AND ABSTRACT The purpose of this K07 proposal is to provide Jeffrey Lee, MD, MAS with the protected time and resources to pursue the additional training needed to reach his long-term goal of becoming an independent clinical investigator, focused on colorectal cancer (CRC) prevention. Screening has been shown to reduce the incidence and mortality for CRC. However, screening has resulted in a growing cohort of patients with adenomatous polyps (adenomas) and little is known about effectively managing their post-polypectomy surveillance. With limited data available in the literature to determine the appropriate timing and frequency of follow-up colonoscopy for patients after adenoma removal, recommendations for post-polypectomy surveillance from our national guidelines have been imprecise at best. For example, the currently recommended range of 5-10 years for a surveillance colonoscopy for patients with a single adenoma covers a two-fold difference in exam frequency, with resultant two-fold impact on patient risk, cost, and colonoscopy capacity. To help optimize the timing of colonoscopic surveillance and guide appropriate utilization of this invasive and costly resource, stratification of CRC risk after colonoscopic polypectomy from a large community-based cohort with long-term follow-up is needed. Building on his prior work in CRC screening, Dr. Lee seeks to fill this knowledge gap by optimizing surveillance practices in post-polypectomy patients according to patient-, polyp-, and colonoscopy exam-related factors. Specifically, he will determine the long- term CRC risk in patients after colonoscopic polypectomy in a very large ?real world? community-based population (Aim 1). He will also identify patient-, polyp-, and exam-related risk factors associated with incident CRC in these patients (Aim 2). Finally, he will develop a CRC risk prediction model that will identify post- polypectomy patients at high and low risk for developing subsequent CRC (Aim 3). To achieve these goals, Dr. Lee and his mentors have designed a career development plan for research and educational training to obtain: 1) knowledge and expertise in advanced epidemiologic methods for design and analysis of cohort studies; 2) knowledge in medical informatics methods; and 3) predictive modeling skills. To achieve the proposed research aims, Dr. Lee will leverage the rich electronic health records of Kaiser Permanente Northern California, a large community-based healthcare system, in which data on patient, physician, colonoscopy, pathology, and CRC status have been collected since 1994. In addition, Dr. Lee will use an established natural language processing tool to efficiently collect data and evaluate potential confounding variables from more than 600,000 colonoscopy reports in order to address one of the main practical challenges that have limited the feasibility of large-scale population-based studies. Thus, completion of these aims has the potential to improve prevention and early detection of CRC, impact current surveillance guidelines for post-polypectomy patients, and reduce overuse and underuse of surveillance colonoscopy. Importantly, this proposal is realistic and feasible within the award period and will allow Dr. Lee to continue to build research skills, generate preliminary data, create additional collaborative relationships, and compete for R01 funding. In summary, this K07 award will support and accelerate the career development activities of Dr. Lee and allow him to successfully launch into the next phase of his career as an independent investigator.
Randomized controlled trials have shown that screening reduces the incidence of and mortality from colorectal cancer (CRC), the second leading cause of cancer deaths in the United States. However, screening has resulted in a growing cohort of patients with adenomatous polyps and little is known about effectively managing their post-polypectomy surveillance. Thus in this proposal, we will determine the long-term risk and risk factors of CRC after colonoscopic polypectomy, and develop a CRC risk prediction model to tailor surveillancein post- polypectomy patients using a large, community-based cohort.
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