Volume 6, Issue 4 (Winter 2018)                   Jorjani Biomed J 2018, 6(4): 29-39 | Back to browse issues page


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Esmaily H, Barzanouni S, Farhangi H. Determining factors contributing to the five-year survival of children suffering from acute lymphoblastic leukemia based on tree survival model in the presence of competing risks. Jorjani Biomed J. 2018; 6 (4) :29-39
URL: http://goums.ac.ir/jorjanijournal/article-1-583-en.html
1- Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
2- Faculty of Health, Mashhad University of Medical Sciences, Mashhad, Iran , s.barzanouni@gmail.com
3- Department of Pediatric Hematology-Oncology, Dr Sheikh Pediatric Hospital, Mashhad University of Medical Sciences, Mashhad, Iran.
Abstract:   (337 Views)
Abstract
Background and objectives: Leukemia is one of the most prevalent cancers worldwide. The relapse of the disease mitigates patient survival time. The convenience of explaining the results obtained from analyzing tree models have encouraged doctors and paramedics to employ them in their research. The current study is an attempt to determine the five-year survival time and factors influencing it in children suffering from acute lymphoblastic leukemia based on tree survival model in the presence of competing risks.
 
Methods: The required data were collected from 255 children younger than 15, who suffered from acute lymphoblastic leukemia and referred to Dr. Sheikh Hospital in Mashhad, Iran during the years 2007-2015. Afterwards, the survival of the patient until the end of March 2015 was scrutinized. In this regard, various variables like sex, age, treatment period, white blood cells count, hemoglobin, platelet count, LDH level, CNS involvement, mediastan mass, rheumatologic symptoms, etc. were also considered.
The relapse of the disease was considered the desired event, whereas the relapse-free death is called competing event. The survival time of the patients from diagnosis date to the date of event (censoring) was calculated on a monthly basis. The fitting of the model is implemented according to maximum within-node homogeneity, which, in turn, is based on the partition function of sum of squares of Event-Specific Martingale Residual changes.
 
Results: The estimated mean survival time during the relapse and relapse-free death periods as well as in the presence of either events was obtained 55.51, 47.53 and 44.20 months, respectively, implying a decrease in the mean survival time in the presence of competing risks. White blood cell count and platelet count were considered the most influential factors contributing to the relapse or survival. Three sub-groups of patients at risk were identified, and those with white blood cells ≥ 50000 were recognized as the ones with the least mean survival time.
 
Conclusion: The factors affecting the survival rate of patients and their spots in the model can be employed in making clinical decisions and proposing therapeutic protocols. Identification of sub-groups with identical mean survival rate is the most salient capabilities of the research model.
 
Full-Text [PDF 745 kb]   (68 Downloads)    
Type of Article: Original article |
Received: 2018/07/1 | Revised: 2018/09/27 | Accepted: 2018/10/13

