910
haematologica | 2017; 102(5)
Received: December 19, 2016.
Accepted: January 25, 2017.
Pre-published: February 2, 2017.
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Correspondence:
monika.engelhardt@uniklinik-freiburg.de
Ferrata Storti
Foundation
EUROPEAN
HEMATOLOGY
ASSOCIATION
Haematologica
2017
Volume 102(5):910-921
ARTICLE
Plasma Cell Disorders
doi:10.3324/haematol.2016.162693
Check the online version for the most updated
information on this article, online supplements,
and information on authorship & disclosures:
www.haematologica.org/content/102/5/910
W
ith growing numbers of elderly multiple myeloma patients, reli-
able tools to assess their vulnerability are required. The objec-
tive of the analysis herein was to develop and validate an easy
to use myeloma risk score (revised Myeloma Comorbidity Index) that
allows for risk prediction of overall survival and progression-free survival
differences in a large patient cohort. We conducted a comprehensive
comorbidity, frailty and disability evaluation in 801 consecutive myeloma
patients, including comorbidity risks obtained at diagnosis. The cohort
was examined within a training and validation set. Multivariate analysis
determined renal, lung and Karnofsky Performance Status impairment,
frailty and age as significant risks for overall survival. These were com-
bined in a weighted revised Myeloma Comorbidity Index, allowing for
the identification of fit (revised Myeloma Comorbidity Index ≤3 [n=247,
30.8%]), intermediate-fit (revised Myeloma Comorbidity Index 4-6
[n=446, 55.7%]) and frail patients (revised Myeloma Comorbidity Index
>6 [n=108, 13.5%]): these subgroups, confirmed via validation analysis,
showed median overall survival rates of 10.1, 4.4 and 1.2 years, respec-
tively. The revised Myeloma Comorbidity Index was compared to other
commonly used comorbidity indices (Charlson Comorbidity Index,
Hematopoietic Cell Transplantation-Specific Comorbidity Index, Kaplan-
Feinstein Index): if each were divided in risk groups based on 25% and
75% quartiles, highest hazard ratios, best prediction and Brier scores were
achieved with the revised Myeloma Comorbidity Index. The advantages
of the revised Myeloma Comorbidity Index include its accurate assess-
ment of patients' physical conditions and simple clinical applicability. We
propose the revised Myeloma Comorbidity Index to be tested with the
"reference" International Myeloma Working Group frailty score in multi-
center analyses and future clinical trials. The study was registered at the
German Clinical Trials Register (DRKS-00003868).
A concise revised Myeloma Comorbidity Index
as a valid prognostic instrument in a large
cohort of 801 multiple myeloma patients
Monika Engelhardt,
1
* Anne-Saskia Domm,
1
* Sandra Maria Dold,
1
Gabriele
Ihorst,
2
Heike Reinhardt,
1
Alexander Zober,
1
Stefanie Hieke,
2,3
Corine Baayen,
3,4
Stefan Jürgen Müller,
1
Hermann Einsele,
5
Pieter Sonneveld,
6
Ola Landgren,
7
Martin Schumacher
3
and Ralph Wäsch
1
1
Department of Medicine I, Hematology, Oncology & Stem Cell Transplantation, Medical
Center - University of Freiburg, Faculty of Medicine, Germany;
2
Clinical Trials Unit, Medical
Center - University of Freiburg, Faculty of Medicine, Germany;
3
Center for Medical
Biometry and Statistics, University of Freiburg, Faculty of Medicine, Germany;
4
Université
de Nantes, UFR des Sciences Pharmaceutiques, Nantes Cedex, France;
5
Department of
Internal Medicine II, University Hospital, Würzburg, Germany;
6
Department of Hematology,
University Rotterdam, The Netherlands and
7
Myeloma Service, Memorial Sloan-Kettering
Cancer Center, New York, NY, USA
* ME and A-SD contributed equally
ABSTRACT
Introduction
Over the past decade, overall survival (OS) has
improved significantly in patients with multiple myeloma
(MM). This is driven by better biological insights in the
disease, implementation of more sensitive tests and tech-
nologies leading to earlier diagnosis, access to better com-
bination therapies and increased access to supportive care
measures.
