A Retrospective Study on the Status of Risk Factor Management in Patients with Ischemic Stroke Based on a Large Linked Dataset of Stroke Patients in Korea

Article information

J Stroke. 2022;24(2):288-291
Publication date (electronic) : 2022 May 31
doi : https://doi.org/10.5853/jos.2021.03741
aDepartment of Neurology, Seoul National University Hospital, Seoul, Korea
bDepartment of Critical Care Medicine, Seoul National University Hospital, Seoul, Korea
cDepartment of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
dDepartment of Critical Care Medicine, Inha University Hospital, Incheon, Korea
eDepartment of Neurology, Hallym University Sacred Heart Hospital, Anyang, Korea
fDepartment of Neurology, Uijeongbu Eulji Medical Center, Eulji University, Uijeongbu, Korea
Correspondence: Sang-Bae Ko Department of Neurology and Critical Care Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea Tel: +82-2-2072-2278 Fax: +82-2-3672-7553 E-mail: sangbai1378@gmail.com
*

These authors contributed equally to the manuscript as first author.

Received 2021 October 25; Revised 2021 December 24; Accepted 2021 January 24.

Dear Sir:

Stroke is the second leading cause of death and disability worldwide. The incidence and recurrence of stroke are strongly associated with the management of modifiable vascular risk factors, including hypertension (HT), diabetes mellitus (DM), dyslipidemia, and atrial fibrillation (AF) [1,2]. Although modifiable risk factors are important to reduce the risk of stroke, awareness of risk factors and the control rate of risk factors in many patients with stroke remain low, especially in young adults [1,2]. Given that there is little information on the clinical factors related to uncontrolled risk factors for stroke, we aimed to investigate the proportion of new diagnoses of major risk factors for ischemic stroke and identify the factors associated with the poor control rate using a linked dataset of stroke in Korea.

The study was approved by the Institutional Review Board (IRB) of Seoul National University Hospital (IRB No. H-1608-078-785), those of other 34 participating hospitals, and the Health Insurance Review and Assessment Service (HIRA). The need for informed consent was waived by the IRBs.

We included data for 42,879 patients with acute ischemic stroke within 7 days after symptom onset from the linked Clinical Research Center for Stroke Registry and the HIRA between January 2008 and December 2014. We evaluated the proportion of patients with newly identified conventional risk factors (HT, DM, and AF) after index ischemic stroke and assessed factors related to poor control of risk factors. Detailed methodological descriptions and statistical analyses are provided in Supplementary methods. Among the total patients included (n=42,879; 59.5%, male [n=25,529]; mean±standard deviation age, 65.7±12.5 years), 78.0% (n=33,462) were hypertensive, 33.2% (n=14,242) were diabetic, and 9.5% (n=4,069) had AF with cardioembolic stroke. Among patients with HT, 8.8% (2,956/33,462) were newly diagnosed with HT. Similarly, 5.0% of the patients (n=709) were newly diagnosed with diabetes, and 15.9% of the patients (n=648) were newly diagnosed with AF after stroke (Supplementary Tables 1-3). The detailed baseline characteristics are discussed in the Supplementary Results. Young-age patients (≤45 years) were more likely to have newly identified vascular risk factors after index stroke (Table 1). After adjusting for confounding variables, younger age (≤45 years) was independently associated with a higher proportion of newly identified risk factors (odds ratio [OR], 1.865; 95% confidence interval [CI], 1.579 to 2.203; P<0.0001 in HT, OR, 1.726; 95% CI, 1.163 to 2.562; P=0.0067 in DM, and OR, 2.503; 95% CI, 1.507 to 4.155; P=0.0004 in AF) (Table 2). Furthermore, factors related to newly diagnosed HT were smoking (OR, 1.336; 95% CI, 1.191 to 1.497; P<0.0001) and other comorbidities were negatively associated with newly diagnosed HT. Among patients with DM, only the presence of HT was negatively associated with newly diagnosed DM, while other factors were not significant. In addition, no factors other than young age were identified for newly diagnosed AF (Table 2).

Newly identified risk factors according to age

Multivariable analyses of the relationship between clinical factors and newly identified risk factors in patients with ischemic stroke

Using the linked dataset, we found that age ≤45 years was an independent factor associated with newly diagnosed major stroke risk factors after index stroke. Furthermore, patients with other comorbidities were less likely to have a new diagnosis of risk factors after index stroke. Traditional vascular risk factors account for up to 90% of the attributable risk of stroke development. Therefore, the most effective way to reduce the incidence of stroke is to control modifiable risk factors [3,4]. However, <50% of the public possesses knowledge of stroke, its risk factors, and warning signs [2,3]. According to public surveys, lower socioeconomic status, lower education, and younger age are associated with a lack of knowledge of stroke risk factors [1,3]. Consistent with previous reports, our findings revealed that age ≤45 years was an independent risk factor for newly identified major stroke in patients with index ischemic stroke [1,2]. Young people do not usually seek medical attention to identify vascular risk factors. Otherwise, they become aware of having risk factors during medical examinations for other purposes [2,3]. This partly explains why patients with comorbidities have a lower probability of a new diagnosis of vascular risk factors. Based on these results, regular check-ups during clinic visits could be an important and effective strategy for stroke prevention by identifying and controlling vascular risk factors.

