Prehospital Electrocardiogram Research Articles

Implementation of a Novel Prehospital Clinical Decision Tool and ECG Transmission for STEMI Significantly Reduces Door-to-Balloon Time and Sex-Based Disparities

Background An important method employed to reduce door to balloon time (DTBT) for ST segment elevation Myocardial Infarctions (STEMIs) is a prehospital MI alert. The purpose of this retrospective study was to examine the effects of an educational intervention using a novel decision support method of STEMI notification and prehospital electrocardiogram (ECG) transmission on DTBT. Methods An ongoing database (April 4, 2000 – present) is maintained to track STEMI alerts. In 2007, an MI alert program began; emergency medicine physicians could activate a “prehospital MI alert”. In October 2015, modems were purchased for Emergency Medical Services personnel to transmit ECGs. There was concurrent implementation of a decision support tool for identifying STEMI. Sex was assigned as indicated in the medical record. Data were analyzed in two groups: Pre-2016 (PRE) and 2016–2022 (POST). Results In total, 3,153 patients (1,301 PRE; 1,852 POST) were assessed; the average age was 65.2 years, 32.6% female, 87.7% white with significant differences in age and race between the two cohorts. Of the total 3,153 MI alerts, 239 were false activations, leaving 2,914 for analysis. 2,115 (72.6%) had cardiac catheterization while 16 (6.7%) of the 239 had a cardiac catheterization. There was an overall decrease in DTBT of 27.5% PRE to POST of prehospital ECG transmission (p < 0.001); PRE median time was 74.5 min vs. 55 min POST. There was no significant difference between rates of cardiac catheterization PRE and POST for all patients. After accounting for age, race, and mode of arrival, DTBT was 12.2% longer in women, as compared to men (p < 0.001) PRE vs. POST. DTBT among women was significantly shorter when comparing PRE to POST periods (median 77 min vs. 60 min; p = 0.0001). There was no significant sex difference in the proportion of those with cardiac catheterization between the two cohorts (62.5% vs. 63.5%; p = 0.73). Conclusion Introduction of a decision support tool with prehospital ECG transmission with prehospital ECG transmission decreased overall DTBT by 20 min (27.5%). Women in the study had a 17-minute decrease in DTBT (22%), but their DTBT remained 12.2% longer than men for reasons that remain unclear.

Machine learning with electrocardiograms to optimize mortality risk stratification in patients with suspected acute coronary syndrome

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart, Lung, and Blood Institute (NHLBI) and National Center for Advancing Translational Sciences (NCATS). Background The importance of risk stratification in patients with suspected acute coronary syndrome (ACS) extends beyond diagnosis and immediate treatment. It influences the precision of care delivery and the allocation of resources to those at highest risk of adverse events. Purpose We sought to evaluate the prognostic value of electrocardiogram (ECG) feature-based machine learning models to risk-stratify long-term mortality in those with suspected ACS. Methods This was a multicenter prospective observational cohort study of consecutive, non-traumatic patients evaluated at the emergency department for suspected ACS. The derivation cohort included 4,015 patients from a University Medical Center (age 59±16 years, 47% women, 80% training with 10-fold cross-validation and 20% testing). Ascertainment of all-cause death was based on numerous data sources, including the Centers for Disease Control and Prevention’s National Death Index registry. We trained six machine learning methods for survival analysis using 73 morphological ECG features from presenting prehospital 10-second 12-lead ECGs. We used predicted risk scores in a variational Bayesian Gaussian mixture model to define three risk groups corresponding to low, moderate, and high-risk and compared the resulting classification performance against the HEART score. The external testing cohort included 3,095 patients from a University (age 59±15 years, 44% women). Results The mortality rate was 20.3% in the derivation cohort after an average follow-up period of 3.53 years (IQR 1.75 - 5.32). Extra Survival Trees outperformed other forecasting models during cross-validation. In the internal testing cohort, the derived risk groups were significantly predictive of survival (Kaplan-Meier log-rank test statistic = 121.14, p&amp;lt;0.001), outperforming the classification performance using the HEART score (Figure 1). Our machine learning-based risk stratification model detected &amp;gt;90% of death events missed by low-risk HEART score (Figure 2). Ruling-out the low-risk group, it achieved a negative predictive value of 93.4% with a sensitivity of 85.9% (compared to 89.0% and 75.0%, respectively, for the HEART score). This classification performance generalized well to our external testing cohort. Compared to those in the low-risk group, patients at moderate (OR = 3.62 [1.35-9.74]) and high (OR = 6.12 [2.38-15.75]) risk had significantly higher odds of 30-day cardiovascular death (n=109, 3.5%). Conclusions Using only features from the 10-second 12-lead ECG, we derived and externally validated a machine learning model that stratifies the mortality risk of patients during long-term follow up. This model outperformed current standard of care based on the HEART score. Future work should validate the potential of this decision-support tool in influencing treatment plans and resource allocation.Figure 1.Clustering performanceFigure 2.Sankey diagram

