Adverse Cardiovascular Outcomes in Young Cannabis Users: A Nationwide Analysis

1. Introduction

Cannabis use has become widely prevalent throughout society, especially in younger adults amidst recent decriminalization/legalization. Parekh at al found a higher percentage of cannabis users to be less than <34 years of age [1], and a recent NIH monitor the future survey reported an all-time high of young adult marijuana users (age 18-30) in 2021[2]. This alludes to higher tendencies of recreational marijuana use in the young adult population.

Despite its growing prevalence, the physiological effects of cannabis use have not been well studied. Few preliminary studies have advocated for the therapeutic benefit of cannabis for its analgesic and anti-inflammatory effects, reporting that in some experimental models of heart disease cannabidiol downregulates oxidative stress, inflammatory processes and apoptosis [3]. While this may be reassuring, recent research describes detrimental effects of cannabis use. Two separate studies, one using a national inpatient sample from 2007-2014 and the other from 2015-2017, demonstrated statistically significant trends in hypertensive emergency-related admissions in cannabis use disorder despite adjusting for comorbid conditions [4,5]. Cannabis use has also been associated with smoking, and odds of cannabis users using cocaine have been significant [6]. Some studies even suggest possible association between cannabis use diabetes via endocannabinoid system modulation [7], and cannabis use and hyperlipidemia via cannabinoid inhibition of reverse cholesterol transport [8].

Despite these known trends, the available research has few (cohort) studies that focus on cannabis use and cardiovascular trends specifically in the young adult population. A recent Cross-sectional study showed an association between cannabis use and ACS admissions; however, this study utilized self-reported data looking at adults ages 18-74 [9]. One retrospective study demonstrated cannabis use associated with acute-MI from a national inpatient sample from 2010-2014 looking at ages 11-70 [10].

Even fewer studies have looked at cannabis use and ventricular arrhythmias, with one isolated case study describing a patient who had an episode of ventricular fibrillation after smoking more cannabis than his usual (and may be the first ever reported) [11]. To further evaluate the younger population, we looked at the prevalence of acute coronary syndrome and V.fib admissions among young adults with CUD+ (known cannabis use disorder) and non-cannabis users (CUD-) with retrospective analysis of a nationally representative cohort from the United States.

2. Materials and Methods

National Inpatient Sample (2019-2020) was used to analyze the data. ICD10 code trunk 124 was used to select admissions for the diagnosis of acute coronary syndrome. ICD 10 code 14901 was used to select admissions for Ventricular fibrillation. Code trunks F12 and F14 were used to select a documented diagnosis of cannabis use disorder (codes for cannabis use in remission have been avoided). Baseline characteristics of admissions in young adults (18-45) with documented diagnosis of cannabis use was done.CMR comorbidity software and ICD 10 codes were used to select admissions with associated cocaine use, smoking, diabetes and uncontrolled hypertension. Population analysis of admissions for young adults (AGE 18- 45) with acs or vfib and its association with cannabis use disorder was done. Probit logistic regression was used to analyze the association. Adjusted odds ratio was done using probit logistic regression model after accounting for age, sex, race and quarterly income. The comorbidities used in the logistic regression were smoking and cocaine, as these have independent association with cannabis use disorder and acs/ vfib. Co-existing diagnoses of uncontrolled hypertension, diabetes and hyperlipidemia were also included in the multivariate logistic regression as these factors have association with acs/ vfib and are also likely to be prevalent in cannabis use disorder. To analyze the causative effect of cannabis use on adverse outcomes in acs admissions in young adults, a propensity score and nearest neighbor matching ( with caliper width of 0.2 ) was used, were exact matching was done for age, sex, race, quarterly income, prevalence of smoking and cocaine abuse: in hospital outcomes studied were all cause mortality, requirement of intra-aortic balloon pump during the admission, mean length of hospital stay and total hospital charges. P value < 0.05 was used to assess significance in all calculations. Among the admissions with documented cannabis use in young patients, factors that were associated with the diagnosis of ACS were also analyzed.

