BACKGROUND: A challenge for emergency medical service (EMS) is accurate identification of acute coronary syndromes (ACS) and ST-segment elevation myocardial infarction (STEMI) for immediate treatment and transport. The electrocardiograph-based acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI) and the thrombolytic predictive instrument (TPI) have been shown to improve diagnosis and treatment in emergency departments (EDs), but their use by paramedics in the community has been less studied. OBJECTIVE: To identify candidates for participation in the Immediate Myocardial Metabolic Enhancement During Initial Assessment and Treatment in Emergency Care (IMMEDIATE) Trial, we implemented EMS use of the ACI-TIPI and the TPI in out-of-hospital electrocardiographs and evaluated its impact on paramedic on-site identification of ACS and STEMI as a community-based approach to improving emergency cardiac care. METHODS: Ambulances in the study municipalities were outfitted with electrocardiographs with ACI-TIPI and TPI software. Using a before-after quasi-experimental design, in Phase 1, for seven months, paramedics were provided with the ACI-TIPI/TPI continuous 0-100% predictions automatically printed on electrocardiogram (ECG) text headers to supplement their identification of ACS; in Phase 2, for 11 months, paramedics were told to identify ACS based on an ACI-TIPI cutoff probability of ACS ≥ 75% and/or TPI detection of STEMI. In Phase 3, this cutoff approach was used in seven additional municipalities. Confirmed diagnoses of ACS, acute myocardial infarction (AMI), and STEMI were made by blinded physician review for 100% of patients. RESULTS: In Phase 1, paramedics identified 107 patients as having ACS; in Phase 2, 104. In Phase 1, 45.8% (49) of patients so identified had ACS confirmed, which increased to 76.0% (79) in Phase 2 (p < 0.001). Of those with ACS, 59.2% (29) had AMI in Phase 1 versus 84.8% (67) with AMI in Phase 2 (p < 0.01), and STEMI was confirmed in 40.8% (20) versus 68.4% (54), respectively (p < 0.01). In Phase 3, of 226 patients identified by paramedics as having ACS, 74.3% (168) had ACS confirmed, of whom 81.0% (136) had AMI and 65.5% (110) had STEMI. Among patients with ACS, the proportion who received percutaneous coronary intervention (PCI) was 30.6% (15) in Phase 1, increasing to 57.0% (45) in Phase 2 (p < 0.004) and 50.6% (85) in Phase 3, and the proportions of patients with STEMI receiving PCI rose from 75.0% (15) to 83.3% (45) (p < 0.4) and 82.7% (91). CONCLUSIONS: In a wide range of EMS systems, use of electrocardiographs with ACI-TIPI and TPI decision support using a 75% ACI-TIPI cutoff improves paramedic diagnostic performance for ACS, AMI, and STEMI and increases the proportions of patients who receive PCI.
BACKGROUND: A challenge for emergency medical service (EMS) is accurate identification of acute coronary syndromes (ACS) and ST-segment elevation myocardial infarction (STEMI) for immediate treatment and transport. The electrocardiograph-based acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI) and the thrombolytic predictive instrument (TPI) have been shown to improve diagnosis and treatment in emergency departments (EDs), but their use by paramedics in the community has been less studied. OBJECTIVE: To identify candidates for participation in the Immediate Myocardial Metabolic Enhancement During Initial Assessment and Treatment in Emergency Care (IMMEDIATE) Trial, we implemented EMS use of the ACI-TIPI and the TPI in out-of-hospital electrocardiographs and evaluated its impact on paramedic on-site identification of ACS and STEMI as a community-based approach to improving emergency cardiac care. METHODS: Ambulances in the study municipalities were outfitted with electrocardiographs with ACI-TIPI and TPI software. Using a before-after quasi-experimental design, in Phase 1, for seven months, paramedics were