Clinical trial NCT06993415: Optimize Risk Prediction After Myocardial Infarction: The ORACLE Study
Official title: Optimize Risk Prediction After Myocardial Infarction Through Artificial Intelligence and Multidimensional Evaluation: The ORACLE Study
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Myocardial Infarction (MI)
Keywords: Myocardial Infarction (MI)
Risk prediction
Artificial Intelligence
ORACLE study
Computational Biomarkers
Ischemic Events
Bleeding Events
Prospective Observational Study
Lead sponsor: Fundación Pública Andaluza para la Investigación de Málaga en Biomedicina y Salud
Sponsor class: OTHER
Collaborators: name: European Research Council
First posted: 2025-05-28
Enrollment: 750
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
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).

Brief 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.


Eligibility criteria: 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:

* 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

Primary outcomes:
measure: Frequency and severity of bleeding and ischemic events
timeFrame: 8 months inclusion and 12 months follow-up after end of study
Secondary outcomes:
measure: Number of death, stroke, recurrent MI, stent thrombosis, heart failure, hospitalization
timeFrame: 8 months inclusion and 12 months follow-up after end of study
Other outcomes:
measure: Quality life and adherence to treatment
timeFrame: 8 months inclusion and 12 months follow-up after end of study
Locations:
city: Málaga
country: Spain
facility: Hospital Universitario Virgen de la Victoria
Study officials:
References:
citation: Writing Group Members; Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, Das SR, de Ferranti S, Despres JP, Fullerton HJ, Howard VJ, Huffman MD, Isasi CR, Jimenez MC, Judd SE, Kissela BM, Lichtman JH, Lisabeth LD, Liu S, Mackey RH, Magid DJ, McGuire DK, Mohler ER 3rd, Moy CS, Muntner P, Mussolino ME, Nasir K, Neumar RW, Nichol G, Palaniappan L, Pandey DK, Reeves MJ, Rodriguez CJ, Rosamond W, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Woo D, Yeh RW, Turner MB; American Heart Association Statistics Committee; Stroke Statistics Subcommittee. Heart Disease and Stroke Statistics-2016 Update: A Report From the American Heart Association. Circulation. 2016 Jan 26;133(4):e38-360. doi: 10.1161/CIR.0000000000000350. Epub 2015 Dec 16. No abstract available.
citation: Valgimigli M, Bueno H, Byrne RA, Collet JP, Costa F, Jeppsson A, Juni P, Kastrati A, Kolh P, Mauri L, Montalescot G, Neumann FJ, Petricevic M, Roffi M, Steg PG, Windecker S, Zamorano JL, Levine GN; ESC Scientific Document Group; ESC Committee for Practice Guidelines (CPG); ESC National Cardiac Societies. 2017 ESC focused update on dual antiplatelet therapy in coronary artery disease developed in collaboration with EACTS: The Task Force for dual antiplatelet therapy in coronary artery disease of the European Society of Cardiology (ESC) and of the European Association for Cardio-Thoracic Surgery (EACTS). Eur Heart J. 2018 Jan 14;39(3):213-260. doi: 10.1093/eurheartj/ehx419. No abstract available.
citation: Simonsson M, Wallentin L, Alfredsson J, Erlinge D, Hellstrom Angerud K, Hofmann R, Kellerth T, Lindhagen L, Ravn-Fischer A, Szummer K, Ueda P, Yndigegn T, Jernberg T. Temporal trends in bleeding events in acute myocardial infarction: insights from the SWEDEHEART registry. Eur Heart J. 2020 Feb 14;41(7):833-843. doi: 10.1093/eurheartj/ehz593.
citation: 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.
citation: Levine GN, Bates ER, Bittl JA, Brindis RG, Fihn SD, Fleisher LA, Granger CB, Lange RA, Mack MJ, Mauri L, Mehran R, Mukherjee D, Newby LK, O'Gara PT, Sabatine MS, Smith PK, Smith SC Jr. 2016 ACC/AHA Guideline Focused Update on Duration of Dual Antiplatelet Therapy in Patients With Coronary Artery Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2016 Sep 6;68(10):1082-115. doi: 10.1016/j.jacc.2016.03.513. Epub 2016 Mar 29. No abstract available.
citation: Fox KA, Dabbous OH, Goldberg RJ, Pieper KS, Eagle KA, Van de Werf F, Avezum A, Goodman SG, Flather MD, Anderson FA Jr, Granger CB. Prediction of risk of death and myocardial infarction in the six months after presentation with acute coronary syndrome: prospective multinational observational study (GRACE). BMJ. 2006 Nov 25;333(7578):1091. doi: 10.1136/bmj.38985.646481.55. Epub 2006 Oct 10.
