Coronary Artery CalcificationNCT07808593
Artificial Intelligence - Based Opportunistic Coronary Artery Calcium Scoring on Routine Chest-CT Scan
An observational study of Coronary Artery Calcification and Cardiovascular Risk, run by University of Cologne.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Coronary Artery Calcification, Cardiovascular Risk, Coronary Artery Disease
- How many people
- 1,950 participants
- Where
- No sites listed yet
- Study dates
- 1 Nov 2026to 1 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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).
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
Carsten Gietzen, MD contact
About this study
Official title: Opportunistic Screening of Coronary Artery Calcium on Non-Gated Routine Chest CT Using Artificial Intelligence: Retrospective External Validation and Clinical Risk Stratification
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.
Read the detailed description
The study analyzes a retrospective cohort of routine clinical CT examinations at University Hospital Cologne. Data originate from the hospital information system and Picture Archiving and Communication System (PACS) and are provided in pseudonymized form via the Medical Data Integration Center (MeDIC), acting as an independent trusted third party; the re-identification key remains under the sole control of MeDIC. Deep-learning inference is performed locally on isolated, access-controlled graphics processing unit (GPU) clusters of the institution (privacy by design / zero data retention); an open-weight model (Swin-UNETR) is used.
Three research questions are addressed in three analytic cohorts:
- Validation (n ≈ 150): diagnostic agreement of the automatically extracted AI-CAC score (from the non-gated CT) with the reference Agatston score from a paired ECG-gated cardiac CT acquired within ≤ 12 months.
- Dialysis (n ≈ 300): 150 hemodialysis patients plus 150 matched kidney-healthy controls with serial non-gated CTs; annualized calcification progression rate and all-cause mortality.
- Emergency department (n ≈ 1,500): patients > 50 years with non-gated chest CTs from the Emergency Department (without a primary cardiac focus); occurrence of in-hospital major adverse cardiac event (MACE) or cardiovascular readmission within 12 months.
Extracted data include demographics (age at examination, sex), cardiovascular risk factors and comorbidities (ICD-10), long-term medication, laboratory values, examination metadata (date, scanner manufacturer, kilovolt peak (kVp), slice thickness), and outcome data (mortality, cardiovascular events, readmissions). Statistical analysis uses Spearman correlation, Cohen's kappa and Bland-Altman analysis for method comparison; t-test / Mann-Whitney-U for group differences in progression; and Kaplan-Meier (log-rank) plus multivariable Cox proportional-hazards and logistic regression for outcome prediction. Legal basis: § 6 (1) no. 2 Health Data Use Act of Germany (GDNG) in conjunction with Art. 9 (2) (j) and Art. 89 (1) GDPR (research privilege); no individual consent (disproportionate effort, Art. 14 (5) (b) GDPR). The AI (artificial intelligence) model carries no CE-marking and is used strictly as a research tool; AI-CAC scores are not systematically fed back into clinical care.
