Abstract
Introduction
Moyamoya has been extensively described in East Asian populations, and despite its accepted clinical presentation and course, it is fundamental to describe major cerebrovascular complications in other ethnically diverse samples. Hence, we sought to determine if distinct ethnic groups are at higher risk of developing stroke using the National Inpatient Sample (NIS) database.
Methods
We included all moyamoya patients admitted from January 2013 until December 2018 in the NIS database. Multivariate regression analysis was used to determine the risk of developing stroke and poor outcomes in different races compared to white patients.
Results
Out of the 6093 admissions with diagnosis of moyamoya disease that were captured, 2,520 were white (41.6%), 2,078 were African American (AA) (34.1%), 721 were Hispanic (11.8%), and 496 were Asian (8.14%). For arterial ischemic stroke (AIS), we found that AA race had a significantly reduced risk of AIS compared to white patients (odds ratio = 0.8, 95% confidence interval: 0.7–0.9, p = 0.031). While being Hispanic or Asian significantly increased 1.5 and 2-fold the risk of hemorrhagic stroke.
Conclusion
This study highlights the unique features and phenotypes of moyamoya cases among different ethnicities. While possibly AA are protected from developing AIS due to underlying causes of moyamoya such as sickle cell disease, Asians seems to be more susceptible to hemorrhagic stroke.
Keywords: Moyamoya, Disease, Stroke, Predictors, Race, Ethnicity
Introduction
Moyamoya disease (MMD) is characterized by a chronic and progressive stenosis of brain vessels with the formation of aberrant collaterals [1]. In some cases, the disease can progress to the complete occlusion of the intracranial internal carotid artery and/or the proximal portion of the anterior cerebral artery (ACA) and middle cerebral artery (MCA), resulting in ischemic neurovascular events such as arterial ischemic stroke (AIS) and transient ischemic attacks (TIA) [2]. In addition, the fragile and dilated newly formed collaterals can also increase the risk of the hemorrhagic stroke (HS) [3]. Due to the recent advances in neuroimaging techniques, the prevalence of MMD has been increasing over the last decades in Asian and Western populations, as shown in recent reports from South Korea, Japan, Taiwan, and the USA [4, 5, 6]. However, to this date, the understanding of risk factors that predict neurological events and worsen outcomes following those events in the context of MMD has not been fully elucidated in diverse populations. Hence, we aimed to contribute to the current knowledge of MMD in western populations by assessing the influence of race on MMD outcomes using a large national sample from the USA.
Methods
Data Source
This is a retrospective study in which we used the US National Inpatient Sample (NIS) database from 2013 to 2018. This database contains a 20% stratified sample of all hospital discharges from participating institutions in the Healthcare Cost and Utilization Project (HCUP). The NIS database contains demographic, diagnostic, and procedural data, with diagnoses and procedures captured in the form of the International Classification of Diseases, 9th and 10th editions (ICD-9/10). For practicality of the study, we named ethnicity and race as synonyms.
Patients Selection
Primary inclusion criteria were patients with a discharge diagnosis of MMD (ICD-9: 437.5 and ICD-10: I67.5) from January 1st, 2013, to December 31st, 2018. Patients were classified into five racial groups: white, African American (AA), Hispanic, Asian, and others.
Examined Data
Demographic data included age, sex, insurance provider, and household income. When associated conditions including sickle cell disease, neurofibromatosis type 1, cranial radiation therapy, or Down syndrome were present, patients were classified as moyamoya syndrome (MMS). Other comorbidities and potential risk factors analyzed were hypertension, diabetes mellitus simple and diabetes mellitus with complications, hyperlipidemia, obesity, congestive heart failure, arrythmia, coronary atherosclerosis, coagulopathy, chronic pulmonary disease, depression, current smoking status, alcohol abuse, drug abuse, peripheral vascular disorders, psychoses, and sleep disturbances. Medication history included long-term (current) use of anticoagulant/antithrombotic agents. Treatments including extracranial-intracranial (EC-IC) bypass surgery and indirect bypass were evaluated together. The NIS database does not separate direct and indirect procedures. All codes for this study are shown in supplementary material (online suppl. Table 1; see www.karger.com/doi/10.1159/000528055 for all online suppl. material).