1-3
However, MM typically affects elderly
patients, who face the challenge that treatment endurance
is poorer and prognosis more unfavorable.
4,5
Moreover, the
simultaneous presence of additional diseases may compli-
cate antimyeloma treatment.
1,3
In general, comorbidities
have been shown to influence cancer patients’ general
health status, limit their physical condition and OS.
6-11
Therefore, with a growing number of elderly patients, reli-
able tools to assess patients' vulnerability as expressed in
chronic conditions and limitations in daily activity are
required to guide therapeutic decisions.
4,12-15
Historically, treatment decisions in symptomatic MM
patients have been largely age-based. Today, disease biol-
ogy and fitness, including patients' Karnofsky
Performance Status (KPS), are considered when assessing
therapeutic options.
3,14
However, the KPS is often overesti-
mated and does not reflect the entire functional status.
10
Therefore, advances in more precise ways of defining fit-
ness are warranted. Moreover, since elderly MM patients
are often excluded from clinical trials due to strict inclu-
sion criteria,
16
these trial results are not necessarily trans-
ferable to elderly patients. In this context, the
International Myeloma Working Group (IMWG),
European Myeloma Network (EMN) and others (e.g., IFM,
HOVON, DSMM, GMMG) recommended that age, phys-
ical condition and comorbidities are included in therapy
decisions.
1,8,10,12,14
Since cytogenetic aberrations are addi-
tional prognostic factors in MM,
17-20
it may also be impor-
tant to include cytogenetics in MM-specific risk scores.
Risk scores for MM have indeed included disease-related
risks (the International Staging System (ISS), lactate dehy-
drogenase (LDH), cytogenetics), combined comorbidities
with cytogenetics or multiple comorbidity screening tests
(IMWG frailty score).
12,17-19
Our prior test
8
and independent
validation analyses
9,10
defined impaired renal function,
lung function or KPS as relevant risks via thoroughly
assessed univariate and multivariate analyses. These vari-
ables were combined in an additive Initial Myeloma
Comorbidity Index (I-MCI),
8-10
which enabled the clear
definition of risk groups with substantially different pro-
gression-free survival (PFS) and OS. Furthermore, it was
found to add valuable information to the ISS.
10
In order to refine and weight our I-MCI, we tested and
validated a 'revised MCI' (R-MCI) based on a large cohort
of 801 MM patients. Additionally, we compared the R-
MCI to other internationally used Comorbidity Indices
(CIs), namely Charlson CI (CCI), Hematopoietic Cell
Transplantation-specific CI (HCT-CI) and Kaplan-
Feinstein Index (KFI). Frailty scores are already used clini-
cally for various cancers, but a comprehensive comorbidi-
ty, frailty and disability evaluation is time-consuming and
less applicable outside centers with oncogeriatric teams,
21
which was the reason why we aimed to establish a con-
cise, time-saving R-MCI.
Since the comparison of the Initial-/R-MCI (I-/R-MCI)
with the IMWG frailty score was already meticulously
performed by us,
22
it was not the focus of this analysis,
rather, the aim was to define a concise, weighted, both
tested and validated MM-specific risk score in a large
cohort which could be subsequently used for the measure-
ment of frailty in multicenter analyses and future clinical
trials.
Methods
Patient population and study design
This prospective assessment was based on the analysis of 801
consecutive MM patients at the time of initial diagnosis and first
presentation at our center between 1997 and 2012. The study was
registered at the German Clinical Trials Register (DRKS-
00003868). The primary objective was to optimize the I-MCI
8-10
within a weighted R-MCI in a large myeloma cohort. Secondary
objectives included the impact of the R-MCI as compared to the I-
MCI, CCI, HCT-CI and KFI (Online Supplementary Table S1), and
their value for PFS and OS. The analysis was carried out according
to the guidelines of the Declaration of Helsinki principles and good
clinical practice. All patients gave their written informed consent
for institutional-initiated research studies and analyses of clinical
outcome studies conforming to the institutional review board
guidelines.
