Risk Factors and Incidence of Acute Ischemic Stroke: A Comparative Study Between Young Adults and Older Adults

Introduction Approximately 5-10% of strokes occur in adults of less than 45 years of age. The rising prevalence of stroke risk factors may increase stroke rates in young adults (YA). We aimed to compare risk factors and outcomes of acute ischemic stroke (AIS) among YA. Methods Adult hospitalizations for AIS and concurrent risk factors were found in the Nationwide Inpatient Sample database. Weighted analysis using chi-square and multivariable survey logistic regression was performed to evaluate AIS-related outcomes and risk factors among YA (18-45 years) and older patients. Results A total of 4,224,924 AIS hospitalizations were identified from 2003 to 2014, out of which 198,378 (4.7%) were YA. Prevalence trend of YA with AIS showed incremental pattern over time (2003: 4.36% to 2014: 4.7%; pTrend<0.0001). In regression analysis, the risk factors associated with AIS in YA were obesity (adjusted odds ratio {aOR}: 2.26; p<0.0001), drug abuse (aOR: 2.56; p<0.0001), history of smoking (aOR: 1.20; p<0.0001), infective endocarditis (aOR: 2.08; p<0.0001), cardiomyopathy (aOR: 2.11; p<0.0001), rheumatic fever (aOR: 4.27; p=0.0014), atrial septal disease (aOR: 2.46; p<0.0001), ventricular septal disease (aOR: 4.99; p<0.0001), HIV infection (aOR: 4.36; p<0.0001), brain tumors (aOR: 7.89; p<0.0001), epilepsy (aOR: 1.43; p<0.0001), end stage renal disease (aOR: 2.19; p<0.0001), systemic lupus erythematous (aOR: 3.76; p<0.0001), polymyositis (aOR: 2.72; p=0.0105), ankylosis spondylosis (aOR: 2.42; p=0.0082), hypercoagulable state (aOR: 4.03; p<0.0001), polyarteritis nodosa (aOR: 5.65; p=0.0004), and fibromuscular dysplasia (aOR: 2.83; p<0.0001). Conclusion There is an increasing trend in AIS prevalence over time among YA. Both traditional and non-traditional risk factors suggest that greater awareness is needed, with prevention strategies for AIS among young adults.


Introduction
Stroke is the second leading cause of death globally, and although it is most common in the elderly, a significant number of young adults (YA) suffer from it every year [1,2]. The risk of stroke increases with age, but can occur at any age. In 2009, 34% of people hospitalized for stroke were less than 65 years old [3]. Approximately 5-10% of strokes occur in adults <45 years of age [4][5][6][7][8]. Despite considerable improvements in primary prevention, diagnostic workup, and treatment, stroke still remains a major cause of morbidity, serious physical and cognitive long-term disability, and loss in work-related productivity especially when it occurs in the younger population [9,10].
In a systematic review of stroke incidence in YA, the proportion of ischemic strokes ranged between 21.0% and 77.9% in patients under 45 years of age with first-ever stroke [11]. There were 59,077 deaths in YA in the United States from 1989 through 2009 due to stroke, contributing to 2868 deaths per year on average with an average annual mortality rate among YA being 0.93 per 100,000 persons for intracerebral hemorrhage (ICH), 1.1 per 100,000 persons for subarachnoid hemorrhage (SAH), and 0.70 per 100,000 persons for ischemic stroke [12]. In a single-center study comparing characteristics of stroke between younger and older patients, there were significant differences in risk factors, etiology, and distribution of sex between these groups [13]. Edwards et al. from the Canadian Institute for Health Information Discharge Abstract Database (n = 26,366) described a higher hazard for recurrent stroke at one year (hazard ratio {HR}: 6.8), at five years (HR: 5.1), stroke survivors had higher mortality and morbidity, and patients with TIA had a higher prevalence (31.5%; 1789/5677) of an adverse event within the first five years [14].
Our study aimed to provide estimates on the burden of stroke among YA in the United States. We performed a comprehensive assessment to compare traditional and non-traditional risk factors and ischemic strokerelated mortality, morbidity, discharge disposition, disability, and risk of death among young adults (YA: 18-45 years) vs. old adults (OA: >45 years) between 2003 and 2014.

