Association of Recreational Marijuana Use with Aneurysmal Subarachnoid Hemorrhage

ARTICLE IN PRESS

Association of Recreational Marijuana Use with Aneurysmal Subarachnoid Hemorrhage Kavelin Rumalla,* Adithi Y. Reddy,* and Manoj K. Mittal,

MD†

Objective: Our objective was to evaluate the effect of cannabis use on hospitalizations for aneurysmal subarachnoid hemorrhage (aSAH). Methods: The Nationwide Inpatient Sample (2004-2011) was used to identify all patients (age 15-54) with a primary diagnosis of aSAH (International Classification of Diseases, Ninth Edition, Clinical Modification 430). We identified patients testing positive for cannabis use using all available diagnosis fields. The incidence and characteristics of aSAH hospitalizations among cannabis users were examined. Bivariate and multivariate analyses were performed to determine the effect of cannabis use on aSAH and in-hospital outcomes. Results: Prior to adjustment, the incidence of aSAH in the cannabis cohort was slightly increased relative to the noncannabis cohort (relative risk: 1.07, 95% confidence interval [CI]: 1.02-1.11). Cannabis use in aSAH was more frequent among younger patients (40.44 ± 10.17 versus 43.74 ± 8.68, P < .0001), males (53.3% versus 40.76%, P < .0001), black patients (35.92% versus 19.10%, P < .0001), and Medicaid enrollees (31.13% versus 18.31%, P < .0001). The cannabis use cohort had greater overall illicit drug use but fewer medical risk factors for aSAH. Cannabis use (odds ratio: 1.18, 95% CI: 1.12-1.24) was found to be an independent predictor of aSAH when adjusting for demographics, substance use, and risk factors. Cannabis use was not associated with symptomatic cerebral vasospasm, inpatient mortality, or adverse discharge disposition. Conclusions: Our analysis suggests that recreational marijuana use is independently associated with an 18% increased likelihood of aSAH. Further case–control studies may analyze inpatient outcomes and other understudied mechanisms behind cannabis-associated stroke. Key Words: Cannabis—marijuana—subarachnoid hemorrhage—aneurysm—substance use—Nationwide Inpatient Sample. © 2015 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Introduction Background Marijuana continues to be the prevailing illicit drug of choice in both the United States and the world, with its From the *University of Missouri-Kansas City School of Medicine, Kansas City, Missouri; and †Department of Neurology, University of Kansas Medical Center, Kansas City, Kansas. Address correspondence to Manoj K. Mittal, MD, University of Kansas Medical Center, Department of Neurology, 3599 Rainbow Blvd; Mailstop 2012, Kansas City, KS 66160, USA. E-mail: kr899@ mail.umkc.edu. Received August 17, 2015; accepted October 22, 2015. 1052-3057/$ - see front matter © 2015 National Stroke Association. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2015.10.019

use largely concentrated within the younger adult population. Despite the widespread belief that marijuana is a relatively safe recreational drug, the acute and chronic use of cannabis may have significant public health implications. Marijuana is a potential trigger of cardiovascular complications in the younger population1 and may act as a possible precipitant for clinical events in coronary heart disease patients.2 Marijuana use is associated with a 4.8-fold increase in risk for myocardial infarction3 and contributes to a dose-dependent rise in heart rate, supine hypertension, postural hypotension, and increased cardiac output.4 Another study found that cerebrovascular resistance was increased in chronic cannabis users.5 However, a prospective case–control study did not find an association between cannabis use and cerebrovascular events that was independent of other drug use.6

Journal of Stroke and Cerebrovascular Diseases, Vol. ■■, No. ■■ (■■), 2015: pp ■■–■■

1

ARTICLE IN PRESS K. RUMALLA ET AL.

2

Rationale Other commonly used recreational drugs such as cocaine, amphetamines, and tobacco have previously been studied and shown to be statistical predictors of ischemic or hemorrhagic stroke.7-12 While a multitude of recent case reports13 have suggested temporal links between cannabis use and cerebrovascular disease,14 an epidemiological relationship has not yet been established.

Objectives Aneurysmal subarachnoid hemorrhage (aSAH) is the most common type of stroke and intracranial injury in people under 50 years of age. We conducted a retrospective crosssectional study using the Nationwide Inpatient Sample (NIS) to examine the incidence of hospitalization for aSAH in cannabis and noncannabis users, to study the characteristics of cannabis users in the aSAH population, to examine cannabis use as an independent predictor of aSAH, and to study the effect of cannabis use on aSAH patient outcomes.