References
1. Alsayyad J, Hamadeh R. Cancer incidence among the Bahraini population: a five-year (1998-2002) experience. Hematology/oncology and stem cell therapy. 2008;1(3):175-82. [DOI:10.1016/S1658-3876(08)50027-6]
2. del Pilar Díaz M, Osella AR, Aballay LR, Muñoz SE, Lantieri MJ, Butinof M, et al .Cancer incidence pattern in Cordoba, Argentina. European Journal of Cancer Prevention. 2009;18(4):259-66. [DOI:10.1097/CEJ.0b013e3283152030]
3. Mousavi SM, Gouya MM, Ramazani R, Davanlou M, Hajsadeghi N, Seddighi Z. Cancer incidence and mortality in Iran. Annals of oncology. 2008;20(3):556.63-6.(persian) [DOI:10.1093/annonc/mdn642]
4. Bao PP, Zheng Y, Wang CF, Gu K, Jin F, Lu W. Time trends and characteristics of childhood cancer among children age 0-14 in Shanghai. Pediatric blood & cancer. 2009;53(1):13-6. [DOI:10.1002/pbc.21939]
5. Buka I, Koranteng S, Vargas ARO. Trends in childhood cancer incidence: review of environmental linkages. Pediatric Clinics of North America. 2007;54(1):177-203. [DOI:10.1016/j.pcl.2006.11.010]
6. Michel G, Von Der Weid N, Zwahlen M, Redmond S, Strippoli MP, Kuehni C. Incidence of childhood cancer in Switzerland: The Swiss childhood cancer registry. Pediatric blood & cancer. 2008;50(1):46-51. [DOI:10.1002/pbc.21129]
7. Moradi A, Semnani S, Roshandel G, Mirbehbehani N, Keshtkar A, Aarabi M, et al. Incidence of childhood cancers in golestan province of Iran. Iranian journal of pediatrics. 2010;20(3):335.(persian)
8. Stack M, Walsh PM, Comber H, Ryan CA, O'Lorcain P. Childhood cancer in Ireland: a population-based study. Archives of disease in childhood. 2007. [DOI:10.1136/adc.2005.087544]
9. Yang L, Fujimoto J, Qiu D, Sakamoto N. Childhood cancer in Japan: focusing on trend in mortality from 1970 to 2006. Annals ofoncology. 2008;20(1):166-74. [DOI:10.1093/annonc/mdn562]
10. Leukemia CL. The leukemia and lymphoma society. InTerneT SITe. 2008.
11. Jemal A, Siegel R, Ward E, Hao Y, Xu J, Murray T, et al. Cancer statistics, 2008. CA: a cancer journal for clinicians. 2008;58(2):71-96. [DOI:10.3322/CA.2007.0010]
12. Jemal A ,Siegel R, Ward E, Hao Y, Xu J, Thun MJ. Cancer statistics, 2009. CA: a cancer journal for clinicians. 2009;59(4):225-49. [DOI:10.3322/caac.20006]
13. Jemal A, Siegel R, Ward E, Murray T, Xu J, Smigal C, et al. Cancer statistics, 2006. CA: a cancer journal for clinicians. 2006;56(2):106-30. [DOI:10.3322/canjclin.56.2.106]
14. Jemal A, Siegel R, Ward E, Murray T, Xu J, Thun MJ. Cancer statistics, 2007. CA: a cancer journal for clinicians. 2007;57(1):43-66. [DOI:10.3322/canjclin.57.1.43]
15. Karimi M, Mehrabani D, Yarmohammadi H, Jahromi FS. The prevalence of signs and symptoms of childhood leukemia and lymphoma in Fars Province, Southern Iran. Cancer detection and prevention. 2008;32(2):178-83(persian). [DOI:10.1016/j.cdp.2008.06.001]
16. Coebergh J-W, Pastore G, Gatta G, Corazziari I, Kamps W, Group EW. Variation in survival of European children with acute lymphoblastic leukaemia ,diagnosed in 1978-1992: the EUROCARE study. European Journal of Cancer. 2001;37(6):687-94. [DOI:10.1016/S0959-8049(01)00013-2]
17. Sajjadi H, Roshanfekr P, Asangari B, Gharai N, Torabi F. Quality of life and satisfaction with services in caregivers of children with cancer. Iran Journal of Nursing. 2011;24(72):8-17.(persian)
18. Malempati S, Gaynon PS, Sather H, La MK, Stork LC. Outcome after relapse among children with standard-risk acute lymphoblastic leukemia: Children's Oncology Group study CCG-1952. Journal of Clinical Oncology. 2007;25(36):5800- 7. [DOI:10.1200/JCO.2007.10.7508]
19. Arellano ML, Langston A, Winton E, Flowers CR, Waller EK. Treatment of relapsed acute leukemia after allogeneic transplantation: a single center experience. Biology of blood and marrow transplantation. 2007;13(1):116-23. [DOI:10.1016/j.bbmt.2006.09.005]
