The Role of the HCC Cancer Registry in Facilitating Cancer Research Linda Cope, CTR HCC Registry Coordinator

Commission on Cancer DHEC’s SC Central Cancer Registry CoC facilities collect data in standardized codes and report to NCDB. SC Law: CHAPTER 35; SECTION 44 ‑ 35 ‑ 5. Makes Cancer reportable to DHEC’s Central Cancer Registry.

The MUSC/HCC Cancer Registry 5,340 new cases abstracted during FY Staff members (7.4FTE) Certified Tumor Registrars One full time Follow Up specialist

MUSC/HCC Cancer Registry Commission on Cancer is curating a nationwide, decades-long big data project which requires patient data from all its facilities to be housed in the National Cancer Data Base (NCDB). More from Shai Cancer Registry identifies all patients with a malignancy at MUSC. Follows analytic patients annually

Currently following >17,000 cancer survivors

MUSC Cancer registry Accuracy requirements Coding instructions Patient data National Cancer Data Base Rapid Quality System Accreditation The Commission on Cancer

RQRS Currently CoC requires breast and colon cases Abstracts data concurrently with treatment Clinically relevant as check for standard therapy. Data available sooner

Patient presents with history of cancer, we only follow Patient presents for consult only Patient only gets diagnosis here and is then referred out Patient presents with recurrent or refractory disease after first course was given elsewhere Patient receives some or all of first course at MUSC: now “ours” for purposes of abstracting and following A basic census is available, with some demographic information, type of cancer, date of diagnosis, and date of first contact with MUSC Full abstract with data fields as shown next slides *Outcomes tracked The MUSC/HCC Cancer Registry: what information is captured?

Data fields for all full abstracts Patient Demographics Cancer Disease Information Staging Information Treatment Outcomes Patient Identifiers behind IRB wall Name Medical Record Number Address Zip code County Phone Secondary contact Date of birth (age at diagnosis) Date of death (if applicable) Sex Race Spanish origin Tobacco history Alcohol history

Data fields for all full abstracts Patient Demographics Cancer Disease Information Staging Information Treatment Outcomes Class of case Site Sequence Histology (ICD-0) Behavior Grade Laterality Date of initial diagnosis

Data fields for all full abstracts Patient Demographics Cancer Disease Information Staging Information Treatment Outcomes Tumor size Tumor extension T eval method Regional nodes examined Regional nodes positive N eval method Mets at diagnosis M eval method Derived TNM stage AJCC Clinical stage AJCC Pathologic stage

Data fields for all full abstracts Patient Demographics Cancer Disease Information Staging Information Treatment Outcomes Dates and Specific Type Biopsy Surgery Chemotherapy Hormonal therapy Immunotherapy Other Surgical margins Sequence of systemic vs surgery Sequence of radiation vs surgery Where treatment happened

Data fields for all full abstracts Patient Demographics Cancer Disease Information Staging Information Treatment Outcomes Follow up is annual Date of last contact Disease status Date of first recurrence Type of first recurrence Second treatment course Survival analysis Kaplan-Meier stratified by stage treatment etc.

Specialized Data Fields Site Specific Data Fields – Defined by Commission on Cancer – Biomarkers – Site specific prognostic factors Custom Data Fields – Defined by individual registry, usually for prospective projects

The MUSC/HCC Registry: how do I request data?

The MUSC/HCC Cancer Registry DATA REQUESTS

The Cancer Registry: what kind of research can I do? Hospital registries pool their data in the National Cancer Data Base, so you can design and power a study based on a huge number of patients and limited number of data fields OR a smaller (local) number of patients with much deeper data. We’ll look at one of each as examples.

The big one: NCDB Participant User File Includes many of the same data fields that Linda just explained. It does NOT include some if they have been determined to be insufficiently reliable at the national level for various reasons. Examples: recurrence, tobacco use, exact chemo regimens Includes some additional fields, derived and assigned by the Commission on Cancer rather than being directly coded by CTRs: education level, income, distance from facility Well-suited to projects about a national research question Keep in mind: demographics>>stage at diagnosis>>first course of treatment>>outcome

MUSC’s First NCDB PUF Project Research problem: In 2004, two landmark papers were published and recommended trimodal therapy for advanced head and neck cancers. Since then, survival rates have increased nationally and trimodal therapy has become more common at MUSC. No broad study had been conducted to evaluate national rates of adherence to the 2004 recommendations. At the same time, an epidemic of HPV+ OPSCC with relatively good outcomes appeared in the USA. Question: were increased survival rates in head and neck cancer due to change in the population or change in the treatment or both?

AB CD Figure 1: treatment trends Percent of patients receiving indicated treatment by year Percent of patients receiving indicated treatment by year Percent of patients receiving indicated treatment by year Percent of patients receiving indicated treatment by year

Figure 2: survival trends by treatment Percent survival at 5 years A *** p <0.001 ** p<0.01 *** B Percent survival at 5 years C *** ** ***

White-Gilbertson S, et. al, J Registry Manag Winter;42(4): Rise in OP cancers seen in the aggregate Stage IV group over time (as expected)

White-Gilbertson S, et. al, J Registry Manag Winter;42(4): No rise in OP cancers seen in Stage IV group treated with trimodal therapy Geographic distribution of trimodal therapy widened over time

Figure 2: survival trends by treatment Percent survival at 5 years A *** p <0.001 ** p<0.01 *** B Percent survival at 5 years C *** ** ***

HCC Registry Data Project Research problem: presentation with late stage breast cancer has been linked to poor insurance status, although results are mixed on the difference between lack of insurance and Medicaid, and this is a difficult thing to analyze in the NCDB due to typical abstraction workflow and insurance changes specific to breast cancer diagnoses. In addition, poor insurance is expected to impact screening practices, but this is not captured in registry databases, although we capture it locally for specific studies. Question: Would real-time abstracting allow us to track the relationship between insurance, method of cancer detection, and stage at diagnosis? If so, we hypothesized that lack of insurance would predict a poorer disease course from the beginning.

AB n % 1.99% 5.49% 55.85% 35.78% 0.90% n % 2.29% 6.60% 52.26% 37.79% 1.06% Figure 1. Insurance vs. Stage with National Data

ns p=0.013 n % 5.90% 4.62% 45.38% 40.26% 3.85% Figure 2. Insurance vs. Stage with Local Data

p=0.027 Figure 3. Insurance vs. Stage after Exclusions

AB p<0.001 ns Figure 4: Testing the Set

p=0.010 Figure 5: Testing the Hypothesis

Questions?