Materials And Methods
Data were obtained from the Agency for Healthcare Research and Quality's Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS) files from January 2003 to December 2014. The NIS is the largest publicly available all-payer inpatient care database in the United States and contains discharge-level data provided by states that participate in the HCUP (including a total of 46 states in 2011). This administrative dataset contains data on approximately eight million hospitalizations in 1000 hospitals that were chosen to approximate a 20% stratified sample of all US community hospitals, representing more than 95% of the national population. Discharge weights are provided for each patient discharge record, which helps to obtain national estimates. Each hospitalization is treated as an individual entry in the database and is coded with one principal diagnosis, up to 24 secondary diagnoses, and 15 procedural diagnoses associated with that stay (detailed information on NIS is available at http://www.hcupus.ahrq.gov/db/nation/nis/nisdde.jsp). The NIS is a de-identified database, so informed consent or IRB approval was not needed for the study. The HCUP Data Use Agreement and training (HCUP-4Q28K90CU) for the data utilized in this study were obtained.

Study population
We used the ninth revision of the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes to identify adult patients admitted with a primary diagnosis of AIS (ICD-9-CM codes 433.01, 433.11, 433.21, 433.31, 433.81, 433.91, 434.01, 434.11, 434.91). These codes have been previously validated and are 35% sensitive, 99% specific, with 96% positive predictive value (PPV), and 79% negative predictive value for the diagnosis of ischemic stroke [15]. We used ICD-9-CM codes to identify traditional and non-traditional risk factors. Table 1 lists all ICD-9-CM codes that were used for this study. Age <18 years and admissions with missing data for age, sex, and race were excluded.

Outcomes
Our primary interest was to compare the prevalence of traditional and non-traditional risk factors of AIS among YA (18-45 years) and OA (>45 years). The secondary interest was to compare outcomes of AIS in YA and OA. The outcomes were all-cause mortality during hospitalization, morbidity (length of stay >10 days {>90th percentile of AIS hospitalization} and discharge to non-home {transfer to short-term hospital, skilled nursing facility, intermediate care facility, or home health care}), discharge disposition (discharge to home vs. non-home), All Patients Refined Diagnosis Related Groups (APR-DRG) risk of mortality, APR-DRG severity of illness (disability), length of stay (LoS), and cost of hospitalization [16]. APR-DRGs were assigned using software developed by 3M Health Information Systems, where score 1 indicates minor loss of function, 2-moderate, 3-major, 4-extreme loss of function or likelihood of death. APR-DRG coding system used in this study to assess the risk of mortality and severity of illness is externally validated. It is a reliable method with accurate and consistent results and is widely used by hospitals, consumers, payers, and regulators [17,18].

Statistical analysis
All statistical analyses were performed using the weighted survey methods in Statistical Analysis System (SAS) Version 9.4 (SAS Institute Inc., Cary, NC). Weighted values of patient-level observations were generated to produce a nationally representative estimate of the entire US population of hospitalized patients. Univariate analysis of differences between categorical variables (including demographics, comorbidities, risk factors, and concurrent conditions) and outcomes was tested using the chi-square test and analysis of differences between continuous variables (LoS and cost of hospitalization) was tested using unpaired student's t-test. Among AIS hospitalizations, the prevalence and mortality trends from 2003 to 2014 for YA and OA were tested and plotted using the Jonckheere trend test.
In order to examine the relationship of age groups (YA vs. OA) with AIS-related risk factors and the relationship of age groups with AIS-related outcomes, we used mixed-effects multivariable survey logistic regression models. The models were weighted and adjusted for demographics (age, sex, race), patient-level hospitalization variables (admission day, primary payer, admission type, median household income category), hospital-level variables (hospital region, teaching versus nonteaching hospital, hospital bed size), comorbidities, traditional and non-traditional risk factors, and CCI in order to estimate the adjusted odds ratio (aOR) and 95% confidence interval (CI). Common conditions covered as risk factors and CCIs were adjusted only once in order to avoid over-adjustment. For each model, the c-index was calculated. All statistical tests used were two-sided, and p<0.05 was deemed statistically significant. No statistical power calculation was conducted prior to the study.

Results
We have described prevalence trends and characteristics of AIS. We have also compared demographics, patient and hospital characteristics, comorbidities, and outcomes of AIS amongst YA and OA below.

