Clinical trial NCT06595134: AI-POD Clinical Validation Study for Obese Patients
Official title: AI for the Prediction of Obesity-Related Vascular Diseases - Validation Study
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Acute Cardiovascular Disease
Keywords: Obesity, Heart CT, Artificial Intelligence
Lead sponsor: University Hospital, Bonn
Sponsor class: OTHER
Collaborators: name: European Union
First posted: 2024-09-19
Enrollment: 1200
Sex: ALL
Minimum age (years): 45
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Obese patients (BMI \>30 kg/m2) with suspected CVD and scheduled for a cardiac CT examination (consisting of calcium scoring and coronary CT angiography).

Brief summary: The study is aimed at patients who suffer from severe obesity and receive a computerised tomography (CT) scan of the heart. A total of 1200 patients across Europe are to take part in the study. It is a multi-centre, controlled, open, randomised study. As part of the study, a citizen app will be installed on a smartphone for randomly selected study participants. A fitness tracker will also be issued so that certain parameters, such as heart rate and daily number of steps, can be recorded.

Detailed description: The clinical validation study of the AI-POD instruments (risk score, CDSS, Citizen App) is designed as a prospective, multicentre observational study. Obese patients (BMI \>30 kg/m2) with suspected CVD who are scheduled for a cardiac CT scan (consisting of calcium scoring and coronary CT angiography) will be included in the study. This clinically indicated cardiac CT scan will serve as a baseline examination, and a second cardiac CT scan will be performed after a follow-up period of 2 years. Both cardiac CT examinations will be performed on a photon-counting CT system (PCCT). In addition to the CT data, clinical data (anthropometry and physical parameters such as body weight, height, age, gender, BMI, waist circumference, waist-to-hip ratio, body impedance analysis, cardiovascular risk factors, medication, previous interventions and operations) will be collected, symptoms and results of echocardiography to determine left ventricular function) as well as laboratory data on (subclinical) inflammation (hsCRP, leukocytes, monocyte activation), lipid disorders (HDL, LDL, total cholesterol, triacylglycerides), glucose homeostasis (HbA1c, fasting glucose, HOMA index), liver and kidney function (creatinine, GFR, GOT/ALAT, GPT/ASAT) are collected at a total of 5 visits over 2 years. Half of all included patients will also be randomised to receive access to the Citizen app and a separate fitness tracker device to record the following data: Activity data such as step count, heart rate, height, weight, gender, age, waist circumference circumference, nutritional data such as calorie intake, nicotine, alcohol, sleep.

Eligibility criteria: Inclusion Criteria:

* Age greater than or equal to 45 years at the time of signing informed consent
* BMI greater than or equal to 30 kg/m2
* Patients with suspected CVD (\>15% pretest probability \[ESC guidelines\]), referred for cardiac CT examinations (consisting of coronary calcium scoring and coronary CT angiography)
* Informed consent of the patient

Exclusion Criteria:

* Any of the following CV conditions within 2 months prior to study inclusion: myocardial infarction, stroke,
* Hospitalization for unstable angina pectoris or transient ischemic attack or due to congestive heart failure.
* Planned coronary, carotid, or peripheral artery revascularisation known on the day of inclusion
* Presently classified NYHA IV heart failure
* Having uncontrolled diabetes mellitus (HbA1c ≥ 11%) at day of inclusion
* Having uncontrolled hypertension (SBP ≥ 160 mmHg and/or DBP ≥ 100 mmHg) as measured at inclusion
* Having severe renal impairment measured as an eGFR \< 30 mL/min/1.73 m2 at inclusion
* Alanine aminotransferase (ALT) or alkaline phosphatase (APT) level \< 3.0 x the upper limit of normal (ULN)for the reference range
* Total bilirubin level \>1.5 x the ULN for the reference range
* History of an active or untreated malignancy or are in remission from a clinically significant malignancy for less than 5 years
* Previous organ transplantation or awaiting an organ transplant
* Pregnancy or breastfeeding

Primary outcomes:
measure: An improved accuracy in prediction of CVD progression by us-ing the AI-POD risk score
timeFrame: Between Visit 1 and Visit 5 (Day 0 and Month 24)
measure: An improved accuracy in prediction of CVD progression by us-ing the AI-POD risk score
timeFrame: Between Visit 1 and Visit 5 (Day 0 and Month 24)
measure: An improved accuracy in prediction of CVD progression by us-ing the AI-POD risk score
timeFrame: Between Visit 1 and Visit 5 (Day 0 and Month 24)
Secondary outcomes:
measure: To validate the Citizen App, to assess the improvement of AI-POD risk score Accuracy and feasibility of collecting data with citizen App
timeFrame: After 2 Years
measure: To validate the Citizen App, to assess the improvement of AI-POD risk score Accuracy and feasibility of collecting data with citizen App
timeFrame: After 2 Years
measure: To validate the Citizen App, to assess the improvement of AI-POD risk score Accuracy and feasibility of collecting data with citizen App
timeFrame: After 2 Years
measure: To validate the Citizen App, to assess the improvement of AI-POD risk score Accuracy and feasibility of collecting data with citizen App
timeFrame: After 2 Years
measure: To validate the Citizen App, to assess the improvement of AI-POD risk score Accuracy and feasibility of collecting data with citizen App
timeFrame: After 2 Years
measure: To validate the Citizen App, to assess the improvement of AI-POD risk score Accuracy and feasibility of collecting data with citizen App
timeFrame: After 2 Years
measure: To validate the Citizen App, to assess the improvement of AI-POD risk score Accuracy and feasibility of collecting data with citizen App
timeFrame: After 2 Years
measure: To validate the Citizen App, to assess the improvement of AI-POD risk score Accuracy and feasibility of collecting data with citizen App
timeFrame: After 2 Years
measure: To validate the Citizen App, to assess the improvement of AI-POD risk score Accuracy and feasibility of collecting data with citizen App
timeFrame: After 2 Years
measure: To validate the Citizen App, to assess the improvement of AI-POD risk score Accuracy and feasibility of collecting data with citizen App
timeFrame: After 2 Years
Other outcomes:
Locations:
city: Vienna
country: Austria
facility: Medical University Vienna
city: Leuven
country: Belgium
facility: University Hospital Leuven
city: Pilsen
country: Czechia
facility: University Hospital Pilsen
city: Bonn
country: Germany
facility: University Hospital Bonn
state: NRW
city: Mannheim
country: Germany
facility: University Hospital Mannheim
city: Zürich
country: Switzerland
facility: University Hospital Zürich
Study officials:
affiliation: ulrike.attenberger@meduniwien.ac.at
name: Ulrike Attenberger, Prof. Dr.
role: PRINCIPAL_INVESTIGATOR
References: