Every year in Europe, between 67 and 170 people per 100,000 suffer an out-of-hospital cardiac arrest, and fewer than one in ten survives. These emergencies appear sudden to bystanders — but new research suggests they may not arrive entirely unannounced for health systems. A nationwide study conducted by researchers at Semmelweis University in Hungary, together with the Budapest University of Technology and Economics and the Hungarian National Ambulance Service, found that routine weather data could help predict, up to three days in advance, when the number of out-of-hospital cardiac arrests is likely to rise above average.
The findings, published in the journal Public Health, are based on an analysis of more than 114,000 cardiac arrest cases recorded between November 2018 and December 2023. For clinicians, ambulance coordinators, public health planners, and families caring for people with cardiovascular disease, the study points toward a practical form of healthcare preparedness: using forecasts that are already available every day to anticipate demand and reduce risk.
Why Cardiac Arrest Prediction Matters for Public Health
Out-of-hospital cardiac arrest (OHCA) is one of the leading causes of death in developed countries. Unlike a heart attack, which typically involves a blocked artery, cardiac arrest is an abrupt electrical failure of the heart: the organ stops pumping effectively, blood flow to the brain ceases, and death follows within minutes unless bystanders begin resuscitation and emergency services arrive quickly.
This is why prevention and preparedness carry so much weight in cardiology and emergency medicine. Every improvement in early recognition, bystander CPR, and ambulance response translates directly into more lives saved. If emergency services know several days ahead that demand is likely to spike, they can:
- Adjust ambulance staffing and vehicle deployment ahead of high-risk periods
- Prepare hospital emergency departments and catheterization labs for increased admissions
- Coordinate with dispatch centers to prioritize suspected cardiac calls
- Issue public advisories encouraging people with heart disease to limit strenuous outdoor activity
Survival from OHCA remains below 10% across Europe. Shifting even part of the response from reactive to proactive could meaningfully improve outcomes. If you work in emergency care or public health and want to discuss how weather-based prediction models could fit into your planning, write to us in the comments section below.
Inside the Study: Matching 114,000 Cardiac Arrests With Daily Weather Data
The research team compared records of out-of-hospital cardiac arrests from November 2018 to December 2023 with daily meteorological indicators, including temperature, wind speed, atmospheric pressure, humidity, and air quality. The goal was not simply to describe weather effects in general terms, but to answer a sharper question: can weather patterns reliably predict when higher-than-average numbers of cases will occur?
That distinction matters. Earlier work by the same group at Semmelweis University’s Heart and Vascular Center had already shown that extreme cold and heat affect the incidence of out-of-hospital cardiac arrest. The new study went a step further, testing whether those relationships are consistent and dependable enough to support an actual forecasting tool.
Dr. Endre Zima, Professor at the Heart and Vascular Center of Semmelweis University and lead researcher of the study, explained that while the earlier research examined how extreme temperatures influence cardiac arrest incidence, this project investigated whether weather patterns could be used to anticipate when higher-than-average case numbers are likely — information that would allow ambulance services and hospitals to prepare for increased demand several days ahead.
Key Findings: Temperature Emerges as the Strongest Risk Signal
Every Degree Counts: The 1.4% Effect
The analysis identified falling temperatures as one of the most important weather-related risk factors examined. Specifically, every 1°C decrease in average temperature was associated with a 1.4% increase in the daily number of out-of-hospital cardiac arrests. On its own, 1.4% may sound modest; applied to a national caseload, it accumulates quickly across a prolonged cold spell.
Winter Versus Summer: An 18% Gap
The seasonal comparison reinforced the point: nearly 18% more cardiac arrests occurred in winter than in summer. Importantly, the researchers emphasized that risk is not limited to sudden cold snaps — lower temperatures in general raise risk. Rapid changes still carry particular weight, however, and a daily temperature drop of more than 5°C was identified as a potential warning signal for an early warning system.
The Three-Day Lag: A Realistic Forecasting Window
Perhaps the most operationally valuable finding is that the effect of weather changes is not immediate. Cardiac arrest numbers tend to rise up to three days after the relevant weather patterns set in. This delay creates a genuine forecasting window — enough time for ambulance services to reposition resources and for hospitals to reinforce staffing before the surge arrives, rather than reacting to it in real time.
From Meteorological Data to a Working Predictive Model
Identifying correlations is only half the work. The researchers then had to determine which atmospheric parameters carried the strongest signal and how to combine them into a model that produces dependable daily 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, described one of the central challenges: deciding which days genuinely counted as outliers — days with unusually high case numbers — and then testing whether those outliers were linked to weather-related factors. From that foundation, the team built a model that uses previous data to estimate expected case numbers.
