Prediction of acute appendicitis among patients with undifferentiated abdominal pain at emergency department

Dai Su,Prashant Mahajan,Kevin He,Xingyu Zhang,Qinmengge Li,Yingchun Chen,Philip Veliz,Tao Zhang

doi:10.1186/s12874-021-01490-9

Abstract

BackgroundEarly screening and accurately identifying Acute Appendicitis (AA) among patients with undifferentiated symptoms associated with appendicitis during their emergency visit will improve patient safety and health care quality. The aim of the study was to compare models that predict AA among patients with undifferentiated symptoms at emergency visits using both structured data and free-text data from a national survey.MethodsWe performed a secondary data analysis on the 2005-2017 United States National Hospital Ambulatory Medical Care Survey (NHAMCS) data to estimate the association between emergency department (ED) patients with the diagnosis of AA, and the demographic and clinical factors present at ED visits during a patient’s ED stay. We used binary logistic regression (LR) and random forest (RF) models incorporating natural language processing (NLP) to predict AA diagnosis among patients with undifferentiated symptoms.ResultsAmong the 40,441 ED patients with assigned International Classification of Diseases (ICD) codes of AA and appendicitis-related symptoms between 2005 and 2017, 655 adults (2.3%) and 256 children (2.2%) had AA. For the LR model identifying AA diagnosis among adult ED patients, the c-statistic was 0.72 (95% CI: 0.69–0.75) for structured variables only, 0.72 (95% CI: 0.69–0.75) for unstructured variables only, and 0.78 (95% CI: 0.76–0.80) when including both structured and unstructured variables. For the LR model identifying AA diagnosis among pediatric ED patients, the c-statistic was 0.84 (95% CI: 0.79–0.89) for including structured variables only, 0.78 (95% CI: 0.72–0.84) for unstructured variables, and 0.87 (95% CI: 0.83–0.91) when including both structured and unstructured variables. The RF method showed similar c-statistic to the corresponding LR model.ConclusionsWe developed predictive models that can predict the AA diagnosis for adult and pediatric ED patients, and the predictive accuracy was improved with the inclusion of NLP elements and approaches.

Highlights

Screening and accurately identifying Acute Appendicitis (AA) among patients with undifferentiated symptoms associated with appendicitis during their emergency visit will improve patient safety and health care quality
Along with the implementation of International Classification of Diseases (ICD)-10-CM since 2015, an ICD-10-CM category of K35-K37 was used to define the diagnosis of primary appendicitis, which is equivalent to the ICD-9-CM category 540-542, according to the ICD-10-CM General Equivalence Mapping (GEM), a crosswalk between the two code standards maintained by the Centers for Medicare and Medicaid Services (CMS) and the Centers for Disease Control and Prevention (CDC)
The proportion of appendicitis patients was highest among Asian adults (4.4%) and highest among white pediatric patients (2.7%)

Summary

Introduction

Screening and accurately identifying Acute Appendicitis (AA) among patients with undifferentiated symptoms associated with appendicitis during their emergency visit will improve patient safety and health care quality. AA is one of the most common surgical emergencies but has a high rate of misdiagnosis in the United States [1]. It is the second most common condition among pediatric malpractice claims and third for adult malpractice claims [2, 3]. Identifying AA among patients with undifferentiated symptoms at emergency visits can potentially improve the patient safety and health care quality

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