Survival Analysis of Patients with Brain Stroke in the Presence of Competing Risks: A Weibull Parametric Model
-
Solmaz Norouzi
,
Ramazan Fallah
,
Seyed Morteza Shamshirgaran
,
Farshid Farzipoor
,
Mohammad Asghari Jafarabadi
Abstract
Introduction: This study aimed to assess the association between the survival of patients and outcomes in Brain Stroke (BS) in the presence of competing risks utilizing a Weibull parametric model.
Methods: In this longitudinal study, 332 patients with BS were attended from Imam Khomeini Hospital in Ardabil, Iran. The stroke was diagnosed according to the medical history, current symptoms, and brain imaging during June 2008 and 2018. The survival of the patients, as the primary outcome, was modeled utilizing the best-chosen Weibull model in the presence of competing risks, including stroke and other factors (heart disease, blood pressure, etc.).
Results: Older age at diagnosis (59-68 years: hazard ratio [HR]=2.27; 90% confidence interval [CI]: 1.65 to 3.12; 69-75 years: HR=4.79; 95% CI: 3.56 to 6.44; ≥76 years: HR, 4.92; 95% CI: 3.55 to 6.80), being a male (HR, 1.39; 95% CI: 1.11 to 1.75), being unemployed (HR, 1.44; 95% CI: 1.39 to 1.82), having heart disease (HR, 1.68; 95% CI: 1.38 to 2.06), and hemorrhagic stroke (HR, 2.21; 95% CI: 1.378to 2.75) were directly related to death from BS. Older age at diagnosis (59-68 years: HR, 18.01; 90% CI, 5.33 to 64.92; 75-69 years: HR, 18.56; 95% CI: 6.97 to 86.57; ≥76 years: HR, 28.90; 95% CI: 15.77 to 218.49), and urban residence (HR, 0.46; 90% CI, 0.28 to 0.77) were directly related to death from other causes.
Conclusion: The recognition of the influential factors on the mortality of BS patients can allow increasing their survival.
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2. Boehme AK, Esenwa C, Elkind MS. Stroke risk factors, genetics, and prevention. Circulation research. 2017;120(3):472-95.
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7. Cho J, Choi YJ, Suh M, Sohn J, Kim H, Cho S-K, et al. Air pollution as a risk factor for depressive episode in patients with cardiovascular disease, diabetes mellitus, or asthma. Journal of affective disorders. 2014;157:45-51.
8. Klein JP, Bajorunaite R. Inference for competing risks. Handbook of statistics. 2003;23:291-311.
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10. Weibull W. The phenomenon of rupture in solids. IVA Handlingar. 1939;153.
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12. Iraji Z, Koshki TJ, Dolatkhah R, Jafarabadi MA. Parametric survival model to identify the predictors of breast cancer mortality: An accelerated failure time approach. Journal of Research in Medical Sciences. 2020;25(1):38.
13. Baghfalaki T. Interpretation of exposure effect in competing risks setting under accelerated failure time models. Journal of Biostatistics and Epidemiology. 2018;4(2):91- 8.
14. Hardie K, Hankey GJ, Jamrozik K, Broadhurst RJ, Anderson C. Ten-year survival after first-ever stroke in the Perth Community Stroke Study. Stroke. 2003;34(8):1842-6.
15. Norouzi S, Jafarabadi MA, Shamshirgaran SM, Farzipoor F, Fallah R. Modeling Survival in Patients With Brain Stroke in the Presence of Competing Risks. J Prev Med Public Health. 2021;54:55-62.
16. Putaala J, Yesilot N, Waje-Andreassen U, Pitkäniemi J, Vassilopoulou S, Nardi K, et al. Demographic and geographic vascular risk factor differences in European young adults with ischemic stroke: the 15 cities young stroke study. Stroke. 2012;43(10):2624-30.
17. Grambauer N, Schumacher M, Beyersmann J. Proportional subdistribution hazards modeling offers a summary analysis, even if misspecified. Statistics in medicine. 2010;29(7‐8):875-84.
18. Farghaly WM, El-Tallawy HN, Shehata GA, Rageh TA, Abdel-Hakeem NM, Abd Elhamed MA, et al. Epidemiology of nonfatal stroke and transient ischemic attack in Al-Kharga District, New Valley, Egypt. Neuropsychiatric disease and treatment. 2013;9:1785.
19. Mogensen UB, Olsen TS, Andersen KK, Gerds TA. Cause-specific mortality after stroke: relation to age, sex, stroke severity, and risk factors in a 10-year follow-up study. Journal of stroke and cerebrovascular diseases. 2013;22(7):e59-e65.
20. Rutten-Jacobs LC, Arntz RM, Maaijwee NA, Schoonderwaldt HC, Dorresteijn LD, van Dijk EJ, et al. Long-term mortality after stroke among adults aged 18 to 50 years. Jama. 2013;309(11):1136-44.
