Rose Angina Questionnaire: Validation In Emergency Department To Detect Myocardial Infarction In A Tertiary Hospital Of Eastern Nepal

HONG KONG JOURNAL OF EMERGENCY MEDICINE(2020)

引用 2|浏览3
暂无评分
摘要
Background: Rose Angina Questionnaire (RAQ) is a useful screening questionnaire for ischemic heart disease validated in different settings; however, its diagnostic ability to predict myocardial infarction (MI) in the emergency is less clear. Objectives: To find out the usefulness of RAQ to predict MI in patients presenting to the emergency. Methods: A cross-sectional study was conducted at the BP Koirala Institute of Health Sciences (BPKIHS), a teaching hospital in eastern Nepal from 1 January to 30 March 2017, after ethical clearance from the Institutional Review Committee. Informed consent was obtained from the patients for their anonymised information to be published in this study The samples were collected from 100 patients with chest pain aged 40 to 70 years presenting to the emergency. RAQ was applied and its performance to detect MI was compared with emergency and cardiologist diagnosis of MI. Sensitivity, specificity, positive predictive value and negative value were calculated along with descriptive analysis. Results: A total of 100 patients were analysed with the mean age of 63.78 years (SD 11.60) and male to female ratio of 1.94. RAQ detected 58 cases (63.8%) with emergency department (ED) diagnosis of MI (true positive) and identified 3 (33.3%) cases with non-MI (true negative). The true positive rate for RAQ to detect MI after cardiologist consultation was 71.6%. RAQ had a sensitivity of 84.91% (95% confidence interval (CI) 72.41% -93.25%) to detect positive troponin, 63.74% (52.99-73.56) to detect positive electrocardiogram (ECG) and 71.60% (95% CI 60.5% to 81.07%) to detect final diagnosis of MI. Conclusion: RAQ is a good screening tool to detect MI in the emergency that can be used in isolation or in combination with other diagnostic modalities to detect it early.
更多
查看译文
关键词
Emergency, MI, Nepal, RAQ
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要