Impact of Quantitative ST-T Analysis in Patients with Suspected Myocardial Infarction Presenting with Right Bundle Branch Block

BackgroundWhile left bundle branch block (LBBB) is a well-known risk feature in patients with acute myocardial infarction, and a rapid invasive management is recommended, data supporting this strategy for patients with right bundle branch block (RBBB) is less robust. MethodsIn total, 2139 patients with suspected ST-elevation myocardial infarction (STEMI) were triaged to acute coronary angiography based on a prehospital 12-lead electrocardiogram (ECG). Sensitivity and specificity for STEMI-ECG criteria were compared in RBBB and non-BBB patients. Adjusted hazard ratios for 1-year overall mortality were computed. ResultsSTEMI was adjudicated in 1832/2139 (85.6%) of all patients and in 102/117 (87.2%) of RBBB patients. ST-segment deviation followed typical ST-T patterns in most RBBB patients. Of 17 RBBB patients without significant ST changes, STEMI was adjudicated in 14 (82%). Diagnostic accuracy of STEMI criteria was comparable in RBBB and non-RBBB patients for inferior (sensitivity: 51.1% vs 59.1%, P = .14; specificity: 66.7% vs 52.1%, P = .33) and anterior STEMI (sensitivity: 35.2% vs 36.6%, P = .80; specificity: 58.3% vs 49.5%, P = .55). Diagnostic performance was lower for lateral STEMI in RBBB patients (sensitivity: 14.8% vs 4.4%, P = .001; specificity: 75.0% vs 98.4%, P < .001). Patients with RBBB had higher 1-year mortality compared with non-BBB patients (hazard ratio 2.3%; 95% confidence interval, 1.25-4.21. ConclusionECG criteria used for detection of STEMI showed comparable diagnostic accuracy in RBBB and non-BBB patients. However, STEMI was frequently present in RBBB patients not fulfilling diagnostic ECG criteria. RBBB patients showed poorer outcome after 1 year. Consequently, the presence of RBBB in suspected STEMI cases signifies a high-risk feature, aligning with established guidelines.

A collaborative approach to develop indicators for quality of care for ST segment Elevation Myocardial Infarction in networks without coronary intervention: A position paper.

Data about performance measures (PM) in patients with ST segment Elevation Myocardial Infarction (STEMI) in low- and middle-income countries is really scarce. One of the reasons is the lack of appropriate measures for these scenarios where coronary intervention is not the standard treatment. This study aimed to develop a set of PM and quality markers for patients with STEMI in these countries. Two investigators systematically reviewed existing guidelines and scientific literature to identify potential PM by referring to documents searched through PubMed from 2010 through 2019, using terms "Myocardial Infarction", "STEMI", "quality indicator", and "performance measure". A modified Delphi technique, involving multidisciplinary panel interview, was used. A 15-member multidisciplinary expert panel individually rated each potential indicator on a scale of 1 (lowest) to 5 (highest) during three rounds. All indicators that received a median score ≥4.5, in final round without significant disagreement were included as PM. Through the consensus-building process, 84 potential indicators were found, of which 10 were proposed as performance measures and 2 as quality metrics, as follows: Pre-Hospital Electrocardiogram; Patients with reperfusion therapy; Pre-hospital Reperfusion; Ischemic time less than 120 minutes; System delay time less than 90 minutes; In-hospital Mortality; Complete in-hospital Treatment; Complete in-hospital Treatment in patients with Heart Failure; 30 day-Re-admissions; 30 day-mortality; Patients with in-hospital stress test performed; and, Patients included in rehabilitation programs. This document provides the official set of PM of attention in ST segment Elevation Myocardial Infarction of the Cuban Society of Cardiology and Cuban National Group of Cardiology.

Smartphone AI vs. Medical Experts: A Comparative Study in Prehospital STEMI Diagnosis.

