation and different drug treatments (statins,41 antiplatelets42 and anticoagulants43), are likely to be important for predicting unfavorable outcomes, which may damper the rigor and scientific nature of the prediction model and limit its extensive application in clinic. Despite these limitations, our nomogram based on easily available variables may be widely used.
In summary, the dynamic nomogram developed in this study, comprised of GHb, ASPECTS, and NIHSS score at day 14, may be able to predict the 3-month unfavorable prognosis in AIS patients. Further external validations are needed to evaluate the universality of the model.
The dynamic nomogram, comprised of glycosylated hemoglobin, ASPECTS, and NIHSS score at day 14, may be able to predict the 3-month unfavorable outcome for acute ischemic stroke patients.
AIS, acute ischemic stroke; NCCT, noncontrast computed tomography; LASSO, least absolute shrinkage and selection operator; UFO, unfavorable outcome; DCA, decision curve analysis; SIH, stress-induced hyperglycemia; mRS, modified Rankin Scale; SD, standard deviation; OR, odds ratio; CI, confidence interval; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; RDW-SD, red blood cell distribution width-standard deviation; RDW-CV, red blood cell distribution width coefficient of variation; PDW, platelet distribution width; PCT, procalcitonin; CRP, c-reactive protein; INR, international normalized ratio; ASPECTS, Alberta Stroke Program Early CT Score; NIHSS, National Institutes of Health Stroke Scale; IV thrombolysis, intravenous thrombolysis; EVT, endovascular thrombectomy; TOAST, Trial of Org 10172 in acute stroke treatment.
Data Sharing Statement
The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding authors.
Our study complies with the Declaration of Helsinki. Approval for the study was obtained from the ethics committee of Guangdong Second Provincial General Hospital (reference number: 2018-SYX-010) and written informed consent was signed by all participants prior to their enrollment.
Consent for Publication
All authors have approved the manuscript for submission.
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
This study was supported by the Medical Scientific Research Foundation of Guangdong Province of China (B2019068) and Doctoral Workstation foundation of Guangdong Second Provincial General Hospital (2020BSGZ035).
The authors report no conflicts of interest in this work.
1. Naghavi M, Abajobir AA, Abbafati Cet al. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the global burden of disease study 2016. Lancet. 2017;390(10100):1151–1210. doi:10.1016/S0140-6736(17)32152-9
2. Benjamin EJ, Virani SS, Callaway CW, et al. Heart disease and stroke statistics-2018 update: a report from the American Heart Association. Circulation. 2018;137(12):e67–e492. doi:10.1161/CIR.0000000000000558
3. Berkhemer OA, Fransen PS, Beumer D, et al. A randomized trial of intraarterial treatment for acute ischemic stroke. N Engl J Med. 2015;372(1):11–20. doi:10.1056/NEJMoa1411587
4. Jovin TG, Chamorro A, Cobo E, et al. Thrombectomy within 8 hours after symptom onset in ischemic stroke. N Engl J Med. 2015;372(24):2296–2306. doi:10.1056/NEJMoa1503780
5. Moussaddy A, Demchuk AM, Hill MD. Thrombolytic therapies for ischemic stroke: triumphs and future challenges. Neuropharmacology. 2018;134(Pt B):272–279. doi:10.1016/j.neuropharm.2017.11.010
6. Powers WJ, Rabinstein AA, Ackerson T, et al. guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2019;50(12):e344–e418. doi:10.1161/STR.0000000000000211
7. Leng T, Xiong ZG. Treatment for ischemic stroke: from thrombolysis to thrombectomy and remaining challenges. Brain Circ. 2019;5(1):8–11. doi:10.4103/bc.bc_36_18
