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Logistic regression model to predict acute uncomplicated and complicated appendicitis

Eddama, M; Fragkos, KC; Renshaw, S; Aldridge, M; Bough, G; Bonthala, L; Wang, A; (2018) Logistic regression model to predict acute uncomplicated and complicated appendicitis. Annals of the Royal College of Surgeons of England 10.1308/rcsann.2018.0152. (In press). Green open access

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Abstract

INTRODUCTION: While patients with acute uncomplicated appendicitis may be treated conservatively, those who suffer from complicated appendicitis require surgery. We describe a logistic regression equation to calculate the likelihood of acute uncomplicated appendicitis and complicated appendicitis in patients presenting to the emergency department with suspected acute appendicitis. MATERIALS AND METHODS: A cohort of 895 patients who underwent appendicectomy were analysed retrospectively. Depending on the final histology, patients were divided into three groups; normal appendix, acute uncomplicated appendicitis and complicated appendicitis. Normal appendix was considered the reference category, while acute uncomplicated appendicitis and complicated appendicitis were the nominal categories. Multivariate and univariate regression models were undertaken to detect independent variables with significant odds ratio that can predict acute uncomplicated appendicitis and complicated appendicitis. Subsequently, a logistic regression equation was generated to produce the likelihood acute uncomplicated appendicitis and complicated appendicitis. RESULTS: Pathological diagnosis of normal appendix, acute uncomplicated appendicitis and complicated appendicitis was identified in 188 (21%), 525 (59%) and 182 patients (20%), respectively. The odds ratio from a univariate analysis to predict complicated appendicitis for age, female gender, log2 white cell count, log2 C-reactive protein and log2 bilirubin were 1.02 (95% confidence interval, CI, 1.01, 1.04), 2.37 (95% CI 1.51, 3.70), 9.74 (95% CI 5.41, 17.5), 1.57 (95% CI 1.40, 1.74), 2.08 (95% CI 1.56, 2.76), respectively. For the same variable, similar odds ratios were demonstrated in a multivariate analysis to predict complicated appendicitis and univariate and multivariate analysis to predict acute uncomplicated appendicitis. CONCLUSIONS: The likelihood of acute uncomplicated appendicitis and complicated appendicitis can be calculated by using the reported predictive equations integrated into a web application at www.appendistat.com. This will enable clinicians to determine the probability of appendicitis and the need for urgent surgery in case of complicated appendicitis. ACKNOWLEDGEMENTS: This work would have not been completed without the help of: Clarissa Y. M. Carvallho, Consultant Anaesthetist at Guy's and St Thomas's Hospital; Bried O'Brien, the Head of Urgent Care Transformation at University College London Hospital; and Guang's Wu, Web Application Developer. Their contribution to building the web application allowed this work to materialise into clinical use to benefit patients.

Type: Article
Title: Logistic regression model to predict acute uncomplicated and complicated appendicitis
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1308/rcsann.2018.0152
Publisher version: https://doi.org/10.1308/rcsann.2018.0152
Language: English
Additional information: This is the published version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Acute abdomen, Acute appendicitis, Complicated appendicitis, Diagnostic strategy, Emergency surgery, Right iliac fossa pain
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Surgical Biotechnology
URI: https://discovery.ucl.ac.uk/id/eprint/10060547
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