Assessment
The I-MCI consists of an additive scoring system, namely
renal, lung and/or KPS impairment.
8-10
In order to weight this in
an even larger cohort, 13 comorbidities were assessed in 801
patients: these were graded and rated according to Common
Terminology Criteria for Adverse Events (CTCAE) 4.03, which
included: renal, lung and KPS impairment, cardiac, liver or gas-
trointestinal disease, disability, frailty, infection, thromboembol-
ic events, peripheral neuropathy, pain and secondary malignan-
cies (Table 1). In addition, age, cytogenetics via fluorescence in
situ hybridization (FISH after CD138 selection), renal function
(via estimated glomerular filtration rate [eGFR
MDRD
]) and lung dis-
ease, including lung function tests, were determined as
described.
8-11,22-25
We performed interphase FISH on CD138+ plas-
ma cells, which were analyzed using DNA probes specific for
the following chromosomal aberrations: t(11;14)(q13;q32),
t(4;14)(p16;q32), t(14;16)(q32;q23) and t(14;20)(q32;q12 (Abbott
Laboratories, IL, USA), XL 5q31/5p12, XCE 9, 11, 15, gain(1q21),
del(1p32), del(13q14), del(17p13) and c-myc rearrangements
(MetaSystems, Altlussheim, Germany). The score of Wuilleme et
al.
26
was used to assess ploidy by using gains of at least two of
the chromosomes. For each probe a minimum of 100 nuclei
were scored. European Myeloma Network (EMN) cutoff values
were applied for the detection of aberrations.
27
Unfavorable
cytogenetics were defined as del(17p13), del(13q14), t(4;14),
t(14;16), t(14;20), hypodiploidy, c-myc and chromosome 1 aber-
rations.
1,8,10,22,24,27-31
The KPS was defined as normal (100%), mildly
(90%), moderately (80%) or more substantially impaired
(≤70%). Frailty and disability were assessed in order to get a
more precise determination of patients’ physical condition. The
Fried definition for frailty was used, which takes into account
the added presence of weakness, poor endurance, low physical
activity, slow gait speed and shrinking, with ≤2 factors defining
frailty as moderate and with ≥3 factors determining frailty as
severe.
32-34
The assessment was performed by a staff member
trained in oncogeriatrics (A-SD, SMD, AZ, SJM), and was per-
formed identically throughout the study period. Patient charac-
teristics included age, myeloma type, stage, β
2
-microglobulin
(β2-MG), creatinine, bone marrow (BM) infiltration, cytogenet-
ics and treatment (Table 2).
Comprehensive appraisal of a weighted myeloma comorbidity index
haematologica | 2017; 102(5)
911
Statistical analysis
Data were analyzed using SAS 9.2 (SAS Institute Inc., NC,
USA). OS was calculated from the date of initial diagnosis until the
date of death from any cause, while PFS was calculated from the
date of initial diagnosis until the date of progression, relapse or
death from any cause. When no event of interest occurred, obser-
vations were censored at the time the patient was last seen
alive/without documented event, or at the latest on June 1st, 2015.
OS and PFS rates were estimated using the Kaplan-Meier method,
and compared using the log-rank test.
