4 | DISCUSSION
Nearly a third of survivors were not seen in a subspecialty clinic five
to seven years after their initial diagnosis and there was insufficient
evidence to support an association between risk strata for serious
adverse health outcomes and likelihood of follow-up. This retrospective
cohort study of survivors demonstrates the feasibility and utility of
cancer registry data to construct a single-institution childhood cancer
survivorship cohort. Furthermore, the integration of EHR and spatial
data to expand analysis to include outcomes beyond mortality and disease
recurrence, such as appropriate follow-up care, holds great promise to
serve as a platform to enhance clinical oncology research with
real-world data. The similar proportions of survivors seen in a
subspecialty clinic between five and seven years, regardless of their
risk stratification, is striking. Both the high proportion of patients
not seen in clinic and the implementation of a validated
risk-stratification system for survivors highlight the need for improved
systems to enhance retention of patients for follow-up care.
While literature to support the benefits of long-term follow-up for
survivors is abundant2,24,41 and guidelines inform
clinicians on surveillance of late effect,11 a uniform
definition of “appropriate” follow-up and “lost to” follow-up
remains elusive. One limitation with the measurement of the primary
“lost to follow-up” outcome variable in our cohort is whether or not
patients seen in clinic were truly lost to follow-up or if they had
moved or transferred care to another tertiary care center. On manual
review of a random sample of thirty-seven patients lost to follow-up
revealed only five had documentation of transfer to another institution.
A CCSS analysis of 6,176 survivors showed approximately 40.3%
self-reported survivor-focused care within the preceding two years at
the baseline questionnaire, which then declined to 30.2% within at the
most recent follow-up questionnaire.42 This markedly
differs from the findings in our cohort with 67% of survivors with a
subspecialty clinic visit between five and seven years after the initial
date of diagnosis. The difference in these results may be due to a
longer length of time since initial diagnosis at the time of the
baseline questionnaire, with a mean of 17.5 years from
diagnosis,43 and the exclusion of survivors
<18 years in the CCSS compared to our cohort. Other
single-institution25 and regional44studies for predictors of follow-up care reported higher rates of
follow-up for younger patients and leukemia survivors, similar to our
cohort, though definitions of appropriate follow-up were not uniform.
The opportunity to implement findings from larger cohort studies, such
as the risk stratification system developed by the
BCCSS,4-6,45 helps to frame the concept of
“appropriate” follow-up care. The heterogeneity of survivors, based on
their primary diagnosis and treatment exposures, merits careful
consideration clinically on an individual level as well as a health
systems level to optimize the follow-up care for this diverse
population. Risk-adapted long-term follow-up care from recent European
guidelines3 and the role of onco-primary care to
facilitate transition of care for low, and potentially intermediate,
risk survivors to primary care providers through the use of survivorship
care plans offers a strategy to improve care. Indeed, the initial
association between low risk survivors and increased likelihood of
follow-up was attenuated by adjustment for primary diagnosis of ALL.
Prioritization of efforts to target high risk survivors to re-engage
them in subspecialty survivorship care is essential.
Routine follow-up in a survivorship clinic serves as an initial step;
however, refinement of therapy-related exposures and ascertainment of
adherence to guideline recommendations is critical to ensure timely
delivery of appropriate care. Dichotomous exposures for chemotherapy,
radiation, and surgery may lead to misclassification of patients into
lower risk strata, despite receipt of high doses of chemotherapy known
to significantly increase risk for late effects (i.e. cumulative
anthracycline exposure and risks for
cardiotoxicity).46 Over the past twenty years,
significant progress in the risk prediction of late effects based on
cumulative doses of specific chemotherapy agents and
radiation20-23 calls for the inclusion of more
granular exposure data to further sharpen risk stratification models.
Successful de-escalation of therapy in the last several decades with
sustained improvement in survival, such as leukemia and
lymphoma,47 the observed reduction in the burden of
late effects based on decade of treatment48, and the
emergence of novel agents with unknown late effects7,8necessitate an adaptable system to incorporate more than simply
dichotomous treatment exposures for risk stratification.
Potential disparities in appropriate follow-up care were identified in
this analysis by race/ethnicity and SES. Specifically survivors who
identified as black or Hispanic, were from locations with a higher area
deprivation index, were of older age or lived further away from the
primary treatment center or COG-affiliated site were less likely to
receive follow-up care five to seven years after the initial diagnosis.
Multivariable analyses were not executed for these associations, as the
primary predictor focused on risk stratification, thus interpretation of
these observed associations must be made with caution. Furthermore, the
complex interactions of race/ethnicity and SES in
general,49,50 and in childhood cancer survivorship
research,51 are often difficult to disentangle. The
development of potential interventions to reengage at-risk patients
through an integrated implementation science and community-based
participatory research approach could help ensure health equity for all
survivors.
Future applications for biomedical informatics tools, such as the
integration of cancer registry and EHR data, include extraction of
cumulative chemotherapy exposures from the EHR as well as discrete data
elements to assess adherence to guideline recommendations based on
specific treatment exposures. Validation of new risk stratification
models based on more granular data with large cohorts, such as the CCSS,
may help bolster new models of care. Single-institution cohorts,
particularly with lack of sufficient follow-up time since the widespread
adoption of the modern EHR, may display limited power to detect rare
events, yet may be sufficiently versatile to translate research through
an implementation science approach. Furthermore, collaboration with
other institutions within the NCDB with uniform cancer registry data and
similar EHR platforms is feasible to maximize the impact of real-world
data. The Cancer Moonshot prioritized the creation of innovative
oncology data-sharing, the reduction of health disparities, and the
identification of health-care delivery models to optimize care for
survivors.52 The National Cancer Institute also
recently launched the Childhood Cancer Data Initiative to foster
collaboration between researchers and build an infrastructure to
integrate data from multiple sources.53 This study
provides a reproducible model to integrate cancer registry and EHR data
to construct risk-adapted survivorship cohorts to assess follow-up care
and aligns with national efforts to apply biomedical informatics methods
to revolutionize clinical research and improve survivorship care.