citation: Subherwal S, Bach RG, Chen AY, Gage BF, Rao SV, Newby LK, Wang TY, Gibler WB, Ohman EM, Roe MT, Pollack CV Jr, Peterson ED, Alexander KP. Baseline risk of major bleeding in non-ST-segment-elevation myocardial infarction: the CRUSADE (Can Rapid risk stratification of Unstable angina patients Suppress ADverse outcomes with Early implementation of the ACC/AHA Guidelines) Bleeding Score. Circulation. 2009 Apr 14;119(14):1873-82. doi: 10.1161/CIRCULATIONAHA.108.828541. Epub 2009 Mar 30.
citation: Baber U, Mehran R, Giustino G, Cohen DJ, Henry TD, Sartori S, Ariti C, Litherland C, Dangas G, Gibson CM, Krucoff MW, Moliterno DJ, Kirtane AJ, Stone GW, Colombo A, Chieffo A, Kini AS, Witzenbichler B, Weisz G, Steg PG, Pocock S. Coronary Thrombosis and Major Bleeding After PCI With Drug-Eluting Stents: Risk Scores From PARIS. J Am Coll Cardiol. 2016 May 17;67(19):2224-2234. doi: 10.1016/j.jacc.2016.02.064. Epub 2016 Apr 11.
citation: Costa F, van Klaveren D, James S, Heg D, Raber L, Feres F, Pilgrim T, Hong MK, Kim HS, Colombo A, Steg PG, Zanchin T, Palmerini T, Wallentin L, Bhatt DL, Stone GW, Windecker S, Steyerberg EW, Valgimigli M; PRECISE-DAPT Study Investigators. Derivation and validation of the predicting bleeding complications in patients undergoing stent implantation and subsequent dual antiplatelet therapy (PRECISE-DAPT) score: a pooled analysis of individual-patient datasets from clinical trials. Lancet. 2017 Mar 11;389(10073):1025-1034. doi: 10.1016/S0140-6736(17)30397-5.
citation: Bianco M, D'ascenzo F, Raposeiras Roubin S, Kinnaird T, Peyracchia M, Ariza-Sole A, Cerrato E, Manzano-Fernandez S, Gravinese C, Templin C, Destefanis P, Velicki L, Luciano A, Xanthopoulou I, Rinaldi M, Rognoni A, Varbella F, Boccuzzi G, Omede P, Montabone A, Bernardi A, Taha S, Rossini R, Durante A, Gili S, Magnani G, Autelli M, Grosso A, Blanco PF, Giustetto C, Garay A, Quadri G, Queija BC, Srdanovic I, Paz RC, Fernandez MC, Pousa IM, Gallo D, Morbiducci U, Dominguez-Rodriguez A, Lopez-Cuenca A, Cequier A, Alexopoulos D, Iniguez-Romo A, Pozzi R, Assi EA, Valgimigli M. Comparative external validation of the PRECISE-DAPT and PARIS risk scores in 4424 acute coronary syndrome patients treated with prasugrel or ticagrelor. Int J Cardiol. 2020 Feb 15;301:200-206. doi: 10.1016/j.ijcard.2019.11.132. Epub 2019 Nov 22.
citation: 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.
citation: 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.
citation: Yoshida R, Ishii H, Morishima I, Tanaka A, Morita Y, Takagi K, Yoshioka N, Hirayama K, Iwakawa N, Tashiro H, Kojima H, Mitsuda T, Hitora Y, Furusawa K, Tsuboi H, Murohara T. Performance of HAS-BLED, ORBIT, PRECISE-DAPT, and PARIS risk score for predicting long-term bleeding events in patients taking an oral anticoagulant undergoing percutaneous coronary intervention. J Cardiol. 2019 Jun;73(6):479-487. doi: 10.1016/j.jjcc.2018.10.013. Epub 2018 Dec 28.
citation: Kawashima H, Gao C, Takahashi K, Tomaniak M, Ono M, Hara H, Wang R, Chichareon P, Suryapranata H, Walsh S, Cotton J, Koning R, Rensing B, Wykrzykowska J, de Winter RJ, Garg S, Anderson R, Hamm C, Steg PG, Onuma Y, Serruys PW. Comparative Assessment of Predictive Performance of PRECISE-DAPT, CRUSADE, and ACUITY Scores in Risk Stratifying 30-Day Bleeding Events. Thromb Haemost. 2020 Jul;120(7):1087-1095. doi: 10.1055/s-0040-1712449. Epub 2020 Jun 22.
citation: 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.
citation: Collet JP, Thiele H, Barbato E, Barthelemy O, Bauersachs J, Bhatt DL, Dendale P, Dorobantu M, Edvardsen T, Folliguet T, Gale CP, Gilard M, Jobs A, Juni P, Lambrinou E, Lewis BS, Mehilli J, Meliga E, Merkely B, Mueller C, Roffi M, Rutten FH, Sibbing D, Siontis GCM; ESC Scientific Document Group. 2020 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation. Eur Heart J. 2021 Apr 7;42(14):1289-1367. doi: 10.1093/eurheartj/ehaa575. No abstract available.
