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NCT Number: NCT06384846

Diagnostic Performance of Artificial Intelligence Algorithms in Prediction of Acute Coronary Syndrome Based on White Blood Cell Properties

The goal of this observational study is to evaluate whether artificial intelligence (AI) algorithms can predict or exclude acute coronary syndrome (ACS) in adults using data generated by routine hematology testing. The main questions the study aims to answer are:

* Can AI algorithms based on white blood cell (WBC) data predict or exclude ACS in subjects with suspected ACS? * Can erythrocyte (EC) and/or thrombocyte (TC) data, where available, improve or complement WBC-based AI prediction of ACS? * How does the diagnostic performance of the AI algorithms compare with high-sensitivity cardiac troponin (hs-cTn), and can the combination of AI algorithms and hs-cTn improve diagnostic performance?

Participants will undergo clinical assessment and blood testing as part of usual clinical care. Their previously generated clinical information, hematology data, and hs-cTn results will be used to train and test the AI algorithms. Participation in the study does not determine the indication for coronary angiography or treatment, and no additional study-specific treatments are performed.

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Key information

About this study

The AI-ACS clinical trial is an observational, prospective, single-center case-control study conducted at the Medical University of Graz. The trial is designed to assess the diagnostic performance of AI algorithms using hematology data to predict or exclude ACS in adult subjects.

The primary focus of the study is the use of WBC data generated by routine hematology testing. In addition, EC and/or TC data may be used, where available, to explore whether these data can predict or exclude ACS independently or improve WBC-based ACS prediction. The diagnostic performance of the AI algorithms will be compared with high-sensitivity cardiac troponin (hs-cTn), and the performance of combined AI and hs-cTn approaches will also be evaluated.

The AI-ACS trial consists of two main phases: training of AI models and testing of AI models.

For training of AI models, subjects will be assigned to the control cohort, case cohort, supplementary cohort, or rule-out cohort. The control cohort includes subjects with suspected ACS but exclusion of a culprit lesion during coronary angiography. The case cohort includes subjects with suspected ACS and identification of a culprit lesion during coronary angiography. The supplementary cohort includes subjects with no or stable angina pectoris and no indication for revascularization during coronary angiography. The rule-out cohort includes subjects with suspected non-ST elevation ACS and NSTEMI rule-out who did not undergo coronary angiography within 72 hours. WBC data from the control, case, and supplementary cohorts will be used to train AI models, while WBC data from the rule-out cohort may be used to optimize AI training by semi-supervised learning. Whenever available, EC and/or TC data may also be used for exploratory AI training, either alone or in combination with WBC data.

For testing of AI models, a separate all-comer cohort will be used. This cohort includes subjects presenting to the emergency department with suspected ACS. WBC and/or EC/TC data from the all-comer cohort will be used to evaluate the diagnostic performance of trained AI models. Data used for testing will not be included in the training set, in order to avoid data leakage and to ensure unbiased evaluation of model performance.

The anticipated maximum number of subjects to be recruited is 3,350. Of these, 2,350 subjects will be recruited for training of AI models and 1,000 subjects will be recruited for testing of AI models. The training population includes the control cohort, case cohort, supplementary cohort, and rule-out cohort. The testing population consists of the separate all-comer cohort.

Hematology data are collected from routine blood tests performed as part of usual clinical care, using hematology analyzers. Clinical assessment, ECG interpretation, hs-cTn measurement, coronary angiography, and treatment decisions are performed according to current clinical guidelines and are independent of study participation. The presence or absence of ACS is determined based on clinical diagnostic procedures, including coronary angiography and review board evaluation where applicable.

The diagnostic performance of the AI models will be evaluated using receiver operating characteristic (ROC) curve analysis, area under the ROC curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value. The diagnostic performance of hs-cTn and combined AI plus hs-cTn approaches will also be assessed. Training of AI models is planned to be repeated at regular intervals as the number of included datasets increases.

The study includes procedures for data validation, source data verification, data management, and quality control to support the accuracy, completeness, and integrity of the data collected. Missing or inconsistent data will be addressed according to the statistical analysis and data management procedures defined in the protocol.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

General inclusion criteria:

  • Male or female, aged 18 years or above.
  • Participant is willing and able to give informed consent for participation in the study.
  • Collection of WBC and hs-cTn data must be possible.
  • Criteria for timing of blood sampling for collection of WBC and hs-cTn data must be fulfilled, where applicable.