Methods NIS The NIS is known to be the most valid and reliable data source for epidemiological estimates that involve inpatient care. The Healthcare Cost and Utilization Project (HCUP) of the Agency for Healthcare Research and Quality collects administrative data on hospital admissions and discharges in the form of the NIS.15 The NIS has provided yearly admission and discharge data from a 20% stratified sample of all hospitals, excluding rehabilitation and long-term care hospitals.15 When discharge weights are applied to unweighted NIS data, the result is a weighted estimate of the total number of discharges representing the U.S. population.15 Unique subject identifiers are used to conceal patient identity and to prevent identification of multiple admissions for the same patient. Records were excluded from the analysis if information for age, gender, discharge disposition, or primary diagnosis was missing. Diagnostic and procedural information in the NIS is identified using the International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) codes and the Clinical Classification Software (HCUP, http://www.hcup-us .ahrq.gov/toolssoftware/ccs/ccsfactsheet.jsp) to collapse the many patient diagnoses and procedures into a smaller number of clinically meaningful categories.15

Inclusion Criteria We queried the NIS from 2004 to 2011 to identify patients with a primary diagnosis of aSAH (ICD-9-CM 430). All cases secondary to trauma were excluded. Current cannabis use was identified using the ICD-9-CM codes 304.30 (cannabis dependence, unspecified), 304.31 (cannabis dependence, continuous), 304.32 (cannabis dependence,

episodic), 305.20 (nondependent cannabis use, unspecified), 305.21 (nondependent cannabis use, continuous), and 305.22 (nondependent cannabis abuse, episodic). Patients coded as “in remission” from drug abuse were excluded. We included patients from 15 to 54 years of age; this age range represented the middle 90th percentile of the cannabis use population. In this study, cannabis abuse was considered equivalent to these ICD-9-CM codes for cannabis abuse were used to represent recreational marijuana use in this study.

Variables Demographic variables included the following NISdefined variables: age, gender (“male” and “female”), race (“white,” “black,” “Hispanic,” “Asian/Pacific Islander,” “Native American,” or “other”), and primary payer status (“Medicare,” “Medicaid,” “private insurance,” “self-pay,” or “no charge”). The Charlson Comorbidity Index was used to determine the standard index of comorbidity in each cohort.16 This variable was defined based on data from the Agency for Healthcare Research and Quality comorbidity measures. We defined suspected risk factors for aSAH (based on previously published data) with codes in all available secondary diagnosis fields in the NIS12,17-19 (Table 1). Outcome variables included discharge disposition, inpatient mortality, and symptomatic cerebral vasospasm. We defined symptomatic cerebral vasospasm using the procedural ICD9-CM codes 00.61 and 00.62 to identify patients treated for vasospasm with angioplasty, as there are no specific ICD-9-CM codes for symptomatic cerebral vasospasm.20

Statistical Analysis All statistical analyses utilized SPSS v.23 (SPSS, Inc., Chicago, IL). Discharge weights provided by HCUP were applied to obtain national estimates of inpatient hospitalizations in the United States. Risk analysis was used to compare aSAH incidence in the NIS cannabis use and noncannabis use cohorts. Age stratification into groups of 15-24, 25-34, 35-44, and 45-54 was performed to assess the effect of age. We utilized indirect standardization to determine the relative risk (RR) of aSAH in cannabis users based on the expected number of aSAH cases in the population of Northern Manhattan. Bivariate and multivariate logistic regression analyses were performed to confirm the suspected risk factors of aSAH in the NIS population, and all variables with a P value less than .05 in the bivariate analysis were further studied in the multivariate model. To study the characteristics of cannabis users in the aSAH population, we compared aSAH patients who were cannabis users and noncannabis users in regard to their demographics, substance use, other aSAH risk factors, and outcomes using bivariate analysis. In all bivariate analyses, the Pearson chi-squared test was employed for categorical variables and the independent sample t-test was utilized for

ARTICLE IN PRESS RECREATIONAL MARIJUANA USE AND ASAH

Table 1. Defined variables by ICD-9-CM and NIS CCS codes

Condition Aneurysmal subarachnoid hemorrhage Substance use Cannabis Cocaine Amphetamines Alcohol

Opioids

Hallucinogens Tobacco Medical aSAH risk factors Hypertension Diabetes mellitus Obesity Coagulopathy Atrial fibrillation Human immunodeficiency virus Type IV Ehlers–Danlos syndrome ADPKD Connective tissue disease Vasculitis History of myocardial infarction Acute myocardial infarction Intracranial tumor Migraine Meningitis Cardiomyopathy, endocarditis, pericarditis, or myocarditis Cardiac and circulatory anomalies Atherosclerosis Transient ischemic attack Elevated cholesterol and lipids Sickle cell disease Oral contraceptive use Viral infection Heart valve disease Symptomatic vasospasm

