unfit ≠ frail: who is the unfit patient? · treatment results in mm w new agents: cts regimen age...

Unfit ≠ Frail: Who is the unfit patient?

Monika Engelhardt

This meeting is organized by Takeda Pharmaceuticals International AG.Takeda medicines will be discussed during this meeting.Full Prescribing Information is available at this meeting.EM/IXZ/0519/0018 June 2019.

Monika Engelhardt

University of Freiburg Medical Center

Emerge meeting Takeda, EHA Amsterdam, 12.06.2019

Unfit ≠ Frail: Who is the unfit patient?

Which elements characterize

fit vs. intermediate-fit vs. frail=unfit patients?

Determining treatment intensity

Determining treatment intensity

Myeloma is a disease of aging adults

MM is a disease of aging adults

Factors that influence outcomes in aging adults with MM

Age, comorbidities (# and extent), QoL or fitness alone may not suffice to determine best

treatment, but their conjunction

-> easy, reproducable scores help to achieve this goal

Abel GA, Klepin HD. Blood 2018;131:515–524.

Factors that incluence outcomes in aging adults w MM

Frailty Phenotype

Frailty phenotype according to Fried

Fried LP et al. J Gerontol A Biol Sci Med Sci 2001;56:146–156.

Frailty highly prevalent in MMR-MCI

renal+lung+KPS+age+frailty + CGn=801fit: OS: 10.1 yrsintermediate: 4.4 yrsfrail: 1.2 yrs

Engelhardt M et al. Haematologica 2017;102:910–921.

Frailty60%: entire (mild - severe)40%: severe

How does the overall finess level

impact the clinical outcome?

Myeloma deaths are greatest in older adults

Augustson BM et al. J Clin Oncol 2005;23:9219–9226; Kleber M,....Engelhardt M. Blood Cancer J 2011;1:e35;Kleber M,....Engelhardt M. Clin Lymphoma Myeloma Leuk 2012;12:38–48;

Kleber M,....Engelhardt M. Clin Lymphoma Myeloma Leuk 2013;13:541–551.

Myeloma deaths are greatest in older adults

Early mortality in MM substantially declined within first 60d of ID: 10% -> 1%Early mortality correlates with frailty and various comorbidity scores (i.e. MCI)

Geriatric Assessment: more informative than age or performance status in predicting Chemo Risk<br />

Geriatric assessment: more informative than age or performance status in

predicting Chemo Risk

Hurria A et al. J Clin Oncol 2011;29:3457–3465.

Physical function predicts transplant LOS

Rosko AE et al. Blood 2015 126:3200;Fernando Dimeo, Charity Berlin;

Mandy Möller. University of Freiburg.

Physical function predicts transplant length of stay (LOS)

Older adults with MM poor HRQoL symptoms

Older adults with MM poor HRQoL symptoms

Kent EE et al. Cancer 2015;121:758–765.

Embed frailty assessments to determine treatment intensity

Singh M et al. Eur Heart J 2014;35:1726–1731.

MM treatment :<br />where you begin may not be where you end

Future of frailty: biomarker of aging

Immune response and profile

Soto Perez de Celis E et al. Lancet Oncol 2018;19:e305–e316.

Future of frailty: biomarker of aging

Survival associated with biomarker + age + performance status

Milani P et al. Am J Hematol 2016;91:1129–1134.

How does age impact the clinical outcome?

Fit Intermediate-fit Unfit=frail• Age decides this all?• Better determinants?• Large variation of elderly pt consitution• Bias of chronological age better approached via definition of biological age

Inability to apply clinical trial results to the general MM population

<65 years 65-74 years +75 years

Rela

tive s

urv

ival ra

te

Kumar SK et al. Leukemia 2014; 28:1122–1128.

Recent trends in MM

all pts <65 years >65 yearsO

vera

ll surv

ival

Follow-up from diagnosis (years)

Costa LJ,....Kumar SK et al. Blood Adv 2017;1:282–287.

