Surveillance Intensity Comparison by Risk for T1NX Locally Excised Rectal Adenocarcinoma: a Cost-Effective Analysis

  • Mason McCain
  • Yohanis O’Neill
  • Hernan Hernandez
  • Ryan Foley
  • Brian M. Sadowski
  • Zachary Torgersen
  • Jennifer Beaty
  • Ruben Rojas Payacan
  • Charles A. Ternent
2019 SSAT Plenary Presentation



Controversy exists regarding the optimal surveillance strategy following local excision of T1NX rectal adenocarcinoma. This study aims to determine the cost-effectiveness of surveillance strategies for locally excised T1NX rectal adenocarcinoma based on histopathologic and local staging risk factors.


A Markov model with 10-year follow-up was developed for cost-effectiveness analysis of high-, medium-, and low-intensity surveillance strategies after local excision of T1NX rectal adenocarcinoma. Literature review and expert consensus were utilized to populate state/transition probabilities and rewards. Based on this data, 87% of T1NX patients undergoing local excision were low risk. Healthcare utilization costs were based on Centers for Medicare and Medicaid Services data. The primary outcomes were costs in 2018 US dollars and effectiveness in life-years presented as net monetary benefit and incremental cost-effectiveness ratios. One-way sensitivity and probabilistic sensitivity analyses were performed.


Net monetary benefit for low-, medium-, and high-intensity surveillance strategies ($393,117.00, $397,978.80, and $397,290.00) shows medium-intensity surveillance to be optimal. One-way sensitivity analysis shows medium-intensity surveillance to be optimal when the cohort is 73–94% low risk. High-intensity surveillance is preferred when less than 73% of the cohort is low risk. Low-intensity surveillance is preferred when greater than 94% is low risk. Probabilistic sensitivity analysis of the base-case shows medium-intensity surveillance is the optimal strategy for 51.5% of the iterations performed.


Medium-intensity surveillance is the most cost-effective surveillance strategy for locally excised T1NX rectal adenocarcinoma in a clinically representative population model.


Cost-effectiveness Rectal cancer Rectal cancer surveillance Markov model Local excision 


Author Contributions

Mason McCain MA: conceptualization; data acquisition, analysis, and interpretation; model creation; model revisions; drafting manuscript; revising manuscript; project administration.

Yohanis O’Neill MD, MPH: conceptualization; design; revising manuscript.

Hernan Hernandez MD: data acquisition and analysis; revising manuscript.

Ryan Foley: data acquisition and analysis; revising manuscript.

Brian Sadowski MD: conceptualization; revising manuscript.

Zachary Torgersen MD: conceptualization; data acquisition, analysis, and interpretation; model revisions; drafting manuscript; revising manuscript; project administration.

Jennifer Beaty MD: conceptualization; analysis, and interpretation; revising manuscript.

Ruben Rojas Paycan MSc: conceptualization; analysis, and interpretation; revising manuscript.

Charles Ternent MD: conceptualization; data interpretation; model creation; model revisions; drafting manuscript; revising manuscript; project administration.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.


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Copyright information

© The Society for Surgery of the Alimentary Tract 2019

Authors and Affiliations

  • Mason McCain
    • 1
  • Yohanis O’Neill
    • 1
    • 2
  • Hernan Hernandez
    • 3
  • Ryan Foley
    • 4
  • Brian M. Sadowski
    • 1
    • 2
    • 3
  • Zachary Torgersen
    • 1
    • 2
    • 3
  • Jennifer Beaty
    • 1
    • 2
    • 3
  • Ruben Rojas Payacan
    • 5
  • Charles A. Ternent
    • 1
    • 2
    • 3
  1. 1.Colon and Rectal Surgery IncOmahaUSA
  2. 2.Department of Surgery, Section of Colon and Rectal SurgeryCreighton University School of MedicineOmahaUSA
  3. 3.Department of SurgeryUniversity of Nebraska Medical CenterOmahaUSA
  4. 4.University of KansasLawrenceUSA
  5. 5.School of Health and Related Research, Section of Health Economics and Decision ScienceThe University of SheffieldSheffieldUK

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