Dataset Information

The value of pancreatic stone protein in predicting acute appendicitis in patients presenting at the emergency department with abdominal pain.

ABSTRACT:

Background

Pancreatic Stone Protein (PSP) is a protein naturally produced mainly in the pancreas and the gut. There is evidence from experimental and clinical trials that blood PSP levels rise in the presence of inflammation or infection. However, it is not known whether PSP is superior to other established blood tests (e.g. White Blood Count, Neutrophils or C - reactive protein) in predicting appendicitis in patients presenting with abdominal pain and a clinical suspicion of appendicitis at the emergency room.

Methods/design

The PSP Appendix Trial is a prospective, multi-center, cohort study to assess the value of PSP in the diagnostic workup of acute appendicitis. 245 patients will be prospectively recruited. Interim analysis will be performed once 123 patients are recruited. The primary endpoint of the study concerns the diagnostic accuracy of PSP in predicting acute appendicitis and therefore the evidence of appendicitis on the histopathological specimen after appendectomy.

Discussion

The PSP Appendix Trial is a prospective, multi-center, cohort study to assess the value of PSP in the diagnostic workup of acute appendicitis.

Trial registration

ClinicalTrials.gov: NCT01610193; Institution Ethical Board Approval ID: KEKZH- Nr. 2011-0501.

SUBMITTER: Tschuor C

PROVIDER: S-EPMC3503734 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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Publications

The value of pancreatic stone protein in predicting acute appendicitis in patients presenting at the emergency department with abdominal pain.

Tschuor Christoph C Raptis Dimitri Aristotle DA Limani Përparim P Bächler Thomas T Oberkofler Christian Eugen CE Breitenstein Stefan S Graf Rolf R

BMC gastroenterology 20121025

<h4>Background</h4>Pancreatic Stone Protein (PSP) is a protein naturally produced mainly in the pancreas and the gut. There is evidence from experimental and clinical trials that blood PSP levels rise in the presence of inflammation or infection. However, it is not known whether PSP is superior to other established blood tests (e.g. White Blood Count, Neutrophils or C - reactive protein) in predicting appendicitis in patients presenting with abdominal pain and a clinical suspicion of appendiciti ...[more]

PMID: 23098130

Similar Datasets

Project description:BackgroundAcute abdominal pain (AAP) constitutes 5-10% of all emergency department (ED) visits, with appendicitis being a prevalent AAP etiology often necessitating surgical intervention. The variability in AAP symptoms and causes, combined with the challenge of identifying appendicitis, complicate timely intervention. To estimate the risk of appendicitis, scoring systems such as the Alvarado score have been developed. However, diagnostic errors and delays remain common. Although various machine learning (ML) models have been proposed to enhance appendicitis detection, none have been seamlessly integrated into the ED workflows for AAP or are specifically designed to diagnose appendicitis as early as possible within the clinical decision-making process. To mimic daily clinical practice, this proof-of-concept study aims to develop ML models that support decision-making using comprehensive clinical data up to key decision points in the ED workflow to detect appendicitis in patients presenting with AAP.MethodsData from the Dutch triage system at the ED, vital signs, complete medical history and physical examination findings and routine laboratory test results were retrospectively extracted from 350 AAP patients presenting to the ED of a Dutch teaching hospital from 2016 to 2023. Two eXtreme Gradient Boosting ML models were developed to differentiate cases with appendicitis from other AAP causes: one model used all data up to and including physical examination, and the other was extended with routine laboratory test results. The performance of both models was evaluated on a validation set (n = 68) and compared to the Alvarado scoring system as well as three ED physicians in a reader study.ResultsThe ML models achieved AUROCs of 0.919 without laboratory test results and 0.923 with the addition of laboratory test results. The Alvarado scoring system attained an AUROC of 0.824. ED physicians achieved AUROCs of 0.894, 0.826, and 0.791 without laboratory test results, increasing to AUROCs of 0.923, 0.892, and 0.859 with laboratory test results.ConclusionsBoth ML models demonstrated comparable high accuracy in predicting appendicitis in patients with AAP, outperforming the Alvarado scoring system. The ML models matched or surpassed ED physician performance in detecting appendicitis, with the largest potential performance gain observed in absence of laboratory test results. Integration could assist ED physicians in early and accurate diagnosis of appendicitis.

Project description: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.

Dataset Information

The value of pancreatic stone protein in predicting acute appendicitis in patients presenting at the emergency department with abdominal pain.

Background

Methods/design

Discussion

Trial registration

Publications

The value of pancreatic stone protein in predicting acute appendicitis in patients presenting at the emergency department with abdominal pain.

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