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Grant Details

Grant Number: 1R21CA220073-01A1 Interpret this number
Primary Investigator: Jeon, Christie
Organization: Cedars-Sinai Medical Center
Project Title: Predicting the Diagnosis of Pancreatic Cancer By Leveraging Big Data
Fiscal Year: 2018
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Abstract

PROJECT SUMMARY 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.

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Publications


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