Outcomes
The primary outcomes were cerebrovascular events including AIS, HS, and TIA. Secondary outcomes included nonroutine discharge and in-hospital mortality.
Statistical Analysis
To assess the differences between races, we performed a univariate analysis; in this step, all continuous values were represented using mean ± standard deviation or median and interquartile range based on their distribution, while categorical variables were described using frequencies and proportions. Age was categorized into five categories: <1 year, 1–17 years, 18–44 years, 45–64 years, and ≥65 years. Household income was stratified into four quartiles (Q1-Q4) based on values derived from ZIP code-demographic data.
Comparisons were performed using Student's t-test, the nonparametric Mann-Whitney test, or χ2 tests as appropriate. Factors with a p value <0.1 were then included in a multiple logistic regression analysis to reflect the adjusted association between outcomes and predictors. Odds ratios and 95% confidence intervals were calculated. A p value <0.05 was considered statistically significant. SPSS v.24 software (IBM, Armonk, NY, USA) was used to perform the statistical analysis.
Results
A total of 6093 admissions with diagnosis of MMD were captured in the NIS database from 2013 until 2018. These patients were classified in five groups: white (n = 2,520, 41.6%), AA (n = 2,078, 34.1%), Hispanic (n = 721, 11.8%), Asian (n = 496, 8.14%), and others (n = 278, 4.6%). We performed a univariate analysis to determine the differences in comorbidities, medical history, and demographic variables among racial groups (Table 1).
Table 1.
Univariate analysis of racial disparities in patients with MMD in the 2013–2018 NIS database
| Variables, n (%) | White (n = 2,520) | AA (n = 2,078) |
Hispanic (n = 721) | Asian (n = 496) | Other (n = 278) | p value |
|---|---|---|---|---|---|---|
| MMS | 345 (13.7) | 1,186 (57.1) | 161 (22.3) | 31 (6.3) | 60 (21.6) | <0.001a |
| Age | ||||||
| <1 year | 49 (1.9) | 20 (1) | 28 (3.9) | 6 (1.2) | 8 (2.9) | <0.001a |
| 1–17 years | 264 (10.5) | 380 (18.3) | 133 (18.5) | 31 (6.3) | 44 (15.8) | |
| 18–44 years | 930 (36.4) | 1,156 (55.7) | 318 (44.2) | 204 (41.1) | 113 (40.6) | |
| 45–64 years | 917 (36.4) | 461(22.2) | 184 (25.6) | 193 (38.9) | 93 (33.5) | |
| >65 years | 360 (14.3) | 60 (2.9) | 56 (7.8) | 62 (12.5) | 20 (7.2) | |
| Sex | ||||||
| Female | 1,745 (69.2) | 1,312 (63.1) | 482 (66.9) | 335 (67.5) | 195 (70.1) | <0.001a |
| Insurance | ||||||
| Medicare/Medicaid | 1,349 (53.6) | 1,523 (73.5) | 464 (64.4) | 224 (45.2) | 145 (52.2) | <0.001a |
| Private | 1,020 (40.5) | 407 (19.6) | 177 (24.5) | 240 (48.8) | 104 (37.4) | |
| Other | 150 (6) | 143 (6.9) | 80 (11.1) | 32 (6.5) | 29 (10.4) | |
| Median household income | ||||||
| Quartile 1 | 654 (26.4) | 1,098 (53.8) | 242 (34.2) | 72 (14.8) | 89 (33.3) | <0.001a |
| Quartile 2 | 627 (25.3) | 437 (21.4) | 195 (27.5) | 73 (15) | 53 (19.9) | |