8. Bhatia R, Hill MD, Shobha N, et al. Low rates of acute recanalization with intravenous recombinant tissue plasminogen activator in ischemic stroke: real-world experience and a call for action. Stroke. 2010;41(10):2254–2258. doi:10.1161/STROKEAHA.110.592535
9. Goyal M, Menon BK, van Zwam WH, et al. Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials. Lancet. 2016;387(10029):1723–1731. doi:10.1016/S0140-6736(16)00163-X
10. Ji X. Forward thinking in stroke treatment: advances in cerebrovascular reperfusion and neurorehabilitation. Brain Circ. 2015;1:1–2. doi:10.4103/2394-8108.166347
11. Zhao W, Wu C, Dornbos D
12. Flint AC, Xiang B, Gupta R, et al. THRIVE score predicts outcomes with a third-generation endovascular stroke treatment device in the TREVO-2 trial. Stroke. 2013;44(12):3370–3375. doi:10.1161/STROKEAHA.113.002796
13. Saposnik G, Guzik AK, Reeves M, Ovbiagele B, Johnston SC. Stroke prognostication using age and NIH stroke scale: SPAN-100. Neurology. 2013;80(1):21–28. doi:10.1212/WNL.0b013e31827b1ace
14. Ntaios G, Faouzi M, Ferrari J, Lang W, Vemmos K, Michel P. An integer-based score to predict functional outcome in acute ischemic stroke: the ASTRAL score. Neurology. 2012;78(24):1916–1922. doi:10.1212/WNL.0b013e318259e221
15. Kusznir Vitturi B, José Gagliardi R. Use of CHADS(2) and CHA(2) DS(2)-VASc scores to predict prognosis after stroke. Rev Neurol. 2020;176(1–2):85–91. doi:10.1016/j.neurol.2019.05.001
16. Broderick JP, Adeoye O, Elm J. Evolution of the modified Rankin Scale and its use in future stroke trials. Stroke. 2017;48(7):2007–2012. doi:10.1161/STROKEAHA.117.017866
17. De Marchis GM, Dankowski T, König IR, et al. A novel biomarker-based prognostic score in acute ischemic stroke: the corisk score. Neurology. 2019;92(13):e1517–e1525. doi:10.1212/WNL.0000000000007177
18. Ryu CW, Kim BM, Kim HG, et al. Optimizing outcome prediction scores in patients undergoing endovascular thrombectomy for large vessel occlusions using collateral grade on computed tomography angiography. Neurosurgery. 2019;85(3):350–358. doi:10.1093/neuros/nyy316
19. Harrell FE
20. van Buuren S, Groothuis-Oudshoorn K. Mice: multivariate imputation by chained equations in R. J Stat Softw. 2011;45(3):1–67.
21. Osei E, den Hertog HM, Berkhemer OA, et al. Admission glucose and effect of intra-arterial treatment in patients with acute ischemic stroke. Stroke. 2017;48(5):1299–1305. doi:10.1161/STROKEAHA.116.016071
22. Lin SF, Chao AC, Hu HH, et al. Hyperglycemia predicts unfavorable outcomes in acute ischemic stroke patients treated with intravenous thrombolysis among a Chinese population: a prospective cohort study. J Neurol Sci. 2018;388:195–202. doi:10.1016/j.jns.2018.03.022
23. Tsivgoulis G, Katsanos AH, Mavridis D, Lambadiari V. Association of baseline hyperglycemia with outcomes of patients with and without diabetes with acute ischemic stroke treated with intravenous thrombolysis: a propensity score-matched analysis from the SITS-ISTR registry. Diabetes. 2019;68(9):1861–1869. doi:10.2337/db19-0440
24. Rinkel LA, Nguyen TTM, Guglielmi V, et al. High admission glucose is associated with poor outcome after endovascular treatment for ischemic stroke. Stroke. 2020;51(11):3215–3223. doi:10.1161/STROKEAHA.120.029944
25. Ferrari F, Moretti A, Villa RF. Hyperglycemia in acute ischemic stroke: physiopathological and therapeutic complexity. Neural Regen Res. 2022;17(2):292–299. doi:10.4103/1673-5374.317959
26. Guo Y, Wang G, Jing J, et al. Stress hyperglycemia may have higher risk of stroke recurrence than previously diagnosed diabetes mellitus. Aging. 2021;13(6):9108–9118. doi:10.18632/aging.202797