20. Lawson S, Harrison G ,Richards S, Oakhill A, Stevens R, Eden O, et al. The UK experience in treating relapsed childhood acute lymphoblastic leukaemia: a report on the medical research council UKALLR1 study. British journal of haematology. 2000;108(3):531-43. [DOI:10.1046/j.1365-2141.2000.01891.x]
21. Kazak AE, Barakat LP, Meeske K, Christakis D, Meadows AT, Casey R, et al. Posttraumatic stress, family functioning, and social support in survivors of childhood leukemia and their mothers and fathers. Journal of consulting and clinical psychology. 1997;65(1):120-9. [DOI:10.1037/0022-006X.65.1.120]
22. Klein JP, Zhang M-J. Survival analysis. Handbook of Statistics. 2007;27:281-317. [DOI:10.1016/S0169-7161(07)27009-9]
23. Abadi A, Dehghani-Arani M, Yavari P, AlaviMajd H, Bajik K. Application of the competing risk models for the analysis of risk factors in patients with breast cancer. Feyz Journals of Kashan University of Medical Sciences. 2013;16 (6).(persian)
24. Saki Malehi A, Hajizadeh E, Fatemi R. Evaluation of prognostic variables for classifying the survival in colorectal patients using the decision tree. Iranian Journal of Epidemiology. 2012;8(2):13-9.(persian)
25. Bacchetti P, Segal MR. Survival trees with time-dependent covariates: application to estimating changes in the incubation period of AIDS. Lifetime data analysis. 1995;1(1):35-47. [DOI:10.1007/BF00985256]
26. Zarei far S, Hashiani almasi A, Karimi M, Tabatabai S ,Qyasvnd R. The five-year survival rate and its influencing factors in pediatric leukemia. Koomesh. 1391;14(1).
27. Hosseini Teshnizi S, Zare S, Tazhibi M. The evaluation of Cox and Weibull proportional hazards models and their applications to identify factors influencing survival time in acute leukem. In: Sci JHUM, editor. 2012. p. 269-78.(persian)
28. Chessells J, Hardisty R, Richards S. Long survival in childhood lymphoblastic leukaemia. British journal of cancer. 1987;55(3):315-9. [DOI:10.1038/bjc.1987.62]
29. Hazar V, Karasu GT, UygunV, Akcan M, Küpesiz A, Yesilipek A. Childhood acute lymphoblastic leukemia in Turkey: factors influencing treatment and outcome: a single center experience. Journal of pediatric hematology/oncology. 2010;32(8):e317-e22. [DOI:10.1097/MPH.0b013e3181ed163c]
30. Hashemi AS, Manuchehri Naini MA ,Islami Z, Lotfi MH, Khairandish M, Rafieean M. Immunofenotype in pediatric patients with acute lymphoblastic leukemia referring to Shahid Sadoughi Hospital in YazdAzam Sadat Hashemi. Journal of Shahid Sadoughi University of Medical Sciences and Health Services. 2007;16(5):56- 60.(persian)
31. Chen B-W, Lin D-T, Lin K, Chuu W, Su S, Lin K. An analysis of risk factor and survival in childhood acute lymphoblastic leukemia. Zhonghua Minguo xiao er ke yi xue hui za zhi [Journal] Zhonghua Minguo xiao er ke yi xue hui . 1989;30(5):299-308
32. Mousavinasab SN, Yazdani Cherati J, Karami H, Khaksar S. Risk Factors Influencing the Survival of Pediatric Acute Leukemia Using Competing Risk Model. Journal of Mazandaran University of Medical Sciences. 2015;24(121):31- 8.(persian)
33. Bahrami M, Moshkani MR, Alam Samimi M. Evaluation of effective factors on the survival time of patients with acute leukemia and the estimated mean survival time by Expectation & Maximation algorithm and Monte Carlo Markov simulation method. Journal of Isfahan Medical School. 2006;25(84).(persian)
34. Bonakchi H, Farhangi H, Esmaily H, Boosti H, Forouzannejhad M. Factors Affecting Survival of Children with Acute Lymphoblastic Leukemia Using Competing Risks Model. ZUMS Journal. 2017;25(110):123-36.(persian)

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