provided with the ACI-TIPI/TPI continuous 0-100% predictions automatically printed on electrocardiogram (ECG) text headers to supplement their identification of ACS; in Phase 2, for 11 months, paramedics were told to identify ACS based on an ACI-TIPI cutoff probability of ACS ≥ 75% and/or TPI detection of STEMI. In Phase 3, this cutoff approach was used in seven additional municipalities. Confirmed diagnoses of ACS, acute myocardial infarction (AMI), and STEMI were made by blinded physician review for 100% of patients. RESULTS: In Phase 1, paramedics identified 107 patients as having ACS; in Phase 2, 104. In Phase 1, 45.8% (49) of patients so identified had ACS confirmed, which increased to 76.0% (79) in Phase 2 (p < 0.001). Of those with ACS, 59.2% (29) had AMI in Phase 1 versus 84.8% (67) with AMI in Phase 2 (p < 0.01), and STEMI was confirmed in 40.8% (20) versus 68.4% (54), respectively (p < 0.01). In Phase 3, of 226 patients identified by paramedics as having ACS, 74.3% (168) had ACS confirmed, of whom 81.0% (136) had AMI and 65.5% (110) had STEMI. Among patients with ACS, the proportion who received percutaneous coronary intervention (PCI) was 30.6% (15) in Phase 1, increasing to 57.0% (45) in Phase 2 (p < 0.004) and 50.6% (85) in Phase 3, and the proportions of patients with STEMI receiving PCI rose from 75.0% (15) to 83.3% (45) (p < 0.4) and 82.7% (91). CONCLUSIONS: In a wide range of EMS systems, use of electrocardiographs with ACI-TIPI and TPI decision support using a 75% ACI-TIPI cutoff improves paramedic diagnostic performance for ACS, AMI, and STEMI and increases the proportions of patients who receive PCI.
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Authors: W D Weaver; M Cerqueira; A P Hallstrom; P E Litwin; J S Martin; P J Kudenchuk; M Eisenberg Journal: JAMA Date: 1993-09-08 Impact factor: 56.272
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Authors: Harry P Selker; Joni R Beshansky; John L Griffith; Ralph B D'Agostino; Joseph M Massaro; James E Udelson; Eric J Rashba; Robin Ruthazer; Patricia R Sheehan; Patrice Desvigne-Nickens; Yves D Rosenberg; James M Atkins; Assaad J Sayah; Tom P Aufderheide; Charles E Rackley; Lionel H Opie; Costas T Lambrew; Leonard A Cobb; Bruce A Macleod; Joanne S Ingwall; Robert J Zalenski; Carl S Apstein Journal: Am Heart J Date: 2012-03 Impact factor: 4.749
Authors: Harry P Selker; James E Udelson; Joseph M Massaro; Robin Ruthazer; Ralph B D'Agostino; John L Griffith; Patricia R Sheehan; Patrice Desvigne-Nickens; Yves Rosenberg; Xin Tian; Ellen M Vickery; James M Atkins; Tom P Aufderheide; Assaad J Sayah; Ronald G Pirrallo; Michael K Levy; Michael E Richards; Darren A Braude; Delanor D Doyle; Ralph J Frascone; Donald J Kosiak; James M Leaming; Carin M Van Gelder; Gert-Paul Walter; Marvin A Wayne; Robert H Woolard; Joni R Beshansky Journal: Am J Cardiol Date: 2014-03-01 Impact factor: 2.778
Authors: Harry P Selker; Joni R Beshansky; Patricia R Sheehan; Joseph M Massaro; John L Griffith; Ralph B D'Agostino; Robin Ruthazer; James M Atkins; Assaad J Sayah; Michael K Levy; Michael E Richards; Tom P Aufderheide; Darren A Braude; Ronald G Pirrallo; Delanor D Doyle; Ralph J Frascone; Donald J Kosiak; James M Leaming; Carin M Van Gelder; Gert-Paul Walter; Marvin A Wayne; Robert H Woolard; Lionel H Opie; Charles E Rackley; Carl S Apstein; James E Udelson Journal: JAMA Date: 2012-03-27 Impact factor: 56.272
Authors: Alison L Sullivan; Joni R Beshansky; Robin Ruthazer; David H Murman; Timothy J Mader; Harry P Selker Journal: Circ Cardiovasc Qual Outcomes Date: 2014-01-14
Authors: Harry P Selker; Manlik Kwong; Robin Ruthazer; Sheeona Gorman; Giuliana Green; Elizabeth Patchen; James E Udelson; Howard A Smithline; Michael R Baumann; Paul A Harris; Rashmee U Shah; Sarah J Nelson; Theodora Cohen; Elizabeth B Jones; Brien A Barnewolt; Andrew E Williams Journal: J Clin Transl Sci Date: 2018-12