Survival Analysis of Patients with Acute Myeloid Leukemia Open Access

Chen, Yufan (2017)

Permanent URL: https://etd.library.emory.edu/concern/etds/4q77fs190?locale=en
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Abstract

Background: Patients with primary refractory/relapsed acute myeloid leukemia (PRRA) usually have poor overall survival (OS) outcomes. This study examined and determined the effects of several risk factors associated with PRRA patients' OS. Sixty-seven patients and 4 different types of treatment were studied based on patients' characteristics.

Methods: Summarization of the demographic and clinical variables was calculated and provided in tabular form. Logistic regression was fitted to determine the risk factor associated with whether patients achieved the first complete remission (CR1). Survival analysis was performed to identify the risk factors associated with patients' OS. After univariate analysis, hazard ratio and p-value for each potential risk factor was calculated. Forward model selection was applied to determine the final multivariable Cox proportional hazard model. Kaplan-Meier curves, the supremum test for the proportional hazards assumption and the plots of the standardized scores process were obtained.

Results: Mean age of patients at diagnosis was 55. Patients who were alive at 1 year was the most important prognostic factor to determine whether patients achieved CR1 with odds ratio of 50 (p-value<0.0001). In the final Cox proportional hazard model, an positive bmt in CR2 (HR: 0.341, 95% CI: (0.138, 0.843)), an favorable Cytogenetics (HR: 0.629, 95% CI: (0.340, 1.163)), an ECOG PS 0-1 (HR: 0.272, 95% CI: (0.130, 0.569), re-induction treatment (HR: 0.440, 95% CI: (0.206, 0.938)) and re-induction and hypomethylating agents Combo treatment (HR: 0.247, CI: (0.108, 0.564)) were associated with patients' OS.

Conclusion: The overall survival for PRRA patients was dismal. In order to improve PRRA patients' overall survival, new and less toxic treatments as well as improving patients' general well-being and daily activities were crucial, as ECOG PS accounted for a large amount of patients' CR1 and OS. Further studies would be needed to find both new treatment strategies and other ways to provide better patients care.

Table of Contents

Table of Contents

1. Introduction. 1

2. Methods. 2

2.1 Data Collection. 3

2.2 Statistical Analysis Method. 4

2.2.1 Descriptive Analysis. 4

2.2.2 Logistic Regression. 4

2.2.3 Cox Proportional Hazards model formulation. 6

2.2.4 Evaluate Assumptions. 7

3. Results. 7

3.1 Descriptive Analysis. 7

3.2 Univariate and multivariable logistic regression. 8

3.3 Survival Analysis. 8

4. Conclusion and Discussion. 10

5. References. 12

6. Tables and Figures. 14

7.Appendix. 20

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