The Stroke Riskometer(TM) App: validation of a data collection tool and stroke risk predictor

Priya Parmar, Rita Krishnamurthi, M Arfan Ikram, Albert Hofman, Saira S Mirza, Yury Varakin, Michael Kravchenko, Michael Piradov, Amanda G Thrift, Bo Norrving, Wenzhi Wang, Dipes Kumar Mandal, Suzanne Barker-Collo, Ramesh Sahathevan, Stephen Davis, Gustavo Saposnik, Miia Kivipelto, Shireen Sindi, Natan M Bornstein, Maurice Giroud, Yannick Béjot, Michael Brainin, Richie Poulton, K M Venkat Narayan, Manuel Correia, António Freire, Yoshihiro Kokubo, David Wiebers, George Mensah, Nasser F BinDhim, P Alan Barber, Jeyaraj Durai Pandian, Graeme J Hankey, Man Mohan Mehndiratta, Shobhana Azhagammal, Norlinah Mohd Ibrahim, Max Abbott, Elaine Rush, Patria Hume, Tasleem Hussein, Rohit Bhattacharjee, Mitali Purohit, Valery L Feigin, Stroke RiskometerTM Collaboration Writing Group, Priya Parmar, Rita Krishnamurthi, M Arfan Ikram, Albert Hofman, Saira S Mirza, Yury Varakin, Michael Kravchenko, Michael Piradov, Amanda G Thrift, Bo Norrving, Wenzhi Wang, Dipes Kumar Mandal, Suzanne Barker-Collo, Ramesh Sahathevan, Stephen Davis, Gustavo Saposnik, Miia Kivipelto, Shireen Sindi, Natan M Bornstein, Maurice Giroud, Yannick Béjot, Michael Brainin, Richie Poulton, K M Venkat Narayan, Manuel Correia, António Freire, Yoshihiro Kokubo, David Wiebers, George Mensah, Nasser F BinDhim, P Alan Barber, Jeyaraj Durai Pandian, Graeme J Hankey, Man Mohan Mehndiratta, Shobhana Azhagammal, Norlinah Mohd Ibrahim, Max Abbott, Elaine Rush, Patria Hume, Tasleem Hussein, Rohit Bhattacharjee, Mitali Purohit, Valery L Feigin, Stroke RiskometerTM Collaboration Writing Group

Abstract

Background: The greatest potential to reduce the burden of stroke is by primary prevention of first-ever stroke, which constitutes three quarters of all stroke. In addition to population-wide prevention strategies (the 'mass' approach), the 'high risk' approach aims to identify individuals at risk of stroke and to modify their risk factors, and risk, accordingly. Current methods of assessing and modifying stroke risk are difficult to access and implement by the general population, amongst whom most future strokes will arise. To help reduce the burden of stroke on individuals and the population a new app, the Stroke Riskometer(TM) , has been developed. We aim to explore the validity of the app for predicting the risk of stroke compared with current best methods.

Methods: 752 stroke outcomes from a sample of 9501 individuals across three countries (New Zealand, Russia and the Netherlands) were utilized to investigate the performance of a novel stroke risk prediction tool algorithm (Stroke Riskometer(TM) ) compared with two established stroke risk score prediction algorithms (Framingham Stroke Risk Score [FSRS] and QStroke). We calculated the receiver operating characteristics (ROC) curves and area under the ROC curve (AUROC) with 95% confidence intervals, Harrels C-statistic and D-statistics for measure of discrimination, R(2) statistics to indicate level of variability accounted for by each prediction algorithm, the Hosmer-Lemeshow statistic for calibration, and the sensitivity and specificity of each algorithm.

Results: The Stroke Riskometer(TM) performed well against the FSRS five-year AUROC for both males (FSRS = 75.0% (95% CI 72.3%-77.6%), Stroke Riskometer(TM) = 74.0(95% CI 71.3%-76.7%) and females [FSRS = 70.3% (95% CI 67.9%-72.8%, Stroke Riskometer(TM) = 71.5% (95% CI 69.0%-73.9%)], and better than QStroke [males - 59.7% (95% CI 57.3%-62.0%) and comparable to females = 71.1% (95% CI 69.0%-73.1%)]. Discriminative ability of all algorithms was low (C-statistic ranging from 0.51-0.56, D-statistic ranging from 0.01-0.12). Hosmer-Lemeshow illustrated that all of the predicted risk scores were not well calibrated with the observed event data (P < 0.006).

