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Multivariable model for time to first treatment in patients with chronic lymphocytic leukemia

  • William G. Wierda(corresponding author)
    ,
  • Susan O'Brien
    ,
  • Xuemei Wang
    ,
  • Stefan Faderl
    ,
  • Alessandra Ferrajoli
    ,
  • Kim Anh Do
*Corresponding author for this work
  • University of Texas MD Anderson Cancer Center
Scholary Output:
Contribution to journal
Article
Peer-review

Sustainable Development Goals

  • SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well

Abstract

Purpose: The clinical course for patients with chronic lymphocytic leukemia (CLL) is diverse; some patients have indolent disease, never needing treatment, whereas others have aggressive disease requiring early treatment. We continue to use criteria for active disease to initiate therapy. Multivariable analysis was performed to identify prognostic factors independently associated with time to first treatment for patients with CLL. Patients and Methods: Traditional laboratory, clinical prognostic, and newer prognostic factors such as fluorescent in situ hybridization (FISH), IGHV mutation status, and ZAP-70 expression evaluated at first patient visit to MD Anderson Cancer Center were correlated by multivariable analysis with time to first treatment. This multivariable model was used to develop a nomogram - a weighted tool to calculate 2- and 4-year probability of treatment and estimate median time to first treatment. Results: There were 930 previously untreated patients who had traditional and new prognostic factors evaluated; they did not have active CLL requiring initiation of treatment within 3 months of first visit and were observed for time to first treatment. The following were independently associated with shorter time to first treatment: three involved lymph node sites, increased size of cervical lymph nodes, presence of 17p deletion or 11q deletion by FISH, increased serum lactate dehydrogenase, and unmutated IGHV mutation status. Conclusion: We developed a multivariable model that incorporates traditional and newer prognostic factors to identify patients at high risk for progression to treatment. This model may be useful to identify patients for early interventional trials.

Publication Information

Output type

Scholary Output:
Contribution to journal
Article
Peer-review

Original language

English (US)

Pages from-to (Number of pages)

Pages 4088-4095 (8 pages)

Journal (Volume, Issue Number)

Journal of Clinical Oncology (Volume 29, Issue 31)

Publication milestones

  • Published - 11/01/2011

Publication status

Published - 11/01/2011

ISSN

0732-183X

Publication IDs

  • Scopus: 80755127112
  • PubMed: 21969505
  • ORCID: /0000-0002-8636-1071/work/68887747

Publication metrics

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Fractional count
1
Fractional count
0.06
Fractional count
15
Fractional count
0.94
Fractional count
1
Fractional count
1
SciVal
citations
97
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FWCI
1.90
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Author count
16
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Paper percentile
96
SciVal
Top percentile
5
Scopus
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