Date of Award
2011
Degree Type
Thesis
Degree Name
Master of Science in Statistics
Department
Computer Science and Statistics
First Advisor
Liliana Gonzalez
Abstract
Appendicitis is common in children, but remains difficult to diagnose accurately. The clinician must integrate information from the history, physical examination and screening laboratory tests to decide whether to reassure, order diagnostic imaging, or proceed to the operating room. This process is best framed as a decision problem with two thresholds; a lower threshold, below which further testing may be unnecessary, and an upper threshold, above which further testing need not delay appendectomy. The goal of this analysis was to model the probability of appendicitis.
This project analyzes observations by 23 physicians on 143 children with abdominal pain evaluated in a Pediatric Emergency Department. Clinicians recorded the presence or absence of various signs and symptoms, and provided their gestalt estimate of the probability of appendicitis (priorprob) prior to obtaining screening laboratory tests such as white blood cell count (wbc). A final diagnosis of appendicitis was confirmed pathologically in 45 (31.5%) patients.
Exploratory plots utilize nonparametric exploratory kernel density and locally weighted scatterplot smoothing. Missing data is imputed using both single and multiple imputation. Receiver Operator Characteristic curves illustrate the superior discrimination of a logistic clinical factors model vs. the Pediatric Appendicitis Score which dichotimizes wbc. The Akaike Information criteria provide support for a model that substitutes gestalt clinical probability (priorprob) for individual clinical factors. The bootstrap is used to produce bias-corrected calibration plots for each model and to estimate confidence intervals for coefficients. To account for the correlation within physicians, Generalized Linear Mixed models with clinician specific random effect(s) weren't using maximum likelihood and Bayesian methods.
The apparent importance of gender in exploratory plots is confirmed using parametric models. Contrary to prior studies, the presence of fever reduces the probability of appendicitis. Conditional predictions from the preferred (random intercept) Bayesian model suggest that one can most confidently omit imaging in girls with low clinical suspicion (priorprob) and low white blood cell counts (wbc). Conversely, the best case for proceeding directly to the operating room can be made for boys with both high priorprob and high wbc. When levels of priorprob and wbc are discordant, imaging, or further observation, will be necessary.
Recommended Citation
Steele, Dale W, "CLINICAL PREDICTION MODELS FOR DIAGNOSIS OF APPENDICITIS IN CHILDREN WITH ABDOMINAL PAIN" (2011). Open Access Master's Theses. Paper 107.
https://digitalcommons.uri.edu/theses/107
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