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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. Wierda,1 Susan O’Brien,1 Xuemei Wang,2 Stefan Faderl,1 Alessandra Ferrajoli,1 Kim-Anh Do,2 Jorge Cortes,1
Deborah Thomas,1 Guillermo Garcia-Manero,1 Charles Koller,1 Miloslav Beran,1 Francis Giles,1 Farhad Ravandi,1
Susan Lerner,1 Hagop Kantarjian,1 and Michael Keating1
1Department of Leukemia, University of Texas, M. D. Anderson Cancer Center, Houston; 2Department 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 pa-
tients 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, ␤-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. (Blood. 2007;109:4679-4685)
© 2007 by The American Society of Hematology
Introduction
Chronic lymphocytic leukemia (CLL) is the most common adult
leukemia in the United States.1 The clinical course is remarkably
variable; some patients live out their life not needing treatment and
die of unrelated causes, whereas others have rapidly progressive
disease requiring treatment within months of diagnosis and succumb to their disease within 2 to 3 years. The Rai2,3 and Binet4
clinical staging systems broadly identify risk groups based on
clinical and laboratory characteristics. Overall, stage correlates
with survival; however, for each stage there is still heterogeneity,
limiting utility in predicting survival. In addition to factors used in
clinical staging, several other patient characteristics and laboratory
tests have been correlated with overall survival, including age,5
sex,5 pattern of bone marrow involvement,6-8 lymphocyte doubling
time,9,10 and the presence of prolymphocytes in blood or bone
marrow.11-13 Other factors that can be measured in the laboratory
have also been correlated with poor prognosis, including the
presence of chromosome abnormalities such as 17p deletion and
11q deletion,14 elevated serum levels of ␤-2 microglobulin (␤-2M),
thymidine kinase, soluble CD23,15,16 unmutated immunoglobulin
heavy chain variable gene (IgVH),17,18 and expression of ZAP7019,20 and CD3818,21 by leukemia cells. Alone, each of these
prognostic factors has limited utility in predicting overall survival.
A nomogram is a graphic representation of a statistical model
with scales for calculating the cumulative affect of weighted
variables on the probability of a particular outcome.22 Nomograms
enable continuous estimation of the probability of a particular
outcome, such as death. The strength of using nomograms is that
they combine multiple independent variables to predict an outcome
and enable appreciation of the prognostic weight for each variable
in calculating the probability of such an outcome.
Prognostic models and nomograms can be developed for a
variety of clinical outcomes, including overall survival,23-25 diseasespecific survival,26-29 probability of developing metastasis,30 and
probability of relapse or recurrence,22,31,32 outcomes that are useful
for patients, physicians, and researchers. They can facilitate
discussion and counseling on the impact of disease on a patient’s
life, when to initiate therapy, in developing patients’ expectations
for outcomes, and in discussions of long-term outlook. They are
useful to clinical scientists in developing expectations for clinical
trials and identifying patients “at risk” who should be targeted for
aggressive therapy or investigational approaches. They also may
give insight into the biology of disease.
Generating predictive models and nomograms for overall
survival in previously untreated patients with CLL is difficult given
the chronic nature of the disease and duration of follow-up to
observe enough events for a reliable model. Owing to development
and maintenance of a unique and complete database of patients
with CLL, we were able to perform an analysis of previously
untreated patients who presented to the University of Texas M. D.
Anderson Cancer Center (MDACC) for evaluation and treatment
Submitted December 28, 2005; accepted January 22, 2007. Prepublished
online as Blood First Edition Paper, February 13, 2007; DOI 10.1182/
blood-2005-12-051458.
payment. Therefore, and solely to indicate this fact, this article is hereby
marked ‘‘advertisement’’ in accordance with 18 USC section 1734.
The publication costs of this article were defrayed in part by page charge
© 2007 by The American Society of Hematology
BLOOD, 1 JUNE 2007 䡠 VOLUME 109, NUMBER 11
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BLOOD, 1 JUNE 2007 䡠 VOLUME 109, NUMBER 11
WIERDA et al
recommendations during more than 20 years. Follow-up for these
patients, including for survival, has been ongoing. Using this
database, we identified presenting characteristics that correlated
with overall survival in univariate and multivariate analyses. A
multivariate Cox proportional hazards model was developed that
included the 6 significant independent covariates to predict overall
survival. A nomogram and prognostic index for overall survival
were developed using this model and may be a useful prognostic
tool for patients, clinicians, and clinical investigators.