Prehospital telecardiology facilitates early ST-elevation myocardial infarction (STEMI) detection, yet its widespread implementation remains challenging. Extracting digital STEMI biomarkers from printed electrocardiograms (ECGs) using phone cameras could offer an affordable and scalable solution. This study assessed the feasibility of this approach with real-world prehospital ECGs. Patients suspected of having STEMI by emergency medical technicians (EMTs) were identified from a policy research dataset. A deep learning-based ECG analyzer (QCG™ analyzer) extracted a STEMI biomarker (qSTEMI) from prehospital ECGs. The biomarker was compared to a group of human experts, including five emergency medical service directors (board-certified emergency physicians) and three interventional cardiologists based on their consensus score (number of participants answering "yes" for STEMI). Non-inferiority of the biomarker was tested using a 0.100 margin of difference in sensitivity and specificity. Among 53 analyzed patients (24 STEMI, 45.3%), the area under the receiver operating characteristic curve of qSTEMI and consensus score were 0.815 (0.691-0.938) and 0.736 (0.594-0.879), respectively (p=0.081). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of qSTEMI were 0.750 (0.583-0.917), 0.862 (0.690-0.966), 0.826 (0.679-0.955), and 0.813 (0.714-0.929), respectively. For the consensus score, sensitivity, specificity, PPV, and NPV were 0.708 (0.500-0.875), 0.793 (0.655-0.966), 0.750 (0.600-0.941), and 0.760 (0.655-0.880), respectively. The 95% confidence interval of sensitivity and specificity differences between qSTEMI and consensus score were 0.042 (-0.099-0.182) and 0.103 (-0.043-0.250), respectively, confirming qSTEMI's non-inferiority. The digital STEMI biomarker, derived from printed prehospital ECGs, demonstrated non-inferiority to expert consensus, indicating a promising approach for enhancing prehospital telecardiology.

A Novel Algorithm for Improving the Prehospital Diagnostic Accuracy of ST-Segment Elevation Myocardial Infarction.

Early detection of ST-segment elevation myocardial infarction (STEMI) on the prehospital electrocardiogram (ECG) improves patient outcomes. Current software algorithms optimize sensitivity but have a high false-positive rate. The authors propose an algorithm to improve the specificity of STEMI diagnosis in the prehospital setting. A dataset of prehospital ECGs with verified outcomes was used to validate an algorithm to identify true and false-positive software interpretations of STEMI. Four criteria implicated in prior research to differentiate STEMI true positives were applied: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. The test characteristics were calculated and regression analysis was used to examine the association between the number of criteria included and test characteristics. There were 44,611 cases available. Of these, 1,193 were identified as STEMI by the software interpretation. Applying all four criteria had the highest positive likelihood ratio of 353 (95% CI, 201-595) and specificity of 99.96% (95% CI, 99.93-99.98), but the lowest sensitivity (14%; 95% CI, 11-17) and worst negative likelihood ratio (0.86; 95% CI, 0.84-0.89). There was a strong correlation between increased positive likelihood ratio (r2 = 0.90) and specificity (r2 = 0.85) with increasing number of criteria. Prehospital ECGs with a high probability of true STEMI can be accurately identified using these four criteria: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. Applying these criteria to prehospital ECGs with software interpretations of STEMI could decrease false-positive field activations, while also reducing the need to rely on transmission for physician over-read. This can have significant clinical and quality implications for Emergency Medical Services (EMS) systems.

The views, opinions and decision-making of UK-based paramedics on the use of pre-hospital 12-lead electrocardiograms in acute stroke patients: a qualitative interview study.

A qualitative exploration into the views, opinions and decision-making of paramedics involved in undertaking pre-hospital 12-lead electrocardiograms (PHECGs) for stroke patients was undertaken, in order to gain a deeper understanding of the clinical and occupational context that the paramedics work within, the acceptability of the paramedics in using PHECGs for stroke patients and the consequences and influences of their decision-making. Data were collected via semi-structured interviews and analysed using the framework method, with the underpinning theoretical framework of cognitive continuum theory. A purposive sample of 14 paramedics was recruited and interviewed. Five themes were generated from the analysis of the interviews: (1) 'time is brain': minimising delays and rapid transport to definitive care; (2) barriers and facilitators to undertaking PHECGs for stroke patients; (3) recognising and gaining cues; (4) maintaining patient dignity, self-protection and fully informed consent; and (5) education, experience and engagement with evidence. The study showed mixed views on the usefulness of PHECGs, but all participants agreed that PHECGs should not cause additional delays. Paramedic decision-making on recording PHECGs relies on intuitive and quasi-rational cognitive modes, and requires a number of clinical, logistical and ethical considerations. The findings suggest careful consideration is needed of the benefits and potential drawbacks of incorporating PHECGs into pre-hospital stroke care.