| Quartile 3 | 650 (26.2) | 303 (14.8) | 194 (27.4) | 117 (24) | 67 (25.1) | |
| Quartile 4 | 564 (22) | 204 (10) | 77 (10.9) | 225 (46.2) | 58 (21.7) | |
| Hypertension | 1,343 (53.3) | 864 (41.6) | 375 (52) | 289 (58.5) | 144 (51.8) | <0.001a |
| Diabetes simple | 401 (15.9) | 253 (12.2) | 132 (18.3) | 89 (17.9) | 47 (16.9) | <0.001a |
| Diabetes complicated | 308 (12.2) | 189 (9.1) | 126 (17.5) | 69 (13.9) | 29 (10.4) | <0.001a |
| Hyperlipidemia | 897 (35.7) | 447 (21.5) | 209 (29) | 193 (38.9) | 91 (32.7) | <0.001a |
| Obesity | 447 (17.7) | 240 (11.5) | 114 (15.8) | 47 (9.5) | 46 (16.5) | <0.001a |
| Congestive heart failure | 214 (8.5) | 177 (8.5) | 53 (7.4) | 31 (6.3) | 14 (5) | 0.118 |
| Arrhythmia | 3 (0.1) | 4 (0.2) | 0 (0) | 1 (0.2) | 0 (0) | 0.713 |
| Coronary atherosclerosis | 241 (9.6) | 115 (5.5) | 42 (5.8) | 38 (7.7) | 21 (7.6) | <0.001a |
| Coagulopathy | 12 (0.5) | 8 (0.4) | 2 (0.3) | 2 (0.4) | 0 (0) | |
| Chronic pulmonary disease | 224 (8.9) | 161 (7.7) | 55 (7.6) | 22 (4.4) | 17 (6.1) | 0.011a |
| Depression | 256 (10.2) | 166 (8) | 67 (9.3) | 27 (5.4) | 18 (6.5) | 0.002a |
| Current smoker | 157 (6.2) | 90 (4.3) | 17 (2.4) | 16 (3.2) | 8 (2.9) | <0.001a |
| Alcohol abuse | 248 (9.8) | 120 (5.8) | 49 (6.8) | 28 (5.6) | 11 (4) | <0.001a |
| Drug abuse | 252 (10) | 185 (8.9) | 46 (6.4) | 35 (7.1) | 16 (5.8) | 0.005a |
| Peripheral vascular disorders | 66 (2.6) | 39 (1.9) | 14 (1.9) | 6 (1.2) | 4 (1.4) | 0.175 |
| Psychoses | 95 (3.8) | 58 (2.8) | 18 (2.5) | 14 (2.8) | 9 (3.2) | 0.271 |
| Sleep disturbances | 222 (8.8) | 121 (5.8) | 57 (7.9) | 27 (5.4) | 16 (5.8) | 0.001a |
| Anticoagulant/antithrombotic | 900 (35.7) | 658 (31.7) | 216 (30) | 169 (34.1) | 77 (27.7) | 0.002a |
| EC-IC bypass surgery | 305 (12.1) | 115 (5.5) | 60 (8.3) | 63 (12.7) | 28 (10.1) | <0.001a |
Included in multivariate analysis.
Regarding outcomes, the highest prevalence of any type of stroke was seen in Asian patients (n = 206, 41.5%), with the lowest prevalence in AA patients (n = 459, 22.1%). The breakdown of cerebrovascular events showed that AA group had a significant lower prevalence of AIS (n = 361, 17.4%, p < 0.001) compared to rest of races that had between 27 and 28.8% of prevalence. HS occurrence was significantly higher in Asian patients (n = 75, 15.1%, p < 0.001). TIA had no significant differences in prevalence among racial groups. Also of interest was the finding that AA (n = 115, 5.5%) were significantly less likely to undergo surgical treatment compared to white patients (n = 305, 12.1%). This could be secondary to the finding that AA had a lower prevalence of ischemic and HS; however, this could also be a racial disparity and reflect a lack of adequate access from this specific patient population. The analysis of secondary outcomes showed that nonroutine discharge was significantly higher in white patients (n = 865, 43.4%) compared to AA (n = 555, 26.7%, p < 0.001), whereas no statistical differences were obtained in rates of in-hospital mortality (Table 2).
Table 2.