27. Bao Y, Gu D. Glycated hemoglobin as a marker for predicting outcomes of patients with stroke (ischemic and hemorrhagic): a systematic review and meta-analysis. Front Neurol. 2021;12:642899. doi:10.3389/fneur.2021.642899
28. Chang JY, Kim WJ, Kwon JH. Association of prestroke glycemic control with vascular events during 1-year follow-up. Neurology. 2021;97(17):e1717–e1726. doi:10.1212/WNL.0000000000012729
29. Barber PA, Demchuk AM, Zhang J, Buchan AM. Validity and reliability of a quantitative computed tomography score in predicting outcome of hyperacute stroke before thrombolytic therapy. ASPECTS study group Alberta stroke programme early CT score. Lancet. 2000;355(9216):1670–1674. doi:10.1016/S0140-6736(00)02237-6
30. Saver JL, Goyal M, Bonafe A, et al. Stent-retriever thrombectomy after intravenous t-PA vst-PA alone in stroke. N Engl J Med. 2015;372(24):2285–2295. doi:10.1056/NEJMoa1415061
31. Goyal M, Demchuk AM, Menon BK, et al. Randomized assessment of rapid endovascular treatment of ischemic stroke. N Engl J Med. 2015;372(11):1019–1030. doi:10.1056/NEJMoa1414905
32. Powers WJ, Derdeyn CP, Biller J, et al. 2015 American Heart Association/American Stroke Association focused update of the 2013 guidelines for the early management of patients with acute ischemic stroke regarding endovascular treatment: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2015;46(10):3020–3035. doi:10.1161/STR.0000000000000074
33. Sarraj A, Albright K, Barreto AD, et al. Optimizing prediction scores for poor outcome after intra-arterial therapy in anterior circulation acute ischemic stroke. Stroke. 2013;44(12):3324–3330. doi:10.1161/STROKEAHA.113.001050
34. Ghodsi H, Abouei Mehrizi MA, Khoshdel AR, Shekarchi B. Evaluation of combining Alberta Stroke Program Early CT Score (ASPECTS) with mean platelet volume, plateletcrit, and platelet count in predicting short- and long-term prognosis of patients with acute ischemic stroke. Clin Neurol Neurosurg. 2021;208:106830. doi:10.1016/j.clineuro.2021.106830
35. Pop NO, Tit DM, Diaconu CC, et al. The Alberta Stroke Program Early CT score (ASPECTS): a predictor of mortality in acute ischemic stroke. Exp Ther Med. 2021;22(6):1371. doi:10.3892/etm.2021.10805
36. Lyden P. Using the National Institutes of Health Stroke Scale: a cautionary tale. Stroke. 2017;48(2):513–519. doi:10.1161/STROKEAHA.116.015434
37. Saber H, Saver JL. Distributional validity and prognostic power of the National Institutes of Health Stroke Scale in US administrative claims data. JAMA Neurol. 2020;77(5):606–612. doi:10.1001/jamaneurol.2019.5061
38. Thorén M, Azevedo E, Dawson J, et al. Predictors for cerebral edema in acute ischemic stroke treated with intravenous thrombolysis. Stroke. 2017;48(9):2464–2471. doi:10.1161/STROKEAHA.117.018223
39. Wu S, Yuan R, Wang Y, et al. Early prediction of malignant brain edema after ischemic stroke. Stroke. 2018;49(12):2918–2927. doi:10.1161/STROKEAHA.118.022001
40. Drozdowska BA, Singh S, Quinn TJ. Thinking about the future: a review of prognostic scales used in acute stroke. Front Neurol. 2019;10:274. doi:10.3389/fneur.2019.00274
41. Vitturi BK, Gagliardi RJ. Effects of statin therapy on outcomes of ischemic stroke: a real-world experience in Brazil. Arq Neuropsiquiatr. 2020;78(8):461–467. doi:10.1590/0004-282×20200027
42. Hackam DG, Spence JD. Antiplatelet therapy in ischemic stroke and transient ischemic attack. Stroke. 2019;50(3):773–778. doi:10.1161/STROKEAHA.118.023954
43. Arihiro S, Todo K, Koga M, et al. Three-month risk-benefit profile of anticoagulation after stroke with atrial fibrillation: the SAMURAI-Nonvalvular Atrial Fibrillation (NVAF) study. Int J Stroke. 2016;11(5):565–574. doi:10.1177/1747493016632239