21. Andersen MN, Andersen KK, Kammersgaard LP, Olsen TS. Sex differences in stroke survival: 10-year follow-up of the Copenhagen stroke study cohort. Journal of Stroke and Cerebrovascular Diseases. 2005;14(5):215-20.
22. Madsen TE, Howard VJ, Jiménez M, Rexrode KM, Acelajado MC, Kleindorfer D, et al. Impact of conventional stroke risk factors on stroke in women: an update. Stroke. 2018;49(3):536-42.
23. de Rivero Vaccari JP, Bramlett HM, Perez-Pinzon MA, Raval AP. Estrogen preconditioning: a promising strategy to reduce inflammation in the ischemic brain. Conditioning medicine. 2019;2(3):106.
24. Kivimäki M, Vahtera J, Virtanen M, Elovainio M, Pentti J, Ferrie JE. Temporary employment and risk of overall and causespecific mortality. American journal of epidemiology. 2003;158(7):663-8.
25. Marshall IJ, Wang Y, Crichton S, McKevitt C, Rudd AG, Wolfe CD. The effects of socioeconomic status on stroke risk and outcomes. The Lancet Neurology. 2015;14(12):1206-18.
26. Cox AM, McKevitt C, Rudd AG, Wolfe CD. Socioeconomic status and stroke. The Lancet Neurology. 2006;5(2):181-8.
27. Kimball MM, Neal D, Waters MF, Hoh BL. Race and income disparity in ischemic stroke care: nationwide inpatient sample database, 2002 to 2008. Journal of Stroke and Cerebrovascular Diseases. 2014;23(1):17-24.
28. Ekker MS, Verhoeven JI, Vaartjes I, Jolink WMT, Klijn CJM, de Leeuw F-E. Association of stroke among adults aged 18 to 49 years with long-term mortality. Jama. 2019;321(21):2113-23.
29. Liu C-H, Lin J-R, Liou C-W, Lee J-D, Peng T-I, Lee M, et al. Causes of death in different subtypes of ischemic and hemorrhagic stroke. Angiology. 2018;69(7):582-90.
30. Koifman J, Hall R, Li S, Stamplecoski M, Fang J, Saltman AP, et al. The association between rural residence and stroke care and outcomes. Journal of the neurological sciences. 2016;363:16-20.
2. Boehme AK, Esenwa C, Elkind MS. Stroke risk factors, genetics, and prevention. Circulation research. 2017;120(3):472-95.
3. Roger VL, Go AS, Lloyd-Jones DM, Adams RJ, Berry JD, Brown TM, et al. Heart disease and stroke statistics—2011update: a report from the American Heart Association. Circulation. 2011;123(4):e18-e209.
4. Ovbiagele B, Goldstein LB, Higashida RT, Howard VJ, Johnston SC, Khavjou OA, et al. Forecasting the future of stroke in the United States: a policy statement from the American Heart Association and American Stroke Association. Stroke. 2013;44(8):2361-75.
5. Azarpazhooh MR, Etemadi MM, Donnan GA, Mokhber N, Majdi MR, GhayourMobarhan M, et al. Excessive incidence of stroke in Iran: evidence from the Mashhad Stroke Incidence Study (MSIS), a populationbased study of stroke in the Middle East. Stroke. 2010;41(1):e3-e10.
6. Delbari A, Roghani RS, Tabatabaei SS, Lökk J. A Stroke Study of an Urban Area
of Iran: Risk Factors, Length of Stay, Case Fatality, and Discharge Destination. Journal of Stroke and Cerebrovascular Diseases. 2010;19(2):104-9.
7. Cho J, Choi YJ, Suh M, Sohn J, Kim H, Cho S-K, et al. Air pollution as a risk factor for depressive episode in patients with cardiovascular disease, diabetes mellitus, or asthma. Journal of affective disorders. 2014;157:45-51.
8. Klein JP, Bajorunaite R. Inference for competing risks. Handbook of statistics. 2003;23:291-311.
9. Kleinbaum DG, Klein M. Survival analysis: Springer; 2010.
10. Weibull W. The phenomenon of rupture in solids. IVA Handlingar. 1939;153.
11. Someeh N, Jafarabadi MA, Shamshirgaran SM, Farzipoor F. The outcome in patients with brain stroke: A deep learning neural network modeling. Journal of Research in Medical Sciences: The Official Journal of Isfahan University of Medical Sciences. 2020;25.
12. Iraji Z, Koshki TJ, Dolatkhah R, Jafarabadi MA. Parametric survival model to identify the predictors of breast cancer mortality: An accelerated failure time approach. Journal of Research in Medical Sciences. 2020;25(1):38.