Distribution of outcomes in racial groups of patients with MMD in the 2013–2018 NIS database
| Variables, n (%) | White (n = 2,520) | AA (n = 2,Q78) | Hispanic (n = 721) | Asian (n = 496) | Other (n = 278) | p value |
|---|---|---|---|---|---|---|
| Any stroke | 878 (34.8) | 459 (22.1) | 251 (34.8) | 206 (41.5) | 106 (38.1) | <0.001a |
| AIS | 692 (27.5) | 361 (17.4) | 195 (27) | 143 (28.8) | 80 (28.8) | <0.001a |
| HS | 174 (6.9) | 102 (4.9) | 64 (8.9) | 75 (15.1) | 21 (7.6) | <0.001a |
| TIA | 86 (3.4) | 49 (2.4) | 21 (2.9) | 15 (3) | 11 (4) | 0.255 |
| Discharge | ||||||
| Routine | 1,653 (65.6) | 1,522 (73.3) | 512 (71) | 299 (60.4) | 187 (67.3) | <0.001a |
| Nonroutine | 865 (43.4) | 555 (26.7) | 209 (29) | 196 (39.6) | 91 (32.7) | |
| In-hospital mortality | 45 (1.8) | 33 (1.6) | 16 (2.2) | 12 (2.4) | 8 (2.9) | 0.429 |
AIS, arterial ischemic stroke; HS, hemorrhagic stroke; TIA, transient ischemic attack.
Statistically significant.
The adjusted regression model for predictors of AIS in MMD (Table 3) showed that AA race had a significantly reduced risk of AIS compared to white patients (odds ratio = 0.8, 95% confidence interval: 0.7–0.9, p = 0.031). Other variables significantly associated with a reduced risk of AIS in MMD included MMS and depression. Whereas independent risk factors found included female sex, private insurance, hypertension, diabetes complicated, alcohol abuse, and EC-IC bypass surgery.
Table 3.
Multivariate analysis of patients with MMD and AIS
| Variables | OR [95% CI] | p value |
|---|---|---|
| Age (compared with age ≥65) | ||
| <1 | 1 [0.6–1.6] | 0.943 |
| 1–17 | 0.9 [0.7–1.2] | 0.819 |
| 18–44 | 0.8 [0.6–1] | 0.184 |
| 45–64 | 0.9 [0.7–1.1 | 0.610 |
| Female | 1.1 [1–1.3] | 0.039a |
| Race (reference: white) | ||
| AA | 0.8 [0.7–0.9] | 0.031a |
| Hispanic | 1 [0.8–1.3] | 0.454 |
| Asian | 1 [0.8–1.3] | 0.972 |
| Others | 1.1 [0.8–1.5] | 0.340 |
| Insurance (compared with Medicare/Medicaid) | ||
| Private | 1.3 [1.1–1.5] | <0.001a |
| Other | 1.7 [1.4–2.2] | <0.001a |
| Median household income (compared with Quartile 1) | ||
| Quartile 2 | 1.1 [0.9–1.3] | 0.236 |
| Quartile 3 | 0.9 [0.8–1.1] | 0.910 |
| Quartile 4 | 0.9 [0.7–1.1] | 0.599 |
| MMS | 0.5 [0.4–0.6] | <0.001a |
| Hypertension | 1.4 [1.2–1.6] | <0.001a |
| Diabetes simple | 1 [0.8–1.2] | 0.873 |
| Diabetes complicated | 1.4 [1.2–1.7] | <0.001a |
| Hyperlipidemia | 1.7 [1.4–2] | <0.001a |
| Obesity | 1 [0.8–1.2] | 0.494 |
| Coronary arteriosclerosis | 0.8 [0.6–1] | 0.151 |
| Chronic pulmonary disease | 0.8 [0.6–1.1] | 0.245 |
| Depression | 0.7 [0.5–0.8] | 0.004a |
| Current smoker | 1.2 [0.7–1.8] | 0.367 |
| Alcohol abuse | 1.4 [1–2] | 0.032a |
| Drug abuse | 1 [0.7–1.4] | 0.739 |
| Sleep disturbances | 0.9 [0.7–1.1] | 0.468 |
| Anticoagulant/antithrombotic | 1 [0.9–1.2] | 0.224 |
| EC-IC bypass surgery | 1.3 [1–1.6] | 0.004a |
OR, odds ratio; CI, confidence interval.
