Trial Title:
Effectiveness of Personalized Surveillance and Aftercare for Breast Cancer
NCT ID:
NCT05975437
Condition:
Breast Cancer
Breast Cancer Female
Survivorship
Conditions: Official terms:
Breast Neoplasms
Conditions: Keywords:
breast cancer
aftercare
surveillance
personalization
cost-effectiveness
Study type:
Observational [Patient Registry]
Overall status:
Enrolling by invitation
Study design:
Time perspective:
Other
Intervention:
Intervention type:
Other
Intervention name:
personalized surveillance plans (PSP) and personalized aftercare plans (PAP)
Description:
The PSP contains decisions on the surveillance trajectory based on individual risks and
needs, assessed with the 'Breast Cancer Surveillance Decision Aid' including the
INFLUENCE prediction tool. The PAP contains decisions on the aftercare trajectory based
on individual needs and preferences and available care resources, which decision-making
is supported by a patient decision aid.
Arm group label:
Personalized care
Summary:
Surveillance and aftercare for curatively treated primary breast cancer patients is
currently mostly 'one-size-fits-all', but can be personalized based on patients' risk of
recurrence (depending on patient-, tumor- and treatment-related characteristics) and
their personal needs and preferences. The use of personalized surveillance (PSP) and
personalized aftercare plans (PAP) based on individual risks and needs might reduce
unnecessary burden to the patient, increase quality of life and lower the costs of
follow-up.
The NABOR study will examine the effectiveness of personalized follow-up care, consisting
of personalized surveillance (PSP) and personalized aftercare plans (PAP) incorporating
individual recurrence risks and personal needs of breast cancer patients. The main
question it aims to answer is: 'what is the effectiveness of personalized surveillance
(PSP) and aftercare plans (PAP), compared to current follow-up care, on cancer worry and
self-rated overall quality of life (EQ-VAS)'. Also the effect of PSP and PAP on
health-related quality of life (EQ-5D), societal participation, risk perception, patient
satisfaction, patients' need for support, shared decision-making, health care costs and
resource use, cost-effectiveness, and number and severity of the detected recurrences
will be investigated. Next, the uptake and appreciation of the personalized plans and
related factors (patient, caregiver, hospital and societal/financial) will be evaluated.
Patients participating in the study will have to fill in several questionnaires and give
consent for requesting data from the Netherlands Cancer Registry and from their
electronic health records (EHR).
The use of personalized surveillance (PSP) and personalized aftercare plans (PAP) will be
implemented stepwise over a period of nine months in ten participating hospitals. To
collect observations of both pre- and post-transition to PSP and PAP, each hospital will
include patients during the nine months before and after its transition to personalized
care.
In the future, the results of this project, i.e. the developed tools, can also be used
for personalization of survivorship care for other cancer survivors. More broadly, all
findings will be actively shared with interested healthcare professionals and other
interested parties in the Netherlands.
Detailed description:
1. Treatment of subjects: personalized follow-up Personalized Surveillance Plan (PSP):
Around the first surveillance mammogram (i.e. one year after end of treatment), the
personalized surveillance plan (PSP) is generated by means of the PSP decision aid,
called the 'Breast Cancer Surveillance Decision Aid' (16). This Aid incorporates the
INFLUENCE tool 3.0, which is a sequel of the INFLUENCE tool 2.0 (15) and will be
developed during this project. Compared to INFLUENCE 2.0, INFLUENCE 3.0 will
additionally include patients treated with neoadjuvant therapy a broader population
than INFLUENCE 2.0 (including patients treated with neoadjuvant therapy) and will
include a more recent population in order to provide more contemporary risk
estimates that are applicable in a broader population. During an outpatient clinic
visit, around the first surveillance mammogram (i.e. approximately one year after
the diagnosis), the INFLUENCE 3.0 prediction tool, as part of the PSP decision aid,
is completed by the HCP (i.e. surgical oncologist or nurse specialist) and patient
together by filling in data on patient, tumour and treatment characteristics. The
estimated personal risk will be explained to the patient and summarized on a leaflet
that also outlines the options possible for the patient (e.g. annual mammogram or
less frequent, duration of follow-up, how to deliver the result from the mammogram).
The patient receives a personal account with which she can, at home, complete the
PSP decision aid that provides information about different surveillance options and
a value clarification exercise to help the patient to get insight in her personal
needs and preferences. This helps the patient to consider the pros and cons of the
different options. A summary sheet based on patients' answers, combined with the
result of the first surveillance mammogram, is used in the next consultation to make
shared decisions on a personalized surveillance plan (PSP).
Personalized Aftercare Plan (PAP): To support creation of this PAP, a patient
decision aid (PtDA) will be used, which assesses patients' needs, offers information
and provides a summary of patients' needs and preferences regarding the aftercare
trajectory. The content of this PtDA will be developed in five cocreation sessions
with a multidisciplinary team of researchers, patient representatives and care
providers. First, needs assessment studies among patients and care providers will be
conducted, which results serve as input for the content of the PtDA. This content
will be critically revised by the team and rewritten to B1 language level (Common
European Framework of Reference for Languages). Usability will be tested, consisting
of think-aloud sessions with patients and interviews by telephone among health care
professionals.