In order to weight the MCI in a large cohort,
8-10
the data set was
randomly split into 2/3 and 1/3, namely a training (n=552) and val-
idation set (n=249). The training set was built by randomly draw-
ing 552 samples. The training set was used to develop the R-MCI,
and the validation set to validate our results. Multivariate Cox pro-
portional hazards regression models with backward variable selec-
tion were applied to the training set to evaluate the prognostic sig-
nificance of the comorbidity factors. Variable selection was based
on complete case analysis. For all other variables, the 552 patients
without any missing data with 294 events (deaths) were used. The
results of the final model with prognostic factors contributing to
the R-MCI were presented as estimated hazard ratios (HRs) with
two-sided 95% confidence interval (CI), corresponding log hazard
ratios and P-values (Table 3). Score weights were determined
based on log hazard ratios, i.e., the regression coefficients of the
prognostic factors, as these reflect the level of association with the
outcome of OS on an additive scale. We assigned a score weight
of 0 if the log hazard ratio was below 0.3, a score weight of 1 if
the log hazard ratio was between 0.31 and 0.7, a score weight of
2 if the log hazard ratio was between 0.71 and 1.07, and a score
weight of 3 if the log hazard ratio was 1.08 or higher, leading to a
maximum of 9 points (Table 3). This rule very closely approxi-
mates the weights as described.
35
In order to additionally evaluate
whether the co-variable cytogenetics can increase the predictive
performance of this score (Table 3), a multivariate Cox model
including a preliminary score as a co-variable was compared to a
multivariable Cox model including both the preliminary score and
cytogenetics: the models were based on 353 patients without any
missing data in all co-variables, with or without the inclusion of
cytogenetics (Table 3).
17–20
Prediction errors based on the Brier
score
36
were used to compare the R-MCI, with and without cyto-
genetics, determining that the R-MCI could be improved with the
inclusion of cytogenetics (Online Supplementary Figure S1). Of note,
our final 9-point weighted R-MCI can be used as a risk tool both
with or without cytogenetics (e.g., if cytogenetics were unavail-
able). Albeit there was no missing data for the prognostic factors
M. Engelhardt et al.
912
haematologica | 2017; 102(5)
Table 1. Definition and grading of 13 comorbidities and physical functions in myeloma patients.
Variables Mild Definition and grading Severe References
Moderate
1. Renal function: CTCAE grade 1 CTCAE grade 2 CTCAE grade 3-4 Kleber
8–10
eGFR / serum creatinine
2. Lung function: dyspnea upon intense activity, dyspnea upon moderate dyspnea at rest/few steps Kleber
8–10
dyspnea or FEV
1
/FVC
a
, mildly altered lung function activity, moderately altered taken/the need for
FEV
1
, TLC, respiratory lung function or respiratory oxygen/non-invasive
insufficiency insufficiency ventilation or FEV
1
<50%
3. Karnofsky Performance 90% 80% ≤70% Kleber
8–10
Status
4. Cardiac function: CTCAE grade 1 CTCAE grade 2 CTCAE grade 3-4 CTCAE, 4.0
arrhythmias, myocardial
infarction/CAD, heart failure
5. Hepatic function: CTCAE grade 1 CTCAE grade 2 CTCAE grade 3-4 CTCAE, 4.0
chronic hepatitis, cirrhosis,
fibrosis, hyperbilirubinemia
6. GI disease: CTCAE grade 1 CTCAE grade 2 CTCAE grade 3 CTCAE, 4.0
nausea, vomiting, diarrhea, ulcer
7. Disability: occasional frequent ≥1x/day Palumbo
12
help in personal care
and household tasks
8. Frailty: 1 factor 2 factors ≥3 factors Rodriguez-Mañas,
32
weakness, poor endurance, Xue
33
low physical activity, slow gait speed
9. Infection local intervention oral intervention i.v. intervention CTCAE, 4.0
10. Thromboembolic event venous thrombosis thrombosis, medical life-threatening, urgent CTCAE, 4.0
intervention indicated intervention indicated Kristinsson
47
11. PNP CTCAE grade 1 CTCAE grade 2 CTCAE grade 3-4 CTCAE, 4.0
12. Pain CTCAE grade 1 CTCAE grade 2 CTCAE grade 3-4 CTCAE, 4.0
13. Secondary malignancy 1. chronological criteria: before, synchronous or after MM Hasskarl
28
2. local criteria: local vs. disseminated cancer Engelhardt
24
3. etiological criteria: hematological, solid or skin tumors Kleber
8–10
a
FEV
1
/FVC: Tiffeneau-Pinelli Index: ratio of the forced expiratory volume in 1 second and the forced vital capacity. CAD: Coronary Artery Disease; CTCAE: Common Terminology Criteria
for Adverse Events; eGFR: estimated glomerular filtration rate; FEV1: forced expiratory volume in one second; FVC: forced vital capacity; GI: gastrointestinal; PNP: peripheral neuropathy;
TLC: total lung capacity; i.v.: intravenous; I-MCI: Initial Myeloma Comorbidity.