Abstract 365: Prehospital Electrocardiogram Integrated With Computer-Assisted Interpretation Shortens Door-to-Balloon Time in Acute ST-Elevation Myocardial Infarction

Introduction: Prehospital electrocardiogram (ECG) has been shown to expedite reperfusion therapy and reduce mortality in acute ST-elevation myocardial infarction (STEMI). The most challenge part for prehospital ECG is timely accurate interpretation and early activation for primary coronary intervention (PCI). In this study we aimed at constructing a prehospital ECG platform integrated with computer-assisted interpretation for strategic PCI activation in an Urban EMS system, and evaluating its efficacy and impact on door-to-balloon time in patients with STEMI. Methods: A prehospital ECG platform with computer-assisted interpretation and wireless transmission was constructed in 2018 in Taipei, together with EMT training for strategic triage of possible STEMI patients to primary PCI capable hospital. The primary outcomes were door-to-balloon time and scene-to-balloon time compared with those without prehospital ECG at the same period. The secondary outcomes were prehospital management time and transportation time. Results: From Feb. 2019 to July 2020, a total of 1670 patients meeting the predesignated criteria received prehospital ECG. Of these, 217 (13.0%) was interpreted as ST elevation out-of-hospital with notification of PCI capable hospital, and 65 (30.0%) was confirmed as STEMI thereafter. The sensitivity of computer-assisted interpretation of STEMI was 96% and specificity was 91%. Compared to the STEMI patients sent by EMS system to the same PCI hospital without prehospital ECG diagnosis and early activation, those with prehospital ECG showed no difference in prehospital management time and transportation time. However, the door-to-balloon-time (58.3 + 10.8 vs. 72.7 + 11.2, P &lt;0.01) and scene-to-balloon time (75.1 + 12.1 vs. 90.5 + 16.1, P &lt;0.05) were both significantly shorter. Conclusion: Prehospital ECG integrated with computer-assisted interpretation enhanced notification of PCI hospital and proper activation of cardiac catheterization team for STEMI patients in an Urban EMS system. Most importantly, it significantly shortens door-to-balloon-time and scene-to-balloon time without lengthening prehospital management and transportation times.

Patient factors associated with conveyance decision-making by Emergency Medical Services professionals in patients with a syncope: a cross-sectional factorial survey design

BackgroundThe clinical decision-making of non-conveyance is perceived as complex and difficult by emergency medical services (EMS) professionals. Patients with a transient loss of consciousness (TLOC) based on syncope constitute a significant part of the non-conveyance population. Risk stratification is the basis of the clinical decision-making process by EMS professionals. This risk stratification is based on various patient factors. This study aimed to explore patient factors significantly associated with conveyance decision-making by EMS professionals in patients with a TLOC based on syncope.MethodsA cross-sectional vignette study with a factorial survey design was conducted. Patient factors were derived from the “National Protocol Ambulance Care”, and all possible combinations of these factors and underlying categories were combined, resulting in 256 unique vignettes (2*4*4*4*2 = 256). Patient factors presented either low-risk or high-risk factors for adverse events. Data were collected through an online questionnaire, in which participants received a random sample of 15 vignettes. For each vignette, the respondent indicated whether the patient would need to be conveyed to the emergency department or not. A multilevel logistic regression analysis with stepwise backward elimination was performed to analyse factors significantly associated with conveyance decision-making. In the logistic model, we modelled the probability of non-conveyance.Results110 respondents were included, with 1646 vignettes being assessed. Mean age 45.5 (SD 9.3), male gender 63.6%, and years of experience 13.2 (SD 8.9). Multilevel analysis showed two patient factors contributing significantly to conveyance decision-making: ‘red flags’ and ‘prehospital electrocardiogram (ECG)’. Of these patient factors, three underlying categories were significantly associated with non-conveyance: ‘sudden cardiac death < 40 years of age in family history’ (OR 0.33, 95% CI 0.22–0.50; p < 0.001), ‘cardiovascular abnormalities, pulmonary embolism or pulmonary hypertension in the medical history’ (OR 0.62, 95% CI 0.43–0.91; p = 0.01), and ‘abnormal prehospital ECG’ (OR 0.54, 95% CI 0.41–0.72; p < 0.001).ConclusionSudden cardiac death < 40 years of age in family history, medical history, and abnormal ECG are significantly negatively associated with non-conveyance decision-making by EMS professionals in patients with a TLOC based on syncope. Low-risk factors do not play a significant role in conveyance decision-making.

Prehospital Electrocardiogram Transmission and Prehospital Scene Time: A Retrospective Cohort Study.

