Clinical trial NCT06531226: Difficult Colonoscopy and Personality Traits
Official title: Personality Traits and Colonoscopy Insertion Time: Applying Machine Learning to Predict the Colonoscopy Time and Difficult Colonoscopy
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Colorectal Disorders
Keywords: machine learning
difficult colonoscopy
Lead sponsor: Qilu Hospital of Shandong University
Sponsor class: OTHER
First posted: 2024-07-31
Enrollment: 450
Sex: ALL
Minimum age (years): 18
Maximum age (years): 60
Standard ages: ADULT
Observational model: CASE_CONTROL
Time perspective: CROSS_SECTIONAL
Sampling method: PROBABILITY_SAMPLE
Study population: The study population consisted of patients who underwent colonoscopy at our hospital. They should meet the aforementioned inclusion criteria did not meet the exclusion criteria. Due to the widespread application of colonoscopy, the above patients may include patients suspected of having various organic or non-organic basic intestinal diseases, as well as health check-up patients.

Brief summary: The cecal intubation time (CIT) refers to the time required for the tip of the colonoscope to reach the cecum from passing through the anus. A prolonged CIT is considered a marker of difficulty in colonoscopy. CIT greater than 10 minutes is considered a difficult colonoscopy. Studies have identified factors that influence CIT, including age, gender, body mass index (BMI), waist circumference, bowel preparation, prior abdominal surgery, etc. Personality traits have been found to be associated with the onset of many diseases, such as hypertension and A-type personality, depression and neurotic personality. According to the Big Five personality theory, personality can be decomposed into five dimensions: openness (O), conscientiousness (C), extraversion (E), agreeableness (A), and neuroticism (N). There is a lack of research on the association between personality traits and colonoscopy insertion time, and the purpose of this study is to investigate whether personality, BMI, age, gender, anxiety and depression index, metabolic diseases, and abdominal pelvic surgery history lead to prolonged colonoscopy insertion time and difficult colonoscopy, and to identify significant variables as predictors by using machine learning methods to build a clinical diagnostic model to predict colonoscopy insertion time and identify difficult colonoscopy patients.

Detailed description: The cecal intubation time (CIT) refers to the time required for the tip of the colonoscope to reach the cecum from passing through the anus. A prolonged CIT is considered a marker of difficulty in colonoscopy. A CIT greater than 10 minutes is considered a difficult colonoscopy. A longer CIT is associated with a lower adenoma detection rate, as it is an indicator of an ineffective colonoscopy, while a shorter CIT will provide the endoscopist with more time for colonoscope retrieval, increasing the likelihood of adenoma detection.

Research has identified factors that influence CIT, including age, gender, body mass index (BMI), waist circumference, bowel preparation, and prior abdominal surgery history.

Personality traits have been found to be associated with the onset of many diseases, such as hypertension and A-type personality, depression, and neurotic personality. According to the Big Five personality theory, personality can be decomposed into five dimensions: openness (O), conscientiousness (C), extraversion (E), agreeableness (A), and neuroticism (N). The Big Five personality theory is a commonly used personality typology in scientific research and has been confirmed in numerous studies. The Big Five Personality Inventory (BFI) is a tool for detecting the classification of the Big Five personality traits. The Chinese version of the Big Five Personality Inventory and norms were jointly revised by Professor Li Jian from the Department of Psychology, Beijing Normal University, and the scale norms were constructed to include people of different age groups. The scale has shown excellent reliability and validity.

The discovery of the brain-gut axis suggests that neural activity can affect the intestinal activity in various ways directly or indirectly, thereby affecting the difficulty of colonoscopy, but there is still a lack of research on the relationship between personality types and difficult colonoscopy, and the specific correlation has not been clarified. What's more important is that previous studies often used ordinary least squares regression (OLS regression) or logistic regression The purpose of this study is to investigate whether personality, BMI, age, gender, anxiety and depression index, metabolic diseases, and history of abdominal and pelvic surgery are associated with prolonged colonoscopy insertion time and difficult colonoscopy, and to construct a clinical diagnostic model by using machine learning methods to predict colonoscopy insertion time and identify difficult colonoscopy patients based on significant variables.

The basic research framework is designed as follows:

Invite patients scheduled for colonoscopy to fill out the "Personality Tendency and Colonoscopy Survey Questionnaire" before colonoscopy.

Record the completion of colonoscopy and CIT for patients during colonoscopy and record the preparation of the colon and the presence of serious organic diseases.

Collect other patient information, including age, gender, and history of major diseases.

All patients included in the study should meet the inclusion criteria and not meet the exclusion criteria.

Organize the data and divide the patients into two groups based on whether their CIT time is greater than 10 minutes.

Use t-tests and chi-square analysis to determine whether each factor is related to difficult colonoscopy, with a significance threshold of P\<0.05.

Include significant variables in logistic regression for initial analysis and calculate OR.

Use LASSO regression for sparse selection and include non-zero features in the machine learning model construction.

Divide the data into training set and test set and construct random forest models, artificial neural network models, and support vector machine models, etc. Through receiver operating curve (ROC), precision, accuracy, recall rates, etc., the model's error can be evaluated, and the model's generalization ability can be compared.

Eligibility criteria: Inclusion Criteria:

* Colonoscopy patients tested 18 to 60 years old, men and women there is no limit
* Insert the cecum complete colonoscopy examination (successfully)
* Agree and complete personality questionnaire and anxiety depression

Exclusion Criteria:

* Maternal/pregnancy/lactation
* Has a history of colorectal resection of colorectal surgery
* Have organic diseases such as severe ulcerative colitis or intestinal tumors
* Inadequate bowel preparation (bowel with feces and less than 90% visible area of mucosa)
* Confirmed with the immune system (SLE), nervous system (Alzheimer's disease), mental system (such as schizophrenia, depression), urinary system disease (CKD), and other digestive system disease
* Unable or unwilling to sign informed consent

Primary outcomes:
measure: The occurrence of difficult colonoscopy
timeFrame: The evaluation of difficult colonoscopies was performed immediately after the patient completed the questionnaire at the next colonoscopy
Secondary outcomes:
Other outcomes:
Locations:
city: Jinan
country: China
facility: Qilu Hospital of Shandong University
state: Shandong
Study officials:
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