Original Contribution Bayesian comparative model of computed tomographic scan and ultrasonography in the assessment of acute appendicitis: results from the Acute Care Diagnostic Collaboration project Laila Cochon, MD, MSc, PhD(c), John Esin, MD, Amado Alejandro Baez, MD, MSc, MPH ⁎ Jackson Memorial Hospital abstract article info Article history: Received 12 April 2016 Received in revised form 8 July 2016 Accepted 9 July 2016 Available online xxxx The objective of this study was to develop a comparative diagnostic model for computed tomography (CT) and ultrasound (US) in the assessment of acute appendicitis using Alvarado risk score as a predictor of pretest probability and Bayesian statistical model as a tool to calculate posttest probabilities for both diagnostic test. Stratification was made by applying the Alvarado score for the prediction of acute appendicitis. Likelihood ratios were calculated using sensitivity and specificity of both CT and US from a Meta-analysis. Posttest probabilities were obtained after inserting Alvarado score and likelihood ratios into Bayesian nomogram. Absolute and relative gains were calculated. ANOVA was used to assess statistical association. 4341 patients from 31 studies yielded a pooled sensitivity and specificity US of 83% (95% CI, 78%-87%) and 93% (95% CI, 90%-96%) and 94% (95% CI, 92%-95%) and 94% (95% CI, 94%-96%), respectively, for CT studies. Positive likelihood ratios (LR) for US were 12 and negative LR was 0.18; for CT +LR was 16 and –LR 0.06. Bayesian statistical modeling posttest probabilities for +LR and low Alvarado risk results yielded a posttest probability for US of 83.72% and 87.27% for CT, intermediate risk gave 95.88% and 96.88%, high risk 99.37% and 99.53 respectively. No statistical differences were found between Ultrasound and CT. This Bayesian analysis demonstrated slight superiority of CT scan over US low-risk patients, whereas no significant advantage was seen when evaluating intermediate and high risk patients. This study also favored elevated accuracy of the Alvarado score. © 2016 Elsevier Inc. All rights reserved. 1. Introduction Acute appendicitis still represents one of the most common causes of surgical emergencies and abdominal pain. It occurs in 7% of the popula- tion accounting for more than 250 000 cases annually, for a total healthcare cost of 5.8 billion [1,9]. Diagnosis of appendicitis cannot be accurately confirmed by any single symptom, sign or test, and if left un- treated can cause significant morbidity and even mortality. Differential diagnosis of appendicitis can become challenging due to other causes of abdominal pain that can mimic appendicitis, leading to potential erroneous treating modalities [2]. Clinical prediction rules or scores estimate in a quantifiable manner the probability of diagnosis based on signs, symptoms or test results [3]. While clinical decision support systems use patient data to analyze and generate case specific clinical advice. Alvarado constructed a 10 point scoring system for patients presenting with suspected acute appendici- tis based on signs, symptoms and diagnostic tests. The Alvarado score combines presence or absence of signs and symptoms for the prediction of acute appendicitis [4]. For its acronym, MANTRELS, Alvarado score evaluates migration of pain, anorexia, nausea, tenderness in right lower quadrant, rebound tenderness, elevated temperature, leukocyto- sis, and shift of white blood cell count to the left. It allows for easy risk stratification in patients presenting to the emergency department with abdominal pain, depending on the estimated probability of the diagnosis patient dispositions are recommended. A clinical policy published in 2010 by the American College of Emergency Physicians highlights the importance of using signs and symptoms to steer decision making in the diagnosis of acute appendicitis [7]. The Acute Care Diagnostic Collaboration is a multicenter, multina- tional research effort that introduces a Bayesian methodology and sta- tistical modeling on pretest probability with emergency medicine clinical decision rules, combining it with assessments on diagnostic quality and cost effectiveness of clinical analytic tools in various patient populations. Employing Bayes' theorem, the initial clinical assessment is graded by means of probability and, when subsequently merged with clinical suspicion and diagnostic test results either rules out or rules in the diagnosis [3,11-15]. In probability theory and statistics, Bayes' theorem describes the probability of an event, based on conditions that might be related to American Journal of Emergency Medicine xxx (2016) xxx–xxx ⁎ Corresponding author. E-mail address: aabaezmd@gmail.com (A.A. Baez). http://dx.doi.org/10.1016/j.ajem.2016.07.012 0735-6757/© 2016 Elsevier Inc. All rights reserved. Contents lists available at ScienceDirect American Journal of Emergency Medicine journal homepage: www.elsevier.com/locate/ajem Please cite this article as: Cochon L, et al, Bayesian comparative model of computed tomographic scan and ultrasonography in the assessment of acute appendicitis: results from ..., Am J Emerg Med (2016), http://dx.doi.org/10.1016/j.ajem.2016.07.012