Introduction
Despite our understanding of cerebrovascular accident also known as stroke, mortality due to it remains high, representing one of the most important causes of death in world(1). In the United States, more than 700,000 strokes that caused more than 165,000 deaths occur each year(2). Worldwide, stroke is the 2nd leading cause of death representing more than 10% of all the causes of fatalities by 2016(3). Deaths due to stroke are either reported prior to the arrival of the patients to the hospitals as well as during the period of hospitalization(4). Several reasons have been linked with an increase of developing stroke, including traditional risk factors such as smoking, diabetes, obesity or sedentarism as well as not traditional risk factors like vitamin D deficiency or altitude exposure(5). Smoking as a major risk factor for stroke, causes endothelial dysfunction increasing the risk of thrombosis, incrementing the chances of occluding arteries or veins(6). Approximately 30% of deaths due to coronary artery disease (CAD) are associated to smoking(7). Smoking also nearly doubles the risk of an acute ischemic stroke(8). Cigarette smoking is the most common preventable cause of any vascular disease(4). Clinical practice guidelines recommend smoking cessation, especially to survivors of stroke, transient ischemic attack (TIA), MI, and other vascular diseases; yet 18-35% of survivors smoke(7).
Smoker’s paradox was first introduced two to three decades ago after observing that smokers experienced decreased mortality following an acute MI, when compared to non-smokers(9). There are other types of paradoxes, for instance obesity and gender paradox have been reported in stroke survivor patients(10)(11)(12). Smoking and its association with acute ischemic stroke have been studied more extensively relative to hemorrhagic stroke. As mentioned above, a smoker’s paradox exists possibly because, in smokers, acute ischemic stroke occurs on average 10 years earlier that in non-smokers(4). There could be a collective effect of younger age, lower clinical risk profiles, and possibly more aggressive treatment that result in a better prognosis in patients who smoke(9)(12)(13).
One study in elderly Chinese patients who smoke showed that cigarette smoking was associated with a higher mortality risk of hemorrhagic stroke and both its major subtypes, i.e. intracerebral hemorrhage (ICH) and subarachnoid hemorrhage (SAH), based on a large community-based sample and a sufficient follow-up period(14). However, a protective effect was observed in patients with intracranial hemorrhage (ICH) a type of stroke that had not been addressed in previous studies(15). Although previous reports have shown that the association between cigarette smoking and hemorrhagic stroke is weak and inconsistent. This report is focused on the association between smoking vs. non-smoking, and the occurrence of in-hospital mortality after a hemorrhagic stroke. No studies have been performed to study the association of smoking and in-hospital mortality in hemorrhagic stroke patients in Florida to our knowledge.
Methods
Population
A secondary data analysis of the Florida Stroke Registry was conducted in those patients who were hospitalized with a diagnosis of stroke from 2008 to 2012. Stroke was defined according to the International Classification of Diseases 10th edition (ICD-10) discharge codes. The total numbers of patients with stroke reached 333,367 and from those patients, 21,013 were diagnosed with intracerebral hemorrhage (ICD-10) code 431, intracranial hemorrhage).
Smoking
The study population was divided in two groups based on their smoking status. Information about smoking status was obtained from the medical record by using ICD-10 codes as well. Both groups were current those patients that were currently smokers (any cigarette use within the year preceding the stroke) versus nonsmokers patients (either never smokers or those who had been abstinent for >1 year prior to stroke). The available data does not distinguish between the 2 subtypes of nonsmokers.
Outcome
The primary outcome of interest for this study was in-hospital mortality, which was defined as patients who died during their stay at the hospital or those who survived and were discharged.
Control variables
Control variables were selected based on an extensive literature review. Demographic factors were self-reported and those included sex and race (white, African American, and all other races were included as other). Insurance status was self-reported and patients were divided into commercial insurance and non-commercial insurance. Non-commercial medical insurance included Medicare, Medicaid, worker’s compensation, TriCare, State/Local government insurance, non-payment, commercial liability coverage, and any other form. Clinical factors such as hypertension, diabetes mellitus, hyperlipidemia, and morbid obesity (BMI>40 or BMI<40) were recorded via ICD-10 codes and were used in the data set accordingly.
Statistical Analysis
Statistical analyses were performed using the software SPSS version 24. Patient demographics and clinical variables were analyzed for both cohorts. Percentages were used for categorical variables and medians with interquartile ranges for continuous variables. Categorical data were analyzed by Pearson χ2 and continuous data by Wilcoxon rank sum test. Logistic regression models were used to compute odds ratios (ORs) and Confidence Intervals (CI) for factors associated with in-hospital mortality in hemorrhagic stroke patients.
Multivariable logistic regression analysis was employed to adjust for potential confounding effect of each one of the control variables, on the estimation of the OR of in-hospital mortality between smokers and non-smokers.
