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

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

doi:10.1186/s12874-021-01490-9

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

BMC Med Res Methodol. 2022 Jan 14;22(1):18. doi: 10.1186/s12874-021-01490-9.

Authors

Dai Su¹, Qinmengge Li^{2

3}, Tao Zhang⁴, Philip Veliz², Yingchun Chen^{5

6}, Kevin He³, Prashant Mahajan⁷, Xingyu Zhang⁸

Affiliations

¹ Department of Health Management and Policy, School of Public Health, Capital Medical University, Beijing, China.
² Department of Systems, Populations, and Leadership, University of Michigan School of Nursing, Ann Arbor, USA.
³ Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, USA.
⁴ Department of Epidemiology and Biostatistics, West China School of Public Health School, Sichuan University, Chengdu, China.
⁵ Department of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
⁶ Research Center for Rural Health Services, Hubei Province Key Research Institute of Humanities and Social Sciences, Wuhan, China.
⁷ Department of Emergency Medicine, University of Michigan School of Medicine, Ann Arbor, USA.
⁸ Thomas E. Starzl Transplantation Institute, University of Pittsburgh Medical Center, Pittsburgh, USA. zhangx28@upmc.edu.

Abstract

Background: Early 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.

Methods: We 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.

Results: Among 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.

Conclusions: We 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.

Keywords: Acute appendicitis; Emergency department; Machine learning; Precision health; Prediction modelling.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Abdominal Pain / diagnosis
Abdominal Pain / epidemiology
Acute Disease
Adult
Appendicitis* / diagnosis
Child
Emergency Service, Hospital
Health Care Surveys
Humans
United States

Grants and funding

UL1 TR002240/TR/NCATS NIH HHS/United States