Middle East Journal of Cancer

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

News

Forms

Journal Metrics

Evaluation and Diagnosis of Prognostic Factors Affecting the Survival of Leukemia Patients Using Cumulative Incidence Function

Document Type : Original Article(s)

Authors

1 Patient Safety Research Center, Department of Biostatistics and Epidemiology, Urmia University of Medical Sciences, Urmia, Iran

2 Department of Pediatric Hematology, Motahari Hospital, Urmia University of Medical Sciences, Urmia, Iran

3 Department of Pediatrics, Urmia University of Medical Sciences, Urmia, Iran

4 Department of Biostatistics, Hamadan University of Medical Sciences, Hamadan, Iran

5 Solid Tumor Research Center, Urmia University of Medical Sciences, Urmia, Iran

6 Urmia University of Medical Sciences, Urmia, Iran

Abstract

Background: Acute lymphoblastic leukemia (ALL) accounts for 25% of cancers among children less than 15 years of age. This study aimed to evaluate and determine the prognostic factors affecting the survival of leukemia patients using cumulative incidence function.
Method: This was a retrospective study done on 176 children under 15 who had ALL between 2011 and 2019. Overall survival, event-free survival, disease-free survival (DFS), and non-relapse mortality served as the study's endpoints. Using the Fine-Gray model, the Kaplan-Meier, single-variable, and multivariable analyses were conducted. Schwenfeld weighted residuals were used to test the proportional hazard hypothesis. SAS was used to conduct the analysis.
Results: The hazard ratio (HR) of DFS for effective variables was calculated (girls compared to boys: 0.37 [95% confidence interval (CI): 0.15-0.91], positive testis test: 10.34 [95% CI: 4.44-24.05], children with central nervous system involvement: 2.95 [95% CI: 1.36-6.40], testicular swelling in children: 11.54 [95% CI: 4.21-31.59], children with hepatosplenomegaly larger than 2 cm: 0.30 [95% CI: 0.10-0.88], high risk of disease compared to low risk: 4.76 [95% CI: 1.12-20.22], children with complete remission in 28th day compared with no complete remission: 0.10 [95% CI: 0.04- 0.25]. Only hemoglobin was substantially linked with DFS in the multivariate DFS HR. Children who got radiation had a 77% reduced risk of non-recurrence death than those who did not (HR: 0.23, 95% CI: 0.08-0.60).
Conclusion: Being a girl, having family history, and not having radiotherapy were the main factors to develop death before the first recurrence in children.

Keywords

How to cite this article:

Khalkhali HR, Noroozi M, Bahadori R, Omidi T, Ghazizadeh F, Hejazi S, et al. Evaluation and diagnosis of prognostic factors affecting the survival of leukemia patients using cumulative incidence function. Middle East J Cancer. 2023;14(1):92-101. doi: 10.30476/ mejc.2022. 89478.1528.

