An observational study in Coronary Artery Calcification, Cardiovascular Risk and Coronary Artery Disease, sponsored by University of Cologne. Not yet recruiting. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-09-09.
Sponsored by University of Cologne · Observational
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.
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:
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.
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.
Exclusion Criteria:
Paired non-gated chest CT and ECG-gated cardiac CT (≤ 12 months apart). AI-CAC score compared against the reference Agatston score. n ≈ 150.
150 hemodialysis patients plus 150 matched kidney-healthy controls with serial non-gated CTs. Calcification progression and all-cause mortality. n ≈ 300.
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
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
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.
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
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.
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
Time frame: 12 months after the index emergency department visit
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
Time frame: At the index non-contrast chest CT (Day 0)
No study locations are listed for this record.
Plan to share: No
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University of Cologne