CClinicalTrials.gg
RecruitingNCT06993415ORACLEUpdated Jun 10, 2025

Optimize Risk Prediction After Myocardial Infarction: The ORACLE Study

An observational study in Myocardial Infarction (MI), sponsored by Fundación Pública Andaluza para la Investigación de Málaga en Biomedicina y Salud. Recruiting at 1 site in Spain. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2025-06-10.

Sponsored by Fundación Pública Andaluza para la Investigación de Málaga en Biomedicina y Salud · Observational

Study type
Observational
Model
Cohort
Time perspective
Prospective
Enrollment
750
Ages
18 Years and older
Sex
All
01

Study summary

Background. Myocardial infarction (MI) is a leading cause of death worldwide. After MI, longterm antithrombotic therapy is crucial to prevent recurrent events, but increases bleeding, that also impacts morbidity and mortality. Giving these competing risks prediction tools to forecast ischemic and bleeding are of paramount importance to inform clinical decisions, but their current precision is limited. Improve events prediction, by discovering novel and innovative markers of risk would have a tremendous impact on therapeutic decisions and patients' outcome.

Objectives. Discover novel "computational biomarkers" of risk and improve current standards of risk prediction by using innovative multidimensional information from wearable devices, biomarkers, behavioural patterns and non-invasive imaging, integrated through artificial intelligence computation.

Outcomes. The primary outcomes of interest for this analysis are bleeding and ischemic events occurring in or outside the hospital at longest available follow-up. Bleeding will be categorised according to the Bleeding Academic Research Consortium (BARC) definition. The occurrence of major adverse cardiovascular events (MACE), a composite of cardiovascular death, MI, definite stent thrombosis and stroke will be collected according to the Academic Research Consortium-2 classification.

02

Conditions studied

  • Myocardial Infarction (MI)

Keywords

  • Myocardial Infarction (MI)
  • Risk prediction
  • Artificial Intelligence
  • ORACLE study
  • Computational Biomarkers
  • Ischemic Events
  • Bleeding Events
  • Prospective Observational Study
03

Who can participate

Ages eligible
18 Years and older
Sexes eligible
All
Accepts healthy volunteers
No
Sampling method
Probability sample

Study population

Patients with Myocardial Infarction (i.e. hospitalization for ST- segment elevated, non-ST-segment elevated myocardial infarction or unstable angina) undergoing invasive management and at high risk of clinical events (i.e. presence of at least two of these high risk criteria: age >65 years, diabetes mellitus, multivessel disease, peripheral artery disease, chronic kidney disease, prior stroke anytime or prior TIA in the last 6 months, prior MI, complex PCI, Prior PCI/CABG, heart failure, BMI>27, anticipated long term use of an oral anticoagulant, haemoglobin less than 11g/dl, spontaneous bleeding requiring hospitalization or transfusion in the past 12 months, bleeding diathesis* active malignancy other than skin, previous spontaneous intracranial hemorrhage).

Inclusion criteria

  • Patients with Myocardial Infarction (i.e. hospitalization for ST- segment elevated, non-ST-segment elevated myocardial infarction or unstable angina) undergoing invasive management and at high risk of clinical events (i.e. presence of at least two of these high risk criteria: age >65 years, diabetes mellitus, multivessel disease, peripheral artery disease, chronic kidney disease, prior stroke anytime or prior TIA in the last 6 months, prior MI, complex PCI, Prior PCI/CABG, heart failure, BMI>27, anticipated long term use of an oral anticoagulant, haemoglobin less than 11g/dl, spontaneous bleeding requiring hospitalization or transfusion in the past 12 months, bleeding diathesis* active malignancy other than skin, previous spontaneous intracranial hemorrhage).

    • Systemic conditions associated with an increased bleeding risk (e.g. haematological disorders, including a history of or current thrombocytopaenia defined as a platelet count \<100,000/mm3 (\<100 x 10\^9/L), or any known coagulation disorder associated with increased bleeding risk.

Exclusion criteria

Exclusion Criteria:

  • Age \< 18 years
  • Low life expectancy (\<1 year)
  • Pregnant or breastfeeding women
  • Evidence at coronary angiography of non-significant coronary artery disease (\<30% in the left main stem or \<50% in the other coronary segments)
  • Subject belongs to a vulnerable population (per investigator's judgment), subject unable to read or write, or other conditions that unable the patient to fully comprehend and comply to the study procedures as per investigator's judgement
04

Study design

Observational model
Cohort
Time perspective
Prospective
Enrollment
750 participants (estimated)
Patient registry
No
Biospecimen retention
Samples with dna

Groups and cohorts

  • Myocardial infarction (MI)

    Patients with Myocardial Infarction (i.e. hospitalization for ST- segment elevated, non-ST-segment elevated myocardial infarction or unstable angina) undergoing invasive management and at high risk of clinical events (i.e. presence of at least two of these high risk criteria: age \>65 years, diabetes mellitus, multivessel disease, peripheral artery disease, chronic kidney disease, prior stroke anytime or prior TIA in the last 6 months, prior MI, complex PCI, Prior PCI/CABG, heart failure, BMI\>27, anticipated long term use of an oral anticoagulant, haemoglobin less than 11g/dl, spontaneous bleeding requiring hospitalization or transfusion in the past 12 months, bleeding diathesis\* active malignancy other than skin, previous spontaneous intracranial hemorrhage)

    Other: data collection

Interventions

  • Otherdata collection

    The ORACLE program is a prospective, deep phenotyping, study based on multimodal information and artificial intelligence computation. We will prospectively collect in-hospital and out-of-hospital data of a large cohort of patients presenting with MI, including data from wearable devices recording continuous ECG, interstitial-fluids, non-invasive blood pressure and mobility, behavioural patterns from a dedicated mobile application, blood and urine biomarkers and non-invasive imaging. We will leverage on AI, using statistical learning methods and neural networks, to explore patterns and higher order interactions within the data to provide novel "computational biomarkers" of ischemic and bleeding risk.