Hypothesis: Improved prognosis also in elderly pts?

Survival improvement in >65 + >75 y MM pts, but age disparities exist

Muchtar E et al. Bone Marrow Transplant 2016;51:1449–1455;Mahajan S et al. Ther Adv Hematol 2018;9:123–133.Merz M et al. Eur J Caner 2016;62:1–8.

Straka C et al. Haematologica 2016;101:1398–1406;

(n=239)

(n=195)

PFS OS

(n=195)

(n=239)

Auner HW et al. Haematologica 2018;103:514–521;Garderet L et al. Haematologica 2016;101:1390–1397.

(n=245)(n=1719)

Mel200 (n=32)

Mel40 (n=18)

OSOS

ASCT in elderly MM pts: hype or hope?Hypothesis: Improved prognosis in elderly pts due to SCTs

The bulk of treatment for MM is non-transplant based

Al-Hamadini M et al. Am J Hematol 2014;89:825–830.

Treatment results in MM w new agents: CTs

Regimen Age (range) # pts Priortherapies

ORR(%) PFS (mo) Ref

Frontline

VCD 54 (32-67) 414 0 85 35 Einsele, BJH 2017

VTD : TD 58 (52-62) 480 0 93 : 79 @3y: 68:56% Cavo, Lancet 2010

VRD : Rd >65: 43% 525 0 82 : 72 43 : 30 Durie, Lancet 2017

VMP : MP 71 (48-91) 682 0 74 : 39 24 : 17 San Miguel, NEJM 2008

Rd : MPT 73 (44-92) 1623 0 75 : 62 26 : 21 Benboubker, NEJM 2014

Relapse

PanVD Panorama 63 768 1 (1-3) 77 : 63 19 : 9 San Miguel BJH 2017

DVD Castor 64 (30-88) 498 2 (1- >3) 83 : 63 nr : 7 Palumbo NEJM 2016

KRd Aspire 64 (38-87) 792 2 (1-3) 87 : 67 26 : 18 Stewart NEJM

Kd Endeavor 65 (60-71) 929 1 (1-3) 61 : 55 19 : 9 Dimopoulos Lancet 2017

DRd Pollux 65 (34-89) 569 1 (1-11) 93 : 76 nr : 18 Dimopoulos 2016

IRd Tourmaline 66 (38-91) 722 1 (1-3) 78 : 72 21 : 15 Moreau NEJM 2016

EloRd Eloquent2 67 (37-88) 646 2 (1-3) 79 : 66 19 : 15 Lonial NEJM 2015

Age both in frontline and relapse clinical trial pts substantially differentNew immunotherapies + NAs may require biomarkers for response prediction

Hypothesis: Improved prognosis is due to numerous effective therapies

Moreau P. Blood 2017;130:1507–1513.

Why is assessment of comorbidities important and

how is it done?

Geriatric Assessments are predictive and prognostic

Hurria A et al. J Clin Oncol 2011;29:3457–3465; Hurria A et al. J Clin Oncol 2016;34:2366–2371;

Extermann M et al. Cancer 2012;118:3377–3386; Wildes TM et al. J Geriatr Oncol 2013;4:227–234;

Soubeyran P et al. J Clin Oncol 2013;30:1829–1834; Palumbo A et al. Blood 2015;25:2068–2074.

Geriatric assessment tool

Kleber M,....Engelhardt M. Blood Cancer J 2011;1:e35;Kleber M,....Engelhardt M. Clin Lymphoma Myeloma Leuk 2012;12:38–48;

Kleber M,....Engelhardt M. Clin Lymphoma Myeloma Leuk 2013;13:541–551.

KPS in MM notoriously overestimated by median +30%

-> i.e. if KPS by physician is 90% -> according to KPS definition: was 60%

Geriatric assessment tool

Documentation of patient- & disease-specific data

Completion of 5 comorbidity indices (CI)

Performance of 12 functional tests

• Age• Gender• Height, weight, BMI• Date of initial diagnosis• Date of last contact• Date of progression• Stage (D&S, ISS)• Plasma cell infiltration• Cytogenetics• ...