ICD-9-CM code or CCS 430

304.30-304.32, 305.20-305.22 304.20-304.22, 305.60-305.62 304.40-304.42, 305.70-305.72 303.00-303.02, 303.90-303.92, 305.00-305.02 304.00-304.02, 305.50-305.52, 304.70-304.72 304.50-304.52, 305.30-305.32 305.1 401.xx-405.xx 250.xx 278.00-278.01 286.x 427.31 V08, 042, 079.53 756.83 753.13 710.x 446.x, 447.6 412 410.xx 191.x, 225.0, 225.2, 228.02 346.xx CCS 76 CCS 97 CCS 213 CCS 114 CCS 112 CCS 53 CCS 61 CCS 176 CCS 7 CCS 96 PR61, PR62

Abbreviations: ADPKD, autosomal dominant polycystic kidney disease; aSAH, aneurysmal subarachnoid hemorrhage; CCS, Clinical Classification Software; ICD-9-CM, International Classification of Diseases, Ninth Edition, Clinical Modification; NIS, Nationwide Inpatient Sample.

3

continuous variables. A multivariate logistic regression was used to assess cannabis use as a predictor of aSAH hospitalization after adjusting for aSAH risk factors. Additional models adjusting for cocaine and tobacco use were also employed. Statistical significance was denoted by an alpha of less than .0001.

Results National Incidence of aSAH A total of 116,163,453 noncannabis users and 2,496,165 cannabis users aged 15-54 were identified in the NIS (20042011). Incidence of aSAH in the cannabis use cohort was significantly greater than the incidence of aSAH in the noncannabis use cohort in the 25-34, 35-44, and 45-54 age groups (Table 2). With indirect standardization to the population in Northern Manhattan, the incidence of aSAH among cannabis users was 10.67 (95% confidence interval [CI]: 10.24-11.09) times greater than expected when adjusted for age, 13.20 (95% CI: 12.67-13.72) times greater than expected when adjusted for gender, and 8.71 (95% CI: 8.34-9.09) times greater than expected when adjusted for race.

Characteristics of Cannabis Users with aSAH Among aSAH admissions, cannabis use (N = 2104) and noncannabis use (N = 91,948) cohorts were identified. Cannabis use was significantly more frequent among younger patients (40.44 ± 10.17 versus 43.74 ± 8.68, P < .0001), males (53.26% versus 40.76%, P < .0001), blacks (35.92% versus 19.10%, P < .0001), and Medicaid enrollees (31.13% versus 18.31%, P < .0001). Substance use was significantly greater in cannabis users than in noncannabis users. This trend was observed in users of amphetamine (11.41% versus 1.01%, P < .0001), cocaine (25.19% versus 2.76%, P < .0001), tobacco (59.27% versus 25.39%, P < .0001), hallucinogen (2.61% versus .27%, P < .0001), and alcohol (25.38% versus 6.59%, P < .0001) (Table 3). With the exception of viral infection (P < .0001), there was not a statistically significant difference in the frequency of pre-existing risk factors for aSAH between the cannabis and noncannabis cohorts (P > .0001; Table 3).

Predictors of aSAH We identified 94,053 patients admitted with aSAH and 118,565,567 patients without aSAH for determination of risk factors for aSAH. The bivariate analysis is shown in Table 4. Cannabis use was not a significant predictor of aSAH when unadjusted for other aSAH predictors (odds ratio [OR]: 1.07, 95% CI: 1.020-1.112, P = .004). Multivariate analysis revealed that cannabis was a predictor of aSAH hospitalization when adjusted for factors of aSAH except for cocaine and tobacco (OR: 1.38, 95% CI: 1.32-1.45, P < .0001), tobacco (OR: 1.28, 95% CI: 1.21-1.34, P < .0001), and with all variables adjusted for (OR: 1.18, 95% CI:

ARTICLE IN PRESS K. RUMALLA ET AL.