During aftercare consultation(s) in the first year after the end of patients'
treatment, the HCP (i.e. nurse or nurse specialist) will introduce the PtDA to the
patient. Next, patients access the online PtDA to complete a needs assessment and
receive information about possible effects of breast cancer, available options and
choices that she has concerning her aftercare trajectory and available resources for
help and support. Patients can weigh options and fill in preferences and
considerations. Once patients have completed the PtDA, a summary sheet will
automatically be created, containing an overview of patient-reported needs,
preferences and considerations, which can be used as a base for final
decision-making on the PAP in a consultation with their care provider. These
decisions will compose the PAP, which will most likely include decisions on
organisation of aftercare (e.g. further support or referrals, mode of contact,
involved care providers) and signals to seek care for and contact details. Since
patients' needs may vary over time, the care provider can introduce the PtDA
multiple times during the aftercare trajectory. Also the PAP might be re-evaluated
and adapted during the aftercare trajectory, depending on patients' needs and
arranged contact frequencies.
2. Sample size calculation: The sample size was estimated using a user-written script
for mITS designs in the Statistical Analysis System (SAS) software program. Since
there are two primary parameters, a Bonferroni correction will be used to correct
for multiple testing and therefore set the statistical significance level at
alpha=0.025 (two sided). The effects the investigators wish to measure are a
difference of 1.52 on the Cancer Worry Scale (CWS; range 6-24) and a difference of
4.8 on the EQ-VAS score (range 0-100), which is part of the EQ-5D. This decision is
based on the aim to detect a small to moderate difference of 0.4 times the standard
deviation, which was found to be around 3.8 for the CWS and 12 for the EQ-VAS score
in previous studies. From a clinical point of view, a difference of 1.52 on the CWS
is relevant for the purpose to estimate the effectiveness of personalized follow-up:
even a small decrease in cancer worry can lead to improved quality of life. A
difference of 4.8 on the VAS score is considered to be clinically relevant as well.
A correlation is expected between the first two measurements within one hospital of
80%, and this correlation is expected to drop to 50% when comparing the first with
the last measurement. In addition, an intraclass correlation coefficient of 0.15 is
assumed. Taken 25% loss-to-follow up into consideration, each hospital will have to
include four patients per period of three weeks in order to detect a difference of
1.52 on the CWS and 4.8 on the VAS score with 84%. The total inclusion time per
hospital is 26 periods (78 weeks, or approximately 18 months), which amounts to a
number of 104 patients per hospital and a study population of N=1,040.
3. Statistical analyses: An overview of the demographic and clinical characteristics
will be provided using descriptive statistics. Continuous data will be expressed as
a mean with the standard deviation (SD), or the Interquartile range (IQR) where
appropriate. Categorical data will be expressed as frequencies (%). All
questionnaires will be analysed in accordance with their corresponding manual.
Self-composed questions (i.e. perceived risk of recurrence, adjusted questions from
the CQ-Breast Index, demographics) will be analysed per item.
To assess the effectiveness of personalized surveillance and aftercare, all outcome
parameters will be compared between the current-care and personalized-care groups.
Time series patterns will be visualized pre- and post-personalization to assess
possible change in pattern after implementation of the personalization, for each
hospital separately and combined. In this way the investigators can identify any
underlying trends, seasonal patterns and outliers.
To test the change in level and slope associated with the personalization and to
control for other (confounding and overall trend) effects, segmented regression
analyses will be used in which piecewise regression lines are fitted to each segment
of time series, allowing each segment to exhibit different trends. To correct for
correlation between repeated measurements residual plots against time will be
visually examined, which can additionally be statistically tested using the
Durbin-Watson statistic. Autocorrelation will consequently be adjusted for by
including the autocorrelation parameter in the segmented regression model.
Intention-to-treat analyses are done to estimate the effectiveness of personalized
follow-up on the outcomes of the questionnaires. The Bonferroni correction will be
used to adjust for multiple testing.
Patients included during the transition phase will be analysed as receiving current
care or personalized care dependent on whether PSP and PAP was applied. Sensitivity
analyses will be performed with and without the patients included during the
transition period, since there may be differences between care provided during and
after the transition phase.
In case of missing data, the investigators will record the percentage of drop-out
and missing at each follow-up timepoint. If necessary, multiple imputation (if the
assumptions for this technique are met) will be performed to ensure accurate
analysis. Thereafter, meta-analyses will be performed to evaluate the average
intervention effect per patient group across all hospitals and the overall effect
across all patient groups and hospitals.
A cost-effectiveness and cost-utility analysis will be performed comparing costs and
effects of "personalized follow-up care" versus "usual care", using a two-year
(study-based; based on data from the current study) and lifetime (model-based; based
on the study and extrapolations by means of data from literature) time horizon.
Information will be derived on the impact on personalized follow-up care on cancer
worries, QoL, healthcare costs and (shift in) resources.