1. Revised-Myeloma CI

2. IMWG frailty score

3. Charlson CI

4. Hematopoietic Cell

f Transplantation-specific CI

5. Kaplan Feinstein Index

1. Patient-rated fitness2. Physician-rated fitness3. Timed Up and Go Test4. Mini-Mental State Examination5. Activity of daily living6. Instrumental activity of daily living7. Pain (NRS)8. Geriatric depression scale9. Karnofsky Performance Status10. SF-12 PCS11. SF-12 MCS12. Malnutrition

Prospective functional assessment (FA) in MM

Kleber M,....Engelhardt M. Blood Cancer J 2011;1:e35; Kleber M,....Engelhardt M. Clin Lymphoma Myeloma Leuk 2012;12:38–48;Kleber M,....Engelhardt M. Clin Lymphoma Myeloma Leuk 2013;13:541–551;

Engelhardt M et al. Haematologica 2016;101:1110–1119; Engelhardt M et al. Haematologica 2017;102:910–921.

→ objective determination of fit, intermediate-fit and frail patients

FitnessPatient-rated fitnessFitness evaluated by the patient based on grades from 1 (very good) to 6 (insufficient)Overall grade: 1-6

Physician-rated fitnessFitness evaluated by physician based on grades from 1 (very good) to 6 (insufficient)Overall grade: 1-6

Timed Up and Go TestTime it takes to rise from a chair, walk 3 meters, turn around, walk back and sit down Total value: time in seconds

Cognitive functionMini-Mental State Examination30 questions to measure cognitive impairment (e.g. memory, reaction, orientation in time & place) Total score: 0-30

Pain scale (NRS)Pain assessment on a scale from 0 (no pain) to 10 (unbearable pain) at the current time Total score : 0-10

Quality of lifeKarnofsky Performance StatusQuantification of pts´ generalwell-being from 100% (perfect health) to 0% (death)Total value: 0-100%

SF-12: Physical composite scaleQuestionnaire with 12 questions to measure physical quality of life Total value: 0-100

SF-12: Mental composite scaleQuestionnaire with 12 questions to measure mental quality of life Total value: 0-100

NutritionMalnutrition10-item questionnaire with regard to pt´s appetite, current medication and drug consumptionTotal score : 0-21

DepressionGeriatric depression scale30-item self-report assessment used to identify depressionTotal score : 0-15

Pain

Self-sufficiencyActivity of daily livingQuestionnaire of 6 self-care tasks to estimate pt´s self-sufficiency Total score : 0-6

Instrumental activity of daily livingQuestionnaire of 8 instrumental self-care tasks to estimate pt´s self-sufficiency Total score : 0-8

Multidimensional functional tests in MM

Introduce and discuss the IMWG Frailty Score

Calculator and others

Palumbo A et al. Blood 2015;25:2068–2074.

IMWG-frailty score predicts OS, PFS, non-/hem tox and treatment discontinuation

Variable Score

Age<7575-80>80

012

Charlson CI<1>2

01

ADL>4<4

01

IADL>5<5

01

DefinitionsFitIntermediate-fitUnfit=Frail

01>2

Independent of

- stage,

- cytogenetics and

- type of treatment

IMWG-frailty IndexCCI, ADL, IADL, Age Factors: 4

Max.Score: 5

Revised Myeloma Comorbidity Index (R-MCI)Renal functionLung functionKPSFrailtyAge+/- Cytogenetics

Factors: 5 (+ CG)Max.Score: 9

Kaplan Feinstein IndexHepatic diseaseCardiac diseaseHypertensionImmobilityCerebrovascular diseasePsychiatric disorderAlcoholismCollagenosis, EpistaxisChronic infectionGI-diseasePulmonary diseaseRenal disease