4

Table 2. Incidence of aSAH hospitalization in cannabis use versus noncannabis use Noncannabis population

Age groups 15-54 15-24 25-34 35-44 45-54

Cannabis population

Total (N)

Incidence per 100,000

Total (N)

Incidence per 100,000

RR of aSAH (95% CI)

P value

116,163,454 22,201,774 31,915,325 27,260,047 34,786,308

79.15 18.45 30.78 93.99 150.65

2,496,166 753,111 743,826 535,790 463,440

84.29 21.25 65.61 109.37 187.73

1.07 (1.02-1.11) 1.15 (.98-1.35) 2.13 (1.95-2.34) 1.16 (1.07-1.26) 1.25 (1.17-1.33)

.004 .08 <.0001 <.0001 <.0001

Abbreviations: aSAH, aneurysmal subarachnoid hemorrhage; CI, confidence interval; RR, relative risk.

1.12-1.24, P < .0001). In the final regression model adjusted for all variables, tobacco (OR: 1.51, 95% CI: 1.49-1.54), cocaine (OR: 1.44, 95% CI: 1.37-1.50) and amphetamine (OR: 3.43, 95% CI: 3.20-3.67) use were also associated with an increased likelihood of aSAH hospitalization. The full multivariate logistic regression model is shown in Table 5.

Outcome Data In regard to discharge disposition, the cannabis use cohort had more routine discharges (50.29% versus 48.65%), transfers to another hospital (12.07% versus 10.23%), discharges to home health care (5.70% versus 4.75%), and more discharges against medical advice (2.61% versus .58%) (P < .0001). However, the cannabis use cohort had fewer transfers to a long-term care facility (14.73 versus 19.36%, P < .0001) and less inpatient mortality (14.59% versus 16.34%, P < .0001). No significant difference in symptomatic vasospasm was observed between the 2 cohorts (P = .808).

Discussion Compared to a standard population in Northern Manhattan, the U.S. incidence of aSAH among hospitalized patients in our cannabis use cohort was 8-13 times greater than the incidence in the standard population of Northern Manhattan. Among NIS admissions, the incidence of aSAH in the cannabis use cohort aged 25-34 was more than two times greater than the noncannabis use cohort, indicating that young adult cannabis users are more susceptible to aSAH than their older counterparts. The determination of cannabis use as an independent predictor of aSAH requires careful consideration of other known confounding aSAH risk factors. According to published literature, aSAH is more common in blacks than in whites, and in females, who are affected 70% of the time.21 However, among our aSAH admissions, we found that the percentage of females in the cannabis use cohort (47%) was lower than that in the noncannabis use cohort (59%). If not adjusted for, female gender would lead to a decreased proportion of aSAH admissions in the cannabis use cohort. The percentage of blacks was higher

in the cannabis use cohort (36%) than in the noncannabis use cohort (19%). If not adjusted for, blacks would lead to a skewed proportion of aSAH cases in the cannabis use cohort because of the increased percentage of blacks in this cohort. Furthermore, the cannabis use cohort had a higher incidence of illicit drug use, whereas the noncannabis use cohort had a higher incidence of other risk factors for aSAH. These results support previous literature that has found an association between regular or heavy cannabis use and risk of using other illicit drugs.22,23 The risk of simultaneously using more than 1 drug typically declines with increasing age. This is somewhat reflected in our results, which indicate the RR of aSAH in cannabis users in the 25-34 age cohort to be over 70% greater than that of both the older (35-44 and 45-55) age cohorts. However, a longitudinal study in New Zealand revealed this trend to begin with adolescence, so the expected result would be observing the highest RR for our 15-24 age cohort, which was unexpectedly lower than the RR of the 25-34 cohort.23 The lower RR for aSAH in the 15-24 age cohort is likely due to the age-related decrease in risk factors for aSAH. After adjusting for other known risk factors of aSAH, cannabis use was associated with an 18% increased likelihood of aSAH hospitalization. This likelihood increased when cannabis use was combined with tobacco use (28%) and further increased when combined with cocaine and tobacco use (38%). These increases in likelihoods can be explained by the known risk of aSAH with tobacco and cocaine use. Another plausible explanation is the synergistic relationship between cannabis and tobacco or cannabis, tobacco, and cocaine. Several animal studies have revealed that the “gateway effect” of cannabis on brain function is caused by its main psychoactive component, delta-9-tetrahydrocannabinol.22 Further, common neural pathways of both cannabis and cocaine underlie the rewarding dopaminergic effects.24 Our results are consistent with a recent populationbased study in Texas (Texas Health Care Information Council inpatient database), which suggested that amphetamine, cocaine, and tobacco were associated with an increased risk of hemorrhagic stroke.12 This study did not

ARTICLE IN PRESS RECREATIONAL MARIJUANA USE AND ASAH

5

Table 3. Characteristics of hospitalizations for aSAH with and without cannabis use Characteristics

