The benefits and harms of lung cancer screening in Florida
Principal Investigator
Name
Dr. Yi Guo
Degrees
PhD
Institution
University of Florida Board of Trustees
Position Title
Associate Professor & Associate Chair for Data Science
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-857
Initial CDAS Request Approval
Nov 30, 2021
Title
The benefits and harms of lung cancer screening in Florida
Summary
Lung cancer is the leading cause of cancer-related death in the United States. Results from the US National Lung Screening Trial (NLST) showed that using low-dose computed tomography (LDCT) as a lung cancer screening (LCS) modality could reduce lung cancer mortality by 20% in the trial. The concerns that complication and false positive rates are higher than the rates reported by NLST in the real-world setting have hindered the implementation of the LCS program. Therefore, it is imperative to understand the contemporary utilization patterns of LCS and to assess the clinical and economic outcomes associated with LCS in real-world settings.
Our study using 2014 - 2021 electronic health records and claims data from the OneFlorida clinical research network, aim to examine LCS usage trends, predictors of appropriate and inappropriate use, LDCT radiology results, postprocedural complications, incidental findings, long-term survival benefits, downstream costs, and cost-effectiveness with data collected from real-world settings in Florida.
We would like access to the NLST data so that we can cross-validate the models we developed between EHRs and trial cohorts, and transfer learning methods to improve the original estimates.
Our study using 2014 - 2021 electronic health records and claims data from the OneFlorida clinical research network, aim to examine LCS usage trends, predictors of appropriate and inappropriate use, LDCT radiology results, postprocedural complications, incidental findings, long-term survival benefits, downstream costs, and cost-effectiveness with data collected from real-world settings in Florida.
We would like access to the NLST data so that we can cross-validate the models we developed between EHRs and trial cohorts, and transfer learning methods to improve the original estimates.
Aims
1) Identify eligible for LCS patients similar to those in the trials from EHRs
2) Develop and validate a lung cancer prediction model.
3) Develop and validate a microsimulation model of the clinical course of LCS leveraging real-world data in OneFlorida to estimate the benefits and cost-effectiveness of LCS.
Collaborators
Yi Guo, Ph.D.,University of Florida
Yonghui Wu, Ph.D., University of Florida
Zhaoyi Chen, Ph.D., University of Florida
Zheng Feng,Ph.D., University of Florida
Xi Yang, Ph.D., University of Florida
Tianchen Lyu, MS, University of Florida
Shuang Yang, MS, University of Florida
Chang Wang, MS, University of Florida