Factors: 12Max.Score: 3

HCT-CICardiac disease(3)Cerebrovascular diseasePsychiatric disorderDiabetes mellitusHepatic disease(2)Second malignancy (2)Rheumatologic diseaseGI-disease(2) Pulmonary disease(2)Renal diseaseObesityInfection


Charlson Comorbidity Index (CCI)Hepatic disease (2)Cardiac disease(2)Diabetes mellitus(2)Malignancy(2)AIDS DementiaPeripheral vascular diseaseCerebrovascular diseaseConnective tissue diseaseHemiplegiaPeptic ulcerPulmonary diseaseRenal disease


Risk assessment tools in MM

+ others

Which ones to develop?Engelhardt M et al. Haematologica 2016;101:1110–1119;


MM-tested (+ validated) risk scores for OS, PFS + tox

Variables R-MCIIMWG frailty

scoreMayo risk score

Binder risk score

UK Myeloma Research

Alliance Risk Profile (MRP)

Risks included

eGFR, Lu,

KPS/PS, Frailty, Age

+/-CG

ADL, IADL, CCI,Age

PS>2, NT-proBNP>300,

Age>70y

StageCytogenetics>20% PCs

Thrombocytopenia

Age

PS, ISS, CRP,Age

Analyses:RetrospectiveProspectiveValidation

+++

++ Larocca ASH 2018

in Engelhardt M 2016*

+--

+--

+--

# of pts assessed >1500 869 351 428 2372CT + non-CT pts both CT non-CT non-CT CT

Homepage http://www.myelomacomorbidityindex.org

http://www.myelomafrailtyscorecalculator.net/

- - -

Engelhardt M et al. Haematologica 2016;101:1110–1119; Engelhardt M et al. Haematologica 2017;102:910–921;Waldschmidt JM et al. Hematologica 2018;103:e473–e479; Greil C et al. Haematologica 2019;104:370–379;

Zweegman S et al. Curr Opin Oncol 2017;29:315–321; Palumbo A et al. Blood 2015;25:2068–2074;Milani P et al. Am J Hematol 2016;91:1129–1134; Cook G et al. Lancet Haematol 2019;6:e154–e166; Abel GA, Klepin HD. Blood 2018;131:515–524.

Hypothesis: Age is one among others in MM risk scores

1. Comorbidity vs. prognostic models, 2. Harmonization, 3. Include biomarkers of aging

• Uni- and multicenter analysis4

• Initial analysis (n=127)1

• Validation analysis (n=466)2

Initial MCI (I-MCI)- eGFR<30

- mod.-sev. lung disease

- KPS≤70%

• Combined training- and validation analysis to improveI-MCI (n=801)3

Revised MCI (R-MCI)

Prospective validation

R-MCI

Development of a MM-specific risk score:Myeloma Comorbidity Index (MCI)

1. Kleber M,....Engelhardt M. Blood Cancer J 2011;1:e35; 2. Kleber M,....Engelhardt M. Clin Lymphoma Myeloma Leuk 2013;13:541–551; 3. Engelhardt M et al. Haematologica 2017;102:910–921; 4. Engelhardt M et al. Haematologica 2016;101:1110–1119.

