Development of a risk grading system to identify patients with acute promyelocytic leukemia at high risk of early death
Authors Zhang Y, Hou W, Wang P, Hou J, Yang H, Zhao H, Jin B, Sun J, Cao F, Zhao Y, Li H, Ge F, Fu J, Zhou J
Received 12 March 2018
Accepted for publication 16 July 2018
Published 17 September 2018 Volume 2018:10 Pages 3619—3627
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Amy Norman
Peer reviewer comments 5
Editor who approved publication: Dr Kenan Onel
Yingmei Zhang,1 Wenyi Hou,2 Ping Wang,2,3 Jinxiao Hou,2 HuiyuanYang,2 Hongli Zhao,2,4 Bo Jin,2 Jiayue Sun,2 Fenglin Cao,1 Yanqiu Zhao,2 Haitao Li,2 Fei Ge,2 Jinyue Fu,2 Jin Zhou1,2
1Department of Central Laboratory, The First Affiliated Hospital, Harbin Medical University, Harbin, People’s Republic of China; 2Department of Hematology, The First Affiliated Hospital, Harbin Medical University, Harbin, People’s Republic of China; 3Department of Neonatology, The First Affiliated Hospital, Harbin Medical University, Harbin, People’s Republic of China; 4Department of Hematology, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, People’s Republic of China
Background: Early death (ED) rate in acute promyelocytic leukemia (APL) remains high. Some studies have identified prognostic factors capable of predicting ED, whereas no risk rating system for ED has been reported in the literature. In this study, a risk classification system was built to identify subgroup at high risk of ED among patients with APL.
Methods: Totally, 364 consecutive APL patients who received arsenic trioxide as induction therapy were included. Ten baseline clinical characteristics were selected for analysis, and they were de novo/relapse, age, sex, white blood cell count, platelet count, serum fibrinogen, creatinine, uric acid, aspartate aminotransferase, and albumin. Using a training cohort (N=275), a multivariable logistic regression model was constructed, which was internally validated by the bootstrap method and externally validated using an independent cohort (N=89). Based on the model, a risk classification system was designed. Then, all patients were regrouped into de novo (N=285) and relapse (N=79) cohorts and the model and risk classification system were applied to both cohorts.
Results: The constructed model included 8 variables without platelet count and sex. The model had excellent discriminatory ability (optimism-corrected area under the receiver operator characteristic curve=0.816±0.028 in the training cohort and area under the receiver operator characteristic curve=0.798 in the independent cohort) and fit well for both the training and independent data sets (Hosmer–Lemeshow test, P=0.718 and 0.25, respectively). The optimism-corrected calibration slope was 0.817±0.12. The risk classification system could identify a subgroup comprising ~25% of patients at high risk of ED in both the training and independent cohorts (OR=0.140, P<0.001 and OR=0.224, P=0.027, respectively). The risk classification system could effectively identify patient subgroups at high risk of ED in not only de novo but also relapse cohorts (OR=0.233, P<0.001 and OR=0.105, P=0.001, respectively).
Conclusion: All the results highlight the high practical value of the risk classification system.
Keywords: acute promyelocytic leukemia, early death, risk classification system, relapse, arsenic trioxide
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