Comput Methods Programs Biomed - A predictive model of longitudinal, patient-specific colonoscopy results.


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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.

Resumo Limpo

suggest model framework individu patient risk colon neoplasia vari base find previous colonoscopi predict longitudin colonoscopi result neoplasia natur histori model describ progress four neoplasia develop state patient age multipl natur histori model paramet set assum act concurr colon paramet set preval combin whose priori likelihood function patient sex provid basi set patientlevel predict novelti approach colonoscopi paramet set combin likelihood model predict can adjust bayesian manner base result condit colonoscopi adjust model predict operation clinic knowledg multipl advanc neoplasia baselin colonoscopi independ predictor multipl advanc neoplasia followup colonoscopyand vice versa negat colonoscopiesand adjust paramet set combin likelihood account possibl patient may differ neoplasia develop rate model accur captur serial colonoscopi result potenti use design evalu postcolonoscopi treatment strategi base risk individu patient support model identif observ longitudin colonoscopi result procedur detail patient characterist collect patient found least two paramet set specif sex model adjust requir captur longitudin colonoscopi data inclus multipl possibl paramet set combin account random variat within popul necessari accur predict secondtim colonoscopi find patient histori advanc adenoma applic model predict crc risk patient adher guidelin recommend followup colonoscopi interv found signific differ risk patient age gender prepar qualiti demonstr need rigor investig recommend

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