Patients, materials, and methods
Patients
Previously untreated patients who presented for initial evaluation to
MDACC from August 1981 through August 2004 were included in this
analysis (Tables 1-2). All patients provided informed consent, in accordance
with MDACC IRB guidelines and the Declaration of Helsinki, and
underwent initial evaluation, including history, physical examination, and
laboratory evaluation of blood counts, chemistries, and bone marrow
examination. There were 383 patients who presented to MDACC from 1981
to 1995, 530 from 1996 to 2000, and 761 from 2001 to 2004. The following
were recorded at presentation: sex; age; Rai and Binet stages; Zubrod
performance status; physical examination, including number of nodal sites
affected, and liver and spleen sizes; and laboratory evaluation, including
complete blood count and measure of serum albumin (ALB; normal range,
35-47 g/L [3.5-4.7 g/dL]), alkaline phosphatase (Alk phos; normal range,
38-126 IU/L), lactate dehydrogenase (LDH; normal range, 313-618 IU/L),
␤-2M (normal range, 51- 170 nM [0.6-2.0 mg/L]), and quantitative
immunoglobulin levels (normal ranges, IgG: 6.24-16.8 g/L [624-1680
mg/dL]; IgA: 0.74-3.27 g/L [74-327 mg/dL]; IgM: 0.29-2.14 g/L [29-214
mg/dL]). Percentage of cellularity and lymphocytes in bone marrow were
recorded for patients who underwent bone marrow aspirate and biopsy.
Patients who did not have an NCI Working Group indication for
treatment33,34 were observed. There were 390 patients whose treatment
started within 30 days of presentation, 34 patients started treatment within
30 to 60 days, and another 31 began therapy within 60 to 90 days of
presentation. Significant heterogeneity in treatment was noted because the
study time spanned more than 20 years. With the currently available
follow-up, 767 patients have not been treated, 719 received treatment on an
MDACC clinical trial, and 183 received treatment either off protocol at
MDACC or with their referring physician. For those patients who were
followed by their referring physician, MDACC follow-up consisted of
scheduled return visits to MDACC or telephone contact with the referring
physicians’ office to obtain clinical notes, laboratory values, and follow-up
survival status.
multivariable model; all had the specified characteristics measured at initial
presentation. The formula to calculate the total point score for a patients is
⫺12.5 ⫹ [1.25 ⫻ age] ⫹ [4.32 ⫻ ␤-2M] ⫹ [8.62 ⫻ (ALC, ⫻ 109/L/100)]
⫹ [7.34 ⫻ I(sex ⫽ male)] ⫹ [11.00 ⫻ I(Rai ⫽ III or IV)] ⫹ [10.84 ⫻
I(nodes ⫽ 3)] where I() is the indicator function, equal to 1 if the condition
in the parenthesis is met and 0 if not.
Validation of the nomogram consisted of generating the concordance
index, which is the probability that, given 2 randomly drawn patients, the
patient who dies first has the higher probability of death. This was
calculated by bootstrapping 200 samples from the original 1561 patients
used to fit the Cox model, and it served as an unbiased measure of the ability
of the nomogram to discriminate among patients. Bootstrappping involves
removing a small random sample of patients from the cohort while the
remaining patients are analyzed as the actual results. Next, we examined the
calibration of the nomogram, which also included 200 bootstrap resamples.
A calibration curve was generated by plotting actuarial survival against
predicted survival probabilities for patients stratified by predicted risk
assessed the prediction accuracy of the nomogram. All analyses were
conducted with S-plus 2000 Professional software (Statistical Sciences,
Seattle, WA).
Results
Patient characteristics
There were 1674 previously untreated patients with CLL at their
initial MDACC presentation included in these analyses (Table 1).
