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

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.

Artificial intelligence for predicting acute appendicitis: a systematic review

“…Ultimately there were 10 studies that fulfilled the inclusion criteria. [11][12][13][14][15][16][17][18][19][20] These studies included 10 derivation/validation studies and 0 implementation studies (Table 1).…”

“…Histopathological confirmation, the most robust method for ascertaining the diagnosis of appendicitis, was used by five included studies. 12,[15][16][17]19 Conversely, other studies employed the use of ICD10 codes as the ground truth by which the diagnosis of appendicitis was established, 20 which may be inaccurate. There were also studies in which there were few, if any, details provided regarding the means of establishing the diagnosis.…”

“…Stiel 18 Some studies also incorporated the use of NLP to extract parameters from unstructured free-text from emergency department triage notes. 18,20 There were other studies that used AI to create models based on more restricted datasets for indirectly improving the diagnosis of appendicitis, and also in differentiating between simple and complicated appendicitis. Hayashi et al used abdominal ultrasound images as input parameter data to develop algorithms that help to identify the appendix on ultrasounds scans.…”

“…Logistic regression and linear models were frequently employed. 12,15,16,18,20 Random forest models were used in 5 studies. 12,15,[18][19][20] Deep learning methodologies, including artificial neural networks, were used in 4 studies.…”

“…12,15,16,18,20 Random forest models were used in 5 studies. 12,15,[18][19][20] Deep learning methodologies, including artificial neural networks, were used in 4 studies. 11,13,14,18 Su et al also used natural language processing (NLP) algorithm for extracting features from unstructured text and 'K nearest neighbour' approach for imputation of missing data prior to creating prediction models using 'binary logistic regression' and 'random forest' algorithms.…”