I-MCI
in our dataset, the provision of weighing unfavorable, favorable vs.
"missing/unavailable cytogenetics" within this R-MCI, illustrates
its usefulness for primary or secondary institutions and non-acad-
emic centers.
The R-MCI was also compared to the I-MCI, CCI, HCT-CI and
KFI, evaluating the prognostic role on OS with Cox regression
models (Table 4) in terms of HRs. The predictive ability of differ-
ent scores was assessed using prediction error curves and Brier
scores (Online Supplementary Figure S2):
36
the smaller this prediction
error is, the better the curves' prediction rate turns out, with the
'reference' constituting a model without co-variables.
Additionally, the R-MCI was assessed on OS in patients with dif-
ferent antimyeloma treatments and ages. Since the comparison of
the MCI with the IMWG frailty score had already been per-
formed,
22
it was not the focus of this analysis, which was rather to
define a weighted, tested and validated MM-specific risk score in
a large myeloma cohort.
Results
Patient characteristics
The analysis included 801 consecutive MM patients.
The median follow up was 6.1 years. The median age was
63 years: 28% of patients were 66-75 years and 13% older
than 75 years, which is very typical for tertiary centers.
7-
11,22,24,37,38
Gender distribution and myeloma subtypes corre-
sponded to the data as described.
7-11,24
Other characteristics
were likewise representative of large MM centers, e.g.,
typical paraprotein frequencies and mostly advanced
Durie-Salmon and ISS II/III disease stages. The median β
2
-
MG level was 4.5mg/dL, renal function showed a median
creatinine level of 0.93mg/dL and BM plasma cell infiltra-
tion of 30%. Patients underwent treatment according to
international guidelines, labels and practices as
described.
1,10,11,22,24
Autologous stem cell transplantation
Comprehensive appraisal of a weighted myeloma comorbidity index
haematologica | 2017; 102(5)
913
Table 2. Patient characteristics.
Entire cohort (n=801) Training set (n=552 / 68.9%) Validation set (n=249 / 31.1%)
n (%) Median (range) n (%) Median (range) n (%) Median (range)
Patient-specific data
Male : female 450 (56.2) : 351 (43.8) 316 (57) : 236 (43) 134 (53.8) : 115 (46.2)
Age (years) 63 (21-93)* 62 (21-93) 63 (32-89)
MM-specific data
Type of myeloma
IgG / IgA 455 (56.8) / 152 (19.0) 309 (56.0) / 105 (19.0) 146 (58.6) / 47 (18.9)
IgM / IgD 6 (0.8) / 2 (0.2) 3 (0.5) / 2 (0.4) 3 (1.2) / 0 (0)
Light-chain MM only 162 (20.2) 117 (21.0) 45 (18.1)
Biclonal (HC) 6 (0.8) 4 (0.7) 2 (0.8)
Non-secretory 18 (2.3) 12 (2.2) 6 (2.4)
κ/λ 502 (62.7) / 276 (34.5) 355 (64.1) / 181 (32.9) 147 (59.0) / 95 (38.2)
Biclonal (LC) 5 (0.6) 4 (0.7) 1 (0.4)
Non-secretory 18 (2.3) 12 (2.2) 6 (2.4)
Durie-Salmon
I 204 (25.5) 139 (25.3) 65 (26.1)
II 117 (14.6) 88 (15.8) 29 (11.7)
III 480 (59.9) 325 (58.9) 155 (62.2)
A / B 665 (83.1) / 136 (16.9) 456 (82.6) / 96 (17.4) 209 (83.9) / 40 (16.1)
ISS 759 (94.8)
a
517 (93.5) 242 (97.2)
I 225 (28.2) 146 (26.5) 79 (31.7)
II 206 (25.8) 139 (25.3) 67 (26.9)