The median time from diagnosis to presentation to MDACC was
3.8 months. Approximately 60% were men, the majority had a
performance status of 0 or 1, and patients of all Rai stages were
represented. The median age was 58 years, younger than the
median age of patients presenting to community practice. Routine
blood counts and chemistries were performed at presentation
(Table 2). The first and third quartiles are shown for appreciation of
Table 1. Patient characteristics (N ⴝ 1674)
Characteristic
Male
Female
Missing data
Descriptive statistics, including median, range, and first and third quartiles
were used to summarize the patient characteristics. Overall survival
probability was estimated by the method of Kaplan and Meier.35 The
difference between patient subgroups for each variable was assessed using
the log-rank test.36 The time interval was measured from the day of
presentation to MDACC until death or last follow-up. Death from all causes
was included.
Univariate and multivariable Cox proportional hazards models were fit
to examine the relationship between survival time and patient characteristics.37 A final multivariable Cox model was obtained by performing a
backward elimination with P value cutoff of .05, then allowing any variable
previously deleted to enter the final model if its P value was less than .05.
Nomogram development began by identifying patient characteristics
predictive for overall survival in the multivariate Cox model. The nomogram was constructed as described by Kattan et al.22 These characteristics
included age, ␤-2M, absolute lymphocyte count (ALC), sex, Rai stage III or
IV, and number of involved lymph node groups. Patients without values
were dropped from the analysis. There were 1561 patients included in this
1029
645
0
Performance status
0
801
1
771
2
28
3
Statistical methods
No. of patients
Sex
Missing data
2
72
Rai stage
0
469
I
739
II
235
III
93
IV
127
Missing data
11
Binet stage
A
1019
B
470
C
168
Missing data
17
No. of nodal sites
0
523
1
280
2
337
3
482
Missing data
52
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BLOOD, 1 JUNE 2007 䡠 VOLUME 109, NUMBER 11
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the distribution of patient characteristics in these analyses. Some
patients were missing information as noted in Tables 1-2; these
patients were not included in the multivariate analysis. Characteristics that had missing data for more than 300 patients were not
included in the multivariate analysis.
Survival
Survival for the entire group was estimated by Kaplan-Meier
analysis (Figure 1). The estimated median overall survival was 10.7
years (95% CI, 9.8-11.2 years). The median follow-up time for all
patients was 4.9 years (95% CI, 4.6-5.1). Of 1674 patients, there
were 443 deaths during the follow-up time. With the currently
available follow-up, 258 of these patients received treatment on an
MDACC clinical trial, 45 were treated off protocol, and 140 died
without documented treatment.
The 443 deaths were reviewed to assess for disease-specific
mortality. This was done by review of death certificates, referring
physician records and notes, and MDACC records. Active CLL
was the cause of death for 165 patients (Table 3). In these cases,
active CLL was characterized by proliferative disease, marrow
failure, or immune dysfunction. The final event for some of these
patients may have been pneumonia, bacteremia, or other infection,
but the dominant underlying process was active CLL. Other deaths
in this category included Richter transformation and bleeding
complications related to low platelet count with active, progressive
CLL. Infection accounted for 72 deaths. In those cases, active CLL
was not the dominant clinical feature. Autoimmune hemolytic
anemia was associated with 4 deaths. Cardiac events accounted for
15 of the deaths, and another 15 died of assorted causes unrelated to
CLL, such as stroke or other medical condition. Second malignancies (solid tumors or hematologic) accounted for 72 of the deaths.
In 99 cases, the cause of death could not be ascertained. Death in
complete remission was rare (⬍ 2%). We also evaluated the causes
of death with regard to age (Table 3). Active CLL was the cause of
death in 41% of patients younger than 50 years old, 40% of patients
aged 50 to 65 years, and 32% of patients older than 65 years (Table
Table 2. Patient ages and characteristics
Characteristic (no. missing)
Median
(range)
25% quartile
75% quartile
Age, y (0)
58 (0-90)
50
66
WBC count, ⫻ 109/L (7)
26 (2-700)
16
55
ALC, ⫻ 109/L (0)
20 (0-623)
10
46
HGB level, g/dL (10)
13.6 (3.4-18.0)
PLT count, ⫻ 109L (9)
193 (2-703)
12.6
149
14.7
Serum ␤-2M, mg/L (0)
2.5 (0.6-16.4)
1.9
3.3
4.2 (1.2-5.4)
4.0
4.5
Serum LDH, IU/L (60)
3). The cause of death was unknown for 24%, 18%, and 26% of
patients younger than 50, 50 to 65, and older than 65 years.
