We all know how weather can impact your weekend plans but have you thought about how weather can impact your heart? Recent studies have shown weather does indeed affect the heart especially if you have cardiovascular disease or if you are considered “at risk.”
The journal Heart (BMJ Journals), recently published a study which determined that machine learning, using a combination of timing and weather data, can accurately predict the risk of out-of-hospital cardiac arrest (OHCA). The researchers suggested that this information could be literally lifechanging as is could be used as an early warning system for people to lower their risk and improve their chances of survival, as well as improve the preparedness of emergency medical services. Out-of-hospital cardiac arrest is common around the world and is generally connected with low rates of survival.
The Japanese research team assessed the capacity of machine learning to predict daily out-of-hospital cardiac arrest using daily weather (e.g., temperature, relative humidity, rainfall, snowfall, cloud cover, wind speed, and atmospheric pressure readings) and timing (e.g., year, season, day of the week, hour of the day, and public holidays) data.
Of the 1,299,784 cases occurring between 2005 and 2013, machine learning was applied to 525,374 of the casts, using either weather or timing data, or both as the model training datasets. The results were then compared with 135,678 cases occurring in 2014-15 to test the accuracy of the model for predicting the number of daily cardiac arrests in other years. They found that the combination of weather and timing data most accurately predicted an out-of-hospital cardiac arrest in both the training and testing datasets.
Additionally, the results showed that Sundays, Mondays, public holidays, winter, low temperatures, and sharp temperature drops within and between days were more strongly associated with cardiac arrest than either the weather or timing data by themselves.
Finally, the researchers suggest their models for daily incidence of out-of-hospital cardiac arrest is widely generalizable for the general population in developed countries, because this study had a large sample size and used comprehensive meteorological data. The researchers concluded that the predictive models may be useful for preventing out-of-hospital cardiac arrest and improving the prognosis of patients with out-of-hospital cardiac arrest via a warning system for citizens and EMS on high-risk days in the future.
This study is just the beginning in the efforts to utilize weather data for medical uses. By correlating weather and climate data with the onset of various medical conditions, there are unlimited ways in which this data could be used for the greater good.
For instance, hospitals can use OnPoint Weather to perform regression analysis on emergency room visits to better understand how emergency room footfall traffic is influenced by weather. This allows providers to create a predictive model to forecast emergency room footfall traffic, with the end results being optimized staff resources and reduced patient wait times.
In addition, medical providers who sell seasonal products can strategically deploy products according to the weather versus a pre-determined data and thus ensure consumers have access to products when they need them. Finally, predictive models could be deployed to create warnings for increased cardiac risk based on the weather.
Organizations that incorporate OnPoint Weather data into their business intelligence can easily apply this data and generate insights to discover how weather impacts their bottom lines, or in this case medical needs for a provider’s patients. The end result is actionable intelligence and healthier patients.