Acute AppendicitisNCT03414853
Free Text Prediction Algorithm for Appendicitis
An observational study of Acute Appendicitis and Abdominal Pain, run by National University Hospital, Singapore at 1 site in Singapore.
Trial 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. Read more about this study
- Who is it for?
- Ages 21 to 99 years
- Conditions the study is for
- Acute Appendicitis, Abdominal Pain
- What is being tested?
- Free text prediction algorithm for appendicitis
- How many people
- 689 participants
- Where
- 1 siteSingapore
- Study dates
- 4 Dec 2017to 1 Jul 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 21 to 99 years
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- Presence of abdominal pain, OR
- Presence of gastrointestinal symptoms such as nausea, vomiting or diarrhea, OR
- Fever with anorexia
Who cannot join
- Previous history of appendicectomy
- Refusal of consent
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Prospective Study of a Free-text Diagnosis Prediction Algorithm for Appendicitis in the Emergency Department
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.
Read the detailed description
Developing machine learning models that have a strong prediction power for diagnosis of appendicitis from physician entered free text input can improve diagnostic accuracy of doctors. It also offers the possibility of using prediction algorithms to improve routine clinical care. In the future, multiple machine learning models can be combined to increase prediction accuracy and prediction algorithms can be extended to other diagnoses.
18,000 cases of emergency department presentations over 10 years were used as a training and validation dataset. To develop the appendicitis prediction model, deep learning neural networks with a customized medical ontology were used. The diagnostic accuracy of the model is expressed as sensitivity (recall), specificity and F1 score (harmonic mean). The developed diagnosis predictive model shows high sensitivity (86.3%), specificity (91.9%) and F1 score (88.8) in diagnosing appendicitis from patients presenting with abdominal pain.
The predictive model algorithm will also highlight words in the free text (entered by the attending physician) that it assigns higher probability for predicting an outcome. The doctors will be instructed to provide a percentage likelihood of appendicitis based on the clinical presentation and any available laboratory investigations. The doctor is then shown the prediction of the algorithm as well as the highlighted words for the patient entered. He/she must then provide another prediction of the likelihood of appendicitis after seeing the algorithm generated prediction.
The aim is to evaluate the performance of the algorithm and to assess if usage of the algorithm is able to help emergency doctors improve their diagnosis of appendicitis. The prediction results will be tabulated to assess accuracy of the algorithm, doctors before algorithm input and doctors after receiving algorithm input. The accuracy will be expressed as sensitivity, specificity, accuracy, positive prediction value, F1 score and F0.5 score.
Approximately 100 emergency doctors will be recruited over the course of 1 year as participants in the study. The doctors will be split randomly assigned to two groups - the algorithm arm and the no algorithm arm. The randomization will be by time (weekly) using variable block randomization of 4 and 6. The patients will be followed up for the final discharge diagnoses.
- Conditions
- Acute Appendicitis, Abdominal Pain
- Being tested
- Free text prediction algorithm for appendicitis
- Keywords
- Appendicitis, abdominal pain, free text prediction
- Medical subject headings
- Appendicitis, Abdominal Pain
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Free text prediction algorithm for appendicitisDiagnostic test
A free-text prediction software that predicts the probability of acute appendicitis
| Study group | Role | What they receive |
|---|---|---|
| With algorithm use | Not described Receives: Diagnostic Test: Free text prediction algorithm for appendicitis | |
| No algorithm use | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of predictive algorithm for acute appendicitis
Accuracy of predictive algorithm and accuracy of doctors with input from the algorithm in diagnosing acute appendicitis
Measured: 30 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Jan 2018
- Study start4 Dec 2017
- Primary completion1 Jul 2019
- Study completion1 Jul 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Acute AppendicitisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 689 participants (actual)
Sponsor and investigators
- Lead sponsor
- National University Hospital, Singapore
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Kee Yuan Ngiam, Dr, Principal investigator, National University Hospital, Singapore
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03414853
- Sponsor's protocol ID
- N-171-000-456-001
- Status last verified
- Mar 2021
- First submitted
- 23 Jan 2018
- Last updated
- 3 Mar 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be made available to other researchers.
