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| ID | Type | Description | Link |
|---|---|---|---|
| 1K23DK147757-01 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) | NIH |
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The goal of this observational study is to learn about an adult's chance of having celiac disease based on blood testing and symptoms. The main question it aims to answer is:
Can a blood test and symptom information separate patients into 3 groups of low, intermediate, and high risk for celiac disease?
Participants already being evaluated for celiac disease as part of their regular medical care will answer online survey questions about their symptoms and have laboratory data collected from their charts.
We hypothesize that a clinical prediction model integrating clinical data with TTG-IgA antibody levels can accurately identify patients with celiac disease offering a personalized approach. We anticipate this prediction model would classify patients into 3 risk groups for celiac disease: 1) Low likelihood (no further testing required), 2) Intermediate likelihood (biopsy required for confirmation), and 3) High likelihood (biopsy can be avoided based on the model's accuracy) thereby reserving endoscopy and biopsies for cases of intermediate probability to improve diagnosis, reduce invasive testing, increase patient focus, and decrease costs.
The specific aims of this study are to: 1) develop and validate a clinical prediction model for celiac disease probability (external validation will be performed by site and time), 2) evaluate the implementation potential of the model, and 3) pilot the model and determine its impact on patient experience.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Aim 1 Retrospective Cohort | 11300 patients evaluated for celiac disease from 1/1/2005-12/31/2021 from various sites, as well as 4000 internal patients evaluated from 1/1/2022-September 2025 will be used to validate the AI model. All 15300 patients are from a previously IRB approved study that had the goal of using for AI model validation. | ||
| Aim 2 Interview Cohort | 30 patients with celiac disease will be recruited to participate in interviews regarding preferences in the diagnostic approach to celiac disease. Approximately 20 physicians (primary care, gastroenterology, and subspeciality physicians from Cleveland Clinic, identified based on their past one-year total number of patients diagnosed with celiac disease, selecting those in the top quartile of each specialty) will be interviewed regarding typical celiac diagnostic approach, concerns about use of a prediction model, etc. Finally, up to 5 physician-leaders (in Gastroenterology, including the Department Chair, General Gastroenterology Section Head, and members of the clinical practice committee) will be interviewed on perspectives on changing the clinical workflow, comfort with use of clinical prediction models for diagnosis, including liability, etc | ||
| Aim 3 Survey Cohort | 100 patients will be asked to complete online surveys and consent to have their data input in the AI model. |
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| Measure | Description | Time Frame |
|---|---|---|
| Aim 1 Prediction Model | The primary outcome being predicted is biopsy-confirmed celiac disease, defined as villous atrophy on duodenal histopathology. A prediction model will be built and after final model construction, we will report its performance using five established measures: sensitivity, specificity, positive predictive value, negative predictive value, and F-measure. A calibration plot will be produced to illustrate if the model's predicted probabilities of an outcome reflect the true outcome probability. We will use the SHapley Additive exPlanation (SHAP) method to provide a list of all model features ranked according to relative importance. | 1 year |
| Aim 2 Interview Transcript Coding | Interview transcripts will be uploaded into NVivo software, a qualitative data analysis tool that facilitates coding of source data and identification of similarities in coded concepts indicative of themes. A research assistant and the PI will independently inductively code interviews in NVivo. Data-driven codes will be combined with a priori codes corresponding to the PRISM domains to develop the study codebook and summarize themes. We will map these codes to the PRISM framework to understand how the intervention, recipients, implementation structure, and external environment interact to support implementation of a prediction model for celiac disease diagnosis. | Years 2-3 |
| Aim 3 Model Performance and Patient Preferences | For aim 3, the primary outcome of interest will be model accuracy reported as AUC, AUPRC, sensitivity, and specificity. We will describe patient-reported preferences for communication and display of the prediction model, as well as ranking of decisional attributes (e.g. discomfort, certainty in results). Best practices for survey reporting will be used. | Years 4-5 |
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For Aim 1:
Inclusion Criteria:
Exclusion Criteria:
For Aim 2:
Inclusion Criteria:
Exclusion Criteria:
- Children and vulnerable populations (e.g. pregnant women or prisoners)
For Aim 3:
Inclusion Criteria:
Exclusion Criteria:
- Children and vulnerable populations (e.g. pregnant women or prisoners)
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The study population for Aim 1 is all adult patients ≥18 who underwent duodenal biopsy during upper endoscopy and had a TTG-IgA antibody test 3 months before or 1 month after biopsy. First pathology data will be reviewed to identify duodenal biopsies conducted during the study period then TTG-IgA antibody availability will be confirmed. We aim to study incident cases. The study population is similar for Aim 3 - patients being evaluated for celiac disease. The study population for Aim 2 includes primary care, gastroenterology, and subspeciality physicians diagnosing celiac disease and patients with or being evaluated for celiac disease.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Claire Jansson-Knodell, MD | Contact | 2164446354 | JANSSOC@ccf.org |
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| ID | Term |
|---|---|
| D002446 | Celiac Disease |
| ID | Term |
|---|---|
| D008286 | Malabsorption Syndromes |
| D007410 | Intestinal Diseases |
| D005767 | Gastrointestinal Diseases |
| D004066 | Digestive System Diseases |
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| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |