Only 10-20% of pancreatic ductal adenocarcinoma (PDAC) cases in the U.S. are diagnosed at a resectable stage. Existing biomarkers such as CA19-9 and CA-125 have not translated to real gains in early detection due to low diagnostic accuracy. Increasing the pre-test probability of PDAC by incorporating patient-centered data can improve existing and future biomarker performance. Leveraging comorbidities that commonly develop in parallel with PDAC pathogenesis is an efficient way to identify persons at high risk for PDAC. Recent initiation of insulin treatment is a notable example, as it is associated with >5 times greater risk of PDAC compared to persons without diabetes. Our own analysis of population-based sample of elderly patients in the U.S. shows that healthcare claims for poorly controlled diabetes increases prior to PDAC, suggesting that quantifying healthcare utilization for diabetes and related conditions may help to identify persons who may be developing PDAC. The overarching aim of this proposal is to develop a systematic data-driven model that relies on cumulative medical histories to identify individuals with undiagnosed PDAC for early detection. Using 16 years' worth of data from the Veterans Affairs Clinical Data Warehouse, we will retrospectively identify persons with progressing diabetes, in whom we will estimate the 6-, 12-, 24-, 36- and 60-month incidence of PDAC. In this population we will extract and harmonize national level data on risk factors and clinical indicators, diagnoses, prescription drug fills. We will then develop a multivariable model for PDAC to estimate the strength of the relationship between covariates and PDAC. Finally, we will validate the model within the Veteran Affairs database to evaluate the predictive performance of the model.
The aims will be carried out by an interdisciplinary team with expertise in epidemiology, statistics, gastroenterology, and oncology with strong shared interests in PDAC epidemiology, prediction modeling and value-centered health care. Ultimately our project will result in a quantitative tool that can estimate the future probability of PDAC given information readily available in medical records. With such a tool we will be able to enhance the performance of existing and future biomarkers and ultimately intervene on a large population of PDAC patients earlier with treatment modalities that have better prognosis for the patients.

Public Health Relevance

The goal of the project is to develop a prediction model that relies on multiple health indicators present in ongoing collections of electronic health data to identify people with a high probability of having undiagnosed pancreatic cancer. With such a tool we will be able to intervene on a large population of pancreatic cancer patients earlier with treatment modalities that have better prognosis for the patients.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21CA220073-01A1
Application #
9530203
Study Section
Cancer, Heart, and Sleep Epidemiology B Study Section (CHSB)
Program Officer
Divi, Rao L
Project Start
2018-04-01
Project End
2020-03-31
Budget Start
2018-04-01
Budget End
2019-03-31
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Cedars-Sinai Medical Center
Department
Type
DUNS #
075307785
City
Los Angeles
State
CA
Country
United States
Zip Code
90048