The resulting model can predict one to three days in advance when the number of out-of-hospital cardiac arrests is likely to exceed the national average. Crucially, the researchers are clear about its scope: the model estimates expected case numbers at the population level — not an individual person’s risk. Dr. Ádám Pál-Jakab, resident physician and PhD student at the Heart and Vascular Center of Semmelweis University and first author of the study, stressed this distinction. If you are caring for a relative with cardiovascular disease, share this article with family members so they understand what these forecasts can — and cannot — tell them.
What the Findings Mean for Healthcare Preparedness
For Ambulance Services and Hospitals
The most immediate application is operational. An early warning system built on weather forecasts would allow ambulance services and hospitals to plan capacity around predicted increases in demand. Practical steps might include adding shifts during flagged periods, pre-positioning vehicles in high-incidence areas, ensuring adequate stocks of defibrillators and resuscitation equipment, and briefing dispatch centers on expected call volumes.
Health systems in Hungary and elsewhere already use forecasting for seasonal flu waves and heatwaves; extending the same logic to cardiac arrest prediction is a natural and comparatively low-cost evolution, since the underlying weather data is collected and published daily at no additional expense.
For People With Cardiovascular Disease and Their Families
In the longer term, the researchers envision alerts that reach patients directly. A household notified that the coming days carry elevated cardiac arrest risk could take sensible precautions: adhering strictly to prescribed medication, avoiding heavy physical exertion outdoors, dressing warmly in cold weather, and making sure family members know CPR and the location of the nearest automated external defibrillator.
None of this replaces medical advice, and a forecast of elevated risk does not mean any individual will experience an emergency. But elevated-risk periods are precisely when preparation pays off most. Explore our related articles on cardiovascular prevention and emergency response to strengthen your household’s readiness.
Broader Implications for Research and Medical Education in Hungary
The study is also a strong example of interdisciplinary collaboration. Cardiologists from Semmelweis University’s Heart and Vascular Center worked with statisticians and data scientists from the Budapest University of Technology and Economics, drawing on real-world ambulance service records spanning five full years. That combination — clinical insight, statistical modeling, and large-scale operational data — is increasingly the template for high-impact health research.
For students considering medicine, biomedical engineering, or public health, the project illustrates where these fields are heading. Future clinicians and researchers will be expected to interpret predictive models, question their assumptions, and translate population-level insights into clinical and policy decisions. Institutions such as Semmelweis University, with its clinical infrastructure and established research programs, sit at the center of that development.
There are also lessons for other countries. Temperature-driven increases in cardiac arrest are documented across temperate climates, and many national ambulance services already collect the case data needed to replicate this analysis. A healthcare preparedness tool based on weather forecasts could, in principle, be adapted to any region with reliable meteorological and emergency medical records.
What Comes Next: Toward an Operational Early Warning System
The research team’s stated next step is to develop an early warning system that uses weather forecasts to help ambulance services and hospitals plan capacity and manage expected increases in demand. Beyond institutional use, such a system could eventually alert people with cardiovascular disease and their families to higher-risk periods, giving them time to prepare before danger peaks.
Several questions remain open and will shape the next phase of work:
- How well does the model perform in real time, outside the historical data on which it was built?
- Which weather thresholds are most useful for triggering operational alerts?
- How should warnings be communicated to the public without causing unnecessary alarm?
- Can air quality data strengthen predictions further in dense urban environments?
Answering these questions will determine how quickly weather-based cardiac arrest prediction moves from research journals into dispatch centers and hospital planning meetings.
Key Takeaways
- A five-year nationwide study of more than 114,000 cases linked weather data to out-of-hospital cardiac arrest incidence in Hungary.
- Each 1°C drop in average temperature was associated with a 1.4% rise in daily cases; winter saw nearly 18% more arrests than summer.
- Cardiac arrest numbers can rise up to three days after relevant weather changes, creating a realistic forecasting window.
- The predictive model estimates population-level case numbers — not individual risk — and is intended to support healthcare preparedness.
- The next phase aims at an operational early warning system for ambulance services, hospitals, and eventually patients and their families.
Weather is one of the few cardiac risk factors that can be observed days in advance at essentially no cost. The work by Semmelweis University and its partners shows how this familiar daily information could become a practical safeguard against one of medicine’s most time-critical emergencies. Subscribe to our newsletter to follow the development of this early warning system, and add your perspective in the comments: would a weather-based risk alert change how you or your family prepare for cold snaps?