Univariate analysis. Univariate analysis was performed to
identify patient characteristics that correlated with survival (Table
4). Longer time from diagnosis to MDACC correlated with higher
risk of death in univariate analysis (Table 4). Women had an
estimated median survival of 12 years (95% CI, 10.5-15.0 years)
versus 10 years (95% CI, 8.6-10.9 years) for men. The median age
at the time of presentation to MDACC for women and men were
not significantly different at 59 and 58 years, respectively. Age was
a significant predictor for survival, the median survivals for
patients younger than 50, 50 to 65, and older than 65 years were
13.3 years (95% CI, 11.8 years to NA), 11.0 years (95% CI,
9.5-12.1 years), and 7.5 years (95% CI, 6.3-8.6 years), respectively.
The estimated median survival times by Rai stage were as
follows: 11.5 years (95% CI, 10.8-13.7 years) for stage 0; 11.0
years (95% CI, 10.2-12.8 years) for stage I; 7.8 years (95% CI,
7.2-10.9 years) for stage II; 5.3 years (95% CI, 5.0-10.0 years) for
stage III; and 7.0 years (95% CI, 4.6-9.3 years) for stage IV (Figure
2). These estimated survival times are similar to those previously
reported with a noted exception of patients with Rai stage IV
having a longer estimated survival than those with Rai stage III
disease. Binet stage also correlated with survival; patients with
stage A, B, and C disease had estimated median survival of 11.5
years (95% CI, 10.8-12.9 years), 8.6 years (95% CI, 7.9-10.9
years), and 7.0 years (95% CI, 5.2-7.6 years), respectively. Zubrod
performance status (PS) predicted for survival; patients with PS 0
241
Serum ALB, g/dL (58)
Serum Alk Phos, IU/L (57)
Figure 1. Kaplan-Meier estimate of overall survival. Estimated overall survival for
all 1674 patients included in the analyses with 95% confidence interval (CI). Median
was 10.7 years (95% CI, 9.8-11.2 years).
78 (20-2073)
65
95
480 (50-3055)
419
568
Table 3. Disease-specific mortality reported by age
Age, y
Younger
than 50
50-65
Older
than 65
Total
Prolymphocytes, % (412)
2 (0-90)
0
5
BM lymphocytes, % (160)
59 (0-97)
44
77
Total no. of patients
395
838
441
1674
BM cellularity, % (304)
40 (0-100)
30
67
Proportion of patients, %
24
50
26
100
Spleen size, cm (33)
0 (0-23)
0
0
Proportion of all deaths, %
18
42
40
100
Liver size, cm (17)
0 (0-17)
0
0
Cause of death, no. of patients
IgG, mg/dL (204)
855 (34-5090)
663
1062
Active CLL
33
76
57
165
IgA, mg/dL (210)
114 (4-1980)
65
179
Infection
10
37
25
72
IgM, mg/dL (208)
51 (1-3180)
32
82
AIHA
0
4
0
4
Cardiac
1
4
10
15
12
28
32
72
4
5
6
15
19
34
46
99
79
188
176
443
To convert HGB and ALB from grams per deciliter to grams per liter, multiple
grams per deciliter by 10. To convert ␤-2M from milligrams per liter to nanomoles per
liter, multiply milligrams per liter by 85. To convert Ig levels from milligrams per
deciliter to grams per liter, divide milligrams per deciliter by 100.
WBC indicates white blood cell; ALC, absolute lymphocyte count; HGB, hemoglobin; PLT, platelet; ␤-2M, ␤-2 microglobulin; ALB, albumin; Alk Phos, alkaline
phosphatase; LDH, lactate dehydrogenase; BM, bone marrow.
Second malignancy
Unrelated to CLL
Unknown
Total deaths
AIHA indicates autoimmune hemolytic anemia.