Case cohort:

  • Suspicion of STEMI or NSTE-ACS according to current ESC guidelines.
  • Coronary angiography must have been performed within 72 hours after initial suspicion of ACS.
  • For patients qualifying for observation according to ESC guidelines, coronary angiography is not mandatory and time limits do not apply.
  • Confirmation of STEMI or NSTE-ACS by identification of a culprit lesion using coronary angiography; identical evaluation results by review board required.
  • For observation patients without coronary angiography, final discharge diagnosis is used to decide about the presence or absence of NSTEMI and/or ACS.
  • Criteria for timing of blood sampling for collection of WBC and hs-cTn data must be fulfilled.

Control cohort:

  • Suspicion of STEMI or NSTE-ACS according to current ESC guidelines.
  • Coronary angiography must have been performed within 72 hours after initial suspicion of ACS.
  • No identification of a culprit lesion compatible with diagnosis of STEMI or NSTE-ACS during coronary angiography; identical evaluation results by review board required.
  • Criteria for timing of blood sampling for collection of WBC and hs-cTn data must be fulfilled.

Supplementary cohort:

  • Subject presents without chest pain or with stable angina pectoris.
  • No indication for revascularization during coronary angiography; identical evaluation results by review board required.
  • Exclusion of elevated hs-cTn.
  • Criteria for timing of blood sampling for collection of WBC and hs-cTn data must be fulfilled.
  • Between initial blood sampling to collect WBC data and coronary angiography, the subject must not develop suspicion of ACS.

Rule-out cohort:

  • Suspicion of NSTE-ACS and NSTEMI rule-out according to current ESC guidelines, i.e. very low initial hs-cTn value, or low initial hs-cTn value and no significant 1-hour/2-hour change in hs-cTn value.
  • No coronary angiography within 72 hours.
  • Criteria for timing of blood sampling for collection of WBC and hs-cTn data must be fulfilled.

All-comer cohort:

  • Subject presents to the emergency department with suspected ACS.
  • Clinical assessments, ECG, and measurements of hs-cTn, single or serial measurement, must be conducted according to ESC guidelines.
  • Collection of WBC data must be performed at initial blood withdrawal after admission to the emergency department.
  • Review board evaluations must confirm the presence or absence of a culprit lesion if coronary angiography was performed, as outlined for the case and control cohorts.

Exclusion criteria

  • Age below 18 years.
  • Subject refuses informed consent.
  • Collection of WBC and hs-cTn data is not possible.
  • Criteria for timing of blood sampling for collection of WBC and hs-cTn data cannot be fulfilled.
  • Suspicion of ACS occurs in subjects with no or stable angina pectoris any time between initial blood sampling and start of coronary angiography.

Treatment and study plan

Primary outcomes

  1. Training of AI models

    Time frame: 36 months

    Diagnostic performance of AI models in predicting ACS, evaluated by area under curve (AUC) under the receiver operating characteristic (ROC) curve

  2. Testing of AI models

    Time frame: 36 months

    Diagnostic performance of AI models in predicting ACS, evaluated by AUC under ROC curve

    ; Specificity and sensitivity of AI models to predict ACS in subjects with suspected ACS, calculated from AUC under ROC curve

Secondary outcomes

  1. Training of AI models

    Time frame: 36 months

    Sensitivity of AI models to predict ACS

    ; Specificity of AI models to predict ACS

  2. Testing of AI models:

    Time frame: 36 months

    • Sensitivity of AI models to predict ACS
    • Specificity of AI models to predict ACS
    • Sensitivity of hs-cTn to predict ACS
    • Specificity of hs-cTn to predict ACS
    • Combined sensitivity of AI models and hs-cTn to predict ACS
    • Combined specificity of AI models and hs-cTn to predict ACS
    • AUC under ROC curve of hs-cTn predicting ACS
    • AUC under ROC curve of AI models and hs-cTn predicting ACS
    • Difference in predicting ACS between hs-cTn and AI models using AUC under ROC curve

Study contacts

Contact information is provided by the study sponsor or research team.

Dimitrij Shulkin, M.Sc.

CONTACT

[email protected]

+43-676-5150578

Johannes Gollmer, Dr. univ.

CONTACT

[email protected]

Sponsors and collaborators

Lead sponsor

RobotDreams GmbH

Industry

Registry information

Official study title

Diagnostic Performance of Artificial Intelligence Algorithms in Prediction of Acute Coronary Syndrome Based on White Blood Cell Properties (AI-ACS Trial)

Important dates

Study start
2024
Primary completion
2026
Study completion
2026
First posted
Apr 25, 2024
Registry last updated
Jun 24, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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