AI Algorithms in Prediction of ACS Based on Leukocyte Properties

NCT06384846 | Recruiting DMC

• 1 other identifier: AI ACS
• Study Type: observational
• Target: 2,700
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this observational study is to find out if artificial intelligence (AI) can accurately predict acute coronary syndrome (ACS) using data on white blood cells in adults. The main question it aims to answer is: \- Can AI algorithms based on white blood cell data predict ACS with accuracy comparable to that of high-sensitivity cardiac troponin (hs-cTn)? Researchers will look at how the AI model's predictions stack up against the standard hs-cTn blood tests to see which is more accurate in diagnosing ACS. Participants in this study will have already had blood tests as part of their usual care. Their previously collected health information and blood test results will be used to help train and test the AI algorithms. Participants will not undergo any new procedures for the study itself.

Conditions

Acute Coronary Syndrome; Angina Pectoris; NSTEMI - Non-ST Segment Elevation MI; STEMI - ST Elevation Myocardial Infarction

Keywords

Artificial Intelligence; AI; hematology analyzer; ACS

Outcome Measures

Primary Outcomes (2)

• Training of AI models

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

  36 months

• Testing of AI models

  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

  36 months

Secondary Outcomes (2)

• Training of AI models

  36 months

• Testing of AI models:

  36 months

Study Arms (3)

Control-Cohort

Subjects with suspected ACS but exclusion of a culprit lesion during coronary angiography.

Case-Cohort

Subjects with suspected ACS and identification of a culprit lesion during coronary angiography.

Supplementary cohort

Subjects with no or stable angina pectoris and no indication for revascularization during coronary angiography.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients of the Landeskrankenhaus Graz of the State Styria. These are usually from Styria, Austria, sometimes bordering Austrian States. The hospital is a maximum-care clinic. The data is collected in the cardiology unit.

You may qualify if:

• Male or Female, aged 18 years or above
• Participant is willing and able to give informed consent for participation in the study
• Subjects presenting without chest pain or with stable angina pectoris but without indication for revascularization during coronary angiography; identical evaluation results by review board required
• Criteria for timing of blood sampling for collection of WBC and hs-cTn data need to be fulfilled (see 5.14)
• o Subjects with no or stable angina pectoris must have provided WBC data and at least one hs-cTn value any time before start of coronary angiography.
• Between initial blood sampling to collect WBC data and coronary angiography, the subject must not develop suspicion of ACS.

You may not qualify if:

• Age \< 18 years old
• 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 occurred in subjects with no or stable angina pectoris any time between initial blood sampling and start of coronary angiography

Sponsors & Collaborators

• RobotDreams GmbH: lead

Study Sites (1)

Landeskrankenhaus-Universitätsklinikum Graz

Graz, Styria / Steiermark, 8036, Austria

RECRUITING

MeSH Terms

Conditions

Acute Coronary Syndrome; Angina Pectoris; Non-ST Elevated Myocardial Infarction; ST Elevation Myocardial Infarction

Condition Hierarchy (Ancestors)

Myocardial Ischemia; Heart Diseases; Cardiovascular Diseases; Vascular Diseases; Chest Pain; Pain; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Myocardial Infarction; Infarction; Ischemia; Pathologic Processes; Necrosis

Central Study Contacts

Dimitrij Shulkin, M.Sc.

CONTACT

+43-676-5150578; shulkin@robotdreams.co

Johannes Gollmer, Dr. univ.

CONTACT

johannes.gollmer@medunigraz.at

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 22, 2024
• First Posted: April 25, 2024
• Study Start: February 1, 2024
• Primary Completion (Estimated): July 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: April 25, 2024

Record last verified: 2024-04

Locations

AU (1)