A simulation model of colorectal cancer surveillance and recurrence

BMC Medical Informatics and Decision Making

Research Article

Xuebei An¹  Qingqing Hong¹  Johnie Rose²  Gregory S Cooper³  Neal J Meropol³  Michael W Kattan⁴  Chung Yin Kong⁵  Knut Magne Augestad⁶ 

[1] Case Western Reserve University School of Medicine, 11000 Cedar Ave., Ste. 402, 44106-7136, Cleveland, OH, USA;Case Western Reserve University School of Medicine, 11000 Cedar Ave., Ste. 402, 44106-7136, Cleveland, OH, USA;Case Comprehensive Cancer Center, Cleveland, OH, USA;Case Western Reserve University School of Medicine, 11000 Cedar Ave., Ste. 402, 44106-7136, Cleveland, OH, USA;Case Comprehensive Cancer Center, Cleveland, OH, USA;Seidman Cancer Center, University Hospitals Case Medical Center, Cleveland, OH, USA;Cleveland Clinic, Cleveland, OH, USA;Massachusetts General Hospital, Boston, MA, USA;Norwegian National Centre of Telemedicine and Integrated Care, University Hospital North Norway, Tromsø, Norway;

关键词: Colorectal cancer;    Recurrence;    Surveillance;    Follow-up;    Model;   

DOI  :  10.1186/1472-6947-14-29

 received in 2013-02-21, accepted in 2014-03-27,  发布年份 2014

来源: Springer

【 摘 要 】

BackgroundApproximately one-third of those treated curatively for colorectal cancer (CRC) will experience recurrence. No evidence-based consensus exists on how best to follow patients after initial treatment to detect asymptomatic recurrence. Here, a new approach for simulating surveillance and recurrence among CRC survivors is outlined, and development and calibration of a simple model applying this approach is described. The model’s ability to predict outcomes for a group of patients under a specified surveillance strategy is validated.MethodsWe developed an individual-based simulation model consisting of two interacting submodels: a continuous-time disease-progression submodel overlain by a discrete-time Markov submodel of surveillance and re-treatment. In the former, some patients develops recurrent disease which probabilistically progresses from detectability to unresectability, and which may produce early symptoms leading to detection independent of surveillance testing. In the latter submodel, patients undergo user-specified surveillance testing regimens. Parameters describing disease progression were preliminarily estimated through calibration to match five-year disease-free survival, overall survival at years 1–5, and proportion of recurring patients undergoing curative salvage surgery from one arm of a published randomized trial. The calibrated model was validated by examining its ability to predict these same outcomes for patients in a different arm of the same trial undergoing less aggressive surveillance.ResultsCalibrated parameter values were consistent with generally observed recurrence patterns. Sensitivity analysis suggested probability of curative salvage surgery was most influenced by sensitivity of carcinoembryonic antigen assay and of clinical interview/examination (i.e. scheduled provider visits). In validation, the model accurately predicted overall survival (59% predicted, 58% observed) and five-year disease-free survival (55% predicted, 53% observed), but was less accurate in predicting curative salvage surgery (10% predicted; 6% observed).ConclusionsInitial validation suggests the feasibility of this approach to modeling alternative surveillance regimens among CRC survivors. Further calibration to individual-level patient data could yield a model useful for predicting outcomes of specific surveillance strategies for risk-based subgroups or for individuals. This approach could be applied toward developing novel, tailored strategies for further clinical study. It has the potential to produce insights which will promote more effective surveillance—leading to higher cure rates for recurrent CRC.

【 授权许可】

CC BY   
© Rose et al.; licensee BioMed Central Ltd. 2014