III 328 (41.0) 232 (42.0) 96 (38.6)
Laboratory parameters
β
2
-microglobulin (mg/dL)
755 (94.3)
e
4.5 (1.1-65.5) 516 (93.5) 4.72 (1.1-65.5) 240 (96.4) 4.20 (1.4-52.6)
Creatinine (mg/dL)
0.93 (0.4-17.9) 0.92 (0.4-17.9) 0.94 (0.5-10.5)
BM infiltration rate (%)
695 (86.8)
d
30 (0-100) 487 (88.2) 30 (0-100) 208 (83.5) 30 (0-90)
Cytogenetics
Favorable 316 (39.5) 214 (38.8) 102 (41.0)
Unfavorable
b
212 (26.5) 140 (25.4) 72 (28.9)
Missing 273 (34.1) 198 (35.9) 75 (30.1)
Therapy
SCT with novel agents 300 (37.5) 194 (35.1) 106 (42.6)
SCT w/o novel agents 83 (10.4) 62 (11.2) 21 (8.4)
Standard with novel agents 173 (21.6) 118 (21.4) 55 (22.1)
Standard w/o novel agents
c
170 (21.2) 127 (23.1) 43 (17.3)
w/o CTx
#
75 (9.4) 51 (9.3) 24 (9.6)
*13% of patients were >75 years, 7% were 76-79 years and 6.3% ≥80 years.
a
Not evaluated in n=42 patients because of missing data.
b
Unfavorable cytogenetics defined as del(17p13),
del(13q14), t(4;14), t(14;16), t(14;20), hypodiploidy, c-myc and chromosome 1 aberrations.
c
Novel agents: e.g., thalidomide, lenalidomide, bortezomib.
d
Not evaluated in n=106 patients
because of missing data.
e
Not evaluated in n=46 patients because of missing data.
#
Radiotherapy and steroids alone. n: number; Ig: immunoglobulin; HC: heavy-chain; MM: multiple myelo-
ma; LC: light-chain; ISS: International Staging System; BM: bone marrow; SCT: stem cell transplantation; w/o CTx: without chemotherapy; κ / λ: kappa / lambda.
(ASCT) was recommended for medically fit, symptomatic
patients up to the age of 70 years.
22,25
Induction usually
consisted of bortezomib-based regimens, such as VCD
(bortezomib, cyclophosphamide and dexamethasone) or
CTD (cyclophosphamide, thalidomide and dexametha-
sone). Mobilization and conditioning were performed as
described.
1,10,11,23,24
Patients ineligible for ASCT received
melphalan, prednisone and bortezomib (MPV), melpha-
lan, prednisone and thalidomide (MPT) or melphalan and
prednisone (MP).
1
Novel agent-based therapies included
immunomodulatory drugs and proteasome inhibitor treat-
ment according to the approved indications and in line
with treatment at other international centers (Table 2). In
order to revise the MCI, the data set was randomly split
into 2/3 and 1/3 of patients, using a training (n=552) and
validation set (n=249). The training set was used to devel-
op the R-MCI and the validation set to validate our results.
Both groups were comparable with respect to relevant
patient-specific and MM-specific data, laboratory parame-
ters and therapy. The data of the entire patient cohort, and
of both the training and validation sets are displayed in
Table 2. Approximately one-half (43%) of the patients
received standard treatment without stem cell transplanta-
tion (SCT), the other percentage of patients includes those
who underwent SCT (Table 2). Patient characteristics
according to treatment are displayed in the Online
Supplementary Table S2. Treatment was not modified
according to the comorbidity scores in line with prior
studies.