citation: Capodanno D, Angiolillo DJ. Tailoring duration of DAPT with risk scores. Lancet. 2017 Mar 11;389(10073):987-989. doi: 10.1016/S0140-6736(17)30591-3. No abstract available.
citation: Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, Ashley E, Dudley JT. Artificial Intelligence in Cardiology. J Am Coll Cardiol. 2018 Jun 12;71(23):2668-2679. doi: 10.1016/j.jacc.2018.03.521.
citation: Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. N Engl J Med. 2019 Apr 4;380(14):1347-1358. doi: 10.1056/NEJMra1814259. No abstract available.
citation: LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539.
citation: Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial Intelligence in Precision Cardiovascular Medicine. J Am Coll Cardiol. 2017 May 30;69(21):2657-2664. doi: 10.1016/j.jacc.2017.03.571.
citation: Rymer JA, Rao SV. Enhancement of Risk Prediction With Machine Learning: Rise of the Machines. JAMA Netw Open. 2019 Jul 3;2(7):e196823. doi: 10.1001/jamanetworkopen.2019.6823. No abstract available.
citation: Goldstein BA, Navar AM, Carter RE. Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. Eur Heart J. 2017 Jun 14;38(23):1805-1814. doi: 10.1093/eurheartj/ehw302.
citation: Pencina MJ, D'Agostino RB Sr. Thoroughly modern risk prediction? Sci Transl Med. 2012 Apr 25;4(131):131fs10. doi: 10.1126/scitranslmed.3004127.
citation: Krittanawong C, Johnson KW, Rosenson RS, Wang Z, Aydar M, Baber U, Min JK, Tang WHW, Halperin JL, Narayan SM. Deep learning for cardiovascular medicine: a practical primer. Eur Heart J. 2019 Jul 1;40(25):2058-2073. doi: 10.1093/eurheartj/ehz056.
citation: Krittanawong C, Rogers AJ, Johnson KW, Wang Z, Turakhia MP, Halperin JL, Narayan SM. Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management. Nat Rev Cardiol. 2021 Feb;18(2):75-91. doi: 10.1038/s41569-020-00445-9. Epub 2020 Oct 9.
citation: Gao W, Emaminejad S, Nyein HYY, Challa S, Chen K, Peck A, Fahad HM, Ota H, Shiraki H, Kiriya D, Lien DH, Brooks GA, Davis RW, Javey A. Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature. 2016 Jan 28;529(7587):509-514. doi: 10.1038/nature16521.
citation: Gerbaud E, Darier R, Montaudon M, Beauvieux MC, Coffin-Boutreux C, Coste P, Douard H, Ouattara A, Catargi B. Glycemic Variability Is a Powerful Independent Predictive Factor of Midterm Major Adverse Cardiac Events in Patients With Diabetes With Acute Coronary Syndrome. Diabetes Care. 2019 Apr;42(4):674-681. doi: 10.2337/dc18-2047. Epub 2019 Feb 6.
citation: Mi SH, Su G, Yang HX, Zhou Y, Tian L, Zhang T, Tao H. Comparison of in-hospital glycemic variability and admission blood glucose in predicting short-term outcomes in non-diabetes patients with ST elevation myocardial infarction underwent percutaneous coronary intervention. Diabetol Metab Syndr. 2017 Mar 21;9:20. doi: 10.1186/s13098-017-0217-1. eCollection 2017.
citation: Hylek EM, Ko D. Atrial Fibrillation and Fall Risk: What Are the Treatment Implications? J Am Coll Cardiol. 2016 Sep 13;68(11):1179-1180. doi: 10.1016/j.jacc.2016.07.714. No abstract available.
citation: Liu C, Chen R, Sera F, Vicedo-Cabrera AM, Guo Y, Tong S, Coelho MSZS, Saldiva PHN, Lavigne E, Matus P, Valdes Ortega N, Osorio Garcia S, Pascal M, Stafoggia M, Scortichini M, Hashizume M, Honda Y, Hurtado-Diaz M, Cruz J, Nunes B, Teixeira JP, Kim H, Tobias A, Iniguez C, Forsberg B, Astrom C, Ragettli MS, Guo YL, Chen BY, Bell ML, Wright CY, Scovronick N, Garland RM, Milojevic A, Kysely J, Urban A, Orru H, Indermitte E, Jaakkola JJK, Ryti NRI, Katsouyanni K, Analitis A, Zanobetti A, Schwartz J, Chen J, Wu T, Cohen A, Gasparrini A, Kan H. Ambient Particulate Air Pollution and Daily Mortality in 652 Cities. N Engl J Med. 2019 Aug 22;381(8):705-715. doi: 10.1056/NEJMoa1817364.