Disease hospitalizations
There were 4,224,924 hospitalizations due to AIS from 2003 to 2014 after excluding patients with age <18 years and admissions with missing data for age, sex, and race ( Figure 1). Out of 4,224,924 AIS hospitalizations, 198,378 (4.7%) were YA (≤45 years) and 4,026,546 (95.3%) were OA. As shown in Figure

Demographics, patient and hospital characteristics, and comorbidities
There was a higher proportion of females among OA with AIS than YA (53.1% vs. 48.5%; p<0.0001) (

Discussion
We performed a population-based retrospective analysis of the nationally-representative NIS database to identify adult AIS hospitalizations and risk factors. Stroke in YA has been observed to be uncommon, and cerebrovascular disease reaches peak incidence later in life [19]. This observation has been confirmed in our study as we identified only 4.7% YA AIS hospitalizations, while 95.3% of hospitalizations were in patients who were 45 years or older. Thus, stroke is not a common health condition among YA. However, for those YA who do suffer a stroke, it is a considerable cause of morbidity and has an impact on the loss of work productivity in these patients [9]. Despite the small number of YA who suffer from stroke, we found an increasing prevalence among YA with AIS. From 2003 to 2014, hospitalizations for AIS in young adults increased from 4.36% to 4.70%. This stands in contrast to previous reports of stable rates of stroke incidence and decreasing rates of stroke hospitalization among adults [9]. A possible reason for this seemingly increasing incidence could be due to rising rates of stroke risk factors, including obesity, hypertension, diabetes, tobacco, and alcohol use [9].  [20]. A possible explanation for this could be more effective treatment guidelines and strategies when presenting to the hospital. Young people may still be participating in high-risk factors that can lead to an increase in AIS hospitalizations, as shown in our study; however, treatment may have improved, with a concomitant decrease in mortality. Our study also indicated that YA with AIS hospitalizations had a lower chance of morbidity, discharge to short/long-term care, and the likelihood of death. YA and OA AIS hospitalizations had a similar mean LoS. However, the cost of hospitalizations was higher in YA ($47365 vs. $37669, p<0.0001). Stroke is thus an important cause of morbidity in young patients, and although having a small prevalence in the population, it affects hospitalization costs and dramatic impacts on quality of life in survivors. YA are also associated with higher long-term cumulative mortality due to stroke compared to the general population [21]. Stroke causes numerous physical and cognitive problems, long-term consequences, and work-related productivity losses especially in younger populations [21].
A major strength of this study was the findings that were nationally representative for the United States. However, there are limitations to this study. AIS was analyzed as a whole, rather than by identifying AIS patients according to subtype or by comparing other types of stroke. Perhaps, YA with AIS hospitalizations were due to a certain cause or presented as a subtype of AIS; however, this could not be elicited through this study. Additionally, being a retrospective study, we have associations between certain concurrent diagnoses and co-morbidities and AIS, yet we do not know if there is a temporal relationship between the two. We have evaluated in-hospital outcomes and do not have post-discharge records of these patients. Likewise, we are missing other details like stroke location, NIH Stroke Scale, concurrent medication use, the severity of risk factors, etc.

Conclusions
AIS has been shown to be an uncommon problem in YA with better outcomes; however, with the rising prevalence trend of AIS over the past decade in young populations, prevention and treatment strategies need to be examined. Young adults have modifiable risk factors such as obesity, drug and smoking abuse, and heart conditions that can be screened. Besides that, non-traditional risk factors suggest that more awareness and prevention strategies can be targeted to the YA population. Further studies should be done to test whether modifying these factors lowers stroke risk in the young population or to determine if awareness campaigns differ based on the age of the patient targeted.

Additional Information Disclosures
Human subjects: Consent was obtained or waived by all participants in this study. Health Care Utilization Project (HCUP) issued approval N/A. The data has been taken from Nationwide Inpatient Sample, which is a deidentified database from "Health Care Utilization Project (HCUP)" sponsored by the Agency for Healthcare Research and Quality, so informed consent or IRB approval was not needed for the study. The relevant ethical oversight and HCUP Data Use Agreement (HCUP-4Q28K90CU) were obtained by Urvish Patel for the data used in this study. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.