The effect of prehospital telecardiology on the mortality and morbidity of ST-segment elevated myocardial infarction patients undergoing primary percutaneous coronary intervention: A cross-sectional study

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.

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

PURPOSE

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.

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSION

The digital STEMI biomarker, derived from printed prehospital ECGs, demonstrated non-inferiority to expert consensus, indicating a promising approach for enhancing prehospital telecardiology.

Telecardiology interventions for patients with cardiovascular Disease: A systematic review on characteristics and effects

INTRODUCTION

The prevalence and mortality of cardiovascular diseases are high worldwide. Telecardiology can be used to diagnose and treat these diseases. This paper aimed to review the effectiveness (positive and negative) of implemented telecardiology services in terms of clinical, economic, and patient-reported aspects.

METHODS

A comprehensive search was conducted in Medline (through PubMed), Scopus, ISI web of science, and IEEE Xplore databases from inception to April 7, 2021. the studies that examined the effectiveness of telecardiology interventions were included.

RESULTS

Fifty studies were included in this systematic review. Most investigations (22%) were conducted in the US. In 22% of studies, telecardiology intervention was used for patients with heart failure. Telecardiology has been used in most studies for tele-monitoring (n = 21, 42%) and tele-consultation (n = 17, 34%) and in 29 studies (58%), was applied for ECG transmission. The highest rate of effects reported by studies was clinical. Thirty-five studies (70%) reported the clinical effects; twenty-one studies reported the positive effects for the economic category, and fifteen studies reported the positive effect for patient-reported class. The most positive clinical effects of telecardiology were early diagnosis, early treatment, and mortality reduction. The most positive effect of the economic class was the reduction of health care costs. The most effects of the patient-reported category were improving the patient's quality of life and patient satisfaction.

CONCLUSION

Telecardiology can help early diagnosis and treatment of cardiovascular diseases. It also has great potential in reducing health care costs and increasing quality of life and patient satisfaction.