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Blood, 1 June 2007, Vol. 109, No. 11, pp. 4679-4685.
Prepublished online as a Blood First Edition Paper on February 13, 2007; DOI 10.1182/blood-2005-12-051458.
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CLINICAL TRIALS AND OBSERVATIONS
Prognostic nomogram and index for overall survival in previously untreated patients with chronic lymphocytic leukemia
William G. Wierda1,
Susan O'Brien1,
Xuemei Wang2,
Stefan Faderl1,
Alessandra Ferrajoli1,
Kim-Anh Do2,
Jorge Cortes1,
Deborah Thomas1,
Guillermo Garcia-Manero1,
Charles Koller1,
Miloslav Beran1,
Francis Giles1,
Farhad Ravandi1,
Susan Lerner1,
Hagop Kantarjian1, and
Michael Keating1
1 Department of Leukemia, University of Texas, M. D. Anderson Cancer Center, Houston;
2 Department of Biostatistics and Applied Mathematics, University of Texas, M. D. Anderson Cancer Center, Houston
The clinical course for patients with chronic lymphocytic leukemia is extremely heterogeneous. The Rai and Binet staging systems have been used to risk-stratify patients; most patients present with early-stage disease. We evaluated a group of previously untreated patients with chronic lymphocytic leukemia (CLL) at initial presentation to University of Texas M. D. Anderson Cancer Center to identify independent characteristics that predict for overall survival. Clinical and routine laboratory characteristics for 1674 previously untreated patients who presented for evaluation of CLL from 1981 to 2004 were included. Univariate and multivariate analyses identified several patient characteristics at presentation that predicted for overall survival in previously untreated patients with CLL. A multivariate Cox proportional hazards model was developed, including the following independent characteristics: age, β-2 microglobulin, absolute lymphocyte count, sex, Rai stage, and number of involved lymph node groups. Inclusion of patients from a single institution and the proportion of patients younger than 65 years may limit this model. A weighted prognostic model, or nomogram, predictive for overall survival was constructed using these 6 characteristics for 5- and 10-year survival probability and estimated median survival time. This prognostic model may help patients and clinicians in clinical decision making as well as in clinical research and clinical trial design.
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