Predicting 30 days Mortality in STEMI Patients using Patient Referral Data to a Primary Percutaneous Coronary Intervention Service

Aleeha Iftikhar, RR Bond, V. E. McGilligan, Khaled Rjoob, Charles Knoery, Stephen Leslie, Anne McShane, Aaron Peace

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Primary percutaneous coronary intervention (PPCI) is a minimally invasive procedure to unblock the arteries which carry blood to the heart. This procedure is carried out once patients are accepted based on the STEMI criteria upon the assessment of 12-lead ECG. This paper reports the analyses of a dataset compiled from patients accepted for PPCI. The primary objective was to explore the features which may predict 30days mortality. The 30 day mortality was?? The main features identified were a patient's age, sex, door to balloon time, call time, pain time, and activation status. Together these features appear to be a predictor of 30day mortality in patients referred for PPCI (76% accuracy, 70% sensitivity and 85% specificity).
Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
EditorsIllhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
Place of PublicationSan Diego, CA, USA
PublisherIEEE Xplore
Pages1315-1317
Number of pages3
ISBN (Electronic)978-1-7281-1867-3
ISBN (Print)978-1-7281-1868-0
DOIs
Publication statusPublished (in print/issue) - 6 Feb 2020
Event2019 IEEE International Conference on Bioinformatics and Biomedicine -
Duration: 18 Nov 201921 Nov 2019
https://ieeebibm.org/BIBM2019/

Conference

Conference2019 IEEE International Conference on Bioinformatics and Biomedicine
Abbreviated titleBIBM
Period18/11/1921/11/19
Internet address

Keywords

  • Machine learning
  • STEMI
  • acute myocardial infarction
  • mortality
  • heart attacks
  • health data analytics

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