This study aims to develop and validate an artificial intelligence (AI)-based predictive model to estimate the risk of incident onset of five major diseases or conditions: cardiovascular disease, type 2 diabetes mellitus, breast cancer, low back pain, and osteoarthritis, in adults aged 30 to 65 years.
For each participant, an index date will be defined as the date of a prior health screening or another protocol-defined baseline clinical date. Incident disease status for each target disease or condition will be ascertained by retrospective review of electronic medical records for up to 10 years after the index date.
The study integrates retrospective clinical, health screening, laboratory, imaging, and electronic medical record data with prospectively collected biospecimen, proteomic, genomic, questionnaire, lifestyle, and digital health data. Prospective study procedures will be completed over approximately 1 week, with up to 2 additional weeks if needed.
By combining multimodal data, this study seeks to improve disease risk prediction and to identify clinical and biological factors associated with disease onset, ultimately supporting personalized risk stratification and preventive healthcare strategies.
Adults aged 30 to 65 years.
Disease group: Participants with a confirmed diagnosis of at least one of the following conditions: type 2 diabetes mellitus, breast cancer, cardiovascular disease, osteoarthritis, or low back pain.
Healthy control group: Participants with no prior diagnosis of type 2 diabetes mellitus, breast cancer, cardiovascular disease, osteoarthritis, or low back pain.
No history or current diagnosis of major medical conditions that may affect study outcomes, including but not limited to chronic kidney disease or liver cirrhosis.
Participants with incomplete or insufficient clinical or health screening data.
Participants considered inappropriate for study participation by the investigator.
This study will involve two distinct groups of participants: a disease group and a healthy control group. 1. Disease Group: The disease group will consist of adults aged 30 to 65 years who have been diagnosed with at least one of the following conditions: Type 2 diabetes mellitus Breast cancer Cardiovascular disease Osteoarthritis Low back pain These participants will undergo questionnaires, digital assessments, physical examinations, and blood tests as part of the study. 2. Healthy Control Group: The healthy control group will consist of adults aged 30 to 65 years with no prior diagnosis of any of the conditions listed above (type 2 diabetes mellitus, breast cancer, cardiovascular disease, osteoarthritis, or low back pain). This group will also undergo similar assessments including questionnaires, physical examinations, and blood tests, but they will not have the aforementioned conditions.
Cohort
Prospective
Not listed
Number of Participants With Incident Target Disease or Condition Identified Up to 10 Years After the Index Date
Incident target disease or condition will be assessed for five prespecified target diseases or conditions: cardiovascular disease, type 2 diabetes mellitus, breast cancer, low back pain, and osteoarthritis. For each target disease or condition, incident occurrence will be defined as a new diagnosis recorded in electronic medical records after the index date among participants without that target disease or condition at the index date. Results will be summarized separately for each target disease or condition as the number and percentage of participants with incident disease or condition.
Discriminative performance of the artificial intelligence model in distinguishing between disease and control groups using baseline data from health screenings and clinical records (AUROC, PR-AUC)
Discriminative performance will be assessed using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (PR-AUC). These metrics will evaluate the ability of the artificial intelligence model to distinguish participants with incident target disease or condition from persistent controls. Incident disease status will be ascertained by retrospective electronic medical record review up to 10 years after the index date.
Diagnostic Performance of the Artificial Intelligence Model for Predicting Incident Target Diseases or Conditions
Diagnostic performance will be assessed using sensitivity, specificity, positive predictive value, and negative predictive value at a prespecified risk threshold. These metrics will evaluate the ability of the artificial intelligence model to classify participants with incident target disease or condition and persistent controls. Incident disease status will be ascertained by retrospective electronic medical record review up to 10 years after the index date.
Brier Score of the Artificial Intelligence Model for Predicting Incident Target Diseases or Conditions
The Brier score will be calculated as the mean squared difference between predicted risk and observed incident disease status for the five target diseases or conditions. Incident disease status will be ascertained by retrospective electronic medical record review up to 10 years after the index date. Lower values indicate better prediction accuracy.
Calibration Slope of the Artificial Intelligence Model for Predicting Incident Target Diseases or Conditions
Calibration slope will be estimated by comparing predicted risk with observed incident disease status for the five target diseases or conditions. Incident disease status will be ascertained by retrospective electronic medical record review up to 10 years after the index date. A value close to 1 indicates better calibration.
Click on the lead sponsor to view all of their trials.
Jae Yong Jeon, MD
Lead sponsor
Asan Medical Center
Sponsor institution