Our study has several limitations. First, there could be a certain degree of unmeasured bias due to the retrospective design using a linked dataset. Second, the linked dataset did not contain data related to knowledge of risk factors in laboratory information and lifestyle aspects of patients, such as healthcare check-up results and socioeconomic status information. Third, we did not investigate risk factor awareness using a questionnaire during hospitalization. However, the status of risk factor management prior to index stroke was evaluated using the linked data. Fourth, we excluded 8.8% of the patients (n=4,578) with inaccurate information due to censored claims data after index stroke. Therefore, these factors may have affected our results.

In conclusion, this study showed that the proportion of patients with uncontrolled risk factors before index ischemic stroke was higher in younger patients (≤45 years). Public education about regular check-ups may contribute to an improvement in the control rate of risk factors to reduce stroke risk. Further large-scale studies are needed to confirm the relationship between clinical factors and control of risk factors among patients with ischemic stroke.

Supplementary materials

Supplementary materials related to this article can be found online at https://doi.org/10.5853/jos.2021.03741.

Supplementary Methods
Supplementary Results
Supplementary Table 1.

Baseline characteristics of patients with hypertension

Supplementary Table 2.

Baseline characteristics of patients with diabetes mellitus

Supplementary Table 3.

Baseline characteristics of patients with atrial fibrillation in cardioembolic stroke

Acknowledgements

We thank our investigators for the CRCS for (Pf. Dae-IL Chang, Pf. Joung-Ho Rha, Pf. Keun-Sik Hong, Pf. Hee-Joon Bae, Pf. Young-Seok Lee, Pf. Ju-Hun Lee, Pf. Sung Il Sohn, Pf. Jong-Moo Park, Pf. Soo Joo Lee, Pf. Dong-Eog Kim, Pf. Jae-Kwan Cha, Pf. Eung-Gyu Kim, Pf. Kyung Bok Lee, Pf. Young Bae Lee, Pf. Tai Hwan Park, Pf. Jun Lee, Pf. Man-Seok Park, Pf. Jay Chol Choi, Pf. Jun Hong Lee, Pf. Chulho Kim, Pf. Dong-Ick Shin, Pf. Hyun Young Kim, Pf. Jee -Hyun Kwon, Pf. Hye-Yeon Choi, Pf. Hahn Young Kim, Pf. Kyung Yoon Eah, Pf. Sang Won Han, Pf. HyungGeun Oh, Pf. Young-Jae Kim, Pf. Byoung-Soo Shin, Pf. Chang Hun Kim, and Pf. Chi Kyung Kim) provided data that greatly assisted in the research, although they may not agree with all interpretations/conclusions of this study.

This work was supported by the Ministry of Health and Welfare (HI 16C1078) of Korea and the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (NRF- 2020R1C1C1003249). The funding organizations had no role in the study or preparation of this report.

Notes

The datasets generated and/or analyzed during the current study are not publicly available due to the data as imposed by ethical approval. Please contact the corresponding author (Sang-Bae Ko), to obtain access to the study data.

Disclosure

The authors have no financial conflicts of interest.

References

1. Hong KS, Bang OY, Kim JS, Heo JH, Yu KH, Bae HJ, et al. Stroke statistics in Korea: Part II stroke awareness and acute stroke care, a report from the Korean Stroke Society and Clinical Research Center For Stroke. J Stroke 2013;15:67–77.
2. Park TH, Ko Y, Lee SJ, Lee KB, Lee J, Han MK, et al. Identifying target risk factors using population attributable risks of ischemic stroke by age and sex. J Stroke 2015;17:302–311.
3. Bucholz EM, Gooding HC, de Ferranti SD. Awareness of cardiovascular risk factors in U.S. young adults aged 18-39 years. Am J Prev Med 2018;54:e67–e77.
4. O’Donnell MJ, Xavier D, Liu L, Zhang H, Chin SL, Rao-Melacini P, et al. Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): a case-control study. Lancet 2010;376:112–123.

Article information Continued

Table 1.

Newly identified risk factors according to age

Variable Age ≤45 years Age >45 years ASD*
Hypertension (n=33,462)
 New onset hypertension 1,764 34,699
  No 1,483 (84.1) 32,024 (92.3) 0.2567
  Yes 281 (15.9) 2,675 (7.7)
Diabetes mellitus (n=14,242) 653 16,296
 New onset diabetes mellitus
  No 611 (93.6) 15,629 (95.9) 0.1049
  Yes 42 (6.4) 667 (4.1)
Atrial fibrillation (n=4,069) in cardioembolic stroke 102 3,967
 New onset atrial fibrillation
  No 69 (67.6) 3,352 (84.5) 0.4029
  Yes 33 (32.4) 615 (15.5)

Values are presented as number (%).