3. Results

Using the above-mentioned ICD 10 codes, 32990 admissions under the age of 45 for the diagnosis of acs were identified, of which 2295 admissions had documented cannabis use disorder. 1939 admissions under the age of 45, with a diagnosis of ventricular fibrillation were identified, of which 125 admissions had a coexisting diagnosis of cannabis use disorder.

Table 1. Baseline characteristics of Admissions with documented Cannabis use disorder (Age 18 - 45) .

Total Number of Admissions	543445
Mean Age	36.574 (36.023 - 36.897)
Sex	Male 56.17% Female 43.83%
Race	Whites 52.93% Blacks 28.77% Hispanics 12.33%
Quarterly income (calculated from the zip codes of the household)	1st quartile 40.25% 2nd quartile 25.67% 3rd quartile 21% 4th quartile 13.08%
Hospital Region	1.Northeast 18.02% 2. Midwest 24.9% 3.South 36.64% 4.West 20.44%
Prevalence of Hypertension	13.86%
Prevalence of diabetes	5.4%
Prevalence of Hyperlipidemia	4.40%
Prevalence of smoking	32.43%
Prevalence of other drug abuse	64.46%
Prevalence of Depression/ Psychosis	15.135%

543445 admissions were identified with documented cannabis use disorder in the age group of 18-45. The prevalence of hypertension, diabetes, hyperlipidemia, smoking, other substance abuse and mental health issues were calculated among the admissions with documented cannabis use using the CMR comorbidity software. .

Table 1. Baseline characteristics of Admissions with documented Cannabis use disorder (Age 18 - 45) .

Total Number of Admissions	543445
Mean Age	36.574 (36.023 - 36.897)
Sex	Male 56.17% Female 43.83%
Race	Whites 52.93% Blacks 28.77% Hispanics 12.33%
Quarterly income (calculated from the zip codes of the household)	1st quartile 40.25% 2nd quartile 25.67% 3rd quartile 21% 4th quartile 13.08%
Hospital Region	1.Northeast 18.02% 2. Midwest 24.9% 3.South 36.64% 4.West 20.44%
Prevalence of Hypertension	13.86%
Prevalence of diabetes	5.4%
Prevalence of Hyperlipidemia	4.40%
Prevalence of smoking	32.43%
Prevalence of other drug abuse	64.46%
Prevalence of Depression/ Psychosis	15.135%

543445 admissions were identified with documented cannabis use disorder in the age group of 18-45. The prevalence of hypertension, diabetes, hyperlipidemia, smoking, other substance abuse and mental health issues were calculated among the admissions with documented cannabis use using the CMR comorbidity software. .

Table 2. Analysis of acute coronary syndrome and ventricular fibrillation admissions (age 18 - 45) with documented diagnosis of cannabis use disorder .

