63 Summer months also presented elevated risk, especially during periods of extreme heat. These patterns underscore that cardiac arrest risk is influenced not only by individual weather events but also by broader seasonal shifts that combine environmental exposures with behavioral and physiological changes. In addition to environmental drivers, the model highlighted the important role of socioeconomic and demographic factors in shaping OHCA risk. The proportion of individuals living below the poverty level emerged as one of the strongest non-environmental predictors, followed by education level (high school attainment), racial composition, and median age. These variables likely reflect differences in baseline health status, access to care, chronic disease burden, and resilience to environmental stressors. Notably, these factors remained consistently predictive across regions of varying population size and composition, demonstrating that social vulnerability and environmental exposure jointly influence OHCA incidence. This reinforces the importance of incorporating both environmental and socioeconomic context into predictive models to better identify high-risk populations and guide targeted prevention and response strategies. Unlike traditional statistical approaches, the machine learning model can continuously improve prediction accuracy through iterative learning. The model was able to generate reliable forecasts of daily OHCA incidence up to seven days in advance across national, state, and local levels, as well as in regions not included in initial training. Figure 1 presents the testing results for same-day, 3-day-ahead, and 7-day-ahead predictions in external settings. The light blue lines indicate the observed daily incidence per 100,000 of out- of-hospital cardiac arrests in the registry- participating areas. The yellow lines indicate the predicted daily incidence per 100,000 by the XGBoost gradient boosting model using predictors selected by invariant causal prediction. This type of predictive capability is particularly influential for public health and emergency response systems, as it enables early warning of high-risk periods, more efficient allocation of EMS resources, and the potential for targeted community interventions during periods of elevated risk. Figure 1. Observed versus predicted incidence of out-of- hospital cardiac arrest in the invariant causal prediction model at varying time intervals. From Nakashima T. et al., Development and evaluation of a machine learning model predicting out-of-hospital cardiac arrest using environmental factors, npj Digital Medicine (2025). Used with Permission. Licensed under CC BY NC ND 4.0. https://pubmed.ncbi.nlm.nih.gov/41430486/
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