Establish Acute Myocardial Infarction Temperatures Early Warning Indicators

Objective:By collecting consecutively admissions for AMI and the weather data from2004to2011, and using atmospheric temperature based on the independent association between admission for acute myocardial infarction (AMI) and the contemporary atmospheric temperature, the paper aims to establish a warning and forecast tool for the incidence of acute myocardial ischemia.Method:1. Literature review. By collecting and sorting out the relative data, the paper researched and summarized the research status at home and abroad about AMI and the contemporary atmospheric temperature.2. Statistical Analyses.(1)To express the characteristics of the patients, we used median and inter-quartile range or mean±standard error of mean to describe continuous variables and frequency and percentages to describe categorical variables.(2) To research the association between the incidence of AMI and daily average temperature, extremely low temperature and daily temperature range, we used chi-square test, t test and rank2classification and the test evaluation and the correlation of variable distribution between group differences in the number of disease.(3)We conducted logistic regressions including these temperature indices, to assess their independent association with the outcomesResult:1. The distribution of clinical status stayed similar across years (p=0.057).2. The number of admission for AMI varied a lot over months, more in winters, and less in summers, which implied an inverse correlation with the Atmospheric temperature. We found no statistically significant difference in the clinical status of patients across seasons or months (p=0.062).3. The daily average temperature was substantially associated inversely with the number of admission in the day1(p=0.017) and day2(p=0.007), but not the number of admission in the same day (day0)(p=0.056).4..During the8-year period, there were284days with an extremely low temperature of less than-10℃, after which the number of admission for AMI in day2was more than after days with normal or above temperature (p=0.043). However the difference were not significant in day0(p=0.304) or day1(p=0.345). The daily temperature range was correlated inversely with the number of admission in day1(p=0.012) and, but not the number of admission in the same day (day0)(p=0.605) or day2(p=0.142)5. In the Multivariable logistic regression model, daily average temperature was independently associated with the AMI incidence in day1. The chance for having admission for AMI decreased by5%, if the temperature the day before was per5℃higher. In another model built for incidence in day1, similar independent correlations existed with both daily average temperature and daily temperature range, and the chance for having admission for AMI increased by4%, if the daily temperature range was2℃higher.Conclusion:According to the influence of AMI caused by the change of temperature, we suggest that build the temperature index to guide people to pay more attention to take measures for AMI patients when the temperature changes, that build the three level pre-warning system including individual, community and hospital, and Emergency Center to take positive emergency measures for the attack of AMI patients caused by the change of temperature, and that build collaboration mechanism including health education and admission nursing.