Young adults (AGE < 45) admitted for-	Acute coronary syndrome admissions	Acute coronary syndrome admissions with cannabis use disorder	Ventricular fibrillation	Ventricular fibrillation with cannabis use disorder
Mean Age	39.83 (38.92 - 40.1)	37.76 (37.12 - 37. 85)	35.664 (34.93 - 36.39)	35.2 (32.38 - 38.017)
Sex	Male 66.93% Female 33.07%	Male 73.64% Female 26.36%	Male 59.96% Female 43.04%	Male 56% Female 44%
Race	Whites 56.99% Blacks 22.54% Hispanics 12.62% Others	Whites 41.37% Blacks 40.93 % Hispanics 14.38% Others	Whites 52.38% Blacks 27.51% Hispanics 11.38% Others	Whites 56% Blacks 32% Hispanics 4% Others
Median Household income	1st quartile 36.6%   2nd quartile 26.36% 3rd quartile 22.69% 4th quartile 14.3%	1st quartile 45.37% 2nd quartile 22.69% 3rd quartile 20.26% 4th quartile 11.6%	1st quartile 28.08% 2nd quartile 27.82% 3rd quartile 23.1% 4th quartile 21%	1st quartile 36% 2nd quartile 20% 3rd quartile 20% 4th quartile 24%
Primary Expected Copayer	1.Medicare 8.3% 2. Medicaid 27.88% 3.Private Insurance43.9% 4. Self Pay 14.68%	1.Medicare 9.61% 2. Medicaid 39.52% 3.Private Insurance22.71% 4. Self Pay 23.48%	1.Medicare 9.59% 2. Medicaid 30.83% 3.Private Insurance 44.3% 4. Self Pay 9.3%	1.Medicare 8% 2. Medicaid 32% 3.Private Insurance40% 4. Self Pay 8%
Charlson Comorbidity index (>=5)	11.2%	17.8%	8%	17%
Hospital Region	1.Northeast 14.41% 2. Midwest 22.52% 3.South 46.21% 4.West 16.85%	1.Northeast 12.2% 2.Midwest 21.13% 3.South 49.89% 4.West 16.78%	1.Northeast 11.08% 2. Midwest 23.71% 3.South 41.75% 4.West 23.45%	1.Northeast 8% 2. Midwest 32% 3.South 28% 4.West 32%

Table 2. Analysis of acute coronary syndrome and ventricular fibrillation admissions (age 18 - 45) with documented diagnosis of cannabis use disorder .

Young adults (AGE < 45) admitted for-	Acute coronary syndrome admissions	Acute coronary syndrome admissions with cannabis use disorder	Ventricular fibrillation	Ventricular fibrillation with cannabis use disorder
Mean Age	39.83 (38.92 - 40.1)	37.76 (37.12 - 37. 85)	35.664 (34.93 - 36.39)	35.2 (32.38 - 38.017)
Sex	Male 66.93% Female 33.07%	Male 73.64% Female 26.36%	Male 59.96% Female 43.04%	Male 56% Female 44%
Race	Whites 56.99% Blacks 22.54% Hispanics 12.62% Others	Whites 41.37% Blacks 40.93 % Hispanics 14.38% Others	Whites 52.38% Blacks 27.51% Hispanics 11.38% Others	Whites 56% Blacks 32% Hispanics 4% Others
Median Household income	1st quartile 36.6%   2nd quartile 26.36% 3rd quartile 22.69% 4th quartile 14.3%	1st quartile 45.37% 2nd quartile 22.69% 3rd quartile 20.26% 4th quartile 11.6%	1st quartile 28.08% 2nd quartile 27.82% 3rd quartile 23.1% 4th quartile 21%	1st quartile 36% 2nd quartile 20% 3rd quartile 20% 4th quartile 24%
Primary Expected Copayer	1.Medicare 8.3% 2. Medicaid 27.88% 3.Private Insurance43.9% 4. Self Pay 14.68%	1.Medicare 9.61% 2. Medicaid 39.52% 3.Private Insurance22.71% 4. Self Pay 23.48%	1.Medicare 9.59% 2. Medicaid 30.83% 3.Private Insurance 44.3% 4. Self Pay 9.3%	1.Medicare 8% 2. Medicaid 32% 3.Private Insurance40% 4. Self Pay 8%
Charlson Comorbidity index (>=5)	11.2%	17.8%	8%	17%
Hospital Region	1.Northeast 14.41% 2. Midwest 22.52% 3.South 46.21% 4.West 16.85%	1.Northeast 12.2% 2.Midwest 21.13% 3.South 49.89% 4.West 16.78%	1.Northeast 11.08% 2. Midwest 23.71% 3.South 41.75% 4.West 23.45%	1.Northeast 8% 2. Midwest 32% 3.South 28% 4.West 32%

Table 3. Association between documented diagnosis of cannabis use disorder and admission diagnosis of ACS. (Age 18-45).

