Due to lacking of evidence on surveillance for gastric cancer (GC), this study aimed to determine the optimal postsurgical surveillance strategy for stage II/III GC patients and compare its cost-effectiveness with traditional surveillance strategies.
Background The development of existing follow-up strategies is mainly based on experts' consensus and researches on recurrence patterns due to the lack of direct evidence and unified standards. On the one hand, it is unreasonable to carry out the same postsurgical surveillance for all stage II/III GC patients. Additionally, In addition to the early detection of recurrence, the optimal follow-up strategy should also balance cost and effectiveness. There were no criteria for the arrangement of follow-up in different guidelines, and the optimal postoperative follow-up strategy for patients with stage II/III GC remains unclear. Methods Prospectively collected data from stage II/III GC patients (n=1,661) who underwent gastrectomy at Fujian Medical University Union Hospital (FJMUUH) between January 2010 and October 2015. For external validation, two independent cohorts were included, which were composed of 380 stage II/III GC patients who underwent gastrectomy at the Mayo Clinic between July 1991 and July 2012 and 270 stage II/III GC patients at the Qinghai university affiliated hospital (QUAH) between May 2010 and Oct 2014. Random survival forest models were used to predict dynamic recurrence hazards and to construct individual surveillance strategies for stage II/III GC. Cost-effectiveness were assessed by Markov model.
Study Type
OBSERVATIONAL
Enrollment
2,311
Department of Gastric Surgery
Fuzhou, Fujian, China
recurrence rate
distant, peritoneal, and local recurrence
Time frame: 5 years
Delayed-detection months
A hypothetical cohort of 1000 GC patients was generated to compare the effectiveness of surveillance among the above-mentioned strategies by calculating the sum of the delayed detection months. Delayed detection months were defined as the duration from the occurrence of failure to the next-nearest follow-up. For instance, if a patient develops distant metastasis in the 3rd month while the next nearest planned visit is in the 5th month, the delayed -detection time for this patient is 2 months. The total number of delayed detection months of our risk-based surveillance schedule and that of the control strategies were simulated and compared.
Time frame: 5 years
Incremental cost-effectiveness ratios (ICERs)
incremental cost-effectiveness ratios (ICERs) were calculated by dividing the difference in cost by the difference in QALY.
Time frame: 5 years
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