Results
In the Florida Stroke Registry, there were 333,367 records of patients that were diagnosed with a stroke. We limited the age group to patients >= 18 years old, resulting in 300,000 patients. Among them, 21,013 patients were diagnosed with hemorrhagic stroke according to appropriate ICD-10 code (intracranial hemorrhage). Prevalence of smoking in our study population was (2285/21013) and overall in-hospital all causes mortality was (4591/21013). The overall prevalence of smoking was 10.9%. The overall in-hospital all causes mortality was 21.8%. Data on demographics, comorbidities, and in-hospital mortality were collected and analyzed.
Smoking | |||
---|---|---|---|
No (N= 18728) | Yes (N= 2285) | p value | |
Characteristics | N (%) | N (%) | |
Age (years) | <0.001 | ||
< = 50 | 10.1 | 25.3 | |
51 - 60 | 13.7 | 31.4 | |
61 - 70 | 17.0 | 23.6 | |
71 - 80 | 26.9 | 13.9 | |
> 80 | 32.2 | 5.8 | |
Gender | <0.001 | ||
Male | 50.0 | 59.1 | |
Female | 50.0 | 40.9 | |
Race | <0.001 | ||
Black or African American | 19.3 | 25.1 | |
White | 74.9 | 69.9 | |
Other | 5.8 | 5.1 | |
Health insurance coverage | <0.001 | ||
Commercial | 9.8 | 12.7 | |
No Commercial | 90.2 | 87.3 | |
BMI(kg/m 2 )>40 | 0.110 | ||
No | 92.2 | 91.2 | |
Yes | 7.8 | 8.8 | |
Diabetes Mellitus | <0.001 | ||
No | 70.7 | 78.6 | |
Yes | 29.3 | 21.4 | |
HTN | <0.001 | ||
No | 93.7 | 90.9 | |
Yes | 6.3 | 9.0 | |
Hyperlipidemia | <0.001 | ||
No | 63.8 | 71.1 | |
Yes | 36.2 | 28.9 |
BMI-Body Mass Index; HTN-Hypertension
Table 1 shows that in this cohort, smokers were substantially younger than those who do not smoke. Younger smokers were also mostly African American men, with hypertension, and overweight (BMI>40). They were also less likely to have a history of diabetes mellitus, and hyperlipidemia. Smokers carried commercial insurance more often than non-smokers.
In-hospital mortality | |||
---|---|---|---|
Alive (N= 16422) | Death (N= 4591) | p value | |
Characteristics | N (%) | N (%) | |
Smoking | <0.001 | ||
No | 77.6 | 22.4 | |
Yes | 83.0 | 17.0 | |
Age | <0.001 | ||
< = 50 | 81.5 | 18.5 | |
51 - 60 | 80.3 | 19.7 | |
61 - 70 | 79.8 | 20.2 | |
71 - 80 | 75.4 | 24.6 | |
> 80 | 77.1 | 22.9 | |
Gender | 0.006 | ||
Male | 78.9 | 21.1 | |
Female | 77.4 | 22.6 | |
Race | 0.003 | ||
Black or African American | 79.7 | 20.3 | |
White | 77.6 | 22.4 | |
Other | 80.4 | 19.6 | |
Health insurance coverage | 0.028 | ||
Commercial | 80.0 | 20.0 | |
No Commercial | 77.8 | 22.2 | |
BMI >40 | <0.001 | ||
No | 77.7 | 22.3 | |
Yes | 83.4 | 16.6 | |
Diabetes Mellitus | 0.03 | ||
No | 78.0 | 22.0 | |
Yes | 79.3 | 20.7 | |
HTN | <0.001 | ||
No | 77.8 | 22.2 | |
Yes | 83.0 | 17.0 | |
Hyperlipidemia | <0.001 | ||
No | 75.9 | 24.1 | |
Yes | 82.7 | 17.3 |
BMI-Body Mass Index; HTN-Hypertension
Table 2 shows that only 17.0% of smokers who were diagnosed with hemorrhagic stroke died during their stay as compared with 22.4% of nonsmokers. In-hospital mortality was increased in patients who were >70 years old, white race, with a BMI<40. Patients who had a history of hypertension, hyperlipidemia, and diabetes mellitus were associated with lower mortality. Patients with in-hospital mortality more commonly carried non-commercial insurance.
A multivariate analysis was performed to assess for any confounding that might have altered the results. The potential confounders that were analyzed included age, gender, race (African American, white, other), health insurance coverage (commercial vs. non-commercial), morbid obesity (BMI>40), diabetes mellitus, hypertension, and hyperlipidemia. Table 3.
