SpringerLink
Log in

Predicting Functional Outcome Based on Linked Data After Acute Ischemic Stroke: S-SMART Score

  • Original Article
  • Published:
Translational Stroke Research Aims and scope Submit manuscript

Abstract

Prediction of outcome after stroke may help clinicians provide effective management and plan long-term care. We aimed to develop and validate a score for predicting good functional outcome available for hospitals after ischemic stroke using linked data. A total of 22,005 patients with acute ischemic stroke from the Clinical Research Center for Stroke Registry between July 2007 and December 2014 were included in the derivation group. We assessed functional outcomes using a modified Rankin scale (mRS) score at 3 months after ischemic stroke. We identified predictors related to good 3-month outcome (mRS score ≤ 2) and developed a score. External validations (geographic and temporal validations) of the developed model were performed. The prediction model performance was assessed using the area under the receiver operating characteristic curve (AUC) and the calibration test. Stroke severity, sex, stroke mechanism, age, pre-stroke mRS, and thrombolysis/thrombectomy treatment were identified as predictors for 3-month good functional outcomes in the S-SMART score (total 34 points). Patients with higher S-SMART scores had an increased likelihood of a good outcome. The AUC of the prediction score was 0.805 (0.798–0.811) in the derivation group and 0.812 (0.795–0.830) in the geographic validation group for good functional outcome. The AUC of the model was 0.812 (0.771–0.854) for the temporal validation group. Moreover, they had good calibration. The S-SMART score is a valid and useful tool to predict good functional outcome following ischemic stroke. This prediction model may assist in the estimation of outcomes to determine care plans after stroke.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Mayo NE, Wood-Dauphinee S, Ahmed S, et al. Disablement following stroke. Disabil Rehabil. 1999;21(5–6):258–68.

    CAS  PubMed  Google Scholar 

  2. Nichols-Larsen DS, Clark P, Zeringue A, Greenspan A, Blanton S. Factors influencing stroke survivors’ quality of life during subacute recovery. Stroke. 2005;36(7):1480–4.

    PubMed  Google Scholar 

  3. Abdul-Rahim AH, Quinn TJ, Alder S, Clark AB, Musgrave SD, Langhorne P, et al. Derivation and validation of a novel prognostic scale (Modified-Stroke Subtype, Oxfordshire Community Stroke Project Classification, Age, and Prestroke Modified Rankin) to predict early mortality in acute stroke. Stroke. 2016;47(1):74–9.

    PubMed  Google Scholar 

  4. de Ridder IR, Dijkland SA, Scheele M, den Hertog H, Dirks M, Westendorp WF, et al. Development and validation of the Dutch Stroke Score for predicting disability and functional outcome after ischemic stroke: a tool to support efficient discharge planning. Eur Stroke J. 2018;3(2):165–73.

    PubMed  PubMed Central  Google Scholar 

  5. Flint A, Cullen S, Faigeles B, Rao VA. Predicting long-term outcome after endovascular stroke treatment: the totaled health risks in vascular events score. AJNR Am J Neuroradiol. 2010;31(7):1192–6.

    PubMed  PubMed Central  Google Scholar 

  6. Flint AC, Faigeles BS, Cullen SP, Kamel H, Rao VA, Gupta R, et al. THRIVE score predicts ischemic stroke outcomes and thrombolytic hemorrhage risk in VISTA. Stroke. 2013;44(12):3365–9.

    CAS  PubMed  Google Scholar 

  7. Lou M, Safdar A, Mehdiratta M, Kumar S, Schlaug G, Caplan L, et al. The HAT Score: a simple grading scale for predicting hemorrhage after thrombolysis. Neurology. 2008;71(18):1417–23.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Myint PK, Clark AB, Kwok CS, Davis J, Durairaj R, Dixit AK, et al. The SOAR (Stroke subtype, Oxford Community Stroke Project classification, Age, prestroke modified Rankin) score strongly predicts early outcomes in acute stroke. Int J Stroke. 2014;9(3):278–83.

    PubMed  Google Scholar 

  9. O’donnell MJ, Fang J, D’uva C, et al. The PLAN score: a bedside prediction rule for death and severe disability following acute ischemic stroke. Arch Intern Med. 2012;172(20):1548–56.

    PubMed  Google Scholar 

  10. Saposnik G, Guzik AK, Reeves M, Ovbiagele B, Johnston SC. Stroke prognostication using age and NIH Stroke Scale: SPAN-100. Neurology. 2013;80(1):21–8.