Noncannabis aSAH

Cannabis aSAH

P value

Total patients (N) Mean age ± SD (year) Age group (%) 15-24 25-34 35-44 45-54 Gender (%) Male (%) Female (%) Race (%) White Black Hispanic Asian or Pacific Islander Native American Other Primary payer (%) Medicare Medicaid Private insurance Self-pay No charge Charlson Comorbidity Index (%) 0 1 2 3 4+ Substance use (%) Tobacco Cocaine Amphetamines Hallucinogens Alcohol Other aSAH risk factors (%) Viral infection Hypertension Diabetes mellitus Coagulopathy History of MI Connective tissue disease Migraine Acute MI Heart valve disease Atherosclerosis Atrial fibrillation Obesity Other aSAH risk factors (%) Oral contraception Intracranial tumor Cardiac and circulatory anomalies Transient ischemic attack Meningitis Vasculitis Vessel dissection ADPKD

91,948 43.74 ± 8.68

2,104 40.44 ± 10.17

NA <.0001 <.0001

4.28 10.69 27.87 56.99

7.60 23.19 27.85 41.35

40.76 59.24

53.26 46.74

56.70 19.10 15.92 3.62 .49 4.17

49.35 35.92 10.13 1.47 .59 2.53

5.58 18.31 53.93 15.19 1.35

6.99 31.13 25.24 28.45 1.39

75.56 15.36 4.21 2.44 2.43

72.90 18.74 3.47 2.52 2.38

25.39 2.76 1.01 .27 6.59

59.27 25.19 11.41 2.61 1.33

<.0001 <.0001 <.0001 <.0001 <.0001

.48 45.23 8.31 3.88 .94 .90 8.14 2.06 1.92 .45 1.76 4.98

1.33 47.39 6.37 2.99 .48 .48 9.84 1.43 1.14 .48 1.71 5.09

<.0001 .049 .002 .036 .028 .042 .005 .043 .009 .848 .867 .826

.13 .35 1.67 8.40 2.49 .31 .87 .11

.29 .24 1.24 7.70 1.90 .24 .00 .00

.067 .386 .125 .25 .089 .56 NA NA

<.0001 <.0001

<.0001

<.0001

Abbreviations: ADPKD, autosomal dominant polycystic kidney disease; aSAH, aneurysmal subarachnoid hemorrhage; MI, myocardial infarction; NA, not enough subjects to perform test; SD, standard deviation.

ARTICLE IN PRESS K. RUMALLA ET AL.

6

Table 4. Bivariate analysis of suspected aSAH risk factors Variables

Non-aSAH

aSAH

P value

Total patients (N) Mean age ± SD (year) Age group (%) 15-24 25-34 35-44 45-54 Female (%) Race (%) White Black Hispanic Asian or Pacific Islander Native American Other Charlson Comorbidity Index (%) 0 1 2 3 4+ Substance abuse (%) Cannabis Tobacco Cocaine Amphetamine Opioids Hallucinogens Alcohol Other suspected aSAH risk factors (%) Hypertension Atherosclerosis Diabetes mellitus Coagulopathy History of MI Connective tissue disease Atrial fibrillation Obesity Oral contraception Intracranial tumor ADPKD Cardiac and circulatory anomalies Transient ischemic attack Meningitis Migraine Acute MI Viral infection Heart valve disease Vasculitis Vessel dissection AIDS Cardiomyopathy, endocarditis, pericarditis, or myocarditis Hypercholesterolemia or hyperlipidemia Type IV Ehlers–Danlos syndrome Sickle cell disease

118,565,567 36.07 ± 11.202

94,052 43.66 ± 8.725

18.64 27.54 23.42 29.69 66.92

4.36 10.97 27.87 56.64 58.91

58.80 18.29 15.70 2.71 .80 3.70

56.53 19.51 15.78 3.57 .49 4.13

75.29 13.62 4.65 2.87 3.58

75.50 15.44 4.20 2.44 2.43

2.10 15.14 1.70 .35 1.96 .022 5.29

2.24 26.14 3.26 1.25 1.06 .011 7.01

.004 <.0001 <.0001 <.0001 <.0001 .021 <.0001

22.21 .25 10.86 2.35 1.34 .71 .93 7.40 2.28 .09 .02 .30 .06 .08 2.91 .27 1.10 1.30 .09 .03 .41 1.61 8.04 .02 .40

45.27 .45 12.01 3.86 .93 .89 1.76 4.98 .14 .35 .11 1.66 8.39 2.47 8.18 2.04 .50 1.91 .31 .85 .47 1.49 8.11 .03 .41

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 .0100 .0050 .4580 .0570 .3740

<.0001 <.0001

<.0001 <.0001

<.0001

Abbreviations: ADPKD, autosomal dominant polycystic kidney disease; AIDS, acquired immune deficiency syndrome; aSAH, aneurysmal subarachnoid hemorrhage; MI, myocardial infarction; SD, standard deviation.