Conclusions: The Stroke Riskometer(TM) is comparable in performance for stroke prediction with FSRS and QStroke. All three algorithms performed equally poorly in predicting stroke events. The Stroke Riskometer(TM) will be continually developed and validated to address the need to improve the current stroke risk scoring systems to more accurately predict stroke, particularly by identifying robust ethnic/race ethnicity group and country specific risk factors.

Keywords: Stroke RiskometerTM App; prevention; stroke prediction; validation.

© 2014 The Authors. International Journal of Stroke published by John Wiley & Sons Ltd on behalf of World Stroke Organization.

Figures

Figure 1
Figure 1
Mean predicted risk score by age for Framingham Stroke Risk Score (FSRS) (black), Stroke Riskometer™ (red) and QStroke (green) for five-years for males and females.
Figure 2
Figure 2
Receiver-operating characteristic (ROC) curves for Framingham Stroke Risk Score (FSRS) (black), Stroke Riskometer™ (red) and QStroke (green) algorithms for 5 and 10-year risks.
Figure 3
Figure 3
Mean predicted risk (%) vs. observed stroke events in deciles of predicted risk for Framingham Stroke Risk Score (FSRS) (black), Stroke Riskometer™ (red) and QStroke (green) algorithms.

References

    1. Lozano R, Naghavi M, Foreman K, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the global burden of disease study 2010. Lancet. 2012;380:2095–2128.
    1. Feigin VL, Forouzanfar MH, Krishnamurthi R, et al. Global and regional burden of stroke during 1990–2010: findings from the global burden of disease study 2010. Lancet. 2014;383:245–254.
    1. Bos MJ, Koudstaal PJ, Hofman A, Ikram MA. Modifiable etiological factors and the burden of stroke from the rotterdam study: a population-based cohort study. PLoS Med. 2014;11:e1001634.
    1. O'Donnell MJ, Xavier D, Liu L, 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.
    1. Danaei G, Finucane MM, Lu Y, et al. National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2¬Σ7 million participants. Lancet. 2011;378:31–40.
    1. Ng M, Fleming T, Robinson M, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the global burden of disease study 2013. Lancet. 2014;384:766–781.
    1. Wolf PA, D'Agostino RB, Belanger AJ, Kannel WB. Probability of stroke: a risk profile from the framingham study. Stroke. 1991;22:312–318.
    1. Hippisley-Cox J, Coupland C, Brindle P. Derivation and validation of qstroke score for predicting risk of ischaemic stroke in primary care and comparison with other risk scores: a prospective open cohort study. BMJ. 2013;346:f2573.
    1. Rose G. Strategy of prevention: lessons from cardiovascular disease. Br Med J (Clin Res Ed) 1981;282:1847–1851.
    1. Brindle P, Emberson J, Lampe F, et al. Predictive accuracy of the framingham coronary risk score in british men: prospective cohort study. BMJ. 2003;327:1267–1270.
    1. Cooney MT, Dudina A, Whincup P, et al. Re-evaluating the rose approach: comparative benefits of the population and high-risk preventive strategies. Eur J Cardiovasc Prev Rehabil. 2009;16:541–549.
    1. Dalton AR, Soljak M, Samarasundera E, Millett C, Majeed A. Prevalence of cardiovascular disease risk amongst the population eligible for the nhs health check programme. Eur J Prev Cardiol. 2013;20:142–150.
    1. Jones SP, Jenkinson AJ, Leathley MJ, Watkins CL. Stroke knowledge and awareness: an integrative review of the evidence. Age Ageing. 2010;39:11–22.
    1. Hickey A, O'Hanlon A, McGee H, et al. Stroke awareness in the general population: knowledge of stroke risk factors and warning signs in older adults. BMC Geriatr. 2009;9:35.
    1. Roger VL, Go AS, Lloyd-Jones DM, et al. Heart disease and stroke statistics – 2011 update: a report from the american heart association. Circulation. 2011;123:e18–209.