    Also known as: Data collection from biological samples, wearable devices and tests

05

What researchers measure

Primary outcomes

  1. Frequency and severity of bleeding and ischemic events

    The primary outcomes of interest for this analysis are bleeding and ischemic events occurring in- or outside the hospital at longest available follow-up. Bleeding will be categorised according to the Bleeding Academic Research Consortium (BARC) definition. The occurrence of major adverse cardiovascular events (MACE), a composite of cardiovascular death, MI, definite stent thrombosis and stroke will be collected according to the Academic Research Consortium-2 classification.

    Time frame: 8 months inclusion and 12 months follow-up after end of study

Secondary outcomes

  1. Number of death, stroke, recurrent MI, stent thrombosis, heart failure, hospitalization

    Death will be defined as death from cardiovascular causes or cerebrovascular causes and any death without another known cause. Stroke will be defined as an acute new neurological deficit ending in death or lasting \>24 hours not due to another readily identifiable cause such as trauma. Recurrent MI is defined according to the fourth universal definition of MI. Stent thrombosis will be classified as definite, probable or possible according to the Academic Research Consortium (ARC) definition. New-onset heart failure requiring re-hospitalisation or unplanned medical contact for heart failure symptoms will be evaluated. Recurrent hospitalization for acute coronary syndrome, unstable angina or clinically-indicated urgent revascularization will also be evaluated.

    Time frame: 8 months inclusion and 12 months follow-up after end of study

Other outcomes

  1. Quality life and adherence to treatment

    Patients' quality of life and adherence to treatment will be evaluated with: * Health mobility and mental scales (i.e. EQ-5D-5L and SF-12v2) * Anginal status according to the Seattle Angina questionnaire (SAQ) * Functional status according to the Kansas City Cardiomiopathy questionnaire (KCCQ) * Modified Borg Dyspnoea Scale

    Time frame: 8 months inclusion and 12 months follow-up after end of study

06

Study locations

1 of 1 sites recruiting
  • Hospital Universitario Virgen de la Victoria
    Málaga, 29010, Spain
    Recruiting
07

References and documents

Publications

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  • Valgimigli M, Costa F, Lokhnygina Y, Clare RM, Wallentin L, Moliterno DJ, Armstrong PW, White HD, Held C, Aylward PE, Van de Werf F, Harrington RA, Mahaffey KW, Tricoci P. Trade-off of myocardial infarction vs. bleeding types on mortality after acute coronary syndrome: lessons from the Thrombin Receptor Antagonist for Clinical Event Reduction in Acute Coronary Syndrome (TRACER) randomized trial. Eur Heart J. 2017 Mar 14;38(11):804-810. doi: 10.1093/eurheartj/ehw525. PubMed 28363222 ↗
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  • Choi KH, Song YB, Lee JM, Park TK, Yang JH, Choi JH, Choi SH, Oh JH, Cho DK, Lee JB, Doh JH, Kim SH, Jeong JO, Bae JH, Kim BO, Cho JH, Suh IW, Kim DI, Park HK, Park JS, Choi WG, Lee WS, Gwon HC, Hahn JY. Clinical Usefulness of PRECISE-DAPT Score for Predicting Bleeding Events in Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention: An Analysis From the SMART-DATE Randomized Trial. Circ Cardiovasc Interv. 2020 May;13(5):e008530. doi: 10.1161/CIRCINTERVENTIONS.119.008530. Epub 2020 May 1. PubMed 32354228 ↗
  • Choi SY, Kim MH, Cho YR, Sung Park J, Min Lee K, Park TH, Yun SC. Performance of PRECISE-DAPT Score for Predicting Bleeding Complication During Dual Antiplatelet Therapy. Circ Cardiovasc Interv. 2018 Dec;11(12):e006837. doi: 10.1161/CIRCINTERVENTIONS.118.006837. PubMed 30545256 ↗
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  • D'Ascenzo F, De Filippo O, Gallone G, Mittone G, Deriu MA, Iannaccone M, Ariza-Sole A, Liebetrau C, Manzano-Fernandez S, Quadri G, Kinnaird T, Campo G, Simao Henriques JP, Hughes JM, Dominguez-Rodriguez A, Aldinucci M, Morbiducci U, Patti G, Raposeiras-Roubin S, Abu-Assi E, De Ferrari GM; PRAISE study group. Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets. Lancet. 2021 Jan 16;397(10270):199-207. doi: 10.1016/S0140-6736(20)32519-8. PubMed 33453782 ↗
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Individual participant data

Plan to share: No

08

Registry details

Key details

Study ID
NCT06993415
Lead sponsor
Fundación Pública Andaluza para la Investigación de Málaga en Biomedicina y Salud
Collaborators
European Research Council
Responsible party
Sponsor
First posted
May 28, 2025
Start date
Jun 2025 (estimated)
Primary completion
Feb 2028 (estimated)
Completion
Feb 2028 (estimated)
Last update
Jun 10, 2025

Study contacts

Dr. Francesco Costa
Contact
dottfrancescocosta@gmail.com
+34

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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