Background: Telemedical transmission of prehospital electrocardiograms (ECGs) to a target clinic may improve clinical workflows and speed of intervention. However, whether ECG transmission delays prehospital workflows remains controversial. Therefore, we aimed to clarify whether ECG transmission prolongs prehospital scene time in patients diagnosed with acute coronary syndrome (ACS). Methods: We retrospectively included all patients diagnosed with ACS by prehospital emergency physicians from July 2016 to June 2019 at a single academic center. The primary endpoint was the effect of ECG transmission on prehospital scene time. The secondary endpoints were the effects of ECG diagnosis on prehospital scene time and quality of care. Multivariable regression was used to account for patients' age, physician specialty, completion of specialty training, and whether emergencies occurred throughout the day or night shifts as potential confounders. Results: A total of 1,106 cases were included, of which 154 ECG transmissions were performed. ECG transmission prolonged prehospital scene time by an average of 3 min: adjusted regression coefficient [95% confidence interval (95% CI)]: 3.24 (1.7-4.8), p < 0.001. Prehospital treatment time was not influenced by prehospital ECG-based diagnosis (ST-elevation myocardial infarction [STEMI] vs. non-ST-elevation ACS [NSTE-ACS]): adjusted regression coefficient (95% CI): 0.7 (-1.3 to 2.7), p = 0.490. Emergency physicians adhered to local standard operating procedures in 739 of 1,007 (73%) patients diagnosed with NSTE-ACS and 93 of 99 (94%) patients diagnosed with STEMI. A STEMI diagnosis compared with NSTE-ACS was associated with five times higher odds of adhering to standard operating procedures (odds ratio [95% CI]: 5.6 [2.7-14.6], p < 0.001). Conclusion: The observed delay of ∼3 min in the prehospital scene time by ECG transmission is clinically irrelevant. For patients prehospitally diagnosed with NSTE-ACS who do not meet STEMI criteria, adherence to standard operating procedures seems unjustifiably low and should be improved.

Spotlight on Research

Journal of Paramedic PracticeVol. 15, No. 1 Spotlight on ResearchSpotlight on ResearchPete GregoryPete GregoryAssociate Dean and Head of School of Allied Health and Midwifery, University of Wolverhampton, UKSearch for more papers by this authorPete GregoryPublished Online:13 Jan 2023https://doi.org/10.12968/jpar.2023.15.1.39AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References Nehme E, Nehme Z, Cox S, Smith K. Outcomes of paediatric patients who are not transported to hospital by Emergency Medical Services: a data linkage study. Emerg Med J. https://doi.org/10.1136/emermed-2022-212350 Google ScholarMunro S, Cooke D, Joy M et al.. The pre-hospital 12-lead electrocardiogram is associated with longer delay and worse outcomes in patients presenting to emergency medical services with acute stroke: a linked cohort study. British Paramed J. 2022;7(2):16–23. https://doi.org/10.29045/14784726.2022.09.7.2.16 Crossref, Google Scholar FiguresReferencesRelatedDetails 2 January 2023Volume 15Issue 1ISSN (print): 1759-1376ISSN (online): 2041-9457 Metrics History Published online 13 January 2023 Published in print 2 January 2023 Information© MA Healthcare LimitedPDF download

Paramedic perspectives on sex and gender equity in prehospital electrocardiogram acquisition

Acute coronary syndrome (ACS) makes up a substantial healthcare burden. Previous research has demonstrated that women fare worse than men across a range of hospital-based ACS processes and outcomes. In the prehospital setting, the 12-lead electrocardiogram (ECG) can identify critical ACS cases and speed access to definitive treatment. However, studies on rates of ECG acquisition among patients at risk of ACS in the prehospital setting have shown that women receive the test less frequently than men. The purpose of this study was to investigate the reasons for observed gender differences in rates of ECG acquisition in the prehospital setting. This is a cross-sectional study of paramedics in an urban emergency medical service agency. An experienced qualitative researcher conducted 26 semi-structured interviews. Using a subjectivist inductive approach, the study team analyzed interview transcripts following the six-step process described by Braun and Clarke (2006). Qualitative rigor was maintained through consistent review, discussion, and revision at each stage. Four themes were identified among diverse views around the 12-lead acquisition. (1) Safety (feelings of physical comfort and psychological safety related to administering a 12-lead). (2) Knowledge (subject-matter knowledge involving the protocols and procedures as well as self-knowledge of participants’ own practice). (3) Training and learning (perceptions of foundational training and on-the-job learning, and how those have influenced their perceptions of how to care for patients experiencing ACS). (4) Professionalism (perceptions of behavior and attitudes and how they influence patient care). These four interrelated themes were integrated to represent a model of prehospital cardiac care. This study addressed a gap in the literature by documenting prehospital providers’ attitudes and beliefs around cardiac care. The resulting model of prehospital cardiac care offers both a template for evaluating the equity of current treatment and a road map for improvement.

