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<p>Climate change is intensifying heat exposure worldwide and is expected to increase the burden of cardiovascular diseases (CVD), particularly in semi-arid urban environments where temperature extremes are common; however, individual-level evidence from hot and dry settings remains limited. This study examined associations between multiple thermal stress indices and CVD hospitalizations (n = 2,760), identified predictors of prolonged length of stay (LOS &gt; 7&#xa0;days) and in-hospital mortality, and explored projected risks under ongoing heat trends in Isfahan, Iran, from 2019 to 2024. Daily meteorological data from ERA5-Land reanalysis and six local weather stations were linked with de-identified hospital admission records. Thermal exposure was assessed using the Heat Index (HI), Humidex, Wet-Bulb Globe Temperature (WBGT), Daily Temperature Range (DTR), and Warm Spell Duration Index (WSDI). Quasi-Poisson regression models were used to estimate associations between heat exposure and CVD admissions, adjusting for age, sex, socioeconomic status, fine particulate matter (PM₂.₅), day of the week, and long-term trends. Logistic regression, Extreme Gradient Boosting (XGBoost), and Random Forest models were applied to predict prolonged LOS, with model performance evaluated using five-fold cross-validation and SHAP-based feature interpretation. Each one–standard deviation increase in same-day HI (≈ 4.3&#xa0;°C) was associated with a 12% increase in CVD admissions (IRR = 1.12; 95% CI: 1.05–1.19), while warm spells lasting five or more consecutive days increased admission risk by 42% (IRR = 1.42; 95% CI: 1.21–1.67). Extreme heat exposure was also associated with prolonged hospitalization (OR = 1.85; 95% CI: 1.21–2.83). Among the machine learning models, XGBoost showed strong discriminatory performance (AUC = 0.84; 95% CI: 0.81–0.87), with HI, WBGT, and age identified as the most influential predictors. Overall, multiple thermal stress indices independently predicted higher CVD admission rates and longer hospital stays in this semi-arid urban setting, supporting the integration of climate-based early warning systems into hospital preparedness strategies to reduce heat-related cardiovascular risks.</p>

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Heat stress and cardiovascular hospitalizations in a semi-arid megacity: a multi-method epidemiological and machine-learning analysis in Isfahan, Iran

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月16日

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  • Pouriya Javari,
  • Majid Javari

摘要

Climate change is intensifying heat exposure worldwide and is expected to increase the burden of cardiovascular diseases (CVD), particularly in semi-arid urban environments where temperature extremes are common; however, individual-level evidence from hot and dry settings remains limited. This study examined associations between multiple thermal stress indices and CVD hospitalizations (n = 2,760), identified predictors of prolonged length of stay (LOS > 7 days) and in-hospital mortality, and explored projected risks under ongoing heat trends in Isfahan, Iran, from 2019 to 2024. Daily meteorological data from ERA5-Land reanalysis and six local weather stations were linked with de-identified hospital admission records. Thermal exposure was assessed using the Heat Index (HI), Humidex, Wet-Bulb Globe Temperature (WBGT), Daily Temperature Range (DTR), and Warm Spell Duration Index (WSDI). Quasi-Poisson regression models were used to estimate associations between heat exposure and CVD admissions, adjusting for age, sex, socioeconomic status, fine particulate matter (PM₂.₅), day of the week, and long-term trends. Logistic regression, Extreme Gradient Boosting (XGBoost), and Random Forest models were applied to predict prolonged LOS, with model performance evaluated using five-fold cross-validation and SHAP-based feature interpretation. Each one–standard deviation increase in same-day HI (≈ 4.3 °C) was associated with a 12% increase in CVD admissions (IRR = 1.12; 95% CI: 1.05–1.19), while warm spells lasting five or more consecutive days increased admission risk by 42% (IRR = 1.42; 95% CI: 1.21–1.67). Extreme heat exposure was also associated with prolonged hospitalization (OR = 1.85; 95% CI: 1.21–2.83). Among the machine learning models, XGBoost showed strong discriminatory performance (AUC = 0.84; 95% CI: 0.81–0.87), with HI, WBGT, and age identified as the most influential predictors. Overall, multiple thermal stress indices independently predicted higher CVD admission rates and longer hospital stays in this semi-arid urban setting, supporting the integration of climate-based early warning systems into hospital preparedness strategies to reduce heat-related cardiovascular risks.