Variables Grading/Definition Literature /References

FCI

1.Renal function mild moderate severeKleber et al. 2011

eGFR or serum creatinin CTCAE grade 1 CTCAE grade 2 CTCAE grade 3-4

2. Lung function mild moderate severe

Kleber et al. 2011dyspnea or FEV1/FVC and FEV1

dyspnea upon intense activity FEV1/FVC <70% and FEV1

≥80%

dyspnea upon moderate activity or FEV1/FVC <70% and FEV1 50% -

<80%

dyspnea at rest or a few steps taken and/or the need for oxygen/non-invasive

ventilation or FEV1 <50%

3. KPS mild moderate severeKleber et al. 2011

80-90 70-79 <70

4. Cardiac function mild moderate severe

CTCAE, 4.0Arrhythmias,myocardial infarction/CAD; heart failure

CTCAE grade 1 CTCAE grade 2 CTCAE grade 3 or 4

5. Hepatic function mild Moderate-severe

CTCAE, 4.0Chronic hepatitis,Cirrhosis, fibrosis, bilirubin

CTCAE grade 1 CTCAE grade 2-4

6. GI- disease mild moderate severeCTCAE, 4.0Nausea, Vomiting, Diarrhea,

Gastric/duodenal ulcerCTCAE grade 1 CTCAE grade 2 CTCAE grade 3

7. Disability mild moderate severePalumbo et al.

Blood 2011help in personal care and household task

occasional frequent ≥ 1x/day

8. Frailty mild moderate severe Woo et al. J AmGeriatr Soc. 2012.Fried LP. et al. J

Gerontol A Biol SciMed Sci 2001

weakness, poor endurance, low physical activity, slow gait speed

1 factor 2 factors ≥3 factors

9. Infection mild moderate severeCTCAE, 4.0

Infection present? localized, local intervention oral intervention i.v intervention

10. Thromboembolic event mild moderate severeCTCAE, 4.0

Landgren et al 2012Venous thrombosisThrombosis, medical intervention

indicatedLife-threatening ; urgent intervention

indicated

11. Peripheral Neuropathy mild/moderate severe CTCAE, 4.0

CTCAE 2-3 CTCAE 4

12. Pain requiring pain medication: yes or no CTCAE, 4.0

Variables Grading/Definition Literature /References

M

C

I

1.Renal function mild moderate severe

Kleber et al. 2011eGFR or serum creatinin CTCAE grade 1 CTCAE grade 2 CTCAE grade 3-4

2. Lung function mild moderate severe

Kleber et al. 2011dyspnea or FEV1/FVC and FEV1

dyspnea upon intense activity FEV1/FVC <70% and FEV1

≥80%

dyspnea upon moderate activity or FEV1/FVC <70% and FEV1 50% -

<80%

dyspnea at rest or a few steps taken and/or the need for oxygen/non-invasive

ventilation or FEV1 <50%

3. KPS mild moderate severeKleber et al. 2011

80-90 70-79 <70

Definition of comorbidities (n=801)

R-MCI: Multivariable Cox proportional hazard model + weighting

n HR(2.5-97.5%)

log(HR) (2.5-97.5%) p-value Weight

1. eGFR≥90

60 to <90<60

184193175

1 (-)1.25 (0.92-1.68)1.96 (1.43-2.68)

0.220.67

<0.001001

2. Lung disease No/mildMod./severe

47082

1 (-)1.65 (1.24-2.18)

0 0.50 <0.001 0

1

3. KPS100%

80-90%≤70%

35207310

1 (-)2.17 (1.04-4.52)2.96(1.43-6.12)

0 0.771.08

<0.001023

4. Age≤60

>60 to ≤70>70

226185141

1 (-)1.43 (1.06-1.92)2.08 (1.50-2.89)

0 0.360.73

<0.001012

5. Frailty (weakness, poor endurance, low physical activity, slow gait speed)

No/mildModerate

Severe

323140

89

1 (-)1.54 (1.17-2.04)2.02 (1.45-2.82)

0 0.430.70

0.002011

6. CytogeneticsFavourable

UnfavourableMissing

010

Max. points 9

12 comorbidities analyzed

Web-based, publicly available scoring: http://www.myelomacomorbidityindex.org

http://www.myelomacomorbidityindex.org/

Practical use in daily practice: R-MCI-Homepage

Pt-specific survival probability (blue graph)

Website: www.myelomacomorbidityindex.org

Engelhardt M et al. Haematologica 2016;101:1110–1119;Engelhardt M et al. Haematologica 2017;102:910–921;

Engelhardt M et al. Dtsch Med Wochenschr 2017;142:e51–e60;Gay F, Engelhardt M, et al. Haematologica 2018;103:197–211;

Ludwig H, .....Engelhardt M, et al....Leukemia 2017; doi: 10.1038/leu.2017.353.

