Financial Disclosure. Learning Objectives. Review and Impact of the NCDB PUF. Moderator: Sandra Wong, MD, MS, FACS, FASCO

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1 Review and Impact of the NCDB PUF Moderator: Sandra Wong, MD, MS, FACS, FASCO Financial Disclosure I do not have personal financial relationships with any commercial interests Learning Objectives At the end of this session, participants will be able to: Describe the difference between PUFs and NCDB data tools Identify and list the areas PUF could be used for future clinical research Understand the impact of the PUFs on quality improvement and clinical research

2 Agenda Using NCDB data Sandra Wong MD MS FACS FASCO Impact of the PUFs Ravinder Kang MD MS Preoperative prediction of node negative disease after neoadjuvant chemotherapy presenting with node negative or node positive breast cancer Brittany Murphy MD and Judy Boughey MD FACS Race-based socioeconomic and treatment disparities in adolescent and young adults with stage II-III rectal cancer Melanie Goldfarb MD MSc FACS FACE Measurement is in the eye of the beholder If you can t measure it, you can t improve it Lord Kelvin

3 Measurement is in the eye of the beholder If you can t measure it, you can t improve it Not everything that counts can be counted and not everything that can be counted counts Lord Kelvin Albert Einstein Why do we measure? Goal: To improve care The short history of hospital quality measurement

4 Why do we measure? Goal: To improve care The short history of hospital quality measurement Today, the question is how do we best use our data? Quality Improvement is Local Requires a burning platform Requires a desire to change and do better Requires Quality Improvement is National Requires

5 And, quality improvement requires the NCDB CoC-accredited programs are required to submit data to the NCDB The aggregated data are used in two primary ways to improve care: At the program level To study national trends Using data for local quality improvement Evaluate and compare your program s care with care at other facilities Are patients getting the right treatment at the right time? Compare your program s performance against national quality measures Using data for local quality improvement Cancer Quality Improvement Program (CQIP) Each program s annual report includes a wealth of data Patient volume In/out migration Survival data Volume-outcomes results (surgical mortality) Performance on quality measures

6 Using data for local quality improvement NCDB web-based tools Hospital Comparison Benchmark Reports Includes cases diagnosed between 2003 and 2013 in 73 different groupings of cancer Using data for local quality improvement NCDB web-based tools Cancer Program Practice Profile Reports (C3PR) Comparative information to assess adherence to CoC measures Sites: Bladder, breast, cervix, endometrial, gastric, lung, melanoma, ovary, rectal Using data for local quality improvement NCDB web-based tools Rapid Quality Reporting System (RQRS) Real time/prospective alerts Breast and colon cancer patients

7 Using data for national efforts in QI Quality measures Using measurement as an effector arm for change Staging Leveraging data to make staging an integral part of cancer management CoC Structure College National Accreditation Program for Breast Centers Cancer Programs American Joint Committee on Cancer Commission on Cancer Executive Committee Nominating Committee Standing Committees Quality Integration Cancer Liaison Member Organization Accreditation Advocacy Education Initiatives Steering Scientific Site-Specific Program Field Staff Constituency Clinical Congress Review Quality Measure Review Subcommittee Education Topics Subcommittee Workgroups Subcommittee Subcommittee Subcommittee Survival Recruitment and Speaker Workgroup Retention Recruitment Subcommittee Subcommittee 11/15/13 Development of quality measures Identify quality gap Test for validity and feasibility with NCDB data Collaborate with clinical experts and professional societies 2015: Approval of 5 new melanoma quality measures

8 Evidence-based guidelines for melanoma Recommendations: SLNB should be performed for tumors 1mm in thickness J Clin Oncol 2012; Ann Surg Oncol 2012 Evidence-based guidelines for melanoma Criticism: Why do we need guidelines like this? Isn t this is all standard practice? Response: Actually, there are huge gaps in practice! Measurement Effector arm for change Baseline data from the NCDB Melanoma diagnoses (C ; C ; C ; C60.0; C60.2; C60.8; C60.9; C63.2) N=50,031 after exclusions Primary disease only Age 18-80

9 How are we doing? Sentinel node biopsy performed Tumor thickness <0.75 mm 17.9% Tumor thickness >1.0 mm 83.5% Sentinel lymph node biopsy guidelines Sentinel lymph node biopsy is appropriate for nodal staging of clinically node negative patients with intermediate thickness melanoma (1.0 to 4.0 mm) Avoid overuse of the procedure when patients have <0.75 mm Using data for national efforts in QI Quality measures Using measurement as an effector arm for change Staging Leveraging data to make staging an integral part of cancer management

10 Why is staging important? Measurement and evaluation Common terminology between providers Guides treatment Informs prognosis Merkel cell carcinoma Rare, aggressive neuroendocrine carcinoma of the skin Tendency for lymph node metastases 5 year overall survival 30-64% Mean age at diagnosis = 74 years Growing incidence 0.15 to 0.44 per 100,000 from 1986 to th edition AJCC could be improved N0 cn0 pn0 No regional lymph node metastasis Nodes negative by clinical exam (no pathological examination done) Nodes negative by pathological exam (a sentinel node biopsy or lymph node dissection was done)

11 N categorization was predicated on management Who is truly node negative? cn0 staging: if only clinical or radiographic evaluation was done pn0 staging: if pathologic nodal evaluation was done (biopsy or complete node dissection) Harms et al. Ann Surg Oncol 2016:23;2564 Clinical T Clinical N Pathological T Stage I T1 N0 T1 N0 Stage IIA T2-3 N0 T2-3 N0 Stage IIB T4 N0 T4 N0 Pathological N Harms et al. Ann Surg Oncol 2016:23;2564

12 Harms et al. Ann Surg Oncol 2016 Revisions to the 8 th edition of the AJCC T Stage IA T1 pn0 Stage IB T1 cn0 Stage IIA T2-3 pn0 Stage IIB T2-3 cn0 Clinical T Clinical N Pathological T Stage I T1 N0 T1 N0 Stage IIA T2-3 N0 T2-3 N0 Stage IIB T4 N0 T4 N0 N Pathological N Use of the NCDB The aggregated data are used in two primary ways to improve care: At the program level (NCDB tools) To study and impact national trends (NCDB Participant User Files) Shameless plug: PUF application cycle is open through Sept 18.

