Artificial Intelligence - Based Opportunistic Coronary Artery Calcium Scoring on Routine Chest-CT Scan

NCT07808593 | Not Yet Recruiting

• 2 other identifiers: AI-CAC-DE22-1182
• Study Type: observational
• Target: 1,950
• Locations: 0 countries
• Sites: N/A

Brief Summary

This retrospective, non-interventional study externally validates a pre-trained open-weight deep-learning algorithm (Swin-UNETR) for the opportunistic quantification of coronary artery calcium (CAC) on non-gated routine chest CT scans acquired at a German academic center, and evaluates the prognostic value of this automated imaging biomarker for cardiovascular risk stratification. Coronary calcium is an established predictor of cardiovascular risk, but is not routinely quantified on the tens of thousands of non-cardiac chest CTs performed each year. Because existing high-performing AI models were trained almost exclusively on U.S. cohorts, external validation on a European scanner fleet is required to exclude scanner bias (domain shift). The study comprises three linked analytic cohorts: (1) a validation cohort comparing the AI-CAC score against the ECG-gated cardiac CT Agatston reference; (2) a dialysis cohort assessing calcification progression and mortality; and (3) an emergency department cohort assessing short-term cardiovascular events. This is an investigator-initiated trial with no intervention on patients.

Conditions

Coronary Artery Calcification; Cardiovascular Risk; Coronary Artery Disease; End-Stage Renal Disease Requiring Haemodialysis; Emergency Care

Keywords

Coronary Artery Calcification; Coronary Artery Disease; Opportunistic Screening; Artificial Intelligence; Deep Learning; Chest CT; Agatston Score; External Validation; Cardiovascular Risk Stratification; Hemodialysis; MACE

Outcome Measures

Primary Outcomes (3)

• Diagnostic agreement of the AI-CAC score with the reference Agatston score

  Agreement between the automatically extracted AI-CAC score from the non-gated chest CT and the reference Agatston score from a paired ECG-gated cardiac CT, reported as Spearman correlation coefficient, Cohen's kappa across established risk classes (0, 1-100, 101-400, \> 400), and Bland-Altman limits of agreement; supplemented by sensitivity, specificity, positive predictive value(PPV)/negative predictive value(NPV) and F1 score

  At the index non-contrast chest CT (Day 0) and at the paired ECG-gated cardiac CT obtained within 12 months after the index CT.

• Annualized calcification progression rate and all-cause mortality

  Difference in the mean annual increase in AI-CAC between hemodialysis patients (dialysis cohort) and matched kidney-healthy controls measured on serial non-gated CTs (t-test / Mann-Whitney-U), and all-cause mortality analyzed by Kaplan-Meier (log-rank) and multivariable Cox proportional-hazards models (hazard ratios adjusted for confounders

  From the index non-contrast chest CT (Day 0) through the last available serial non-gated chest CT and the end of individual follow-up, up to 10 years per participant.

• In-hospital Major Adverse Cardiac Event or cardiovascular readmission within 12 months (Emergency Department cohort).

  Occurrence of in-hospital Major Adverse Cardiovascular Events (myocardial infarction, stroke, resuscitation) or cardiovascular readmission within 12 months of the index Emergency Department visit, in relation to an unrecognized high AI calcium score (\> 400); reported as adjusted odds ratios and hazard ratios from logistic regression and Cox models

  12 months after the index emergency department visit

Secondary Outcomes (1)

• Technical feasibility and inference time of the open-source AI model on local GPU clusters

  At the index non-contrast chest CT (Day 0)

Study Arms (3)

Validation Cohort

Paired non-gated chest CT and ECG-gated cardiac CT (≤ 12 months apart). AI-CAC score compared against the reference Agatston score. n ≈ 150.

Dialysis Cohort

150 hemodialysis patients plus 150 matched kidney-healthy controls with serial non-gated CTs. Calcification progression and all-cause mortality. n ≈ 300.

Emergency Department Cohort

Patients \> 50 years with non-gated chest CTs from the emergency department without primary cardiac focus. In-hospital MACE / cardiovascular readmission within 12 months. n ≈ 1,500

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Retrospective patients at University Hospital Cologne with routine clinical CT imaging (01 January 2015 - 31 December 2025), across three analytic cohorts (validation, dialysis, emergency department); approximately 1,950 cases.

You may qualify if:

• Validation cohort: patients with a paired non-gated chest CT and an ECG-gated cardiac CT acquired within ≤ 12 months.
• Dialysis cohort: hemodialysis patients with serial non-gated CTs, plus matched kidney-healthy controls.
• Emergency department cohort: patients \> 50 years with non-gated chest CTs from the Emergency Department without a primary cardiac focus.

You may not qualify if:

• Documented objection to the scientific use of the data pursuant to Art. 21 GDPR.
• Cases lacking the minimum data required for analysis (insufficient image quality or missing reference/outcome data).

Sponsors & Collaborators

• University of Cologne: lead
• Institute for Diagnostic and Interventional Radiology, University Hospital Cologne: collaborator
• Medical Data Integration Center (MeDIC), University Hospital Cologne: collaborator

MeSH Terms

Conditions

Coronary Artery Disease

Condition Hierarchy (Ancestors)

Coronary Disease; Myocardial Ischemia; Heart Diseases; Cardiovascular Diseases; Arteriosclerosis; Arterial Occlusive Diseases; Vascular Diseases

Study Officials

• Cem Özel, MD

  Department of Internal Medicine II, University Hospital Cologne

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Cem Özel, MD

CONTACT

+49 176 2113 7580; cem.oezel@uk-koeln.de

Carsten Gietzen, MD

CONTACT

carsten.gietzen@uk-koeln.de

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Prof. Dr.

Study Record Dates

• First Submitted: September 1, 2026
• First Posted: September 9, 2026
• Study Start (Estimated): November 1, 2026
• Primary Completion (Estimated): November 1, 2027
• Study Completion (Estimated): December 1, 2027
• Last Updated: September 9, 2026

Record last verified: 2026-09

Data Sharing

• IPD Sharing: Will not share