Why use this?

R-MCI development via OSTraining set (n=552) Validation set (n=249)

Engelhardt M et al. Haematologica 2017;102:910–921;Zweegman S et al. Curr Opin Oncol 2017;29:315–321;

Larocca A,...Engelhardt M. Leukemia 2018;32:1696–1712;Schoeller K et al. DGHO 2019, Holler M et al. DGHO 2019, Engelhardt M et al. DGHO 2019.

Q:1. Comparison of scores in MM 2. Retrospective, prospective and CT use3. Routine use in clinical practice4. Implementation into treatment-decisions, -monitoring, -toxicity prevention

Prospective comparison R-MCI : IMWG-frailty index

OS via IMWG

OS via R-MCI

(n=125)

PFS via IMWG

PFS via R-MCI


R-MCI

R-MCI

Examples in daily clinics, where frailty scores

and/or functional assessment tools are valuable

1. Ease therapeutic choices: exchange IFM + UKF

Website: www.myelomacomorbidityindex.org

8/9 = high-risk/frail -> slow go

Pt #1: ♀, 73 yrs, KPS: 70%, strong pain restricting long walks: pain: 6/10Hb 9.6g/dl, Krea 3.2mg/dl, eGFR 15ml/min, Ca+LDH: ↑, osteolyses, BMPCs: 40%, CG: HR: del17p. IgAl MM, D&S IIIB, ISS: III, R-ISS: III

T. Facon (personal communication 6/2018), for ASH 2018

1

2

3

4

1.L-Therapy options Depending on pt 'constitution'

VCD, (V)RD + SCT fit

VCD, (V)RD Ø SCT intermediate-fit

Dara-VCD/VMP intermediate-fit

(V)Rd* frail

*VRd not as yet approved, but aimed for registration

2. Choose therapy intensityFrailty index risk factors

R-MCI 0-3 4-6 7-9

Definition “go-go” “intermediate-go” “slow-go”

Treatment doses Level 0 Level -1 Level -2

Dexamethasone 40mg day 1, 8, 15, 22 of 28d cycle 20mg day 1, 8, 15, 22 of 28d cycle 8-10mg day 1, 8, 15, 22 of 28d cycle

Melphalan 0.25mg/kg days 1-4 of 4-6 wk cycle 0.18mg/kg days 1-4 of 4-6 wk cycle 0.13mg/kg days 1-4 of 4-6 wk cycle

Bortezomib1.3mg/m2 twice weekly

Day 1, 4, 8, 11 every 3 weeks

1.3mg/m2 once weekly


1.0mg/m2 once weekly


Thalidomide 100 (-200) mg/day 50 (-100) mg/day 50 mg qod (-50mg/day)

Lenalidomide 25mg days 1-21 of a 28-day cycle 15mg days 1-21 of a 28-day cycle 10mg days 1-21 of a 28-day cycle

Ixazomib 4 mg day 1, 8, 15, every 4 weeks 3 mg day 1, 8, 15, every 4 weeks 2.3 mg day 1, 8, 15, every 4 weeks

Daratumumab16mg/kg bw cy1+2 weekly, out-pt

treatment/in combo16mg/kg bw cy1+2, weekly

8mg cy d1-> increase 16mgd8,

possibly: start in-pt-care

Various others such as Ab, check point inhibitors, immunotherapy and study option

Zweegman S et al. Curr Opin Oncol 2017;29:315–321;Larocca A,...Engelhardt M. Leukemia 2018;32:1696–1712;

Zweegman S, Larocca A. Lancet Haematol 2019;6:e117–e118.

unfit ≠ frail: who is the unfit patient? · treatment results in mm w new agents: cts regimen age...

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