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Stroke prevention in rural residents: development of a simplified risk assessment tool with artificial intelligence

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Abstract

Background

Limited studies have focused on the risk assessment of stroke in rural regions. Moreover, the application of artificial intelligence in stroke risk scoring system is still insufficient. This study aims to develop a simplified and visualized risk score with good performance and convenience for rural stroke risk assessment, which is combined with a machine learning (ML) algorithm.

Methods

Participants of the Henan Rural Cohort were enrolled in this study. The total participants (n = 38,322) were randomly split into a train set and a test set in the ratio of 7:3. An ML algorithm was used to select variables and the logistic regression was then applied to construct the scoring system. The C-statistic and the Brier score (BS) were used to evaluate the discrimination and calibration. The Framingham stroke risk profile (FSRP) and the self-reported stroke risk function (SRSRF) were chosen to be compared.

Results

The Rural Stroke Risk Score (RSRS) was produced in this study, including age, drinking status, triglyceride, type 2 diabetes mellitus, hypertension, waist circumference, and family history of stroke. On validation, the C-statistic was 0.757 (95% CI 0.749–0.765) and the BS was 0.058 in the test set. In addition, the discrimination of RSRS was 6.02% and 7.34% higher than that of the FSRP and SRSRF, respectively.

Conclusions

A well-performed scoring system for assessing stroke risk in rural residents was developed in this study. This risk score would facilitate stroke screening and the prevention of cardiovascular disease in economically underdeveloped areas.

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Data availability

The data analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Hankey GJ (2017) Stroke. Lancet 389(10069):641–654

    Article  PubMed  Google Scholar 

  2. Tsao CW, Aday AW, Almarzooq ZI et al (2022) Heart disease and stroke statistics-2022 update: a report from the American Heart Association. Circulation 145(8):e153-e639

  3. Campbell BCV, De Silva DA, Macleod MR et al (2019) Ischaemic stroke. Nat Rev Dis Primers 5(1):70

    Article  PubMed  Google Scholar 

  4. Wang W, Jiang B, Sun H et al (2017) Prevalence, incidence, and mortality of stroke in China: results from a nationwide population-based survey of 480 687 adults. Circulation 135(8):759–771

    Article  PubMed  Google Scholar 

  5. Schulz UG, Fischer U (2017) Posterior circulation cerebrovascular syndromes: diagnosis and management. J Neurol Neurosurg Psychiatry 88(1):45–53

    Article  PubMed  Google Scholar 

  6. Flueckiger P, Longstreth W, Herrington D et al (2018) Revised Framingham stroke risk score, nontraditional risk markers, and incident stroke in a multiethnic cohort. Stroke 49(2):363–369

    Article  PubMed  PubMed Central  Google Scholar 

  7. Wolf PA, D’Agostino RB, Belanger AJ et al (1991) Probability of stroke: a risk profile from the Framingham study. Stroke 22(3):312–318

    Article  CAS  PubMed  Google Scholar 

  8. Dufouil C, Beiser A, McLure LA et al (2017) Revised Framingham stroke risk profile to reflect temporal trends. Circulation 135(12):1145–1159

    Article  PubMed  PubMed Central  Google Scholar 

  9. Hijazi Z, Lindbäck J, Alexander JH et al (2016) The ABC (age, biomarkers, clinical history) stroke risk score: a biomarker-based risk score for predicting stroke in atrial fibrillation. Eur Heart J 37(20):1582–1590

    Article  PubMed  PubMed Central  Google Scholar 

  10. Lip GYH, Nieuwlaat R, Pisters R et al (2010) Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation. Chest 137(2):263–272

    Article  PubMed  Google Scholar 

  11. Kelly PJ, Camps-Renom P, Giannotti N et al (2020) A risk score including carotid plaque inflammation and stenosis severity improves identification of recurrent stroke. Stroke 51(3):838–845

    Article  CAS  PubMed  Google Scholar 

  12. Ambale-Venkatesh B, Yang X, Wu CO et al (2017) Cardiovascular event prediction by machine learning: the multi-ethnic study of atherosclerosis. Circ Res 121(9):1092–1101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Li D, Xiong G, Zeng H et al (2021) Machine learning-aided risk stratification system for the prediction of coronary artery disease. Int J Cardiol 326:30–4

    Article  PubMed  Google Scholar 

  14. Howard G (2021) Rural-urban differences in stroke risk. Prev Med 152(Pt 2):106661

    Article  PubMed  PubMed Central  Google Scholar 

  15. Wu S, Wu B, Liu M et al (2019) Stroke in China: advances and challenges in epidemiology, prevention, and management. Lancet Neurol 18(4):394–405

    Article  PubMed  Google Scholar 

  16. Hammond G, Luke AA, Elson L et al (2020) Urban-rural inequities in acute stroke care and in-hospital mortality. Stroke 51(7):2131–2138

    Article  PubMed  Google Scholar 

  17. Wang Y, Li Y, Liu X et al (2019) Prevalence and influencing factors of coronary heart disease and stroke in Chinese rural adults: the henan rural cohort study. Front Public Health 7:411

  18. Zhang L, Wang Y, Niu M et al (2021) Nonlaboratory-based risk assessment model for type 2 diabetes mellitus screening in Chinese rural population: a joint bagging-boosting model. IEEE J Biomed Health Inform 25(10):4005–4016

    Article  PubMed  Google Scholar 

  19. Kang N, Chen G, Tu R et al (2022) Adverse associations of different obesity measures and the interactions with long-term exposure to air pollutants with prevalent type 2 diabetes mellitus: the Henan Rural Cohort study. Environ Res 207:112640