13 Agenda Impact of the PUFs Ravinder Kang MD MS Preoperative prediction of node negative disease after neoadjuvant chemotherapy presenting with node negative or node positive breast cancer Brittany Murphy MD and Judy Boughey MD FACS Race-based socioeconomic and treatment disparities in adolescent and young adults with stage II-III rectal cancer Melanie Goldfarb MD MSc FACS FACE Impact of the NCDB PUF Ravinder Kang, MD, MS

14 Participant User Files (PUFS) Retrospective data Variety of disease site specific files Cancer Registries Two large cancer registries in the United States 1. Survival, Epidemiology, and End Results (SEER) 2. National Cancer Database (NCDB) SEER Began collecting data in 1973

15 SEER 18 Cancer Registries in 14 States SEER Began collecting data in 1973 Epidemiological tool Monitors the incidence and mortality of cancer in the US Captures ~ 30% of new cancer diagnoses NCDB Established in 1988 Joint venture of the American College of Surgeons & the American Cancer Society Quality Assurance/Improvement tool Provides process measures as well as outcomes Captures 70% of new cancer diagnoses

16 NCDB 1,500 CoC Hospitals in all 50 States The Emergence of PUFs Sponsored Research (Supported by NCDB analytic staff & Multidisciplinary disease site teams) Participant User Files HIPAA-Compliant de-identified data sets PUFs Alpha tested in 2010 An incremental strategy applied to PUF release Allowed for an assessment of the procedures and policies surrounding data distribution. Evaluate the usefulness and potential limitations of the PUF dataset for clinical research

17 Access to PUFs Starting in 2013, investigators from CoC-accredited facilities have applied for PUF data PUFs updated annually Access to PUFs More than 35 million patient records The largest clinical cancer registry in the world If you build it Will they come? Are PUFs used? A systematic review of the literature, as of September Full Manuscripts 85 Abstracts

18 Are PUFs used? How does this compare to other cancer registries? Number of NCDB publications in 2016 nearly equal to number of SEER + SEER-Medicare publication Only 3 years after PUFs became available to the research community at large, the number of publications using PUFs match publications using more established registries. Visibility of Publications 74% of publications in journals with an Impact Factor > 3 28% of publications in journals with an Impact Factor > 5 Impact Factor > 3 Annals of Surgical Oncology Annals of Thoracic Surgery Disease of the Colon & Rectum Journal of Surgical Oncology Impact Factor > 5 Annals of Surgery JAMA Journal of Clinical Oncology The Lancet Oncology

19 Which Cancers? Five most commonly studied cancers using PUFs Lung Breast Colorectal Prostate Thyroid These 5 cancers account for 46 % of the research published using PUFs Common Cancers Studied with SEER Five most commonly studied cancers using SEER Breast Multiple Cancer Types Lymphoma Colorectal Lung Value of Aggregating Data Aggregating data from the 1,500 CoC-accredited facilities, allows us to study rare cancers 17% of the PUFbased publications focused on rare cancers

20 Value of Aggregating Data Study potentially low-risk populations 4% of the PUFbased publications include pediatric patients Value of Aggregating Data Provides large cohorts, to adequately power research questions 37% of the PUFbased publications had cohorts of > 50,000 patients Value of Aggregating Data Allows investigators to assess adherence to process measures and set benchmarks 60% of the PUF-based publications utilize data on neoadjuvant & adjuvant chemotherapy 61% of the PUF-based publications utilize data on neoadjuvant & adjuvant radiation

21 Ensuring Quality Data 2/3 of studies assess chemoradiation, yet these treatments may occur outside of the CoC facility The onus to capture all aspects of cancer care, even the care provided at other facilities (including non-coc facilities) is on all participating centers Value of NCDB Data Provides investigators with socioeconomic details of patients Income level Education level Insurance status 97% of the PUF-based publications reported insurance status for the study cohort Ensuring Quality Data Demographic, socioeconomic and tumor details are vital to capture to prevent duplicate patient entries. Propensity algorithm used to identify duplicate patients who received care at more than 1 CoC Hospital

22 Cancer Stages Studies 7.3% of studies focus exclusively on Stage I cancer 1.4% of studies focus exclusively on Stage II cancer 8.1% of studies focus exclusively on Stage III cancer 3.6% of studies focus exclusively on Stage IV cancer The vast majority of studies (>70%) consider multiple stages of cancer Surgical Treatment NCDB captures site-specific surgical treatments. More recently also reports surgical approach (robotic or minimally invasive). 75% of NCDB published studies evaluate surgical treatments Ensuring Quality Data Reason no Surgery variable, allows investigators to distinguish between patients who were too sick for surgery vs. refused surgery. The comorbidity index doesn t fully capture the overall health status of patients

23 The Future of PUFs