Association between acs admissions and cannabis use disorder diagnosis	OR	P value	Confidence Interval
Unadjusted Odds	2.95068	0.000	2.683 - 3.244
Adjusted Odds for Age, Sex, Race and household income	2.67347	0.000	2.4255 2.9504
Additional adjustment for coexisting diagnosis of smoking, cocaine use, uncontrolled hypertension, diabetes and Hyperlipidemia	2.749	0.000	2.485- 3.040

Table 3. Association between documented diagnosis of cannabis use disorder and admission diagnosis of ACS. (Age 18-45).

Association between acs admissions and cannabis use disorder diagnosis	OR	P value	Confidence Interval
Unadjusted Odds	2.95068	0.000	2.683 - 3.244
Adjusted Odds for Age, Sex, Race and household income	2.67347	0.000	2.4255 2.9504
Additional adjustment for coexisting diagnosis of smoking, cocaine use, uncontrolled hypertension, diabetes and Hyperlipidemia	2.749	0.000	2.485- 3.040

The association between admission diagnosis of ACS and co-existing diagnosis of cannabis use was found to be statistically significantly, after matching for potential confounders.

Table 4. association between admission diagnosis of ventricular fibrillation and documented diagnosis of cannabis use disorder (Age 18-45).

Association between vfib admissions and cannabis use disorder diagnosis	OR	P value	Confidence Interval
Unadjusted Odds	2.713	0.000	1.809- 4.069
Adjusted Odds for Age, Sex, Race and household income	2.561	0.000	1.873 - 3.876
Additional adjustment for coexisting diagnosis of smoking, cocaine use, uncontrolled hypertension, diabetes and Hyperlipidemia	2.2974	0.000	1.5103 -3.493

Table 4. association between admission diagnosis of ventricular fibrillation and documented diagnosis of cannabis use disorder (Age 18-45).

Association between vfib admissions and cannabis use disorder diagnosis	OR	P value	Confidence Interval
Unadjusted Odds	2.713	0.000	1.809- 4.069
Adjusted Odds for Age, Sex, Race and household income	2.561	0.000	1.873 - 3.876
Additional adjustment for coexisting diagnosis of smoking, cocaine use, uncontrolled hypertension, diabetes and Hyperlipidemia	2.2974	0.000	1.5103 -3.493

The association between admission diagnosis of Ventricular fibrillation and co-existing diagnosis of cannabis use was found to be statistically significantly, after matching for potential confounders.

Table 5. Analysis was done in the population selected based on diagnosis of cannabis use disorder. Analysis of comorbid conditions and their associations with acs as the admitting diagnosis: analysis done among admissions with documented diagnosis of cannabis use disorder : (age 18 -45).

CHARACTERISTICS OF ADMISSIONS WITH CANNABIS USE DISORDER	ASSOCIATION WITH ACS AS ADMISSION DIAGNOSIS: UNADJUSTED ODDS RATIO	ASSOCIATION WITH ACS AS ADMISSION DIAGNOSIS: ADJUSTED ODDS RATIO
Admissions with Black as the documented race	1.73848 (1.4428 - 2.09711) p value 0.000	1.73522 (1.4391- 2.0935) p value 0.000
Admissions with diagnosis of uncontrolled hypertension	4.082651 (3.3829 - 4.927039) P value 0.00	4.08102 (3.37617 - 4.9185) p value 0.000
Admissions with diagnosis of diabetes	2.45692 (1.8591 - 3.24958) P value 0.000	2.37651 (1.8532 - 3.2458) P value 0.000
Admissions with diagnosis of smoking	1.5346 (0.04912 - 4.7935) P value 0.000
Admissions with diagnosis of cocaine use	1.3361 (0.097481 - 1.82455) P value 0.000

Odds ratio was adjusted for age, sex, household income in probit logistic regression. Among the population of admissions with documented diagnosis of cannabis use disorder (age 18 - 45), those admissions with documented black race, uncontrolled hypertension and diabetes had statistically significant association with ACS as the admitting diagnosis. Co diagnosis of smoking or cocaine use did not reach statistical significance.