- 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
- Medical subject headings
- Coronary Artery Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 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
Measured: 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
Measured: 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
Inference time per case and technical feasibility of running the open-weight deep-learning model (Swin-UNETR) as an isolated container on the institution's local GPU infrastructure
Measured: At the index non-contrast chest CT (Day 0)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Sep 2026
- Study start1 Nov 2026 (estimated)
- Primary completion1 Nov 2027 (estimated)
- Study completion1 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Coronary Artery CalcificationStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,950 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Cologne
- Sponsor type
- University, hospital or other
- Collaborators
- University Hospital of Cologne
- Responsible party
- Principal investigator, Volker Burst, University of Cologne
- Investigators
- Cem Özel, MD, Principal investigator, Department of Internal Medicine II, University Hospital Cologne
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07808593
- Sponsor's protocol ID
- AI-CAC-DE
- Other IDs
- 22-1182
- Status last verified
- Sep 2026
- First submitted
- 1 Sep 2026
- Last updated
- 9 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 9 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Opportunistic Screening of Coronary Artery Calcium on Non-Gated Routine Chest CT Using Artificial Intelligence: Retrospective External Validation and Clinical Risk Stratification
- ClinicalTrials.gov ID
- NCT07808593
- Acronym / sponsor ID
- AI-CAC-DE
- 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
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,950 participants (estimated)
- Lead sponsor
- University of CologneUniversity, hospital or other
- Collaborators
- University Hospital of Cologne
- Study start
- 1 Nov 2026 (estimated)
- Primary completion
- 1 Nov 2027 (estimated)
- Study completion
- 1 Dec 2027 (estimated)
- First posted
- 9 Sep 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
The study analyzes a retrospective cohort of routine clinical CT examinations at University Hospital Cologne. Data originate from the hospital information system and Picture Archiving and Communication System (PACS) and are provided in pseudonymized form via the Medical Data Integration Center (MeDIC), acting as an independent trusted third party; the re-identification key remains under the sole control of MeDIC. Deep-learning inference is performed locally on isolated, access-controlled graphics processing unit (GPU) clusters of the institution (privacy by design / zero data retention); an open-weight model (Swin-UNETR) is used.
Three research questions are addressed in three analytic cohorts:
- Validation (n ≈ 150): diagnostic agreement of the automatically extracted AI-CAC score (from the non-gated CT) with the reference Agatston score from a paired ECG-gated cardiac CT acquired within ≤ 12 months.
- Dialysis (n ≈ 300): 150 hemodialysis patients plus 150 matched kidney-healthy controls with serial non-gated CTs; annualized calcification progression rate and all-cause mortality.
- Emergency department (n ≈ 1,500): patients > 50 years with non-gated chest CTs from the Emergency Department (without a primary cardiac focus); occurrence of in-hospital major adverse cardiac event (MACE) or cardiovascular readmission within 12 months.
Extracted data include demographics (age at examination, sex), cardiovascular risk factors and comorbidities (ICD-10), long-term medication, laboratory values, examination metadata (date, scanner manufacturer, kilovolt peak (kVp), slice thickness), and outcome data (mortality, cardiovascular events, readmissions). Statistical analysis uses Spearman correlation, Cohen's kappa and Bland-Altman analysis for method comparison; t-test / Mann-Whitney-U for group differences in progression; and Kaplan-Meier (log-rank) plus multivariable Cox proportional-hazards and logistic regression for outcome prediction. Legal basis: § 6 (1) no. 2 Health Data Use Act of Germany (GDNG) in conjunction with Art. 9 (2) (j) and Art. 89 (1) GDPR (research privilege); no individual consent (disproportionate effort, Art. 14 (5) (b) GDPR). The AI (artificial intelligence) model carries no CE-marking and is used strictly as a research tool; AI-CAC scores are not systematically fed back into clinical care.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| 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 |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (2 listed)
- 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).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Diagnostic agreement of the AI-CAC score with the reference Agatston score
Time frame: 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.
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
Annualized calcification progression rate and all-cause mortality
Time frame: 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.
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
In-hospital Major Adverse Cardiac Event or cardiovascular readmission within 12 months (Emergency Department cohort).
Time frame: 12 months after the index emergency department visit
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
1 secondary outcome
Technical feasibility and inference time of the open-source AI model on local GPU clusters
Time frame: At the index non-contrast chest CT (Day 0)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Sep 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Carsten Gietzen, MD contact
Cem Özel, MD principal investigator
Department of Internal Medicine II, University Hospital Cologne
Questions about this trial
What is this clinical trial studying?
An observational study of Coronary Artery Calcification and Cardiovascular Risk, run by University of Cologne.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 9 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Cem Özel, MD (+49 176 2113 7580, cem.oezel@uk-koeln.de), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic agreement of the AI-CAC score with the reference Agatston score (time frame: 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.). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Sep 2026.