Direct healthcare costs based on activities extracted from the EHRs (e.g. number of
mammograms, consultations) will be multiplied by costs described in 'Benchmark costs
from the Netherlands' or from the 'Nederlandse Zorgautoriteit' (NZa). For the costs
of the interventions (decision support tools), an activity-based costing method will
be performed for development, use and maintenance of the decision support platforms.
Indirect costs that will be taken into consideration are healthcare consumption
outside the hospital and health-related productivity losses. Productivity losses
will be calculated by means of the Friction cost method, according to the
International Society of Pharmaco-economical Organization and Research (ISPOR)
guidelines.
The cost-effectiveness will be expressed in incremental costs per patient with a
clinically relevant improvement on the CWS as primary outcome of the NABOR study.
The cost-utility will be expressed in incremental costs per quality adjusted life
years (QALYs) gained, obtained from the EQ-5D-5L (including the VAS). Long term
consequences will be based on both the study results and literature to extrapolate
the patient outcomes for a lifelong time horizon.
The cost-effectiveness analysis will be performed according to the guidelines for
economic evaluations of the Dutch Zorginstituut (ZIN).
4. Handling of data
Data sources for our study:
- NCR (Netherlands Cancer Registry): data (e.g. patient-, tumor- and treatment-related
characteristics) are already gathered and part of the general data collection of the
NCR
- Additional patient data from the EHR: collected during the project
- PROFILES: a data registry collecting answers on questionnaires during the project
Handling of data: For obtaining the data form the NCR, additional patient data from
the EHR and answers on questionnaires through PROFILES, the study participants will
provide Informed Consent. Registration will be done by the PROFILES registry. In
order to send questionnaires to participants, participants will be asked permission
for registration of their name and email-address or postal address in the PROFILES
registry. In order to combine answers on questionnaires with data from NCR and the
EHR later on, the PROFILES registry also needs their patient number and date of
birth. Participants will also be asked permission for registration of their patient
number and date of birth in the PROFILES registry. After the patient signs Informed
Consent, the HCP will register the patient in PROFILES. HCPs can obtain the personal
data and sequence number in the PROFILES registry, but have no access to the answers
on questionnaires.
Accessibility of data: All the collected data is patient data, this cannot be made
publicly available. Data from the NCR and additional EHR data can be requested, a local
privacy committee evaluates requests. PROFILES data (i.e. answers on questionnaires) can
be requested as well, and linked to NCR data if needed.
Criteria for eligibility:
Study pop:
All new female patients who were curatively treated for non-metastasized primary breast
cancer and start follow-up care in the hospital.
Sampling method:
Non-Probability Sample
Criteria:
Inclusion Criteria:
- female,
- aged 40 years or older (because of higher risk on recurrence),
- facing the decision for the organization of post-treatment surveillance and
aftercare,
- being curatively treated including breast surgery, for invasive non-metastasized
breast cancer
- able to understand the Dutch language in speech and writing.
Exclusion Criteria:
- bilateral breast cancers,
- BRCA1/2 or CHEK2 carriers,
- having an indication for MRI
- participation in another study that requires fixed scheduled follow-up consultations
and/or imaging.
Gender:
Female
Minimum age:
40 Years
Maximum age:
N/A
Healthy volunteers:
No
Locations:
Facility:
Name:
Jeroen Bosch Ziekenhuis
Address:
City:
Den Bosch
Country:
Netherlands
Facility:
Name:
Bernhoven Ziekenhuis
Address:
City:
Uden
Country:
Netherlands
Facility:
Name:
Gelre Ziekenhuizen
Address:
City:
Apeldoorn
Country:
Netherlands
Facility:
Name:
Rijnstate
Address:
City:
Arnhem
Country:
Netherlands
Facility:
Name:
Noordwest Ziekenhuisgroep
Address:
City:
Alkmaar
Country:
Netherlands
Facility:
Name:
Ziekenhuisgroep Twente
Address:
City:
Hengelo
Country:
Netherlands
Facility:
Name:
Isala Klinieken
Address:
City:
Zwolle
Country:
Netherlands
Facility:
Name:
Haaglanden Medisch Centrum
Address:
City:
Den Haag
Country:
Netherlands
Facility:
Name:
Albert Schweitzer Ziekenhuis
Address:
City:
Dordrecht
Country:
Netherlands
Facility:
Name:
Alrijne Ziekenhuis
Address:
City:
Leiderdorp
Country:
Netherlands
Start date:
March 6, 2023
Completion date:
March 6, 2027
Lead sponsor:
Agency:
Comprehensive Cancer Centre The Netherlands
Agency class:
Other
Collaborator:
Agency:
University of Twente
Agency class:
Other
Collaborator:
Agency:
Dutch Breast Cancer Association
Agency class:
Other
Collaborator:
Agency:
Borstkanker Onderzoek Groep
Agency class:
Other
Collaborator:
Agency:
Netherlands Institute for Health Services Research
Agency class:
Other
Source:
Comprehensive Cancer Centre The Netherlands
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05975437