ASD, absolute standardized difference.

*

ASD >0.1, considered meaningful imbalances.

Table 2.

Multivariable analyses of the relationship between clinical factors and newly identified risk factors in patients with ischemic stroke

Variable Crude analysis
Adjusted analysis
OR 95% CI P OR 95% CI P
Hypertension
 Age ≤45 years 2.673 2.333–3.063 <0.0001 1.865 1.579–2.203 <0.0001
 Female 0.673 0.621–0.729 <0.0001 0.930 0.818–1.056 0.2628
 Diabetes mellitus 0.533 0.489–0.581 <0.0001 0.572 0.514–0.636 <0.0001
 Dyslipidemia 0.679 0.623–0.740 <0.0001 0.741 0.666–0.824 <0.0001
 Atrial fibrillation 0.549 0.487–0.618 <0.0001 0.480 0.386–0.595 <0.0001
 Coronary artery disease 0.319 0.259–0.393 <0.0001 0.399 0.311–0.512 <0.0001
 Previous stroke/TIA 0.391 0.342–0.447 <0.0001 0.448 0.378–0.532 <0.0001
 Smoking 1.660 1.539–1.790 <0.0001 1.336 1.191–1.497 <0.0001
 Education years
  0–3 0.553 0.430–0.712 <0.0001 0.745 0.571–0.970 0.0289
  4–6 0.626 0.545–0.720 <0.0001 0.813 0.697–0.949 0.0085
  7–9 0.882 0.763–1.021 0.0931 1.078 0.923–1.258 0.3444
  9–12 1.042 0.915–1.186 0.5394 1.128 0.985–1.292 0.0807
  ≥13 Reference Reference
Diabetes mellitus
 Age ≤45 years 1.790 1.293–2.478 0.0005 1.726 1.163–2.562 0.0067
 Female 1.018 0.873–1.187 0.8231 0.965 0.757–1.229 0.7707
 Hypertension 0.625 0.527–0.741 <0.0001 0.558 0.451–0.689 <0.0001
 Dyslipidemia 0.775 0.660–0.910 0.0018 0.855 0.703–1.041 0.1184
 Atrial fibrillation 1.552 1.283–1.878 <0.0001 1.398 0.962–2.031 0.0789
 Coronary artery disease 0.890 0.680–1.164 0.3935 0.795 0.562–1.124 0.1935
 Previous stroke/TIA 0.816 0.667–0.997 0.0466 0.914 0.705–1.186 0.4994
 Smoking 0.983 0.843–1.147 0.8310 0.938 0.749–1.173 0.5738
 Education years
  0–3 0.990 0.619–1.581 0.9651 1.001 0.614–1.633 0.9954
  4–6 1.062 0.802–1.406 0.6752 1.128 0.830–1.533 0.4426
  7–9 0.993 0.730–1.350 0.9619 1.058 0.769–1.455 0.7308
  9–12 1.041 0.784–1.381 0.7825 1.091 0.817–1.457 0.5537
  ≥13 Reference Reference
Atrial fibrillation
 Age ≤45 years 2.607 1.707–3.982 <0.0001 2.503 1.507–4.155 0.0004
 Female 0.798 0.673–0.946 0.0092 0.873 0.678–1.125 0.2951
 Hypertension 0.934 0.769–1.134 0.4893 1.010 0.794–1.285 0.9331
 Diabetes mellitus 1.023 0.849–1.232 0.8114 1.120 0.894–1.404 0.3242
 Dyslipidemia 1.003 0.835–1.205 0.9740 1.013 0.812–1.265 0.9066
 Coronary artery disease 0.840 0.656–1.077 0.1688 0.924 0.693–1.231 0.5874
 Previous stroke/TIA 0.981 0.785–1.224 0.8622 0.954 0.795–1.384 0.7344
 Smoking 1.233 1.031–1.475 0.0220 1.049 0.971–1.563 0.0862
 Education years
  0–3 0.660 0.422–1.033 0.0688 0.782 0.490–1.248 0.3029
  4–6 0.810 0.608–1.080 0.1510 0.957 0.701–1.307 0.7824
  7–9 0.927 0.673–1.277 0.6426 1.034 0.743–1.438 0.8430
  9–12 0.931 0.695–1.248 0.6338 0.969 0.720–1.305 0.8365
  ≥13 Reference Reference

Adjusted for age, sex, hypertension, diabetes mellitus, dyslipidemia, atrial fibrillation, coronary artery disease, previous stroke/TIA, history of smoking, initial National Institutes of Health Stroke Scale, pre-stroke modified Rankin Scale, stroke mechanisms, education years, and reperfusion therapy.

OR, odds rtio; CI, confidence interval; TIA, transient ischemic attack.