Identifying and Solving Gaps in Pre- and In-Hospital Acute Myocardial Infarction Care in Asia-Pacific Countries.

Acute myocardial infarction (AMI) is a major cause of morbidity and mortality in the Asia-Pacific region, and mortality rates differ between countries in the region. Systems of care have been shown to play a major role in determining AMI outcomes, and this review aims to highlight pre-hospital and in-hospital system deficiencies and suggest possible improvements to enhance quality of care, focusing on Korea, Japan, Singapore and Malaysia as representative countries. Time to first medical contact can be shortened by improving patient awareness of AMI symptoms and the need to activate emergency medical services (EMS), as well as by developing robust, well-coordinated and centralized EMS systems. Additionally, performing and transmitting pre-hospital electrocardiograms, algorithmically identifying patients with high risk AMI and developing hospital networks that appropriately divert such patients to percutaneous coronary intervention-capable hospitals have been shown to be beneficial. Within the hospital environment, developing and following clinical practice guidelines ensures that treatment plans can be standardised, whilst integrated care pathways can aid in coordinating care within the healthcare institution and can guide care even after discharge. Prescription of guideline directed medical therapy for secondary prevention and patient compliance to medications can be further optimised. Finally, the authors advocate for the establishment of more regional, national and international AMI registries for the formal collection of data to facilitate audit and clinical improvement.

Abstract 12562: To Call ‘911’ or Not: What Influences Decision Making in Patients With St-Elevation Myocardial Infarction (STEMI)?

Introduction: It is well-established that total ischemic time (TT) is related to mortality of STEMI patients treated with primary percutaneous coronary intervention (PPCI). TT is composed of the time from symptom onset (Sx) to first medical contact (FMC) and FMC to device. Shortest times are achieved when patients decide to call ‘911’, have a pre-hospital electrocardiogram and are transported directly for PPCI. Efforts have primarily focused on improvement of FMC-Device time. SxFMC time is less understood, collected and studied. Our goal was to identify factors that influence STEMI patients’ decision making to employ emergency medical services (EMS) vs self-presenting to an emergency department (ED). Methods: Between 1/1/2013- 12/31/2014, we collected data on 481 consecutive low risk STEMI patients, excluding cardiogenic shock, cardiac arrest and unsuccessful PPCI. Results: The median SxFMC was 53 minutes (interquartile range (IQR) 23-120) in EMS cohort versus 139 minutes (IQR 72-297) in ED group (p&lt;0.001). EMS patients were more often older (median age 64, IQR 56-74 vs 60 IQR 52-68) vs ED, p&lt;0.001. Presenting features such as sex, anterior STEMI, and Killip Class were not statistically different between EMS and ED. Risk factors (RF) for coronary disease including hypertension, dyslipidemia, diabetes, smoking, previous diagnosis of myocardial infarction (MI), previous PCI or coronary artery bypass grafting (CABG) were not statistically different between the groups. There was no difference in time of the day of symptoms onset between the groups. Conclusions: Accordingly, demographic, cardiac risk factors, time of onset of symptoms, location and severity of infarction are not associated with patient decisions to employ EMS. However, older patients were more likely to employ EMS. Further research is required to understand these decision-making processes to develop targeted public awareness interventions that are directed to improve SxFMC in STEMI patients.

Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care.

