Free Text Prediction Algorithm for Appendicitis

NCT03414853 | Completed

• 1 other identifier: N-171-000-456-001
• Study Type: observational
• Target: 689
• Locations: 1 country
• Sites: 1

Brief Summary

Computer-aided diagnostic software has been used to assist physicians in various ways. Text-based prediction algorithms have been trained on past medical records through data mining and feature analysis. Currently, all text-based machine learning prediction problem models have been built on extracted data with no research completed on free text based prediction algorithms. This study aims to determine the accuracy of a free text prediction algorithm in predicting the probability of appendicitis in patients presenting to the Emergency Department with abdominal pain and gastrointestinal symptoms.

Conditions

Acute Appendicitis; Abdominal Pain

Keywords

Appendicitis, abdominal pain, free text prediction

Outcome Measures

Primary Outcomes (1)

• Accuracy of predictive algorithm for acute appendicitis

  Accuracy of predictive algorithm and accuracy of doctors with input from the algorithm in diagnosing acute appendicitis

  30 days

Study Arms (2)

With algorithm use

Diagnostic Test: Free text prediction algorithm for appendicitis

No algorithm use

Interventions

Free text prediction algorithm for appendicitis DIAGNOSTIC_TEST

A free-text prediction software that predicts the probability of acute appendicitis

With algorithm use

Eligibility Criteria

• Age: 21 Years - 99 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Attending physicians will be recruited as study participants and randomised weekly into "algorithm use" versus "no algorithm use". Patients who fulfilled the above eligibility criteria will have their data collected and entered into the predictive algorithm.

You may not qualify if:

• Eligibility criteria of patients-
• Presence of abdominal pain, OR
• Presence of gastrointestinal symptoms such as nausea, vomiting or diarrhea, OR
• Fever with anorexia
• Previous history of appendicectomy
• Refusal of consent

Sponsors & Collaborators

• National University Hospital, Singapore: lead

Study Sites (1)

National University Hospital

Singapore, 119074, Singapore

Location

MeSH Terms

Conditions

Appendicitis; Abdominal Pain

Condition Hierarchy (Ancestors)

Intraabdominal Infections; Infections; Gastroenteritis; Gastrointestinal Diseases; Digestive System Diseases; Cecal Diseases; Intestinal Diseases; Pain; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Signs and Symptoms, Digestive

Study Officials

• Kee Yuan Ngiam, Dr

  National University Hospital, Singapore

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 23, 2018
• First Posted: January 30, 2018
• Study Start: December 4, 2017
• Primary Completion: July 1, 2019
• Study Completion: July 1, 2020
• Last Updated: March 3, 2021

Record last verified: 2021-03

Data Sharing

• IPD Sharing: Will not share

Locations

SG (1)