    PubMed  PubMed Central  Google Scholar 

  11. Saposnik G, Kapral MK, Liu Y, Hall R, O’Donnell M, Raptis S, et al. IScore: a risk score to predict death early after hospitalization for an acute ischemic stroke. Circulation. 2011;123(7):739–49.

    PubMed  Google Scholar 

  12. Strbian D, Meretoja A, Ahlhelm F, Pitkäniemi J, Lyrer P, Kaste M, et al. Predicting outcome of IV thrombolysis–treated ischemic stroke patients the DRAGON score. Neurology. 2012;78(6):427–32.

    CAS  PubMed  Google Scholar 

  13. Counsell C, Dennis M, McDowall M, Warlow C. Predicting outcome after acute and subacute stroke: development and validation of new prognostic models. Stroke. 2002;33(4):1041–7.

    PubMed  Google Scholar 

  14. Quinn TJ, Singh S, Lees KR, et al. Validating and comparing stroke prognosis scales. Neurology. 2017;89(10):997–1002.

    PubMed  Google Scholar 

  15. Ntaios G, Faouzi M, Ferrari J, et al. An integer-based score to predict functional outcome in acute ischemic stroke the astral score. Neurology. 2012;78(24):1916–22.

    CAS  PubMed  Google Scholar 

  16. Rha J-H, Saver JL. The impact of recanalization on ischemic stroke outcome: a meta-analysis. Stroke. 2007;38(3):967–73.

    PubMed  Google Scholar 

  17. Goyal M, Menon BK, Van Zwam WH, et al. Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials. Lancet. 2016;387(10029):1723–31.

    PubMed  Google Scholar 

  18. Emberson J, Lees KR, Lyden P, Blackwell L, Albers G, Bluhmki E, et al. Effect of treatment delay, age, and stroke severity on the effects of intravenous thrombolysis with alteplase for acute ischaemic stroke: a meta-analysis of individual patient data from randomised trials. Lancet. 2014;384(9958):1929–35.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Kim TJ, Lee JS, Kim J-W, et al. Building linked big data for stroke in Korea: Linkage of Stroke Registry and National Health Insurance Claims Data. J Korean Med Sci. 2018;33(53):33 e343.

    Google Scholar 

  20. Kim BJ, Park J-M, Kang K, Lee SJ, Ko Y, Kim JG, et al. Case characteristics, hyperacute treatment, and outcome information from the clinical research center for stroke-fifth division registry in South Korea. J Stroke. 2015;17(1):38–53.

    PubMed  PubMed Central  Google Scholar 

  21. Steyerberg EW, Vergouwe YJ. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J. 2014;35(29):1925–31.

    PubMed  PubMed Central  Google Scholar 

  22. Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162(1):W1–W73.

    PubMed  Google Scholar 

  23. Collins GS, Altman DG. Predicting the 10 year risk of cardiovascular disease in the United Kingdom: independent and external validation of an updated version of QRISK2. BMJ. 2012;344:e4181.

    PubMed  PubMed Central  Google Scholar 

  24. Cook JA, Collins GS. Some important deficiencies in the development, validation, and reporting of a prediction model. JAMA Surg. 2015;150(9):915.

    PubMed  Google Scholar 

  25. Vergouwe Y, Steyerberg EW, Eijkemans MJ, Habbema JD. Substantial effective sample sizes were required for external validation studies of predictive logistic regression models. J Clin Epidemiol. 2005;58(5):475–83.

    PubMed  Google Scholar 

  26. Saposnik G, Cote R, Phillips S, Gubitz G, Bayer N, Minuk J, et al. Stroke outcome in those over 80: a multicenter cohort study across Canada. Stroke. 2008;39(8):2310–7.

    PubMed  Google Scholar 

  27. Kawase S, Kowa H, Suto Y, Fukuda H, Kusumi M, Nakayasu H, et al. Association between body mass index and outcome in Japanese ischemic stroke patients. Geriatr Gerontol Int. 2017;17(3):369–74.

    PubMed  Google Scholar 

  28. Fonarow GC, Saver JL, Smith EE, Broderick JP, Kleindorfer DO, Sacco RL, et al. Relationship of National Institutes of Health Stroke Scale to 30-day mortality in Medicare beneficiaries with acute ischemic stroke. J Am Heart Assoc. 2012;1(1):42–50.

    PubMed  PubMed Central  Google Scholar 

  29. Rubin G, Firlik AD, Levy EI, Pindzola RR, Yonas H. Relationship between cerebral blood flow and clinical outcome in acute stroke. Cerebrovasc Dis. 2000;10(4):298–306.