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Table 4. Univariate Cox proportional hazards models for survival
Variable
Relative risk
95% CI
P
Age, y
1.05
1.04-1.06
⬍ .001*
Ln WBC count
1.37
1.23-1.53
⬍ .001*
ALC
1.59
1.39-1.83
⬍ .001*
HGB
0.84
0.80-0.88
⬍ .001*
PLT count
0.998
0.997-1.00
.01*
␤-2M
1.29
1.25-1.34
⬍ .001*
Ln ALB
0.07
0.04-0.13
⬍ .001*
Ln Alk Phos
2.75
2.11-3.60
⬍ .001*
Ln LDH
2.50
1.96-3.18
⬍ .001*
Ln prolymphs
1.28
1.13-1.46
.001*
BM lymphs
1.01
1.01-1.02
⬍ .001*
BM cellularity
1.01
1.01-1.02
⬍ .001*
Ln spleen size
1.31
1.17-1.46
⬍ .001*
Ln liver size
1.64
1.34-2.00
⬍ .001*
Ln IgG
1.04
0.81-1.33
.76
Ln IgA
0.85
0.75-0.97
.02*
Ln IgM
0.93
0.81-1.07
.32
Time from Dx to MDACC
1.04
1.01-1.08
.02*
CI indicates confidence interval; Ln, natural log; WBC, white blood cell; ALC,
absolute lymphocyte count; HGB, hemoglobin; PLT, platelet; ␤-2M, ␤-2 microglobulin; ALB, albumin; Alk Phos, alkaline phosphatase; LDH, lactate dehydrogenase; BM
lymphs, bone marrow lymphocytes; prolymphs, prolymphocytes; Dx, diagnosis;
MDACC, M. D. Anderson Cancer Center.
*Statistically significant.
to 1 had median survival of 10.8 years (95% CI, 10.0-11.7 years)
versus 6.0 years (95% CI, 2.6 years to NA) for those with PS 2 to 3.
Most laboratory parameters as well as liver and spleen measurements correlated with survival in univariate Cox proportional
hazards analysis except quantitative immunoglobulin levels (Table
4). Natural log transformation was performed for several of the
laboratory values to minimize the affect of skewing of data points.
The number of involved lymph node sites also correlated with
survival. The estimated median survival by number of involved
lymph node sites were as follows: none, 11.3 years (95% CI,
10.3-12.9 years), 1 node site, 10.9 years (95% CI, 8.8-14.5), 2 node
sites, 11.0 years (95% CI, 10.0-15.4 years), and 3 node sites, 8.5
years (95% CI, 7.6-10.0 years).
Martingale residual analysis was used to investigate for meaningful cut points for statistically significant continuous variables in
their correlation with overall survival such as age and ␤-2M. With
this analysis, there were no variables that had distinct cut points
identified for overall survival (data not shown).
Multivariate analysis. Multivariate regression analysis was
performed to examine the relationship of independent variables
with overall survival in Cox proportional hazards modeling. All
significant characteristics identified in the univariate analysis were
used to develop the multivariable model for survival. A total of
1617 patients with pertinent available data were included in the
model; there were 432 (26.7%) deaths in this group during the
follow-up period. Table 5 indicates the best model after eliminating
variables that were not statistically significant.
Predictive nomogram
A nomogram (Figure 3) was developed to predict for survival using
the 6 independent covariates identified in the multivariate model
(Table 5). The nomogram is used by totaling the points identified
on the top scale for each independent covariate. This total point
score is then identified on the total points scale to identify the
probability of 5- and 10-year survival and to estimate median
survival. The contribution of each covariate to the total score can be
visually appreciated and was potentially greatest for age, ␤-2M,
and ALC, followed by sex, Rai stage (III or IV), and the presence of
3 palpable lymph node groups. The median total points for the 1617
patients used to fit the multivariable Cox model was 82.9 (range,
31.5-187.2), the first and third quartiles were 70.9 and 96.6,
respectively.
The concordance index for this nomogram was 0.84 based on
the fitted multivariable Cox model. The calibration curve (Figure 4)
illustrates how the predictions from the nomogram compare with
actual outcomes for the 1617 patients. The dashed line represents
the performance of an ideal nomogram, in which predicted
outcomes perfectly match with the actual outcomes. The dots were
calculated from subcohorts of our dataset and represent the
performance of our nomogram based on the Cox model, including
age, ␤-2M, ALC, sex, Rai stage (0-II versus III and IV), and
number of involved lymph node groups (0-2 versus ⱖ 3). The dots
were close to the dashed line, which implies that the prediction
from our nomogram approximates the actual outcome. The X’s
were bootstrap-corrected predictions, which are more appropriate
estimates of actual survival. Most of the X’s are close to the dots,
indicating that the predictions based on the use of the nomogram
and modeled data (the dots) are near that expected from the use of
the new data (the X’s).
