TY - JOUR
T1 - Characteristics associated with important clinical end points in patients with chronic lymphocytic leukemia at initial treatment
AU - Wierda, William G.
AU - O'Brien, Susan
AU - Wang, Xuemei
AU - Faderl, Stefan
AU - Ferrajoli, Alessandra
AU - Do, Kim Anh
AU - Garcia-Manero, Guillermo
AU - Cortes, Jorge
AU - Thomas, Deborah
AU - Koller, Charles
AU - Burger, Jan
AU - Lerner, Susan
AU - Kantarjian, Hagop
AU - Keating, Michael
PY - 2009/4/1
Y1 - 2009/4/1
N2 - Purpose Response to front-line treatment and subsequent clinical course for patients with chronic lymphocytic leukemia (CLL) are heterogeneous. Identifying pretreatment patient characteristics or prognostic factors associated with clinical outcomes is important for counseling patients, conducting clinical research, and evaluating trial results. Patients and Methods We evaluated the pretreatment characteristics of 595 previously untreated patients who had National Cancer Institute Working Group indications to initiate front-line therapy for predictors of complete response (CR), time to treatment failure (TTF), and overall survival (OS). Multivariable models were developed for all three end points. Results CR is an important treatment end point correlated with longer TTF and OS. In this retrospective analysis, front-line treatment regimen was a significant independent predictive factor for all three end points; chemoimmunotherapy was the superior treatment regimen. Considering front-line treatment regimen, other independent patient characteristics associated with CR included age and β-microglobulin (β-2M). TTF was independently associated with age, β-2M, percent lymphocytes in bone marrow, and treatment regimen. Improved OS was independently associated with younger age, lower β-2M, and treatment regimen. Two weighted prognostic models or nomograms, one including and one excluding treatment regimen, were constructed using significant characteristics to predict 5- and 10-year survival probability and estimate median survival time. Conclusion Identifying pretreatment patient characteristics associated with CR, TTF, and OS establishes a baseline to compare and incorporate new prognostic factors. Treatment had an impact on the significance of these factors. Prognostic models may help patients and clinicians in decision making as well as facilitate clinical research through design and analyses of clinical trials.
AB - Purpose Response to front-line treatment and subsequent clinical course for patients with chronic lymphocytic leukemia (CLL) are heterogeneous. Identifying pretreatment patient characteristics or prognostic factors associated with clinical outcomes is important for counseling patients, conducting clinical research, and evaluating trial results. Patients and Methods We evaluated the pretreatment characteristics of 595 previously untreated patients who had National Cancer Institute Working Group indications to initiate front-line therapy for predictors of complete response (CR), time to treatment failure (TTF), and overall survival (OS). Multivariable models were developed for all three end points. Results CR is an important treatment end point correlated with longer TTF and OS. In this retrospective analysis, front-line treatment regimen was a significant independent predictive factor for all three end points; chemoimmunotherapy was the superior treatment regimen. Considering front-line treatment regimen, other independent patient characteristics associated with CR included age and β-microglobulin (β-2M). TTF was independently associated with age, β-2M, percent lymphocytes in bone marrow, and treatment regimen. Improved OS was independently associated with younger age, lower β-2M, and treatment regimen. Two weighted prognostic models or nomograms, one including and one excluding treatment regimen, were constructed using significant characteristics to predict 5- and 10-year survival probability and estimate median survival time. Conclusion Identifying pretreatment patient characteristics associated with CR, TTF, and OS establishes a baseline to compare and incorporate new prognostic factors. Treatment had an impact on the significance of these factors. Prognostic models may help patients and clinicians in decision making as well as facilitate clinical research through design and analyses of clinical trials.
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U2 - 10.1200/JCO.2008.18.1701
DO - 10.1200/JCO.2008.18.1701
M3 - Article
C2 - 19224852
AN - SCOPUS:63749084353
SN - 0732-183X
VL - 27
SP - 1637
EP - 1643
JO - Journal of Clinical Oncology
JF - Journal of Clinical Oncology
IS - 10
ER -