    CAS  PubMed  Google Scholar 

  30. Alberti KGMM, Zimmet PF. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO consultation. Diabet Med. 1998;15(7):539–53.

    CAS  PubMed  Google Scholar 

  31. Committee IE. International expert Committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care. 2009;32(7):1327–34.

    Google Scholar 

  32. Adams HP Jr, Bendixen BH, Kappelle LJ, et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke. 1993;24(1):35–41.

    PubMed  Google Scholar 

  33. Arboix A, Alió J. Cardioembolic stroke: clinical features, specific cardiac disorders and prognosis. Curr Cardiol Rev. 2010;6(3):150–61.

    PubMed  PubMed Central  Google Scholar 

  34. Norrving B. Long-term prognosis after lacunar infarction. Lancet Neurol. 2003;2(4):238–45.

    PubMed  Google Scholar 

  35. Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med. 2009;28(25):3083–107.

    PubMed  PubMed Central  Google Scholar 

  36. Kim L, Kim J-A, Kim SJE, et al. A guide for the utilization of health insurance review and assessment service national patient samples. Epidemiol Health. 2014;36:e2014008.

    PubMed  PubMed Central  Google Scholar 

  37. Flint AC, Rao VA, Chan SL, Cullen SP, Faigeles BS, Smith WS, et al. Improved ischemic stroke outcome prediction using model estimation of outcome probability: the THRIVE-c calculation. Int J Stroke. 2015;10(6):815–21.

    PubMed  Google Scholar 

  38. König IR, Ziegler A, Bluhmki E, Hacke W, Bath PM, Sacco RL, et al. Predicting long-term outcome after acute ischemic stroke: a simple index works in patients from controlled clinical trials. Stroke. 2008;39(6):1821–6.

    PubMed  Google Scholar 

  39. Powers WJ, Rabinstein AA, Ackerson T, Adeoye OM, Bambakidis NC, Becker K, et al. 2018 guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2018;49(3):e46–99.

    PubMed  Google Scholar 

  40. Kim BJ, Kim JS. Ischemic stroke subtype classification: an Asian viewpoint. J Stroke. 2014;16(1):8–17.

    PubMed  PubMed Central  Google Scholar 

  41. Lattanzi S, Pulcini A, Corradetti T, Rinaldi C, Zedde ML, Ciliberti G, et al. Prediction of outcome in embolic strokes of undetermined source. J Stroke Cerebrovasc Dis. 2020;29(1):104486.

    PubMed  Google Scholar 

  42. Bender M, Jusufovic E, Railic V, Kelava S, Tinjak S, Dzevdetbegovic D, et al. High burden of stroke risk factors in develo** country: the case study of Bosnia-Herzegovina. Mater Sociomed. 2017;29(4):277–9.

    PubMed  PubMed Central  Google Scholar 

  43. Feigin VLJTL. Stroke epidemiology in the develo** world. Lancet. 2005;365(9478):2160–1.

    PubMed  Google Scholar 

  44. Johnson W, Onuma O, Owolabi M, Sachdev S. Stroke: a global response is needed. Bull World Health Organ. 2016;94(9):634–634A.

    PubMed  PubMed Central  Google Scholar 

  45. Lattanzi S, Bartolini M, Provinciali L, Silvestrini M. Glycosylated hemoglobin and functional outcome after acute ischemic stroke. J Stroke Cerebrovasc Dis. 2016;25(7):1786–91.

    PubMed  Google Scholar 

  46. Lattanzi S, Brigo F, Trinka E, Cagnetti C, di Napoli M, Silvestrini M. Neutrophil-to-lymphocyte ratio in acute cerebral hemorrhage: a system review. Transl Stroke Res. 2019;10(2):137–45.

    CAS  PubMed  Google Scholar 

  47. Zangari R, Zanier ER, Torgano G, et al. Early ficolin-1 is a sensitive prognostic marker for functional outcome in ischemic stroke. J Neuroinflammation. 2016;13:16.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Lattanzi S, Carbonari L, Pagliariccio G, et al. Predictors of cognitive functioning after carotid revascularization. J Neurol Sci. 2019;405:116435.

    PubMed  Google Scholar 

  49. Divani AA, Liu X, Di Napoli M, et al. Blood pressure variability predicts poor in-hospital outcome in spontaneous intracerebral hemorrhage. Stroke. 2019;50(8):2023–9.

    PubMed  Google Scholar 

  50. Buratti L, Cagnetti C, Balucani C, et al. Blood pressure variability and stroke outcome in patients with internal carotid artery occlusion. J Neurol Sci. 2014;339(1–2):164–8.