ARTICLE IN PRESS RECREATIONAL MARIJUANA USE AND ASAH

7

Table 5. Multivariate predictors of aSAH

find cannabis use as a significant predictor of hemorrhagic stroke. However, descriptive analysis of their results suggests a possible association between cannabis use and hemorrhagic stroke.12 The mechanism behind cannabis-associated aSAH is currently unknown. A previous study established that cocaineinduced cerebral vasospasm was associated with dopaminerich areas of the brain and postulated that these dopaminergic pathways are responsible for cerebral autoregulation.25 Marijuana is known to increase dopamine release in certain areas of the brain,26 leading us to suspect that cannabis use may also lead to increased cerebral vasospasm, thereby leading to increased aSAH. Cannabis use has already been identified as a causative factor in reversible cerebral vasoconstriction syndrome (RCVS).27 Furthermore, RCVS has been shown to lead to aSAH and associated cerebral vasospasm.28,29 RCVS was not defined as a variable in our analysis because it does not have a specific ICD-9-CM code, which is accepted as a limitation of our study. An increase in cerebral vasospasm was not observed in our cannabis use cohort compared to the noncannabis use cohort. However, we have reason to suspect that cannabis use associated with aSAH is at least partially a result of RCVS. In a French prospective study of RCVS, vasoactive substances, mostly cannabis, accounted for the majority (55%) of the RCVS cases.30 An increase in inpatient mortality or symptomatic vasospasm was not observed in cannabis users admitted for aSAH. The strengths of this study include the use of the NIS database. The NIS is the largest all-payer national inpatient care database whose sample size provides unparalleled statistical power in analyzing associations between conditions. Due to the lack of studies—especially epidemiological ones—testing for a link between aSAH and cannabis use, our use of the NIS is an effective means of acquiring data to demonstrate the relationship between the two, independent of confounding variables, and to contribute to existing data regarding other illicit drugs. The exhaustive list of statistically determined covariates used in our logistic regression analyses affirms the legitimacy of our findings. The National Hospital Ambulatory Medical Care Survey and the American Hospital Association Annual Survey databases, which are used to evaluate and benchmark the NIS, monitor inaccuracies in the NIS database. Other databases, including the American Hospital Association Survey and the Area Resource File, are linked to the NIS with accuracy via the benchmark process. The present study also possesses several limitations. As with all large administrative databases, there is possibility of inaccuracy of diagnoses and procedural codes used to identify our patients from the NIS database. Previous studies have demonstrated that aSAH can be accurately identified in the NIS with ICD-9-CM primary diagnosis codes.20,31 Furthermore, only patients with a primary diagnosis of aSAH were included in our

Predictors Mean age (per 1 yr) Charlson Comorbidity Index (per additional comorbidity) Gender Male Female Race White Black Hispanic Asian or Pacific Islander Native American Other Primary payer Medicare Medicaid Private insurance Self-pay No charge Other Substance use No substance use Cannabis Tobacco Cocaine Amphetamines Hallucinogens Alcohol Other aSAH risk factors No risk factor Viral infection Hypertension Diabetes mellitus Coagulopathy History of MI Connective tissue disease Migraine Acute MI Heart valve disease Atherosclerosis Atrial fibrillation Obesity Oral contraception Intracranial tumor Cardiac and circulatory anomalies Transient ischemic attack Meningitis Vasculitis Vessel dissection ADPKD

Odds ratio (95% CI)

P value

.94 (.91-.97) 1.11 (1.10-1.13)

<.0001 <.0001

Reference 1.06 (1.06-1.06)

<.0001

Reference 1.16 (1.13-1.18) 1.57 (1.53-1.60) 1.77 (1.70-1.84) .75 (.69-.85) 1.45 (1.39-1.50)

<.0001 <.0001 <.0001 <.0001 <.0001

Reference 2.02 (1.95-2.09) 2.63 (2.55-2.72) 3.38 (3.25-3.50 2.75 (2.57-2.94) 2.48 (2.37-2.59)