ObjectiveTo implement an all-day online artificial intelligence (AI)-assisted detection of ST-elevation myocardial infarction (STEMI) by prehospital 12-lead electrocardiograms (ECGs) to facilitate patient triage for timely reperfusion therapy.MethodsThe proposed AI model combines a convolutional neural network and long short-term memory (CNN-LSTM) to predict STEMI on prehospital 12-lead ECGs obtained from mini-12-lead ECG devices equipped in ambulance vehicles in Central Taiwan. Emergency medical technicians (EMTs) from the 14 AI-implemented fire stations performed the on-site 12-lead ECG examinations using the mini portable device. The 12-lead ECG signals were transmitted to the AI center of China Medical University Hospital to classify the recordings as “STEMI” or “Not STEMI”. In 11 non-AI fire stations, the ECG data were transmitted to a secure network and read by available on-line emergency physicians. The response time was defined as the time interval between the ECG transmission and ECG interpretation feedback.ResultsBetween July 17, 2021, and March 26, 2022, the AI model classified 362 prehospital 12-lead ECGs obtained from 275 consecutive patients who had called the 119 dispatch centers of fire stations in Central Taiwan for symptoms of chest pain or shortness of breath. The AI's response time to the EMTs in ambulance vehicles was 37.2 ± 11.3 s, which was shorter than the online physicians' response time from 11 other fire stations with no AI implementation (113.2 ± 369.4 s, P < 0.001) after analyzing another set of 335 prehospital 12-lead ECGs. The evaluation metrics including accuracy, precision, specificity, recall, area under the receiver operating characteristic curve, and F1 score to assess the overall AI performance in the remote detection of STEMI were 0.992, 0.889, 0.994, 0.941, 0.997, and 0.914, respectively. During the study period, the AI model promptly identified 10 STEMI patients who underwent primary percutaneous coronary intervention (PPCI) with a median contact-to-door time of 18.5 (IQR: 16–20.8) minutes.ConclusionImplementation of an all-day real-time AI-assisted remote detection of STEMI on prehospital 12-lead ECGs in the field is feasible with a high diagnostic accuracy rate. This approach may help minimize preventable delays in contact-to-treatment times for STEMI patients who require PPCI.

The pre-hospital 12-lead electrocardiogram is associated with longer delay and worse outcomes in patients presenting to emergency medical services with acute stroke: a linked cohort study.

To investigate the association between pre-hospital 12-lead electrocardiogram (PHECG) use in patients presenting to emergency medical services (EMS) with acute stroke, and clinical outcomes and system delays. Multi-centre linked cohort study. Patients with verified acute stroke admitted to hospital via EMS were identified through routinely collected hospital data and linked to EMS clinical records via EMS unique identifiers. Ordinal and logistic regression analyses were undertaken to analyse the relationship between having a PHECG and modified Rankin Scale (mRS); hospital mortality; pre-hospital time intervals; door-to-scan and door-to-needle times; and rates of thrombolysis. Of 1161 eligible patients admitted between 29 December 2013 and 30 January 2017, PHECG was performed in 558 (48%). PHECG was associated with an increase in mRS (adjusted odds ratio [aOR] 1.30, 95% confidence interval [CI] 1.01-1.66, p = 0.04) and hospital mortality (aOR 1.83, 95% CI 1.26-2.67, p = 0.002). There was no association between PHECG and administration of thrombolytic treatment (aOR 1.06, 95% CI 0.75-1.52, p = 0.73). Patients who had PHECG recorded spent longer under the care of EMS (median 49 vs 43 minutes, p = 0.006). No difference in times to receiving brain scan (median 28 with PHECG vs 29 minutes no PHECG, p = 0.32) or thrombolysis (median 46 vs 48 minutes, p = 0.37) were observed. The PHECG was associated with worse outcomes and longer delays in patients with acute ischaemic stroke.

Systematic Review and Meta-Analysis of Diagnostic Accuracy to Identify ST-Segment Elevation Myocardial Infarction on Interpretations of Prehospital Electrocardiograms.

Background: The aim of this study was to assess and discuss the diagnostic accuracy of prehospital ECG interpretation through systematic review and meta-analyses.Methods and Results: Relevant literature published up to July 2020 was identified using PubMed. All human studies of prehospital adult patients suspected of ST-segment elevation myocardial infarction in which prehospital electrocardiogram (ECG) interpretation by paramedics or computers was evaluated and reporting all 4 (true-positive, false-positive, false-negative, and true-negative) values were included. Meta-analyses were conducted separately for the diagnostic accuracy of prehospital ECG interpretation by paramedics (Clinical Question [CQ] 1) and computers (CQ2). After screening, 4 studies for CQ1 and 6 studies for CQ2 were finally included in the meta-analysis. Regarding CQ1, the pooled sensitivity and specificity were 95.5% (95% confidence interval [CI] 82.5–99.0%) and 95.8% (95% CI 82.3–99.1%), respectively. Regarding CQ2, the pooled sensitivity and specificity were 85.4% (95% CI 74.1–92.3%) and 95.4% (95% CI 87.3–98.4%), respectively.Conclusions: This meta-analysis suggests that the diagnostic accuracy of paramedic prehospital ECG interpretations is favorable, with high pooled sensitivity and specificity, with an acceptable estimated number of false positives and false negatives. Computer-assisted ECG interpretation showed high pooled specificity with an acceptable estimated number of false positives, whereas the pooled sensitivity was relatively low.

