Johnie Rose1,2, Laura Homa3, Chung Yin Kong4, Gregory S Cooper3,5,6, Michael W Kattan5,7, Bridget O Ermlich8, Jeffrey P Meyers9, John N Primrose10, Sian A Pugh10, Bethany Shinkins11, Uriel Kim3, Neal J Meropol3,5,12. 1. Case Western Reserve University School of Medicine, Cleveland, OH, USA. johnie.rose@case.edu. 2. Case Comprehensive Cancer Center, Cleveland, OH, USA. johnie.rose@case.edu. 3. Case Western Reserve University School of Medicine, Cleveland, OH, USA. 4. Massachusetts General Hospital Institute for Technology Assessment, Boston, MA, USA. 5. Case Comprehensive Cancer Center, Cleveland, OH, USA. 6. University Hospitals Seidman Cancer Center, Cleveland, OH, USA. 7. Cleveland Clinic Foundation, Dept. of Quantitative Health Sciences, Cleveland, OH, USA. 8. University Hospitals Cleveland Medical Center, Cleveland, OH, USA. 9. Mayo Clinic Minnesota, Rochester, MN, USA. 10. University Surgery, Cancer Sciences, University of Southampton, Southampton, UK. 11. Test Evaluation Group, University of Oxford, Oxfordshire, UK. 12. Flatiron Health, New York, NY, USA.
Abstract
PURPOSE: Clinical trials suggest that intensive surveillance of colon cancer (CC) survivors to detect recurrence increases curative-intent treatment, although any survival benefit of surveillance as currently practiced appears modest. Realizing the potential of surveillance will require tools for identifying patients likely to benefit and for optimizing testing regimens. We describe and validate a model for predicting outcomes for any schedule of surveillance in CC survivors with specified age and cancer stage. METHODS: A Markov process parameterized based on individual-level clinical trial data generates natural history events for simulated patients. A utilization submodel simulates surveillance and diagnostic testing. We validate the model against outcomes from the follow-up after colorectal surgery (FACS) trial. RESULTS: Prevalidation sensitivity analysis showed no parameter influencing curative-intent treatment by > 5.0% or overall five-year survival (OS5) by > 1.5%. In validation, the proportion of recurring subjects predicted to receive curative-intent treatment fell within FACS 95% CI for carcinoembryonic antigen (CEA)-intensive, computed tomography (CT)-intensive, and combined CEA+CT regimens, but not for a minimum surveillance regimen, where the model overestimated recurrence and curative treatment. The observed OS5 fell within 95% prediction intervals for all regimens. CONCLUSION: The model performed well in predicting curative surgery for three of four FACS arms. It performed well in predicting OS5 for all arms.
RCT Entities:
PURPOSE: Clinical trials suggest that intensive surveillance of colon cancer (CC) survivors to detect recurrence increases curative-intent treatment, although any survival benefit of surveillance as currently practiced appears modest. Realizing the potential of surveillance will require tools for identifying patients likely to benefit and for optimizing testing regimens. We describe and validate a model for predicting outcomes for any schedule of surveillance in CC survivors with specified age and cancer stage. METHODS: A Markov process parameterized based on individual-level clinical trial data generates natural history events for simulated patients. A utilization submodel simulates surveillance and diagnostic testing. We validate the model against outcomes from the follow-up after colorectal surgery (FACS) trial. RESULTS: Prevalidation sensitivity analysis showed no parameter influencing curative-intent treatment by > 5.0% or overall five-year survival (OS5) by > 1.5%. In validation, the proportion of recurring subjects predicted to receive curative-intent treatment fell within FACS 95% CI for carcinoembryonic antigen (CEA)-intensive, computed tomography (CT)-intensive, and combined CEA+CT regimens, but not for a minimum surveillance regimen, where the model overestimated recurrence and curative treatment. The observed OS5 fell within 95% prediction intervals for all regimens. CONCLUSION: The model performed well in predicting curative surgery for three of four FACS arms. It performed well in predicting OS5 for all arms.
Authors: Zihan Wang; Jin Zhang; Gaoyu Zhang; Tianyi Lan; Ziyi Sun; Xiaoyan Lu; Li Huang; Lin Li Journal: Evid Based Complement Alternat Med Date: 2022-09-13 Impact factor: 2.650