    PubMed  Google Scholar 

  51. Lattanzi S, Silvestrini M, Provinciali L. Elevated blood pressure in the acute phase of stroke and the role of angiotensin receptor blockers. Int J Hypertens. 2013;2013:941783.

    PubMed  PubMed Central  Google Scholar 

  52. Grube MM, Koennecke HC, Walter G, et al. Influence of acute complications on outcome 3 months after ischemic stroke. PLoS One. 2013;8(9):e75719.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Langhorne P, Stott DJ, Robertson L, MacDonald J, Jones L, McAlpine C, et al. Medical complications after stroke: a multicenter study. Stroke. 2000;31(6):1223–9.

    CAS  PubMed  Google Scholar 

  54. Yagi M, Yasunaga H, Matsui H, Morita K, Fushimi K, Fujimoto M, et al. Impact of rehabilitation on outcomes in patients with ischemic stroke: a nationwide retrospective cohort study in Japan. Stroke. 2017;48(3):740–6.

    PubMed  Google Scholar 

  55. Musicco M, Emberti L, Nappi G, Caltagirone C, Italian Multicenter Study on Outcomes of Rehabilitation of Neurological Patients. Italian multicenter study on outcomes of rehabilitation of neurological patients. Early and long-term outcome of rehabilitation in stroke patients: the role of patient characteristics, time of initiation, and duration of interventions. Arch Phys Med Rehabil. 2003;84(4):551–8.

    PubMed  Google Scholar 

Download references

Acknowledgments

We thank our investigators of CRCS from 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. Hyung-Geun Oh, Pf. Young-Jae Kim, Pf. Byoung-Soo Shin, Pf. Chang Hun Kim, and Pf. Chi Kyung Kim who provided data that greatly assisted the research, although they may not agree with all of the interpretations/conclusions of this paper.

Availability of Data and Materials

The datasets generated and/or analyzed during the current study are not publicly available due to the data as imposed by ethical approval. To inquire access to the study data, contact the corresponding author (Byung-Woo Yoon).

Author information

Author notes

  1. Tae Jung Kim and Ji Sung Lee contributed equally to this work.

Authors and Affiliations

  1. Department of Neurology, Seoul National University Hospital, 101 Daehakno, Jongno-Gu, Seoul, 03080, South Korea

    Tae Jung Kim, Jae Sun Yoon, Chan-Hyuk Lee, Heejung Mo, Han-Yeong Jeong, Keun-Hwa Jung, Sang-Bae Ko & Byung-Woo Yoon

  2. Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea

    Ji Sung Lee

  3. Department of Neurology, Hallym University Sacred Heart Hospital, Anyang, South Korea

    Mi-Sun Oh, Jae-Sung Lim, Kyung-Ho Yu & Byung-Chul Lee

  4. Health Insurance Review and Assessment Service, Wonju, South Korea

    Ji-Woo Kim, Log Young Kim, Mi Ra An, Young Hee Park, Tae Seon Lee & Yun Jung Heo

  5. Department of Neurology, Kangdong Sacred Heart Hospital, Seoul, South Korea

    Yerim Kim

  6. Department of Neurology, Hallym University College of Medicine, Chuncheon, South Korea

    Sang-Hwa Lee

Authors
  1. Tae Jung Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ji Sung Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mi-Sun Oh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ji-Woo Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jae Sun Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jae-Sung Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chan-Hyuk Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Heejung Mo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Han-Yeong Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yerim Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Sang-Hwa Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Keun-Hwa Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Log Young Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Mi Ra An
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Young Hee Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Tae Seon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Yun Jung Heo
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Sang-Bae Ko
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Kyung-Ho Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Byung-Chul Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Byung-Woo Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

B-W Y., B-C L., K-H Y., S-B K., K-H J., M-S O., JS L., and TJ K. contributed to the study concept and design. TJ K., JS L., M-S O., and JS Y. contributed to data analysis. TJ K., M-S O., J-S L., C-H L., H-H M., H-Y J., Y K., S-H L. LY K., MR A., YH P., TS L., and YJ H. contributed to data collection. TJ K., JS L., and B-W Y. drafted the manuscript. All authors read and approved the manuscript.

Corresponding author

Correspondence to Byung-Woo Yoon.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

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

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic Supplementary Material

ESM 1

(DOCX 889 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, T.J., Lee, J.S., Oh, MS. et al. Predicting Functional Outcome Based on Linked Data After Acute Ischemic Stroke: S-SMART Score. Transl. Stroke Res. 11, 1296–1305 (2020). https://doi.org/10.1007/s12975-020-00815-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12975-020-00815-y

Keywords

Search

Navigation