<.0001 <.0001 <.0001 <.0001 <.0001

Reference 1.18 (1.12-1.24) 1.51 (1.49-1.54) 1.44 (1.37-1.50) 3.43 (3.20-3.67) 1.14 (.88-1.48) .98 (.97-1.00)

<.0001 <.0001 <.0001 <.0001 .311 .010

Reference 72.52 (70.41-74.70) 21.47 (20.36-22.64) 15.45 (14.16-16.86) 5.09 (4.12-6.27) 4.48 (4.25-4.73) 3.64 (3.43-3.87) 2.83 (2.49-3.21) 2.24 (2.20-2.27) 2.21 (2.15-2.27) 2.18 (1.90-2.50) 1.53 (1.47-1.59) 1.17 (1.10-1.24) .86 (.84-.88) .56 (.51-.62) .50 (.48-.52)

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

.35 (.32-.37) .097 (.079-.119) 1.00 (.90-1.12) 1.00 (.89-1.05) .96 (.91-1.02)

<.0001 <.0001 .976 .393 .192

Abbreviations: ADPKD, autosomal dominant polycystic kidney disease; AIDS, acquired immune deficiency syndrome; aSAH, aneurysmal subarachnoid hemorrhage; CI, confidence interval; MI, myocardial infarction; SD, standard deviation.

ARTICLE IN PRESS K. RUMALLA ET AL.

8

analyses, so there is a possibility of an underdiagnosis of aSAH patients in the NIS database. There is also a chance of misclassification and underclassification of drug use using secondary ICD-9-CM codes as it is often self-reported.32 However, if the sensitivity for substance use in the ICD-9-CM coding were higher, it would likely strengthen the association between substance use and aSAH. No information pertaining to the time from last drug use to aSAH was available. However, we eliminated all cases of drug use coded as “in remission” to identify only active drug users. We are also limited by the specificity of ICD-9-CM coding practices. For example, there is no specific diagnosis code denoting reversible cerebral vasoconstriction syndrome (RCVS), which is hypothesized to be one of the mechanisms behind cannabisinduced aSAH. Furthermore, an assessment of preadmission functional status and severity of aSAH at admission could not be determined using the NIS.

Conclusion Recreational marijuana is falsely perceived as having fewer side effects than other commonly used substances such as tobacco and cocaine. To our knowledge, this study provides the first epidemiological evidence of an association between recreational cannabis use (the most commonly used illicit drug) and aSAH (the most common type of stroke in the young adult population). This association is independent of other substance use, including tobacco, alcohol, amphetamine, cocaine, and hallucinogen use. Four states have already legalized marijuana for recreational use despite its numerous adverse health effects. Further studies may examine hemorrhagic stroke and cannabis use using a prospective case–control study design to analyze inpatient outcomes and other understudied mechanisms behind stroke, including RCVS.

References 1. Jouanjus E, Lapeyre-Mestre M, Micallef J, et al. Cannabis use: signal of increasing risk of serious cardiovascular disorders. J Am Heart Assoc 2014;3:e000638. 2. Sidney S. Cardiovascular consequences of marijuana use. J Clin Pharmacol 2002;42(11 Suppl). 3. Mittleman MA, Lewis RA, Maclure M, et al. Triggering myocardial infarction by marijuana. Circulation 2001;103:2805-2809. 4. Jones RT. Cardiovascular system effects of marijuana. J Clin Pharmacol 2002;42(11 Suppl). 5. Herning RI, Better WE, Tate K, et al. Cerebrovascular perfusion in marijuana users during a month of monitored abstinence. Neurology 2005;64:488-493. 6. Barber PA, Pridmore HM, Krishnamurthy V, et al. Cannabis, ischemic stroke, and transient ischemic attack: a case-control study. Stroke 2013;44:2327-2329. 7. Moon K, Albuquerque FC, Mitkov M, et al. Methamphetamine use is an independent predictor of poor outcome after aneurysmal subarachnoid hemorrhage. J Neurointerv Surg 2015;7:346-350.