Independent predictors for AIS in MMD
The adjusted regression model for HS in MMD (Table 4) revealed that compared to white patients, Hispanic and Asian races significantly increased 1.5- and 2-fold the risk of HS, respectively. This analysis showed as well that private insurance, household income Q2, hypertension, and alcohol abuse were also independent risk factors. While other variables including age groups between 1 and 44 years-old, MMS, diabetes simple or complicated, hyperlipidemia, depression, anticoagulant/antithrombotic use, and EC-IC bypass surgery were significantly associated with reduced risk of HS. Finally, adjusted regression analyses for nonroutine discharge (online suppl. Table 2) and in-hospital mortality (online suppl. Table 3) showed no significant differences between races in the risk of these adverse outcomes.
Table 4.
Multivariate analysis of patients with MMD and HS
| Variables | OR [95% Cl] | p value |
|---|---|---|
| Age (compared with >65) | ||
| <1 | 0.8 [0.4–1.6] | 0.657 |
| 1–17 | 0.2 [0.1–0.4] | <0.001a |
| 18–44 | 0.4 [0.3–0.6] | <0.001a |
| 45–64 | 0.7 [0.5–1] | 0.081 |
| Female | 1 [0.8–1.2] | 0.955 |
| Race (compared with white) | ||
| AA | 1.1 [0.8–1.5] | 0.244 |
| Hispanic | 1.5 [1.1–2.1] | 0.007a |
| Asian | 2 [1.5–2.8] | <0.001a |
| Others | 1.2 [0.7–1.9] | 0.418 |
| Insurance (compared with Medicare/Medicaid) | ||
| Private | 1.3 [1–1.6] | 0.028a |
| Other | 1 [0.6–1.5] | 0.948 |
| Median household income (compared with Quartile 1) | ||
| Quartile 2 | 1.3 [1–1.7] | 0.030a |
| Quartile 3 | 1.1 [0.8–1.5] | 0.429 |
| Quartile 4 | 1.1 [0.8–1.5] | 0.379 |
| MMS | 0.3 [0.2–0.4] | <0.001a |
| Hypertension | 1.8 [1.4–2.3] | <0.001a |
| Diabetes simple | 0.7 [0.5–0.9] | 0.030a |
| Diabetes complicated | 0.5 [0.4–0.8] | 0.004a |
| Hyperlipidemia | 0.7 [0.5–0.8] | 0.004a |
| Obesity | 0.8 [0.6–1.1] | 0.387 |
| Coronary arteriosclerosis | 0.7 [0.4–1.1] | 0.166 |
| Chronic pulmonary disease | 0.7 [0.5–1.2] | 0.294 |
| Depression | 0.5 [0.3–0.9] | 0.017a |
| Current smoker | 0.8 [0.4–1.5] | 0.526 |
| Alcohol abuse | 1.7 [1–2.7] | 0.030a |
| Drug abuse | 0.8 [0.5–1.4] | 0.644 |
| Sleep disturbances | 0.7 [0.4–1.2] | 0.245 |
| Anticoagulant/antithrombotic | 0.5 [0.4–0.6] | <0.001a |
| EC-IC bypass surgery | 0.5 [0.3–0.8] | 0.006a |
OR, odds ratio; Cl, confidence interval.
independent risk factors for SH.
Discussion
With the move toward the development of refined genetic tests, neuroimaging techniques, and interventions that allow early and accurate characterization of anomalies in brain vasculature, the elucidation of prognostic factors including demographics and comorbidities, among others, that help identify the best candidates to surgically intervene is a major clinical objective for patients with MMD. The course and predictors for cerebrovascular events have been extensively described in Asian populations [7]; however, in diverse western populations, this disease is less well understood. Risk factors for ischemic events in MMD affecting western populations have been described only in few reports [8], and furthermore, how much race and ethnicity can influence the disease course and outcomes has not been explored in large diverse samples. In this context, we aimed to characterize the association between race/ethnicity and stroke events using a large national US database. We found that compared to whites, AA had a significantly reduced risk of AIS, while Hispanics and Asians had a 1.5- and 2-fold increased risk of HS, respectively.