    1. Cooney MT, Dudina A, D'Agostino R, Graham IM. Cardiovascular risk-estimation systems in primary prevention: do they differ? Do they make a difference? Can we see the future? Circulation. 2010;122:300–310.
    1. Brouillette RM, Foil H, Fontenot S, et al. Feasibility, reliability, and validity of a smartphone based application for the assessment of cognitive function in the elderly. PLoS ONE. 2013;8:e65925.
    1. Leung R, Tang C, Haddad S, McGrenere J, Graf P, Ingriany V. How older adults learn to use mobile devices: survey and field investigations. ACM Trans Access Comput. 2012;4:1–33.
    1. Arab F, Malik Y, Abdulrazak B. Evaluation of phonage: an adapted smartphone interface for elderly people. Lect Notes Comput Sci. 2013;8120:547–554.
    1. Be he@lthy, be mobile. 2014. ITU. Available at .
    1. Hill S, Spink J, Cadilhac D, et al. Absolute risk representation in cardiovascular disease prevention: comprehension and preferences of health care consumers and general practitioners involved in a focus group study. BMC Public Health. 2010;10:1–13.
    1. Fagerlin A, Zikmund-Fisher BJ, Ubel PA. Helping patients decide: ten steps to better risk communication. J Natl Cancer Inst. 2011;103:1436–1443.
    1. Goldstein LB, Bushnell CD, Adams RJ, et al. Guidelines for the primary prevention of stroke: a guideline for healthcare professionals from the american heart association/american stroke association.[erratum appears in stroke. 2011 feb;42(2):E26] Stroke. 2011;42:517–584.
    1. Eckel RH, Jakicic JM, Ard JD, et al. 2013 aha/acc guideline on lifestyle management to reduce cardiovascular risk: a report of the american college of cardiology/american heart association task force on practice guidelines. J Am Coll Cardiol. 2014;63:2960–2984.
    1. Furie KL, Kasner SE, Adams RJ, et al. Guidelines for the prevention of stroke in patients with stroke or transient ischemic attack: a guideline for healthcare professionals from the american heart association/american stroke association. Stroke. 2011;42:227–276.
    1. Graham I, Atar D, Borch-Johnsen K, et al. European guidelines on cardiovascular disease prevention in clinical practice: executive summary: fourth joint task force of the european society of cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of nine societies and by invited experts) Eur Heart J. 2007;28:2375–2414.
    1. Perk J, De Backer G, Gohlke H, et al. European guidelines on cardiovascular disease prevention in clinical practice (version 2012). The fifth joint task force of the european society of cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of nine societies and by invited experts).[erratum appears in eur heart j. 2012 sep;33(17):2126] Eur Heart J. 2012;33:1635–1701.
    1. WHO. 2007. Prevention of cardiovascular disease: pocket guidelines for assessment and management of cardiovascular risk. Available at .
    1. Feigin VL, Norrving B. A new paradigm for primary prevention strategy in people with elevated risk of stroke. Int J Stroke. 2014;9:624–626.
    1. Krishnamurthi R, Jones A, Barber A, et al. Methodology of a population-based stroke and tia incidence and outcomes study: the auckland regional community stroke study (arcos iv) 2011–2012. Int J Stroke. 2014;9:140–147.
    1. Hofman A, Darwish Murad S, van Duijn CM, et al. The rotterdam study: 2014 objectives and design update. Eur J Epidemiol. 2013;28:889–926.
    1. Clark TG, Altman DG, De Stavola BL. Quantification of the completeness of follow-up. Lancet. 2002;359:1309–1310.
    1. Hatano S. Experience from a multicentre stroke register: a preliminary report. Bull World Health Organ. 1976;54:541–553.
    1. WHO. Geneva World Health Organization. 2013. Global Action Plan for Prevention and Control of Noncommunicable Diseases 2013–2020.
    1. van Asch CJJ, Luitse MJ, Rinkel GJ, van der Tweel I, Algra A, Klijn CJ. Incidence, case fatality, and functional outcome of intracerebral haemorrhage over time, according to age, sex, and ethnic origin: a systematic review and meta-analysis. Lancet Neurol. 2010;9:167–176.