    Article  CAS  PubMed  Google Scholar 

  20. Liu X, Mao Z, Li Y et al (2019) Cohort profile: the Henan Rural Cohort: a prospective study of chronic non-communicable diseases. Int J Epidemiol 48(6):1756-1756j

  21. Liao S, Jin L, Dai W-Q et al (2021) A machine learning-based risk scoring system for infertility considering different age groups. Int J Intell Syst 36(3):1331–1344

    Article  Google Scholar 

  22. Sullivan LM, Massaro JM, D’Agostino RB (2004) Presentation of multivariate data for clinical use: the Framingham study risk score functions. Stat Med 23(10):1631–1660

    Article  PubMed  Google Scholar 

  23. Segar MW, Vaduganathan M, Patel KV et al (2019) Machine learning to predict the risk of incident heart failure hospitalization among patients with diabetes: the WATCH-DM risk score. Diabetes Care 42(12):2298–2306

    Article  PubMed  PubMed Central  Google Scholar 

  24. Howard G, McClure LA, Moy CS et al (2017) Self-reported stroke risk stratification: reasons for geographic and racial differences in stroke study. Stroke 48(7):1737–1743

    Article  PubMed  PubMed Central  Google Scholar 

  25. Xing X, Yang X, Liu F et al (2019) Predicting 10-year and lifetime stroke risk in Chinese population. Stroke 50(9):2371–2378

    Article  PubMed  Google Scholar 

  26. Chao TF, Chiang CE, Chen TJ et al (2021) Clinical risk score for the prediction of incident atrial fibrillation: derivation in 7 220 654 Taiwan patients with 438 930 incident atrial fibrillations during a 16-year follow-up. J Am Heart Assoc 10(17):e020194

    Article  PubMed  PubMed Central  Google Scholar 

  27. Saini V, Guada L, Yavagal DR (2021) Global epidemiology of stroke and access to acute ischemic stroke interventions. Neurology 97(20 Suppl 2):S6-s16

    Article  PubMed  Google Scholar 

  28. Adler ED, Voors AA, Klein L et al (2020) Improving risk prediction in heart failure using machine learning. Eur J Heart Fail 22(1):139–147

    Article  PubMed  Google Scholar 

  29. Kim W, Park JJ, Lee H-Y et al (2021) Predicting survival in heart failure: a risk score based on machine-learning and change point algorithm. Clin Res Cardiol 110(8):1321–1333

    Article  PubMed  Google Scholar 

  30. Parmar P, Krishnamurthi R, Ikram MA et al (2015) The stroke riskometer(TM) App: validation of a data collection tool and stroke risk predictor. Int J Stroke 10(2):231–244

    Article  PubMed  Google Scholar 

  31. Feigin VL, Norrving B (2014) A new paradigm for primary prevention strategy in people with elevated risk of stroke. Int J Stroke 9(5):624–626

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank all of the participants, coordinators, and administrators for their support and help during the research.

Funding

This research was supported by the National Natural Science Foundation of China (Grant NO: 81930092, 81973128), Foundation of National Key Program of Research and Development of China (Grant NO: 2016YFC0900803), Science and Technology Innovation Team Support Plan of Colleges and Universities in Henan Province (Grant NO:21IRTSTHN029), Key Research Program of Colleges and Universities in Henan Province (Grant NO: 21A330007), and Discipline Key Research and Development Program of Zhengzhou University (Grant NO: XKZDQY202008, XKZDQY202002). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Author notes

  1. Zhongao Ding and Liying Zhang contributed equally to this work.

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Henan, 450001, Zhengzhou, People’s Republic of China

    Zhongao Ding, Liying Zhang, Miaomiao Niu, Xiaotian Liu, Wenqian Huo, Jian Hou, Zhenxing Mao & Chongjian Wang

  2. Department of Software Engineering, School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China

    Liying Zhang & Zhenfei Wang

  3. Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL, USA

    Bo Zhao

  4. NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, Zhengzhou, Henan, People’s Republic of China

    Chongjian Wang

Authors
  1. Zhongao Ding
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Contributions

Zhongao Ding: conceptualization, investigation, data curation, methodology, formal analysis, visualization, writing—original draft. Liying Zhang: investigation, data curation, formal analysis, code, writing—review and editing. Miaomiao Niu: visualization, writing—review and editing. Bo Zhao: writing—review and editing. Xiaotian Liu: writing—review and editing. Wenqian Huo: investigation, writing—review and editing. Zhenxing Mao: investigation, writing—review and editing. Jian Hou: writing—review and editing. Zhenfei Wang: writing—review and editing. Chongjian Wang: conceptualization, methodology, investigation, validation, supervision, funding acquisition, project administration, writing—review and editing.

Corresponding author

Correspondence to Chongjian Wang.

Ethics declarations

Ethical approval

Ethics approval of the study was obtained from Zhengzhou University Life Science Ethics Committee, and has, therefore, been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. The Henan Rural Cohort has been registered at the Chinese Clinical Trial Register. (Trial registration: ChiCTR-OOC-15006699. Registered 6 July 2015 -Retrospectively registered).

Informal consent

All study subjects gave their informed consent prior to their inclusion in the study.

Conflict of interest

The authors declare no competing interests.

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Ding, Z., Zhang, L., Niu, M. et al. Stroke prevention in rural residents: development of a simplified risk assessment tool with artificial intelligence. Neurol Sci 44, 1687–1694 (2023). https://doi.org/10.1007/s10072-023-06610-5

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