Table 5. Analysis was done in the population selected based on diagnosis of cannabis use disorder. Analysis of comorbid conditions and their associations with acs as the admitting diagnosis: analysis done among admissions with documented diagnosis of cannabis use disorder : (age 18 -45).

CHARACTERISTICS OF ADMISSIONS WITH CANNABIS USE DISORDER	ASSOCIATION WITH ACS AS ADMISSION DIAGNOSIS: UNADJUSTED ODDS RATIO	ASSOCIATION WITH ACS AS ADMISSION DIAGNOSIS: ADJUSTED ODDS RATIO
Admissions with Black as the documented race	1.73848 (1.4428 - 2.09711) p value 0.000	1.73522 (1.4391- 2.0935) p value 0.000
Admissions with diagnosis of uncontrolled hypertension	4.082651 (3.3829 - 4.927039) P value 0.00	4.08102 (3.37617 - 4.9185) p value 0.000
Admissions with diagnosis of diabetes	2.45692 (1.8591 - 3.24958) P value 0.000	2.37651 (1.8532 - 3.2458) P value 0.000
Admissions with diagnosis of smoking	1.5346 (0.04912 - 4.7935) P value 0.000
Admissions with diagnosis of cocaine use	1.3361 (0.097481 - 1.82455) P value 0.000

Odds ratio was adjusted for age, sex, household income in probit logistic regression. Among the population of admissions with documented diagnosis of cannabis use disorder (age 18 - 45), those admissions with documented black race, uncontrolled hypertension and diabetes had statistically significant association with ACS as the admitting diagnosis. Co diagnosis of smoking or cocaine use did not reach statistical significance.

Propensity score and nearest neighbor matching and analysis among acs admissions in young adults.

Since the total number of acs admissions in young adults was much higher than compared to the subpopulation with cannabis use disorder, to analyze the in-hospital outcomes of acs, namely all-cause mortality, requirement of IABP, mean length of stay and mean of total charges, propensity score matched samples from the main sample were selected.

Propensity score and nearest neighbor matching ( caliper width 0.2) was done among the acs admissions in young adults: exact matching was done for Age, Sex, Race, Income, Prevalence of smoking and cocaine use: each cohort had 380 subjects, differing only in cannabis use ( CUD + and CUD - cohorts): outcomes of all-cause mortality, mean length of stay, total charges and requirement of intra-aortic balloon pump ( IABP) during the admission was studied. (Overall balance test- Hansen and Bowers: chi square 2.52, dF 6.00), using the relative multivariate imbalance (Lacus, king and porro 2010) had .161 after matching): no covariates exhibited a large imbalance > .25). SPSS 29 and R 4.3 were used to create the propensity score matched cohorts.

	CUD + cohort	CUD - cohort
Total cohort size	380	380
Mean Age	38.8236	38.8236
Sex	Male 74.73% Female 25.26%	Male 74.73% Female 25.26%
Race	Whites 43.68% Blacks 40.23% Hispanics 13.94%	Whites 43.68% Blacks 40.23% Hispanics 13.94%
Quarterly Income	1st quartile 45.51%   2nd quartile 23.42% 3rd quartile 19.73% 4th quartile 11.31%	1st quartile 45.51%   2nd quartile 23.42% 3rd quartile 19.73% 4th quartile 11.31%
Proportion of smokers	0.005263	0.005263
Proportion of cocaine	0.03157	0.03157
Proportion of all-cause mortality	0.0078497	0.0078497
Proportion of patients requiring IABP	2.10526%	1.57895%
Mean Length of stay (days)	3.281579	2.6947
Mean of total hospital charges	92390.64	908865.44

The requirement of IABP during the admissions, mean length of hospital stay and mean of total charges were higher in the cohort with cannabis use disorder among acs admissions, when compared to the cohort without cannabis use. No difference in all-cause mortality was seen between the two cohorts.