Prognostic index
A simplified prognostic index was developed using the 6 prognostic factors (Table 6). The index score is based on the sum total of
factors with one point given for each of the following: ␤-2M 1 to 2
times upper limit of normal (ULN), age younger than 50 years,
ALC 20 to 50 ⫻ 109/L, Rai stage III to IV, and 3 or more involved
nodal groups, male sex; 2 points are given for each of the
following: ␤-2M greater than 2 times ULN, age 50 to 65 years, and
Table 5. Multivariate Cox proportional hazards model for survival
(N ⴝ 1617)
Variable
Age, y
Figure 2. Kaplan-Meier estimates of overall survival by Rai stage (n ⴝ 1659).
Estimated median survival for Rai stage 0 was 11.5 years; I, 11.0 years; II, 7.8 years;
III, 5.3 years; and IV, 7.0 years.
Relative risk
95% CI
P
1.05
1.04-1.06
⬍ .001
ALC
1.37
1.17-1.60
⬍ .001
␤-2M
1.17
1.12-1.23
⬍ .001
Nodal groups of 3 versus less than 3
1.48
1.21-1.83
.002
Rai stage III or IV versus 0 to II
1.49
1.16-1.92
.002
Sex, male versus female
1.31
1.07-1.60
.009
CI indicates confidence interval; ALC, absolute lymphocyte count; ␤-2M, ␤-2
microglobulin; Rai Stage III or IV, hemoglobin level less than 110 g/L (11 g/dL) or
platelet count less than 100 ⫻ 109/L.
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Figure 3. Nomogram for survival of untreated patients with
CLL. The nomogram is used by totaling the points identified on
the top scale for each independent covariate. This total is then
identified on the total points scale to identify the estimated median
survival time (years) and the probability of 5- and 10-year survival.
ALC greater than 50 000/␮L; and 3 points for age older than 65
years. Risk is assigned as follows: index score 1 to 3, low; index
score 4 to 7, intermediate; and index score 8 or greater, high risk
(Figure 5). The estimated median survival times by risk group were
as follows: not reached for low risk; 10.3 years (95% CI, 9.5-11.0
years) for intermediate risk; and 5.4 years (95% CI, 4.7-7.4 years)
for high risk. Percentage of 5- and 10-year survival probabilities
are provided in Table 7.
Discussion
Prognostic models can facilitate discussion between physicians and
patients, help to identify high-risk patients for whom new treatments and clinical trials can be developed, and may provide insight
into the biology of disease. Nomograms have been developed to
predict various clinical end points for patients with other types of
malignancies.22-32
Several prognostic factors for survival have been identified for
patients with CLL. Clinical stage has been used to stratify patients
according to risk for progression and death from their disease. We
developed a nomogram for untreated patients to estimate 5- and
10-year survival probability and estimate median survival time
based on a multivariate Cox proportional hazards model that
included 6 independent patient characteristics measured at presentation to MDACC. This modeling aims to account for at least some
of the heterogeneity seen within clinical stages and provides as
accurate a predictor as possible for survival based on a large patient
population experience. The model and nomogram have been
validated as reliable predictors for survival in previously untreated
patients with CLL, independent of an indication for treatment.
We also developed a prognostic index based on the 6 significant
factors as a simplified tool to assess risk. A prognostic index was
developed for patients with follicular lymphoma, referred to as the
Follicular Lymphoma International Prognostic Index (FLIPI), to
stratify patients into low-, intermediate-, and poor-risk groups.38
Remarkably, the characteristics identified in the FLIPI, namely age,
Ann Arbor stage, HGB level, number of affected nodal areas, and
serum LDH, are similar to those identified for the CLL nomogram.
