The research was conducted by a team from Semmelweis University, the Budapest University of Technology and Economics, and the Hungarian National Ambulance Service. Drawing on one of the most comprehensive datasets assembled for this purpose, the investigators analyzed more than 114,000 cases of out-of-hospital cardiac arrest that occurred across Hungary between November 2018 and December 2023. They then compared the daily counts of these events with a battery of meteorological indicators, including air temperature, wind speed, atmospheric pressure, humidity, and air quality measurements. The goal was not simply to confirm that weather and cardiac arrest are connected, but to determine whether that connection is strong and consistent enough to serve as the basis for a predictive tool.

The stakes are considerable. Out-of-hospital cardiac arrest is one of the leading causes of death in developed countries. In Europe, an estimated 67 to 170 cases occur per 100,000 people each year, and survival remains below 10 percent. Because every minute without circulation dramatically reduces the chances of survival, the speed of emergency response is critical. If emergency medical services could anticipate a spike in case numbers even a few days in advance, they could adjust staffing, reposition ambulances, and prepare hospital resources accordingly. Public awareness campaigns could also be timed to coincide with high-risk periods, reinforcing the importance of rapid intervention and bystander resuscitation, both of which are decisive factors in survival.

The analysis produced a strikingly clear signal. Lower temperatures emerged as one of the most important weather-related risk factors examined. For every 1 degree Celsius decrease in average temperature, the daily number of out-of-hospital cardiac arrests increased by 1.4 percent. That may sound modest, but across a population of millions and over an entire cold season, the cumulative effect translates into a substantial additional burden on emergency services. The seasonal comparison reinforced the pattern: nearly 18 percent more cardiac arrests occurred in winter than in summer, a gap that aligns with the temperature findings and underscores how strongly cold conditions shape cardiovascular emergencies at the population level.

Importantly, the researchers found that it is not only sudden drops in temperature that pose a risk, but lower temperatures in general. This distinction matters for how early warning systems might be designed. A system tuned exclusively to detect abrupt cold snaps would miss the broader, slower-moving risk that accompanies persistently cold days. At the same time, rapid changes remain particularly significant: the study identified a daily temperature drop of more than 5 degrees Celsius as a potential warning signal, one that could be flagged within an early warning framework as a day of heightened concern. In practice, this means both the level of cold and the pace at which it arrives carry predictive information.

Perhaps the most consequential discovery was that the effects of weather changes are not immediate. The number of out-of-hospital cardiac arrests may rise up to three days after the relevant weather conditions occur. This lag between meteorological trigger and clinical outcome is precisely what makes prediction feasible. Weather forecasts are already reliable on a one-to-three-day horizon, so if the physiological and behavioral consequences of cold exposure take days to manifest in case counts, forecast data can be fed into a model that estimates demand before the surge materializes. As Dr. Endre Zima, Professor at the Heart and Vascular Center of Semmelweis University and lead researcher of the study, explained, a previous study by the group examined how extreme cold and heat affect the incidence of out-of-hospital cardiac arrest, and this new work went a step further by investigating whether weather patterns could predict when higher-than-average numbers of cases are likely to occur. If proven feasible, he noted, ambulance services and hospitals could prepare for increased demand several days in advance.

To translate these associations into a practical tool, the team built a predictive model that uses meteorological data to estimate the expected daily number of out-of-hospital cardiac arrests. Dr. Ádám Pál-Jakab, resident physician and PhD student at the Heart and Vascular Center of Semmelweis University and first author of the study, described how combining meteorological and ambulance service data allows the model to predict one to three days in advance when case numbers are likely to rise above average. He emphasized an important limitation and strength of the approach at once: the model does not estimate an individual person’s risk, but rather the number of cases expected nationwide. This population-level framing is what makes the tool useful for health system planning rather than personal diagnosis, and it reflects the statistical nature of the underlying analysis, which treats cardiac arrest counts as a time series shaped by environmental conditions.

Building such a model was far from straightforward. The researchers had to determine not only which atmospheric parameters exerted the greatest influence on cardiac arrest numbers, but also how to use them in a way that produces reliable estimates. Dr. Brigitta Szilágyi, Associate Professor at the Budapest University of Technology and Economics and Corvinus University of Budapest and co-author of the study, identified one of the biggest challenges as determining which days could genuinely be considered outliers, and then examining whether these anomalous days were associated with weather-related factors. Only after establishing that connection could the team construct a model that uses previous data to estimate expected case numbers. This careful separation of true anomalies from ordinary variation is a cornerstone of sound time-series modeling, and it helps ensure that the predictions rest on genuine signal rather than noise.

The next step, according to the researchers, could be the development of an operational early warning system that ingests weather forecasts and issues alerts when conditions associated with elevated cardiac arrest risk are expected. For ambulance services and hospitals, such a system would support capacity planning and help manage expected increases in demand, from scheduling additional crews to ensuring that emergency departments are ready for an influx of resuscitation cases. For the public, the potential applications are equally meaningful. In the future, the system could alert people with cardiovascular disease and their families to higher-risk periods, encouraging them to pay closer attention to warning symptoms and to seek medical attention sooner when necessary. Simple behavioral adjustments during flagged periods, such as avoiding strenuous outdoor exertion in severe cold, could complement the system-level benefits.

The study was conducted as part of the National Multidisciplinary Laboratory for Climate Change, with the University of Pannonia serving as consortium leader, a detail that situates the work within a broader research effort to understand how a changing climate affects human health. As global temperatures become more volatile and extreme weather events grow more frequent, the ability to anticipate the health consequences of atmospheric conditions will only grow in importance. This Hungarian study demonstrates that the connection between weather and cardiac arrest is not merely a statistical curiosity but a predictable, quantifiable relationship with a built-in delay that medicine can exploit. If early warning systems built on this research prove effective in practice, the humble weather forecast could become a routine component of cardiovascular prevention and emergency preparedness, turning days of cold air into days of warning.

Subject of Research: Predicting out-of-hospital cardiac arrest incidence from meteorological conditions using nationwide time-series data

Article Title: Weather data could warn of cardiac arrest risk several days in advance

Article References: Weather data could warn of cardiac arrest risk several days in advance. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: out-of-hospital cardiac arrest, weather forecasting, temperature, early warning system, emergency medical services, cardiovascular disease, public health, time-series analysis, cold exposure, Hungary, Semmelweis University, predictive modeling