Prediction of neutropenia and of febrile neutropenia€¦ · Prediction of neutropenia and of...
Transcript of Prediction of neutropenia and of febrile neutropenia€¦ · Prediction of neutropenia and of...
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Prediction of neutropenia and of febrile neutropenia
Marianne Paesmans – MASCC meeting 30/06/2018
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Background
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u Neutropenic complications -> reduction of chemotherapy dose intensity
u Febrile neutropenia increases risk for early mortality u Myeloid growth factors reduce occurrence of
neutropenic complications, impact on mortality less obvious
u Costly, not adverse events free u Need to administer them to the right patients
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Background
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u Current guidelines : use them when u Risk of febrile neutropenia (FN) from chemotherapy > 20%
(updated EORTC guidelines, NCCN) u Risk of febrile neutropenia from chemotherapy 10%-20%
and other risk factors exist (older age, poor PS, comorbidities, female gender, …)
u Requirement of accurate estimate of the risk of FN
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Difficulties
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u Baseline risk from chemotherapy regimen : u In the guidelines, estimated through clinical trials reports u Not often reported in detail u Statistical accuracy may be lacking (large confidence
intervals) u Guidelines updates not frequent enough u Patients included in clinical trials are different from those
treated in real clinical practice -> expectation of increased rate of FN
u Currently, better reimbursement of myeloid growth factors -> selection bias for further observational studies
u Difficult to integrate it into a risk prediction model : how to combine drugs, doses, …
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u Assessment of clinical trials and observational studies using the same chemotherapy regimens in breast cancer
u Inclusion of a regimen : clinical trial + observational study
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u Clinical trials n=42257, observational studies n=7812
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u N=3760, patients with solid tumor or lymphoma u Development and validation sets u Controlled sample size (10% risk versus 20% risk) u Prospective study u Outcome : severe or febrile neutropenia – cycle 1 u Data collection up to 4 cycles u Inclusion of patients receiving prophylaxis u Rates of outcome : 19.5% / 21.2%
Cancer2011
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Highrisk:predictedrisk>10%
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Clinicalusefulness?
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Cri;calReviewsinOncologyHematology,2014
u Studies reporting univariate / multivariate results u Heterogeneity in populations, in definition of outcome u Age, gender, performance status, laboratory abnormalities
(lymphocyte & monocyte counts, ANC), low BMI u Chemotherapy drugs : anthracyclines, taxanes, alkylators,
topoisomerase inhibitors, impact of growth factors u Tumor type, advanced disease u Genetic factors (MBL gene for instance)
u Validated models seldom
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Validated risk models for FN
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Suppor;veCareCancer,2011
Suppor;veCareCancer,2015
Leukemia&Lymphoma2013
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Hosmer Schwenkglenks BozcukDesign Retrospective Prospective ProspectiveValidation Internal External External
Patients
Elderly;breast,lung,colorectal,prostate
cancer NHLBreast,lung,
colorectalcancer
DatasourceSEER;geographical
area
Sitesfrom14Europeancountries
andAustraliaPatientsfrom2institutions
Inclusionperiod 1994-2005 ? 5/2010-1/2011
Setting FirstlineCT
R-CHOPchemotherapy,any
line AnylineCTGrowthfactors No Yes YesOutcome FirstcycleFN FirstandanycycleFN AnycycleFNNtraining 58053 240 1089pts-3882cyclesNvalidation 28910 1829 960pts-1444cycles
Comment
Limitednumberofcovariates(no
biologicalfactors)
36covariatesincludinglaboratory
values
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Hosmer model
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Hosmer model
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Schwenkglenks model
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Schwenkglenks model
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Bozcuk model
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Bozcuk model
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u No «universal » model u Outcome should be FN at 1st cycle u Modelling impact of chemotherapy regimen is complex u Model should be developed without inclusion of patients
receiving growth factors u Model should be externally validated u More frequent use of growth factors decreases the impact
of a model (choice of risk factors adequate ?) u Within the MASCC Study Group for Infections, very
pragmatic study ongoing, observational on patients without growth factors
Comments
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u Feasible to estimate the risk through an algorithm applied on the EHR -> easy to guide growth factors prediction
u Stratification of the patients into 3 risk groups u Inclusion of patients receiving growth factors u Compliance with guidelines moderate
Such a model might be easy to use …
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u 124 physicians u 944 patients u Poor correlation with physician assessed risk and validated
model assessed risk : 0.25 (95% CI : 0.18-0.32) u Moderate correlation between physician assessed risk and
subsequent order for growth factors administration : 0.31 (95% CI : 0.14-0.47)
And helpful …
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u Some risk factors are clearly identified
u Limited tools to predict FN
u Further research on that topic ?
u Use of growth factors might be improved
Conclusions