E-HEART score: A novel scoring system for undifferentiated chest pain in the emergency department.

OBJECTIVE: Cardiovascular disease is the leading cause of death worldwide. As there is an increase in the global burden of ischemic heart disease, there are multiple scoring systems established in the emergency department (ED) to risk stratify and manage acute coronary syndrome (ACS) in patients with chest pain. The objective of this study was to integrate point-of-care echo into the existing history, electrocardiogram, age, risk factors, and troponin (HEART) score and evaluate a novel scoring system, the echo HEART (E-HEART) score in risk stratification of patients presenting with undifferentiated chest pain to the ED. The E-HEART Score was also compared with existing traditional scoring systems for risk-stratifying acute chest pain. METHODS: A diagnostic accuracy study involving 250 patients with chest pain at the ED of a single tertiary care teaching hospital in India was conducted. The emergency physicians assessed the E-HEART score after integrating their point-of-care echo/focused echo findings into the conventional HEART score on presentation. The primary endpoint was the occurrence of major adverse cardiovascular events (MACE) within 4 weeks of initial presentation. The accuracy of the E-HEART score was compared with other conventional risk stratification scoring systems such as the thrombolysis in myocardial infarction (TIMI), history, electrocardiogram, age, and risk factors, Troponin Only Manchester ACS (T-MACS), and HEART scores. RESULTS: A total of 250 patients with a median age of 53 years (42.25-63.00) were part of the study. Low E-HEART scores (values 0-3) were calculated in 121 patients with no occurrence of MACE in this category. Eighty-one patients with moderate E-HEART scores (4-6) were found to have 30.9% MACE. In 48 patients with high E-HEART scores (values 7-11), MACE occurred in 97.9%. The area under receiver operating characteristics (AUROC) of E-HEART score is 0.992 (95% confidence interval: 0.98-0.99), which is significantly higher than AUROC values for HEART (0.978), TIMI (0.889), T-MACS (0.959), and HEAR (0.861), respectively (P < 0.0001). At a cutoff of E-HEART score >6, it accurately predicted ACS with a sensitivity of 92% and a specificity of 99% with a diagnostic accuracy of 97%. CONCLUSION: The E-HEART score gives the clinician a quick and accurate forecast of outcomes in undifferentiated chest pain presenting to the ED. Low E-HEART scores (0-3) have an extremely low probability for short-term MACE and may aid in faster disposition from the ED. The elevated risk of MACE in patients with high E-HEART scores (7-11) may facilitate more aggressive workup measures and avoid disposition errors. E-HEART is an easily adaptable scoring system with improved accuracy compared to conventional scoring systems.

Evaluating the Use of E-Learning in Indian Emergency Medicine Residency Programs During the COVID-19 Pandemic: A National Cross-Sectional Survey.

OBJECTIVE: The coronavirus disease (COVID-19) pandemic has necessitated e-learning strategies in academic emergency medicine (EM) programs. A study was conducted during the COVID-19 pandemic to understand e-learning in the Indian EM context. METHODS: After IEC/IRB approval, we conducted a multicenter national survey validated by experts and underwent multiple reviews by the research team. The final survey was converted into Google Forms for dissemination via email to National Medical Commission (NMC) approved EM residency program as of 2020-2021. Data were exported into Excel format and analyzed. RESULTS: Residents and faculty comprised 41.5% and 58.5% of 94 respondents. The COVID-19 pandemic's second wave in India significantly impacted response rates. Internet connectivity was cited as a significant barrier to e-learning, while flexible timings and better engagement were facilitators identified by the survey. The attitude among residents and faculty toward e-learning was also evaluated. CONCLUSION: This survey reveals a significant positive shift in medical education from conventional teaching strategies toward e-learning, specifically during the pandemic. It also shows the need for all stakeholders (learners/educators) to better understand e-learning and adapt to its requirements. We need more data on the efficacy of e-learning compared to traditional methods. Until then, innovative hybrid/blended strategies would be the way forward.