AUTHOR=Li Chen-Yu , Wu Po-Jui , Chang Chi-Jen , Lee Chien-Ho , Chung Wen-Jung , Chen Tien-Yu , Tseng Chien-Hao , Wu Chia-Chen , Cheng Cheng-I 

TITLE=Weather Impact on Acute Myocardial Infarction Hospital Admissions With a New Model for Prediction: A Nationwide Study

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 8 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2021.725419

DOI=10.3389/fcvm.2021.725419

ISSN=2297-055X

ABSTRACT=Introduction: Cardiovascular disease is one of the leading causes of mortality worldwide. Furthermore, acute myocardial infarction (AMI) is associated with weather change. Hence, this study aimed to investigate the impact of timing, weather change, and risk factors of coronary artery disease on AMI occurrence in Taiwan and to generate a model to predict the probability of AMI in specific weather and clinical conditions. 
Method: This observational study utilized the database from the National Health Insurance Research Database and Taiwan Central Weather Bureau to evaluate the discharge records of patients diagnosed with AMI from various hospitals in Taiwan between January 1, 2008 and December 31, 2011. Generalized additive models (GAMs) were used to estimate the effective parameters on the trend of the AMI incidence rate with respect to the weather and health factors in the time-series data and to build a model for predicting AMI probability. 
Results: A total of 40,328 discharges were listed. The minimum temperature, maximum wind speed, and antiplatelet therapy were negatively related to the daily AMI rate. Every 1° of temperature drop below 15°C was associated with 1.6% increase in relative AMI incidence in Taiwan. By using the meaningful parameters including medical and weather factors, an estimated GAM was built. The model showed adequate correlation in both internal and external validation. 
Conclusion: A colder weather is significantly associated with an increased AMI rate, but the influence of wind speed on AMI remains uncertain. Our study showed that the novel model can reasonably predict daily rates of AMI.