4. Discussion

As far as we know, this is the largest population-based analysis to date examining CUD+ and ACS/V. Fib admissions in the young adult population.

In this retrospective analysis, we found a highly significant odds ratio between cannabis users ages 18-44 and ACS-related admissions. This odds ratio remained highly significant even after running logistic regression models accounting for demographics (race, sex, age, socioeconomic status), comorbid conditions, cocaine use, marijuana use, etc. (OR 2.95, aOR 2.749). A previous retrospective NIS analysis including ages 11-70 demonstrated a significant odds ratio at 1.041 for acute-MI and cannabis use disorder after adjusting for confounders [10]. After similar adjustments were made in our analysis, the odds ratio for CUD+ and ACS was even more significant at 2.749 when looking only at ages 18-44. These odds imply stronger association between cannabis use and ACS admissions and younger adults. This may be due to stronger associations of confounders such as diabetes, hypertension, etc. in older adults, again suggesting that cannabis use has a more significant role in younger populations with precipitating ACS.

In the ACS and CUD+ group, there were higher percentages of males (73.64% vs. 66.93%), blacks (40.93% vs 22.54%), 1st quartile income (45.6% vs. 35.57%) and Medicaid recipients (39.52% vs. 27.88%). Further analysis revealed significant higher odds of blacks with CUD+ and ACS admissions, confirming our initial sample findings. Similar findings have been previously documented [12].

To further delineate in-hospital outcomes in ACS admissions with cannabis use, we used propensity score analysis with exact matching to compare all-cause mortality, requirement of IABP, mean length of stay and mean of total charges between CUD+ and CUD- patients. Patients in the ACS and CUD+ group had more IABP requirements (2.10% vs 1.58%), longer length of stay (~3.3 vs. ~2.7 days), and subsequent higher average hospital costs ($92,340 vs. $90,864).

Some studies have provided insight into what may be the cause of these significant trends. In one case-crossover study, the risk of myocardial infarction onset was elevated 4.8 times over baseline within the first 60 minutes of marijuana use, suggesting that it may be a rare trigger of MI [13]. One proposed mechanism is that CB1 receptors, a receptor in the human body that plays a protective role in myocardial ischemia and implicated to modulate chemotaxis, can be overactivated with exogenous delta-9-tetrahydrocannabinol (THC) consumption. In turn, inflammatory molecules are increased in the body promoting endothelial dysfunction and subsequent exacerbation of atherosclerosis [14].

Other studies discuss how smoking marijuana facilitates carboxyhemoglobin via combustion as well as upregulating sympathetic response [15,17]. This sympathetic response primarily manifests as tachycardia as cannabis increases sinus node automaticity via b-adrenergic stimulation [16,17]. Increased tachycardia coupled with possible decreased cellular oxygenation may provide the perfect storm for myocardial supply-demand mismatch, thereby inducing a type II myocardial infarction. There have also been animal models demonstrating larger atherosclerotic plaques compared to control when injected with THC comparable to one joint [14].

V. Fib association: Our analysis also looked at CUD+ and Ventricular fibrillation hospital admissions. Unadjusted odds were statistically significant at 2.713, however after adjusting for demographics and comorbidities the odds remained significant at 2.29. This correlates with previously documented findings, in which a sample population who underwent ambulatory Zio patch monitoring had a higher burden of arrhythmias with current Cannabis use [18]. As mentioned prior, this may be driven by the acute sympathetic changes that cannabis causes [17].

Limitations

One of the limitations with utilizing the national inpatient sample (NIS) is the inability to further evaluate CUD+ patients with amount using or duration of use. Over/under-reporting is also possible if there are ICD-10 coding errors in a database such as NIS. Additionally, while there is a significant adjusted odds ratio, no clear causation can be drawn from the data itself given the nature of the study, which would require more prospective/ randomized controlled trials.