Roni Shouval1, Amir Hadanny2, Nir Shlomo3, Zaza Iakobishvili4, Ron Unger5, Doron Zahger6, Ronny Alcalai7, Shaul Atar8, Shmuel Gottlieb9, Shlomi Matetzky10, Ilan Goldenberg11, Roy Beigel10. 1. Internal Medicine "F" Department, the 2013 Pinchas Borenstein Talpiot Medical Leadership Program, Sheba Medical Center, Ramat-Gan, Israel; The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel; Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel. Electronic address: Roni.Shoval@sheba.health.gov.il. 2. Sagol Center for Hyperbaric Medicine and Research, Assaf HaRofe Medical Center, Ramle, Israel; The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel; Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel. 3. Israeli Association for Cardiovascular Trials, Sheba Medical Center, Tel Hashomer, Israel. 4. Department of Cardiology, Beilinson Hospital, Rabin Medical Center, affiliated to the Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel. 5. The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel. 6. Department of Cardiology, Soroka University Medical Center, Faculty of Health Sciences, Ben Gurion University of the Negev, Israel. 7. Heart Institute, Hadassah Hebrew University Medical Center, 91120 Jerusalem, Israel. 8. Department of Cardiology, Galilee Medical Center, Nahariya, affiliated with the Faculty of Medicine of the Galilee, Bar-Ilan University, Ramat Gan, Israel. 9. Department of Cardiology, Shaare-Zedek Medical Center, the Hebrew University School of Medicine, Jerusalem, Israel; Israeli Association for Cardiovascular Trials, Sheba Medical Center, Tel Hashomer, Israel. 10. The Heart Institute, Sheba Medical Center, Tel Hashomer, Sackler School of Medicine, Tel Aviv University, Israel. 11. The Heart Institute, Sheba Medical Center, Tel Hashomer, Sackler School of Medicine, Tel Aviv University, Israel; Israeli Association for Cardiovascular Trials, Sheba Medical Center, Tel Hashomer, Israel.
Abstract
BACKGROUND: Risk scores for prediction of mortality 30-days following a ST-segment elevation myocardial infarction (STEMI) have been developed using a conventional statistical approach. OBJECTIVE: To evaluate an array of machine learning (ML) algorithms for prediction of mortality at 30-days in STEMI patients and to compare these to the conventional validated risk scores. METHODS: This was a retrospective, supervised learning, data mining study. Out of a cohort of 13,422 patients from the Acute Coronary Syndrome Israeli Survey (ACSIS) registry, 2782 patients fulfilled inclusion criteria and 54 variables were considered. Prediction models for overall mortality 30days after STEMI were developed using 6 ML algorithms. Models were compared to each other and to the Global Registry of Acute Coronary Events (GRACE) and Thrombolysis In Myocardial Infarction (TIMI) scores. RESULTS: Depending on the algorithm, using all available variables, prediction models' performance measured in an area under the receiver operating characteristic curve (AUC) ranged from 0.64 to 0.91. The best models performed similarly to the Global Registry of Acute Coronary Events (GRACE) score (0.87 SD 0.06) and outperformed the Thrombolysis In Myocardial Infarction (TIMI) score (0.82 SD 0.06, p<0.05). Performance of most algorithms plateaued when introduced with 15 variables. Among the top predictors were creatinine, Killip class on admission, blood pressure, glucose level, and age. CONCLUSIONS: We present a data mining approach for prediction of mortality post-ST-segment elevation myocardial infarction. The algorithms selected showed competence in prediction across an increasing number of variables. ML may be used for outcome prediction in complex cardiology settings.
BACKGROUND: Risk scores for prediction of mortality 30-days following a ST-segment elevation myocardial infarction (STEMI) have been developed using a conventional statistical approach. OBJECTIVE: To evaluate an array of machine learning (ML) algorithms for prediction of mortality at 30-days in STEMI patients and to compare these to the conventional validated risk scores. METHODS: This was a retrospective, supervised learning, data mining study. Out of a cohort of 13,422 patients from the Acute Coronary Syndrome Israeli Survey (ACSIS) registry, 2782 patients fulfilled inclusion criteria and 54 variables were considered. Prediction models for overall mortality 30days after STEMI were developed using 6 ML algorithms. Models were compared to each other and to the Global Registry of Acute Coronary Events (GRACE) and Thrombolysis In Myocardial Infarction (TIMI) scores. RESULTS: Depending on the algorithm, using all available variables, prediction models' performance measured in an area under the receiver operating characteristic curve (AUC) ranged from 0.64 to 0.91. The best models performed similarly to the Global Registry of Acute Coronary Events (GRACE) score (0.87 SD 0.06) and outperformed the Thrombolysis In Myocardial Infarction (TIMI) score (0.82 SD 0.06, p<0.05). Performance of most algorithms plateaued when introduced with 15 variables. Among the top predictors were creatinine, Killip class on admission, blood pressure, glucose level, and age. CONCLUSIONS: We present a data mining approach for prediction of mortality post-ST-segment elevation myocardial infarction. The algorithms selected showed competence in prediction across an increasing number of variables. ML may be used for outcome prediction in complex cardiology settings.
Authors: Iulia Andras; Elio Mazzone; Fijs W B van Leeuwen; Geert De Naeyer; Matthias N van Oosterom; Sergi Beato; Tessa Buckle; Shane O'Sullivan; Pim J van Leeuwen; Alexander Beulens; Nicolae Crisan; Frederiek D'Hondt; Peter Schatteman; Henk van Der Poel; Paolo Dell'Oglio; Alexandre Mottrie Journal: World J Urol Date: 2019-11-27 Impact factor: 4.226
Authors: Peter A Kavsak; Johannes T Neumann; Louise Cullen; Martin Than; Colleen Shortt; Jaimi H Greenslade; John W Pickering; Francisco Ojeda; Jinhui Ma; Natasha Clayton; Jonathan Sherbino; Stephen A Hill; Matthew McQueen; Dirk Westermann; Nils A Sörensen; William A Parsonage; Lauren Griffith; Shamir R Mehta; P J Devereaux; Mark Richards; Richard Troughton; Chris Pemberton; Sally Aldous; Stefan Blankenberg; Andrew Worster Journal: CMAJ Date: 2018-08-20 Impact factor: 8.262
Authors: Mithat Ekşi; Abdullah Hizir Yavuzsan; İsmail Evren; Ali Ayten; Ali Emre Fakir; Fatih Akkaş; Kerem Bursali; Azad Akdağ; Selcuk Sahin; Ali İhsan Taşçi Journal: Pediatr Surg Int Date: 2022-08-02 Impact factor: 2.003
Authors: Mubarak A Bidmos; Oladiran I Olateju; Sabiha Latiff; Tawsifur Rahman; Muhammad E H Chowdhury Journal: Int J Legal Med Date: 2022-10-07 Impact factor: 2.791
Authors: Dagmar F Hernandez-Suarez; Sagar Ranka; Yeunjung Kim; Azeem Latib; Jose Wiley; Angel Lopez-Candales; Duane S Pinto; Maday C Gonzalez; Harish Ramakrishna; Cristina Sanina; Brenda G Nieves-Rodriguez; Jovaniel Rodriguez-Maldonado; Roberto Feliu Maldonado; Israel J Rodriguez-Ruiz; Istoni da Luz Sant'Ana; Karlo A Wiley; Pedro Cox-Alomar; Pedro A Villablanca; Abiel Roche-Lima Journal: Cardiovasc Revasc Med Date: 2020-06-15