13. Baghfalaki T. Interpretation of exposure effect in competing risks setting under accelerated failure time models. Journal of Biostatistics and Epidemiology. 2018;4(2):91- 8.
14. Hardie K, Hankey GJ, Jamrozik K, Broadhurst RJ, Anderson C. Ten-year survival after first-ever stroke in the Perth Community Stroke Study. Stroke. 2003;34(8):1842-6.
15. Norouzi S, Jafarabadi MA, Shamshirgaran SM, Farzipoor F, Fallah R. Modeling Survival in Patients With Brain Stroke in the Presence of Competing Risks. J Prev Med Public Health. 2021;54:55-62.
16. Putaala J, Yesilot N, Waje-Andreassen U, Pitkäniemi J, Vassilopoulou S, Nardi K, et al. Demographic and geographic vascular risk factor differences in European young adults with ischemic stroke: the 15 cities young stroke study. Stroke. 2012;43(10):2624-30.
17. Grambauer N, Schumacher M, Beyersmann J. Proportional subdistribution hazards modeling offers a summary analysis, even if misspecified. Statistics in medicine. 2010;29(7‐8):875-84.
18. Farghaly WM, El-Tallawy HN, Shehata GA, Rageh TA, Abdel-Hakeem NM, Abd Elhamed MA, et al. Epidemiology of nonfatal stroke and transient ischemic attack in Al-Kharga District, New Valley, Egypt. Neuropsychiatric disease and treatment. 2013;9:1785.
19. Mogensen UB, Olsen TS, Andersen KK, Gerds TA. Cause-specific mortality after stroke: relation to age, sex, stroke severity, and risk factors in a 10-year follow-up study. Journal of stroke and cerebrovascular diseases. 2013;22(7):e59-e65.
20. Rutten-Jacobs LC, Arntz RM, Maaijwee NA, Schoonderwaldt HC, Dorresteijn LD, van Dijk EJ, et al. Long-term mortality after stroke among adults aged 18 to 50 years. Jama. 2013;309(11):1136-44.
21. Andersen MN, Andersen KK, Kammersgaard LP, Olsen TS. Sex differences in stroke survival: 10-year follow-up of the Copenhagen stroke study cohort. Journal of Stroke and Cerebrovascular Diseases. 2005;14(5):215-20.
22. Madsen TE, Howard VJ, Jiménez M, Rexrode KM, Acelajado MC, Kleindorfer D, et al. Impact of conventional stroke risk factors on stroke in women: an update. Stroke. 2018;49(3):536-42.
23. de Rivero Vaccari JP, Bramlett HM, Perez-Pinzon MA, Raval AP. Estrogen preconditioning: a promising strategy to reduce inflammation in the ischemic brain. Conditioning medicine. 2019;2(3):106.
24. Kivimäki M, Vahtera J, Virtanen M, Elovainio M, Pentti J, Ferrie JE. Temporary employment and risk of overall and causespecific mortality. American journal of epidemiology. 2003;158(7):663-8.
25. Marshall IJ, Wang Y, Crichton S, McKevitt C, Rudd AG, Wolfe CD. The effects of socioeconomic status on stroke risk and outcomes. The Lancet Neurology. 2015;14(12):1206-18.
26. Cox AM, McKevitt C, Rudd AG, Wolfe CD. Socioeconomic status and stroke. The Lancet Neurology. 2006;5(2):181-8.
27. Kimball MM, Neal D, Waters MF, Hoh BL. Race and income disparity in ischemic stroke care: nationwide inpatient sample database, 2002 to 2008. Journal of Stroke and Cerebrovascular Diseases. 2014;23(1):17-24.
28. Ekker MS, Verhoeven JI, Vaartjes I, Jolink WMT, Klijn CJM, de Leeuw F-E. Association of stroke among adults aged 18 to 49 years with long-term mortality. Jama. 2019;321(21):2113-23.
29. Liu C-H, Lin J-R, Liou C-W, Lee J-D, Peng T-I, Lee M, et al. Causes of death in different subtypes of ischemic and hemorrhagic stroke. Angiology. 2018;69(7):582-90.
30. Koifman J, Hall R, Li S, Stamplecoski M, Fang J, Saltman AP, et al. The association between rural residence and stroke care and outcomes. Journal of the neurological sciences. 2016;363:16-20.
| Files | ||
| Issue | Vol 7 No 3 (2021) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/jbe.v7i3.7295 | |
| Keywords | ||
| Stroke Risk factors Survival analysis Competing risk Weibull Model | ||
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How to Cite
1.
Norouzi S, Fallah R, Shamshirgaran SM, Farzipoor F, Asghari Jafarabadi M. Survival Analysis of Patients with Brain Stroke in the Presence of Competing Risks: A Weibull Parametric Model. JBE. 2021;7(3):235-243.
