A study published in npj Digital Medicine found that abrupt changes in temperature seem to be a risk factor for out-of-hospital cardiac arrest (OHCA).
OHCA is a “leading cause of mortality worldwide and 90% of cases are fatal,” mostly because patients progressively lose cardiac function and circulation, meaning it must be treated quickly for a good outcome to be likely.
The University of Michigan research team used “advanced machine learning techniques capable of analyzing multiple interacting variables” to study the link. Their model was built using “more than 190,000 cases from 2013 to 2017 and identified 17 factors that can predict OHCA risk.”
The model “consistently predicted nationwide OHCA incidence, even in areas not included in the training data. Mean ambient temperature, including both colder days and extremely warm ones, as well as higher relative humidity, influenced the number of OHCA incidents.” The model was also able to “predict OHCA patterns up to seven days in advance.”