Utility of prehospital electrocardiogram interpretation in ST-segment elevation myocardial infarction utilizing computer interpretation and transmission for interventional cardiologist consultation.

ObjectivesWe examined the appropriateness of prehospital cardiac catheter laboratory activation (CCL‐A) in ST‐segment elevation myocardial infarction (STEMI) utilizing the University of Glasgow algorithm (UGA) and remote interventional cardiologist consultation.BackgroundThe incremental benefit of prehospital electrocardiogram (PH‐ECG) transmission on the diagnostic accuracy and appropriateness of CCL‐A has been examined in a small number of studies with conflicting results.MethodsWe identified consecutive PH‐ECG transmissions between June 2, 2010 and October 6, 2016. Blinded adjudication of ECGs, appropriateness of CCL‐A, and index diagnoses were performed using the fourth universal definition of MI. The primary outcome was the appropriate CCL‐A rate. Secondary outcomes included rates of false‐positive CCL‐A, inappropriate CCL‐A, and inappropriate CCL nonactivation.ResultsAmong 1088 PH‐ECG transmissions, there were 565 (52%) CCL‐As and 523 (48%) CCL nonactivations. The appropriate CCL‐A rate was 97% (550 of 565 CCL‐As), of which 4.9% (n = 27) were false‐positive. The inappropriate CCL‐A rate was 2.7% (15 of 565 CCL‐As) and the inappropriate CCL nonactivation rate was 3.6% (19 of 523 CCL nonactivations). Reasons for appropriate CCL nonactivation (n = 504) included nondiagnostic ST‐segment elevation (n = 128, 25%), bundle branch block (n = 132, 26%), repolarization abnormality (n = 61, 12%), artefact (n = 72, 14%), no ischemic symptoms (n = 32, 6.3%), severe comorbidities (n = 26, 5.2%), transient ST‐segment elevation (n = 20, 4.0%), and others.ConclusionsPH‐ECG interpretation utilizing UGA with interventional cardiologist consultation accurately identified STEMI with low rates of inappropriate and false‐positive CCL‐As, whereas using UGA alone would have almost doubled CCL‐As. The benefits of cardiologist consultation were identifying “masquerading” STEMI and avoiding unnecessary CCL‐As.

An Evaluation of Prehospital Adenosine Use

Background Adenosine has been safely used by paramedics for the treatment of stable supraventricular tachycardia since the mid-1990s. However, there continues to be variability in paramedics’ ability to identify appropriate indications for adenosine administration. As the first of a planned series of studies aimed at improving the accuracy of SVT diagnosis and successful administration of adenosine by paramedics, this study details the current usage patterns of adenosine by paramedics. Methods This cross-sectional retrospective study investigated adenosine use within a large northeast EMS region from January 1, 2019, through September 30, 2021. Excluding pediatric and duplicate case reports, we created a dataset containing patient age, sex, and vital signs before, during, and after adenosine administration; intravenous line location; and coded medical history from paramedic narrative documentation, including a history of atrial fibrillation, suspected arrhythmia diagnosis, and effect of adenosine. In cases with available prehospital electrocardiograms (EKGs) for review, two physicians independently coded the arrhythmia diagnosis and outcome of adenosine administration. Statistical analysis included interrater reliability with Cohen’s kappa statistic. Results One hundred eighty-three cases were included for final analysis, 84 did not have a documented EKG for review. Categorization of presenting rhythms in these cases occurred by a physician reviewing EMS narrative and documentation. Forty of these 84 cases (48%) were adjudicated as SVT likely, 32 (38%) as SVT unlikely and 12 (14%) as uncategorized due to lack of supporting documentation. Of the 99 cases with EKGs available to review, there was substantial agreement of arrhythmia diagnosis interpretation between physician reviewers (Cohen’s kappa 0.77–1.0); 54 cases were adjudicated as SVT by two physician reviewers. Other identified cardiac rhythms included atrial fibrillation (16), sinus tachycardia (11), and ventricular tachycardia (2). Adenosine cardioversion occurred in 47 of the 99 cases with EKGs available for physician review (47.5%). Adenosine cardioversion was also deemed to occur in 87% (47/54) of cases when the EKG rhythm was physician adjudicated SVT. Conclusions This study supports the use of adenosine as a prehospital treatment for SVT while highlighting the need for continued efforts to improve paramedics’ identification and management of tachyarrhythmias.

Pre-hospital electrocardiogram in patients with acute myocardial infarction during the COVID-19 pandemic.

Pre-hospital electrocardiogram in patients with acute myocardial infarction during the COVID-19 pandemic.