An advantage of the nomogram is that it is a weighted model,
combining independent prognostic factors and enabling appreciation of the magnitude of impact of each of the factors on the
probability of survival. The 2 major, heavily weighted, factors in
this model were ␤-2M and age. Not surprisingly, these 2 prognostic
factors have reproducibly been correlated with overall survival for
patients enrolled on clinical trials. This was most recently appreciated with the chemoimmunotherapy regimen fludarabine, cyclophosphamide, and rituximab for previously untreated and previously
treated patients with CLL.39,40
In this study, overall survival was the analysis end point. We did
not distinguish disease-specific mortality from other causes of
Table 6. Prognostic index based on presence of risk factors
Point contribution
Characteristic
Age, y
␤-2M, mg/L
ALC, ⫻ 109/L
Sex
Figure 4. Calibration curve for 5-year survival. The calibration curve illustrates
how the predictions from the nomogram compare with actual outcomes for the 1617
patients. The concordance index was 0.84. The solid line represents the performance
of the present nomogram, and the dashed line represents the performance of an ideal
nomogram.
0
1
2
3
—
⬍ 50
50-65
⬎ 65
⬍ ULN
1-2 ⫻ ULN
⬎ 2 ⫻ ULN
—
⬍ 20
20-50
⬎ 50
—
Female
Male
—
—
Rai stage
0-II
III-IV
—
—
No. of involved nodal groups
ⱕ2
3
—
—
Index score is the sum total of the point contribution for each of the 6
characteristics. An index score of 1-3 indicates low risk; 4-7, intermediate risk; and
ⱖ 8, high risk.
To convert ␤-2M from milligrams per liter to nanomoles per liter, multiply
milligrams per liter by 85. ␤-2M indicates ␤-2 microglobulin; LDH, lactate dehydrogenase; ALC, absolute lymphocyte count; —, not applicable; ULN, upper limit of normal.
From www.bloodjournal.org by guest on April 14, 2017. For personal use only.
4684
BLOOD, 1 JUNE 2007 䡠 VOLUME 109, NUMBER 11
WIERDA et al
mortality. Many patients with CLL succumb to infection-related
complications, which are likely related to immune suppression
from underlying disease as well as treatment. The vast majority of
patients have disease present at the time of death, and it is
conceivable that the presence of the disease was in some way
related to death. We reviewed the 443 deaths in this study to assess
cause; 165 could be directly attributed to active CLL, others were
disease-related complications such as infection, second malignancies, or unrelated causes (Table 3). A multivariate model developed
for CLL-specific survival (censoring other deaths) revealed the
following significant (P ⬍ .05) independent predictors for survival:
age, ␤-2M, ALC, and number of affected lymph node groups (data
not shown). There were significantly fewer events (165 deaths) in
this model, and, as such, Rai stage and sex fell out of the model as
significant predictors. Notably, when evaluating for CLL-related
mortality, age remains a significant prognostic factor. We feel that it
is more clinically relevant and useful to analyze and have a
predictive tool for overall survival from all causes, rather than to
distinguish disease-related mortality.
There were 441 patients older than 65 years, among whom 176
(40%) died. The median age in this subgroup was 71 years (range,
66-90 years). The median time to death was 7.5 years (95% CI,
6.3-8.6 years). A multivariate model for survival in patients older
than 65 years included the following significant (P ⬍ .05) independent predictors for survival: age, ␤-2M, and performance status
(data not shown). The concordance index for the subpopulation of
patients older than 65 years using the original 6-factor nomogram
was 0.82, indicating a well-fitted model that can be validly applied
to patients older than 65 years of age.
This analysis included patient characteristics measured at the
time of presentation to MDACC, not at the time of diagnosis.
Therefore, a range of time from diagnosis to MDACC evaluation
was noted with a median time to MDACC of 3.8 months. Longer
time to MDACC predicted for shorter survival in univariate
analysis but not in multivariate analysis. The characteristics
included in the final model are all characteristics that can easily and
rapidly be assessed on clinical presentation. This model may be
valid for previously untreated patients, independent of time from
diagnosis, and this model may be used serially for the same patient
over time, prior to treatment. This will need to be validated in a
prospective fashion.
Prognostic modeling has some limitations. This is a single
center study of patients who presented to a referral institution. As
such, they are younger (median age, 58 years) than patients who
present to community practice (median age, older than 65 years).
