Roudebush Veterans Affairs Medical Center, Indianapolis, IN, USA.
Comput Methods Programs Biomed. 2013 Dec;112(3):563-79. doi: 10.1016/j.cmpb.2013.07.007. Epub 2013 Aug 20.
We suggest a model framework, in which an individual patient's risk for colonic neoplasia varies based on findings from his previous colonoscopies, to predict longitudinal colonoscopy results. The neoplasia natural history model describes progression through four neoplasia development states with patient age. Multiple natural history model parameter sets are assumed to act concurrently on the colon and parameter set prevalence combinations, whose a priori likelihoods are a function of patient sex, provide a basis set for patient-level predictions. The novelty in this approach is that after a colonoscopy, both the parameter set combination likelihoods and their model predictions can adjust in a Bayesian manner based on the results and conditions of the colonoscopy. The adjustment of model predictions operationalizes the clinical knowledge that multiple or advanced neoplasia at baseline colonoscopy is an independent predictor of multiple or advanced neoplasia at follow-up colonoscopy--and vice versa for negative colonoscopies--and the adjustment of parameter set combination likelihoods accounts for the possibility that patients may have different neoplasia development rates. A model that accurately captures serial colonoscopy results could potentially be used to design and evaluate post-colonoscopy treatment strategies based on the risk of individual patients. To support model identification, observational longitudinal colonoscopy results, procedure details, and patient characteristics were collected for 4084 patients. We found that at least two parameter sets specific to each sex with model adjustments was required to capture the longitudinal colonoscopy data and inclusion of multiple possible parameter set combinations, which account for random variations within the population, was necessary to accurately predict the second-time colonoscopy findings for patients with a history of advanced adenomas. Application of this model to predict CRC risks for patients adhering to guideline recommended follow-up colonoscopy intervals found that there are significant differences in risk with patient age, gender, and preparation quality and demonstrates the need for a more rigorous investigation into these recommendations.
我们提出了一个模型框架，该框架基于个体患者之前结肠镜检查的结果，预测其结直肠腺瘤的纵向结肠镜检查结果。该腺瘤自然史模型描述了患者年龄的四个腺瘤发展状态的进展。多个自然史模型参数集被假设同时作用于结肠，并且参数集流行组合的先验可能性是患者性别、为患者水平预测提供基础的函数。该方法的新颖之处在于，在结肠镜检查后，基于结肠镜检查的结果和条件，可以以贝叶斯方式调整参数集组合可能性及其模型预测。模型预测的调整实现了这样的临床知识，即基线结肠镜检查中存在多个或高级别腺瘤是随访结肠镜检查中存在多个或高级别腺瘤的独立预测因素，反之亦然，而阴性结肠镜检查则反之亦然，并且参数集组合可能性的调整考虑了患者可能具有不同的腺瘤发展速度的可能性。一个能够准确捕捉系列结肠镜检查结果的模型，有可能被用于设计和评估基于个体患者风险的结肠镜检查后治疗策略。为了支持模型识别，我们收集了 4084 名患者的观察性纵向结肠镜检查结果、程序细节和患者特征。我们发现，至少需要为每个性别指定两个具有模型调整的参数集，才能捕捉到纵向结肠镜检查数据，并且需要包含多个可能的参数集组合，以准确预测有高级别腺瘤病史的患者第二次结肠镜检查结果，这些参数集组合可以解释人群内的随机变化。将该模型应用于预测遵循指南推荐的结肠镜检查间隔的患者的 CRC 风险，发现风险存在显著差异，与患者年龄、性别和准备质量有关，并证明需要更严格地研究这些建议。
