An observational study in Acute Appendicitis and Abdominal Pain, sponsored by National University Hospital, Singapore. Completed at 1 site in Singapore. Open to participants aged 21 Years to 99 Years. Per ClinicalTrials.gov, last updated 2021-03-03.
Sponsored by National University Hospital, Singapore · Observational
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.
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.
419 studies on the registry are indexed under Appendicitis; 67 are open to participants now.
This study's enrollment of 689 is above the median of 200 across 151 observational studies indexed under Appendicitis.
Browse Appendicitis studies →National University Hospital, Singapore is the lead sponsor of 454 studies on the registry; 99 are open to participants now.
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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.
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Eligibility criteria of doctors- Inclusion criteria: Junior doctors working in the Emergency Department Exclusion criteria: Refusal of consent
Eligibility criteria of patients-
Inclusion Criteria:
Exclusion Criteria:
Diagnostic Test: Free text prediction algorithm for appendicitis
A free-text prediction software that predicts the probability of acute appendicitis
Accuracy of predictive algorithm for acute appendicitis
Accuracy of predictive algorithm and accuracy of doctors with input from the algorithm in diagnosing acute appendicitis
Time frame: 30 days
Plan to share: No — Individual participant data will not be made available to other researchers.
No publications or documents are linked to this record.
Ran from Dec 2017 to primary completion in Jul 2019, 18 months.
Of 54 non-industry observational studies under Appendicitis, half reached primary completion within 23 months of starting (the median), counting those still running; a quarter within 7 and three quarters within 56. This study's 18 months is below that.
Counting every sponsor, half of 56 studies reached it within 23 months; a quarter within 7 and three quarters within 56.
How these figures are worked out, and which studies are left out of them.
This study is completed, as verified in Mar 2021. You cannot join it, but the record below documents what was studied.
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