Study at a glance
From the registry record, last updated by the sponsor on 3 Mar 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective Study of a Free-text Diagnosis Prediction Algorithm for Appendicitis in the Emergency Department
- ClinicalTrials.gov ID
- NCT03414853
- Acronym / sponsor ID
- N-171-000-456-001
- Conditions
- Acute Appendicitis, Abdominal Pain
- Keywords
- Appendicitis, abdominal pain, free text prediction
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 689 participants (actual)
- Lead sponsor
- National University Hospital, SingaporeUniversity, hospital or other
- Study start
- 4 Dec 2017 (actual)
- Primary completion
- 1 Jul 2019 (actual)
- Study completion
- 1 Jul 2020 (actual)
- First posted
- 30 Jan 2018 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Developing machine learning models that have a strong prediction power for diagnosis of appendicitis from physician entered free text input can improve diagnostic accuracy of doctors. It also offers the possibility of using prediction algorithms to improve routine clinical care. In the future, multiple machine learning models can be combined to increase prediction accuracy and prediction algorithms can be extended to other diagnoses.
18,000 cases of emergency department presentations over 10 years were used as a training and validation dataset. To develop the appendicitis prediction model, deep learning neural networks with a customized medical ontology were used. The diagnostic accuracy of the model is expressed as sensitivity (recall), specificity and F1 score (harmonic mean). The developed diagnosis predictive model shows high sensitivity (86.3%), specificity (91.9%) and F1 score (88.8) in diagnosing appendicitis from patients presenting with abdominal pain.
The predictive model algorithm will also highlight words in the free text (entered by the attending physician) that it assigns higher probability for predicting an outcome. The doctors will be instructed to provide a percentage likelihood of appendicitis based on the clinical presentation and any available laboratory investigations. The doctor is then shown the prediction of the algorithm as well as the highlighted words for the patient entered. He/she must then provide another prediction of the likelihood of appendicitis after seeing the algorithm generated prediction.
The aim is to evaluate the performance of the algorithm and to assess if usage of the algorithm is able to help emergency doctors improve their diagnosis of appendicitis. The prediction results will be tabulated to assess accuracy of the algorithm, doctors before algorithm input and doctors after receiving algorithm input. The accuracy will be expressed as sensitivity, specificity, accuracy, positive prediction value, F1 score and F0.5 score.
Approximately 100 emergency doctors will be recruited over the course of 1 year as participants in the study. The doctors will be split randomly assigned to two groups - the algorithm arm and the no algorithm arm. The randomization will be by time (weekly) using variable block randomization of 4 and 6. The patients will be followed up for the final discharge diagnoses.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| With algorithm use | – | Diagnostic Test: Free text prediction algorithm for appendicitis |
| No algorithm use | – | – |
Who may qualify
- Ages
- 21 to 99 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- Presence of abdominal pain, OR
- Presence of gastrointestinal symptoms such as nausea, vomiting or diarrhea, OR
- Fever with anorexia
Key exclusion criteria (2 listed)
- Previous history of appendicectomy
- Refusal of consent
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Free text prediction algorithm for appendicitisA free-text prediction software that predicts the probability of acute appendicitis | Diagnostic test | With algorithm use |
Primary outcome
Accuracy of predictive algorithm for acute appendicitis
Time frame: 30 days
Accuracy of predictive algorithm and accuracy of doctors with input from the algorithm in diagnosing acute appendicitis
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Mar 2021.
- Individual participant dataSharing planned: No. Individual participant data will not be made available to other researchers.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Kee Yuan Ngiam, Dr principal investigator
National University Hospital, Singapore
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Acute Appendicitis and Abdominal Pain, run by National University Hospital, Singapore at 1 site in Singapore.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 3 Mar 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Singapore. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of predictive algorithm for acute appendicitis (time frame: 30 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 3 Mar 2021; AzTrials last refreshed its copy on 27 Oct 2026.