Unadjusted | Adjusted | ||||
Characteristics | OR (95% CI) | p-value | OR (95% CI) | p-value | |
Smoking | |||||
No | Reference | ||||
Yes | 0.71 (0.63-0.79) | <0.001 | 0.75 (0.67-0.85) | <0.001 | |
Age | |||||
< = 50 | Reference | ||||
51 - 60 | 1.08 (0.95-1.24) | 0.242 | 1.09 (0.94-1.26) | 0.243 | |
61 - 70 | 1.12 (0.98-1.27) | 0.095 | 1.12 (0.97-1.28) | 0.132 | |
71 - 80 | 1.43 (1.27-1.61) | <0.001 | 1.33 (1.16-1.52) | <0.001 | |
> 80 | 1.31 (1.16-1.47) | <0.001 | 1.12 (0.98-1.29) | 0.096 | |
Gender | |||||
Male | 0.91 (0.91-0.98) | 0.006 | 0.96 (0.89-1.02) | 0.193 | |
Female | Reference | ||||
Race | |||||
Black or African American | 0.88 (0.81-0.96) | 0.004 | 1.13 (0.95-1.33) | 0.172 | |
White | Reference | ||||
Other | 0.84 (0.73-0.98) | 0.027 | 1.20 (1.02-1.40) | 0.024 | |
Health insurance coverage | |||||
Commercial | Reference | ||||
No Commercial | 1.14 (1.01 -1.30) | 0.028 | 1.09 (0.96-1.23) | 0.194 | |
BMI >40 | |||||
No | Reference | ||||
Yes | 0.69 (0.61-0.79) | <0.001 | 0.81 (0.71-0.94) | 0.005 | |
Diabetes Mellitus | |||||
No | Reference | ||||
Yes | 0.92 (0.86-0.99) | 0.030 | 0.97 (0.90-1.05) | 0.494 | |
HTN | |||||
No | Reference | ||||
Yes | 0.72 (0.62-0.83) | <0.001 | 0.78 (0.67-0.91) | 0.001 | |
Hyperlipidemia | |||||
No | Reference | ||||
Yes | 0.66 (0.61 -0.71) | <0.001 | 0.64 (0.59-0.69) | <0.001 |
BMI-Body Mass Index; HTN-Hypertension
Smoking was associated with lower in-hospital mortality in hemorrhagic patients. (UOR= 0.7, 95% CI, 0.6-0.8). After adjusting for age, gender, race, health insurance coverage, BMI > 40, diabetes mellitus, hypertension and hyperlipidemia, the magnitude of the association remained almost the same (OR= 0.75, 95% CI, 0.67-0.85). As observed in the tables, many of the confounders, hypertension, BMI>40, and hyperlipidemia, remained significantly associated after modeling, and some others like gender, race, health insurance coverage, and diabetes became insignificant.
Discussion
This study investigates if there is an association between smoking and in-hospital mortality in patients admitted with hemorrhagic stroke and registered in the Florida Stroke Registry from the years 2008-2012. Our analysis showed a decrease in in-hospital mortality in smokers when compared to non-smokers, resulting in an OR=0.71 and after adjusting for confounders, the OR=0.8 (95% CI, 0.67- 0.85). There was no significant increase in the odds ratio after adjusting for all the potential confounders leading us to conclude that smoking by itself is potentially a protective factor. Although smoking in this case is considered a protective factor, we highly recommend against the use cigarettes because of the hazardous effects of smoking that are manifested as the occurrence of stroke in patients’ years earlier than might otherwise have occurred.
A “smoker’s paradox” was observed in ischemic stroke by previous studies conducted by Ali et al. (2013, 2015)(4)(16). Edjoc et al(15). observed a protective effect in patients with intracranial hemorrhage (ICH) patients, a finding not present in previous studies. Our study reports lower in-hospital mortality in hemorrhagic stroke in smoker’s patients, being this a novel finding because no previous studies have assessed in-hospital mortality of hemorrhagic patients to our knowledge. The pathophysiology behind this occurrence is unknown. It could be due to chronic changes in vasomotor tone that might lead to preconditioning in smokers, as well as the development of improved small vessel cerebral collaterals and better cerebral perfusion(17)(18). It could be hypothesized that chronic long term hypercapnia or relatively low but persistent hypoxia might trigger angiogenesis(5)(19). Metabolic changes to in the brain might limit the initial injury and influence stroke progression and mortality(12)(16).
As incidental findings we observed that patients with hypertension, morbid obesity and hyperlipidemia had lower in-hospital mortality. The expectation would be that patients with comorbidities would have a higher chance of in-hospital mortality. These findings are counterintuitive, suggesting that besides the so-called “smoking paradox”, there could be other paradoxes, deserving further research(10)(11).
The Strengths of our study include an extensive database in the Florida Stroke Registry, a well collected dataset the include demographic, socioeconomic and clinical relevant data. This study had some important limitations. One of the limitations was that there is no severity scale for Stroke, which prevents us from properly evaluating patients with different severities of stroke. Another limitation is the unknown amount of cigarettes smoked by patients during their lifetime, or if they were former smokers, as the database reports only if they were smokers or non-smokers according to the patients themselves. There was also no report of pre-admission mortality, or post-hospital follow-up in patients who were still alive. Therefore, our recommendation would be to perform a study on mortality of patients with hemorrhagic stroke after 6 months and 12 months.