References
  1. Azeem S, Gillani SW, Siddiqui A, Jandrajupalli SB, Poh V, Syed Sulaiman SA. Diet and colorectal cancer risk in Asia--a systematic review. Asian Pac J Cancer Prev. 2015;16(13):5389-96. doi: 10.7314/apjcp.2015. 16.13.5389.
  2. Mousavi SM, Gouya MM, Ramazani R, Davanlou M, Hajsadeghi N, Seddighi Z. Cancer incidence and mortality in Iran. Ann Oncol. 2009;20(3):556-63. doi: 10.1093/annonc/mdn642.
  3. Moradi A, Semnani S, Roshandel G, Mirbehbehani N, Keshtkar A, Aarabi M, et al. Incidence of childhood cancers in golestan province of iran. Iran J Pediatr. 2010;20(3):335-42.
  4. Yang L, Fujimoto J, Qiu D, Sakamoto N. Childhood cancer in Japan: focusing on trend in mortality from 1970 to 2006. Ann Oncol. 2009;20(1):166-74. doi: 10.1093/annonc/mdn562.
  5. Jemal A, Siegel R, Ward E, Hao Y, Xu J, Thun MJ. Cancer statistics, 2009. CA Cancer J Clin. 2009;59(4): 225-49. doi: 10.3322/caac.20006.
  6. Hunger SP, Lu X, Devidas M, Camitta BM, Gaynon PS, Winick NJ. Improved survival for children and adolescents with acute lymphoblastic leukemia between 1990 and 2005: a report from the children's oncology group. J Clin Oncol. 2012;30(14):1663-9. doi: 10.1200/JCO.2011.37.8018.
  7. Malempati S, Gaynon PS, Sather H, La MK, Stork LC; Children's Oncology Group. Outcome after relapse among children with standard-risk acute lymphoblastic leukemia: Children's Oncology Group study CCG- 1952. J Clin Oncol. 2007;25(36):5800-7. doi: 10.1200/JCO.2007.10.7508.
  8. Lugthart S, Cheok MH, den Boer ML, Yang W, Holleman A, Cheng C, et al. Identification of genes associated with chemotherapy crossresistance and treatment response in childhood acute lymphoblastic leukemia. Cancer Cell. 2005;7(4):375-86. doi: 10.1016/j.ccr.2005.03.002.
  9. Pui CH, Pei D, Sandlund JT, Ribeiro RC, Rubnitz JE, Raimondi SC, et al. Long-term results of St Jude total therapy studies 11, 12, 13A, 13B, and 14 for childhood acute lymphoblastic leukemia. Leukemia. 2010;24(2):371-82. doi: 10.1038/leu.2009.252.
  10. Möricke A, Zimmermann M, Reiter A, Henze G, Schrauder A, Gadner H, et al. Long-term results of five consecutive trials in childhood acute lymphoblastic leukemia performed by the ALL-BFM study group from 1981 to 2000. Leukemia. 2010;24(2):265-84. doi: 10.1038/leu.2009.257.
  11. Klein P, Moeschberger L. Survival analysis: techniques for censored and truncated data. New York: Springer; 2003.p.26-60.
  12. Kleinbaum G, Klein M. Survival analysis: a selflearning text. Gail M, Krickeberg K, Samet JA, et al, editors. 3rd ed. New York: Springer Science and Business Media; 2006.p.11-17.
  13. Abadi A, Dehghani-Arani M, Yavari P, Alavi-Majd H, Bajik K. Application of the competing risk models for the analysis of risk factors in patients with breast cancer. Feyz J. 2013;16:546-52.
  14. Pintilie M. Competing risks: a practical perspective. Ontario Cancer Institute, Canada: John Wiley & Sons, Ltd; 2006.
  15. He P, Eriksson F, Scheike TH, Zhang MJ. A Proportional hazards regression model for the subdistribution with covariates adjusted censoring weight for competing risks data. Scand Stat Theory Appl. 2016;43(1):103-22. doi: 10.1111/sjos.12167.
  16. Satagopan JM, Ben-Porat L, Berwick M, Robson M, Kutler D, Auerbach AD. A note on competing risks in survival data analysis. Br J Cancer. 2004;91(7):1229- 35. doi: 10.1038/sj.bjc.6602102.
  17. Prentice RL, Kalbfleisch JD, Peterson AV Jr, Flournoy N, Farewell VT, Breslow NE. The analysis of failure times in the presence of competing risks. Biometrics. 1978;34(4):541-54.
  18. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1999;94(446):496-509. doi: 10.1080/01621459. 1999.10474144.
  19. Fürstová J, Valenta Z. Statistical analysis of competing risks: overall survival in a group of chronic myeloid leukemia patients. J Biomed Inform. 2011;7(1).
  20. Shin A, Joo J, Yang HR, Bak J, Park Y, Kim J, et al. Risk prediction model for colorectal cancer: National Health Insurance Corporation study, Korea. PLoS One. 2014;9(2):e88079. doi: 10.1371/journal.pone.0088079.
  21. Pizzo P, Pollock DG. Principles and practice of pediatric oncology. 4th ed. Philadelphia: Lippincott Williams & Wilkins;2002.p.1091-105.
  22. Ziaei JE. High frequency of acute promyelocytic leukemia in northwest Iran. Asian Pac J Cancer Prev. 2004;5:188-9.
  23. AkbarzadehBaghban A, Hosseinifard H, Baghestani AR, Ahmadi S, Rezaei Tavirani M. Factors that affecting survival of patients with acute myeloid leukemia. [In Persian] Koomesh. 2016;17: 596-602.
  24. Abdali F, Taghavi S, Vazifekhah S, Naghavi Behzad M, Mirza Aghazadeh Attari M. Effect of progesterone on latent phase prolongation in patients with preterm premature rupture of membranes. Acta Med Iran. 2017;55(12):772-8.
  25. Rauscher GH, Sandler DP, Poole C, Pankow J, Mitchell B, Bloomfield CD, et al. Family history of cancer and incidence of acute leukemia in adults. Am J Epidemiol. 2002;156:517-26. doi: 10.1093/aje/kwf075.
  26. Kleinbaum G, Klein M. Survival analysis: a selflearning text. New York: Springer; 2012. p.52-124.
  27. Bhatia S, Sather HN, Heerema NA, Trigg ME, Gaynon PS, Robison LL. Racial and ethnic differences in survival of children with acute lymphoblastic leukemia. Blood. 2002;100(6):1957-64. doi: 10.1182/blood-2002- 02-0395.
  28. Zareifar S, Almasi-Hashiani A, Karimi M, Tabatabaee S H, Ghiasvand R. Five-year survival rate of pediatric leukemia and its determinants. [In Persian] Koomesh. 2012;14(1):13-9.
  29. Hazar V, Karasu GT, Uygun V, Akcan M, Küpesiz A, Yesilipek A. Childhood acute lymphoblastic leukemia in turkey: factors influencing treatment and outcome a single center experience. J Pediatr Hematol Oncol. 2010;32(8):e317-22. doi: 10.1097/MPH.0b013e 3181ed163c.
  30. Hussein H, Sidhom I, Naga SA, Amin M, Ebied E, Khairy A, et al. Outcome and prognostic factors of acute lymphoblastic leukemia in children at the National Cancer Institute, Egypt. J Pediatr Hematol Oncol. 2004;26(8):507-14. doi: 10.1097/01.mph. 0000132735.93396.92.
  31. Khalkhali HR, Gharaaghaji R, Valizadeh R, Kousehlou Z, Ayatollahi H. Ten years’ survival in patients with cervical cancer and related factors in West Azerbaijan Province: Using of cox proportion hazard model. Asian Pac J Cancer Prev. 2019;20(5):1345.doi: 10.31557/ APJCP.2019.20.5.1345.
  32. Noroozi M, Khalkhali HR, Bahadori R, Omidi T, Ghazizadeh F, Hejazi S, et al. The survival of childhood acute lymphoblastic leukemia and its related factors using competing risks model: A retrospective study from 2011 to 2019 in northwestern Iran. Middle East J Cancer. 2022:13(3):531-42. doi: 10.30476/mejc. 2022.88069.1455.
Middle East Journal of Cancer
Volume 14, Issue 1 - Serial Number 53
January 2023
Pages 92-101
Files
Share
How to cite
Statistics