Use of fecal and serologic biomarkers in the prediction clinical outcomes in children presenting with abdominal pain and/or diarrhea
Rogerson, Sara M.
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INTRODUCTION: Abdominal pain and diarrhea are two of the most common pediatric complaints. They are often associated with a diagnosis of Crohn Disease or Ulcerative Colitis, collectively known as inflammatory bowel disease (IBD). IBD is set of diseases with ill-defined pathogenesis but similar clinical presentation. Clinicians rely on colonoscopic evaluation to distinguish between the two disorders, and the rate of colonoscopies has been increasing over the past several years. With the risks and costs associated with colonoscopic evaluation, our study sought to identify physiologic variables with significant predictive value in order to better determine those most likely to have an abnormal colonoscopy. Those variables could then be incorporated into a predictive model to stratify the risk of a patient having an abnormal colonoscopy and be used as a decision assist tool for physicians. METHODS: We conducted a retrospective cohort study examining 443 patients who underwent a colonoscopy between the years of 2012 and 2016 at Boston Children’s Hospital. Data on demographics, lab work, and stool studies was collected into an online database for three separate data sets. It was analyzed using SAS 9.4 and logistic regression was performed to identify four variables with the most predictive value relating to abnormal colonoscopy. Those variables were incorporated into a predictive model. RESULTS: Several variables were determined to be statistically significant in the prediction of abnormal colonoscopy. The four variables with the most predictive value based on calculated odds ratios were family history of IBD in a first-degree relative, serum albumin, fecal lactoferrin, and platelet count. When ROC curves were generated to validate the model using the four variables for each of the data sets, the area under the ROC curve was used to assess the robustness of the predictive model. The area under the curve (AUC) for the training data set was .81, the first validation set was .79, and the second validation set was .6. DISCUSSION: ROC curves were generated for each of the data sets in order to assess the predictive ability of the model, and the AUCS were calculated. An AUC of 1.0 would indicate a predictive model with perfect predictability. The AUC of the model building set at .81 and the first validation set at .79 are indicative of a predictive model with strong predictive value. The second validation set, used to assess the success of the model on an external data set, had an AUC of .6, which is less robust in its predictive value but is of more predictive utility than a coin flip. CONCLUSION: Logistic regression yielded a parsimonious model consisting of four variables with the strongest predictive value in terms of having an abnormal colonoscopy. The variables are metrics that are routinely collected as part of ambulatory and inpatient clinic visits. When the model was validated using an external data set, it did not perform as well as expected based on the results of the training and first validation set. If the robustness of the model can be improved when validated using an external data set, it could be of great clinical utility to physicians as a decision assist tool and help to limit the number of less clinically indicated colonoscopies being performed in the future.