Several factors can influence the progression of stenotic arteries to develop AIS in MMD [9]. Although AA patients had the lowest rates of vascular risk factors in our study, the adjusted regression analysis suggests that the vascular damage and disease progression are milder compared to whites. Furthermore, in line with previous findings, we found that AA had the highest prevalence of MMS, possibly due to the strong association between sickle cell and AA ethnicity [10]; this scenario has also been associated with lower risk of AIS in the moyamoya phenomenon [1]. Based on these observations, possible genetic variations in different racial groups might explain MMD phenotype differences. Single nucleotide polymorphisms in genes involved in vascular endothelial proteins, promoting inflammatory signals, tissue remodeling, and growth factors have been found in MMD patients [11]. However, to this date, genetic data in MMD is inconsistent and limited to small sample sizes, mostly to East Asian populations [12]. Genome-wide association studies could potentially allow us to understand to what the extent common genetic variations can contribute to the overall risk of AIS in MMD and, moreover, if MMD-associated single nucleotide polymorphisms differ from one race/ethnicity to another, as has been found with the ring finger protein 213 (RNFP213) variant p.R4810K which has been detected in up to 90% of Asian cases but has not been found in Caucasian MMD patients [8]. Nevertheless, the likely multifactorial nature of MMD [13] challenges the possibility of monogenic causes that could be used as early markers or as targets for gene therapy trials.
While ischemia is the most frequent MMD manifestation, prevalence of MMD-associated intracerebral hemorrhage (ICH) has been reported to be as high as 62% in adult patients [14]. However, HS prevalence varies significantly among Asian and Caucasian MMD populations [15]. Previous single center studies have shown ∼15% of HS cases in North American patients [16] and 8.5% in European Caucasians. Similarly, to a 1988–2004 NIS study [17] that found 7.4% of ICH in MMD, in our study, the overall prevalence was 8.7%. Interestingly, the Asian population was the most affected with 15% rate of ICH; this confirms previous reports that have suggested an increased susceptibility of Asians to develop HS compared to Caucasians [6]. The reason of this susceptibility could be associated to the intrinsic development of angioarchitecture in hemorrhagic cases. Fujimura et al. [18] showed that in hemorrhagic onset, thalamic and choroidal anastomosis are seen in higher proportion than in ischemic onset cases; moreover, interethnic differences in dilation patterns of periventricular anastomosis have been proposed, as shown in Hori et al. [19], where Japanese patients had marked dilation of the anterior choroidal and posterior communicating arteries compared to European Caucasians. However, further studies are required to unveil pathophysiological processes in Western and Eastern cases [8].
The present study aimed to overcome sample size limitations found in previous reports and furthermore to characterize the heterogeneity of Western MMD cases; however, the scope is still limited given its retrospective nature. In the NIS database, the angiographic categorization commonly used in these patients, the Suzuki classification, was not available, and therefore, the severity of the MMD could not be included in our analyses. In addition, limitations of the US medical record system may not reflect all components of the moyamoya phenomenon. Nevertheless, we highlight unique characteristics of Western MMD cases with different clinical presentations that could potentially aid to stratify treatment planning with a greater degree of granularity; hence, we believe the communication of these results will improve the current understanding of the influence of race/ethnicity in the disease progression and thereby neurological sequelae in MMD.
Statement of Ethics
Due to the de-identified nature of the NIS, this study did not require institutional review board (IRB) approval or exempt determination. Ethical approval and consent were not required as this study was based on publicly available data. Patient consents were not required as this study was based on publicly available data.
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Funding Sources
The authors have no funding sources to declare.
Author Contributions
Santiago R. Unda: conception, acquisition, analysis, interpretation, writing draft, and final version. Aldana M. Antoniazzi: acquisition, analysis, and interpretation. Rose Fluss, Neeky Yassari, and Neil Haranhalli: writing draft and final version. Charles Esenwa: interpretation, writing draft, and final version. David J. Altschul: conception, interpretation, writing draft, and final version.
Data Availability Statement
Complete data can be acquired at the Healthcare Cost & Utilization Project (HCUP) webpage (https://www.hcup-us.ahrq.gov/nisoverview.jsp). Further inquiries can be directed to the corresponding author.
Supplementary Material
Supplementary data
Funding Statement
The authors have no funding sources to declare.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary data
Data Availability Statement
Complete data can be acquired at the Healthcare Cost & Utilization Project (HCUP) webpage (https://www.hcup-us.ahrq.gov/nisoverview.jsp). Further inquiries can be directed to the corresponding author.