8. Yeung JT, Williams J, Bowling WM. Effect of cocaine use on outcomes in traumatic brain injury. J Emerg Trauma Shock 2013;6:189-194. 9. Shah RS, Cole JW. Smoking and stroke: the more you smoke the more you stroke. Expert Rev Cardiovasc Ther 2010;8:917-932. 10. Esse K, Fossati-Bellani M, Traylor A, et al. Epidemic of illicit drug use, mechanisms of action/addiction and stroke as a health hazard. Brain Behav 2011;1:44-54. 11. Broderick JP, Viscoli CM, Brott T, et al. Major risk factors for aneurysmal subarachnoid hemorrhage in the young are modifiable. Stroke 2003;34:1375-1381. 12. Westover AN, McBride S, Haley RW. Stroke in young adults who abuse amphetamines or cocaine: a population-based study of hospitalized patients. Arch Gen Psychiatry 2007;64. 13. Hackam DG. Cannabis and stroke: systematic appraisal of case reports. Stroke 2015;46:852-856. 14. Mateo I, Pinedo A, Gomez-Beldarrain M, et al. Recurrent stroke associated with cannabis use. J Neurol Neurosurg Psychiatry 2005;76:435-437. 15. Agency for Healthcare Research and Quality. Introduction to the HCUP Nationwide Inpatient Sample (NIS) Agency for Healthcare Research and Quality [Internet]. 2015. Available at: https://www.hcup-us.ahrq.gov/ nisoverview.jsp. Accessed April 23, 2015. 16. Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40:373-383. 17. Connolly ESJ, Rabinstein AA, Carhuapoma JR, et al. Guidelines for the management of aneurysmal subarachnoid hemorrhage: a guideline for healthcare professionals from the American Heart Association/ American Stroke Association. Stroke 2012;43:1711-1737. 18. Feigin VL, Rinkel GJE, Lawes CMM, et al. Risk factors for subarachnoid hemorrhage: an updated systematic review of epidemiological studies. Stroke 2005;36:27732780. 19. Teunissen LL, Rinkel GJ, Algra A, et al. Risk factors for subarachnoid hemorrhage: a systematic review. Stroke 1996;27:544-549. 20. Murthy SB, Moradiya Y, Shah S, et al. In-hospital outcomes of aneurysmal subarachnoid hemorrhage associated with cocaine use in the USA. J Clin Neurosci 2014;21:2088-2091. 21. Bederson JB, Connolly ESJ, Batjer HH, et al. Guidelines for the management of aneurysmal subarachnoid hemorrhage: a statement for healthcare professionals from a special writing group of the Stroke Council, American Heart Association. Stroke 2009;40. 22. Hall WD, Lynskey M. Is cannabis a gateway drug? Testing hypotheses about the relationship between cannabis use and the use of other illicit drugs. Drug Alcohol Rev 2005;24. 23. Fergusson DM, Boden JM, Horwood LJ. Cannabis use and other illicit drug use: testing the cannabis gateway hypothesis. Addict Abingdon Engl 2006;101:556-569. 24. Levesque RJR, Herpertz-Dahlmann B, Berman SL, et al. Encyclopedia of adolescence. 1st ed. New York: SpringerVerlag, 2011. 25. Johnson B, Lamki L, Fang B, et al. Demonstration of dose-dependent global and regional cocaine-induced reductions in brain blood flow using a novel approach to quantitative single photon emission computerized tomography. Neuropsychopharmacology 1998;18:377384.

ARTICLE IN PRESS RECREATIONAL MARIJUANA USE AND ASAH 26. Jentsch JD, Andrusiak E, Tran A, et al. Delta 9-tetrahydrocannabinol increases prefrontal cortical catecholaminergic utilization and impairs spatial working memory in the rat: blockade of dopaminergic effects with HA966. Neuropsychopharmacology 1997;16:426-432. 27. Robert T, Kawkabani Marchini A, Oumarou G, et al. Reversible cerebral vasoconstriction syndrome identification of prognostic factors. Clin Neurol Neurosurg 2013;115:2351-2357. 28. Edlow B, Kasner S, Hurst R, et al. Reversible cerebral vasoconstriction syndrome associated with subarachnoid hemorrhage. Neurocrit Care 2007;7:203-210. 29. Forget P, Goffette P, van de Wyngaert F, et al. Possible overlap between reversible cerebral vasoconstriction

9 syndrome and symptomatic vasospasm after aneurysmal subarachnoid hemorrhage. J Headache Pain 2009; 10. 30. Ducros A, Boukobza M, Porcher R, et al. The clinical and radiological spectrum of reversible cerebral vasoconstriction syndrome. A prospective series of 67 patients. Brain 2007;130(Pt 12):3091-3101. 31. McDonald RJ, McDonald JS, Bida JP, et al. Subarachnoid hemorrhage incidence in the United States does not vary with season or temperature. AJNR Am J Neuroradiol 2012;33:1663-1668. 32. O’Malley KJ, Cook KF, Price MD, et al. Measuring diagnoses: ICD code accuracy. Health Serv Res 2005;40 (5 Pt 2):1620-1639.