    1. Shinton R, Sagar G, Beevers G. The relation of alcohol consumption to cardiovascular risk factors and stroke. The west birmingham stroke project. J Neurol Neurosurg Psychiatry. 1993;56:458–462.
    1. Bazzano LA, Gu D, Reynolds K, et al. Alcohol consumption and risk for stroke among chinese men. Ann Neurol. 2007;62:569–578.
    1. Mvundura M, McGruder H, Khoury MJ, Valdez R, Yoon PW. Family history as a risk factor for early-onset stroke/transient ischemic attack among adults in the united states. Public Health Genomics. 2010;13:13–20.
    1. Liao D, Myers R, Hunt S, et al. Familial history of stroke and stroke risk: the family heart study. Stroke. 1997;28:1908–1912.
    1. Kayaba K. Family history of stroke: an old and still unproven risk factor. Hypertens Res. 2008;31:1489–1490.
    1. Cook NR, Paynter NP, Eaton CB, et al. Comparison of the framingham and reynolds risk scores for global cardiovascular risk prediction in the multiethnic women's health initiative. Circulation. 2012;125:1748–1756. , S1741–1711.
    1. Pendlebury ST, Rothwell PM. Risk of recurrent stroke, other vascular events and dementia after transient ischaemic attack and stroke. Cerebrovasc Dis. 2009;27(Suppl. 3):1–11.
    1. Ferrucci L, Guralnik JM, Salive ME, et al. Cognitive impairment and risk of stroke in the older population. J Am Geriatr Soc. 1996;44:237–241.
    1. Chen YH, Kang JH, Lin HC. Patients with traumatic brain injury: population-based study suggests increased risk of stroke. Stroke. 2011;42:2733–2739.
    1. De Koning L, Merchant AT, Pogue J, Anand SS. Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies. Eur Heart J. 2007;28:850–856.
    1. Asplund K, Karvanen J, Giampaoli S, et al. Relative risks for stroke by age, sex, and population based on follow-up of 18 european populations in the morgam project. Stroke. 2009;40:2319–2326.
    1. D'Agostino RB, Sr, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the framingham heart study. Circulation. 2008;117:743–753.
    1. Chang M, Hahn RA, Teutsch SM, Hutwagner LC. Multiple risk factors and population attributable risk for ischemic heart disease mortality in the united states, 1971–1992. J Clin Epidemiol. 2001;54:634–644.
    1. Pennells L, Kaptoge S, White IR, Thompson SG, Wood AM, Factors ER. Assessing risk prediction models using individual participant data from multiple studies. Am J Epidemiol. 2014;179:621–632.
    1. R Development Core Team. 2013. Vienna, Austria R Foundation for Statistical Computing R: A Language and Environment for Statistical Computing, 2013.
    1. Bineau S, Dufouil C, Helmer C, et al. Framingham stroke risk function in a large population-based cohort of elderly people: the 3c study. Stroke. 2009;40:1564–1570.
    1. Majed B, Tafflet M, Kee F, et al. External validation of the 2008 framingham cardiovascular risk equation for chd and stroke events in a european population of middle-aged men. The prime study. Prev Med. 2013;57:49–54.
    1. McClure LA, Kleindorfer DO, Kissela BM, Cushman M, Soliman EZ, Howard G. Assessing the performance of the framingham stroke risk score in the reasons for geographic and racial differences in stroke cohort. Stroke. 2014;45:1716–1720.
    1. Arts EE, Popa C, Den Broeder AA, et al. Performance of four current risk algorithms in predicting cardiovascular events in patients with early rheumatoid arthritis. Ann Rheum Dis. 2014;0:1–7.
    1. Yusuf S, Rangarajan S, Teo K, et al. Cardiovascular risk and events in 17 low-, middle-, and high-income countries. N Engl J Med. 2014;371:818–827.
    1. Collins GS, Altman DG. An independent and external validation of qrisk2 cardiovascular disease risk score: a prospective open cohort study. BMJ. 2010;340:c2442.

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