7-10,12,35,39,40
Frequency of specific comorbidities
Frequent comorbidities (>30%) of all grades were KPS
impairment (94%), renal impairment (68%), frailty (62%),
cardiac impairment (45%), disability (43%) and lung
impairment (32%). More severely graded comorbidities
were again KPS impairment, frailty, disability, renal
impairment, lung impairment, cardiac impairment and
infections. Other comorbid conditions, such as liver and
gastrointestinal impairment and thrombosis occurred to a
lesser extent and severity (Figure 1).
Multivariate analysis for OS, weighting and risk
stratification via MCI
The multivariate Cox proportional hazards model based
on backward selection revealed five highly significant risks
as relevant for OS (Table 3). Score weights for comorbidities
(Table 3) were determined based on regression coefficients
of the prognostic factors, i.e., log hazard ratios. In a separate
M. Engelhardt et al.
914
haematologica | 2017; 102(5)
Table 3. Multivariate Cox proportional hazards model of the training set analysis (n=552) based on backward selection for overall survival (OS),
and the value of inclusion of cytogenetics (n=353).
Multivariate Cox proportional hazards model of the training set analysis (n=552)
Definition n=552 (%) HR P-value log(HR) Score weight
(2.5-97.5%)
1. Renal disease (eGFR
MDRD
)
a
≥90 184 (33) 1 (-) 00
60-89 193 (35) 1.25 (0.92-1.68) <0.0001 0.22 0
<60 175 (32) 1.96 (1.43-2.68) 0.67 1
2. Lung disease No/mild 470 (85) 1 (-) 0.0005 00
Moderate/severe 82 (15) 1.65 (1.24-2.18) 0.50 1
3. KPS 100% 35 (6) 1 (-) 0.0036 00
80-90% 207 (38) 2.17 (1.04-4.52) 0.77 2
≤70% 310 (56) 2.96 (1.43-6.12) 1.08 3
4. Age (years) <60 226 (41) 1 (-) <0.0001 00
60-69 185 (33) 1.43 (1.06-1.92) 0.36 1
≥70 141 (26) 2.08 (1.50-2.89) 0.73 2
5. Frailty No/mild 323 (59) 1 (-) <0.0001 00
Moderate 140 (25) 1.54 (1.17-2.04) 0.43 1
Severe 89 (16) 2.02 (1.45-2.82) 0.70 1
± Cytogenetics Favorable 0
Unfavorable 1
Unavailable 0
Maximum points 9
Univariate and bivariate Cox model with and without inclusion of cytogenetics (n=353)
log(HR) HR P-value
(2.5-97.5%)
Univariate Cox model Preliminary score 0.10 1.11 <0.0001
with preliminary score (1.08-1.14)
Multivariable Cox model Preliminary score 0.10 1.11 <0.0001
with inclusion of cytogenetics (1.08-1.13)
Cytogenetics unfavorable 0.44 1.56 0.006
(1.13-2.15)
a
eGFR calculated as MDRD 186 × (serum creatinine level [mg/dl])
-1.154
× (age [y])
-0.203
× (0.742 if female, 1.21 if black person), log hazard ratios: parameter estimates.
We assigned a score weight of 0, if the log hazard ratio was below 0.3, a score weight of 1, if the log hazard ratio was between 0.31 and 0.7, a score weight of 2, if the log hazard
ratio was between 0.71 and 1.07, and a score weight of 3, if the log hazard ratio was 1.08 or higher, leading to a maximum of 9 points. This rule very closely approximates the
weights as previously described.
35
n: number; HR: hazard ratio; KPS: Karnofsky Performance Status; eGFR
MDRD
: estimated glomerular filtration rate by MDRD (Modification of Diet
in Renal Disease).