Despite this, the concordance index for patients older than 65 years
with this model was very high at 0.82. This model is internally
Table 7. Overall survival probability and relative risk of death
according to risk group (N ⴝ 1617)
Index
score
No. of
patients
5-y OS
(SE)
10-y OS
(SE)
RR
95% CI
1-3
194
0.97 (0.01)
0.80 (0.05)
1.00
Reference
Intermediate
4-7
1236
0.80 (0.01)
0.52 (0.03)
3.89
2.42-6.26
High
ⱖ8
187
0.55 (0.04)
0.26 (0.06)
10.48
6.27-17.53
Risk group
Low
OS indicates overall survival; SE, standard error; RR, relative risk; CI, confidence
interval.
validated; however, it would be strengthened by external validation
with patients evaluated and monitored at multiple institutions.
Finally, the laboratory assays used to measure ␤-2M may differ
between laboratory facilities. In addition, different laboratories
potentially have different normal ranges. This may affect the
portability and ability to generally apply the nomogram.
Some of the patients included in this analysis began treatment
soon after their initial evaluation at MDACC. Others continued to
be monitored with observation and have not received treatment.
The patients enrolled on this study span more than 20 years, during
which, significant changes and advances in treatment were made.
This prognostic model does not account for potential treatment
advances and may therefore underestimate survival, assuming a
positive impact of therapy on survival.
New prognostic factors have been identified that were not
measured on initial presentation to MDACC in this study group.
Examples include IgVH mutational status, ZAP-70 expression, and
chromosome abnormalities identified by interphase fluorescence in
situ hybridization (FISH) analysis. Without these test results
available at initial presentation and without cryopreserved material
obtained at presentation for retrospective testing, we were unable to
incorporate them into this prognostic modeling. Although we have
characterized IgVH mutation status in a subgroup of 401, ZAP70
expression in 303, and interphase FISH analysis in 123 of these
patients, there are too few events (deaths) among the patients in
these subgroups to reliably incorporate any of these factors in a
multivariate analysis. This will require continued follow-up. Predicting survival may be improved by incorporating these prognostic
factors in future predictive models.
Future work will focus on validating this model, both externally
and in a prospective manner. We are also currently investigating
incorporation of newer prognostic factors into this type of prognostic modeling, including cytogenetic analysis by FISH, IgVH
mutational status, ZAP-70 expression, and CD38 expression. In
addition, models for survival are being developed for patients at
initial treatment, at treatment for relapsed disease, and for other end
points such as time to treatment, time to progression, time to
treatment failure.
Acknowledgment
W.G.W. is a Leukemia and Lymphoma Society Scholar in Clinical
Research.
Authorship
Figure 5. Kaplan-Meier estimates for overall survival by index score (n ⴝ 1617).
Contribution: W.G.W. designed research, initiated analysis, summarized results, and wrote manuscript; S.O. reviewed manuscript and
From www.bloodjournal.org by guest on April 14, 2017. For personal use only.
BLOOD, 1 JUNE 2007 䡠 VOLUME 109, NUMBER 11
NOMOGRAM FOR SURVIVAL IN UNTREATED CLL
managed patients; X.W. did statistical analysis; S.F., A.F., J.C.,
D.T., G.G.-M., C.K., M.B., F.G., F.R., and H.K. managed patients;
K.-A.D. supervised statistical analysis; S.L. supervised data collection; and M.K. engaged in helpful clinical discussions and managed patients.
4685
Conflict-of-interest disclosure: The authors declare no competing financial interests.
Correspondence: William G. Wierda, Department of Leukemia,
1515 Holcombe Blvd, Unit 428, Houston, TX 77030; e-mail:
[email protected].
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2007 109: 4679-4685
doi:10.1182/blood-2005-12-051458 originally published
online February 13, 2007
Prognostic nomogram and index for overall survival in previously
untreated patients with chronic lymphocytic leukemia
William G. Wierda, Susan O'Brien, Xuemei Wang, Stefan Faderl, Alessandra Ferrajoli, Kim-Anh Do,
Jorge Cortes, Deborah Thomas, Guillermo Garcia-Manero, Charles Koller, Miloslav Beran, Francis
Giles, Farhad Ravandi, Susan Lerner, Hagop Kantarjian and Michael Keating
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