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Effects of Extreme Humidity and Heat on Ventricular Arrhythmia Risk in Patients With Cardiac Devices.

JACC. Advances2025-01-06PubMed
Total: 84.5Innovation: 9Impact: 8Rigor: 8Citation: 9

Summary

In a case time-series of 5,944 device patients linked to geocoded weather data, extreme humidity (95th percentile, 90% RH) increased VT/VF odds over the subsequent 7 days (aOR 1.23; 95% CI 1.00–1.51), with strongest effects in individuals with CAD, HF, diabetes, hypertension, and in socioeconomically deprived areas. High temperatures alone were not associated with VT/VF.

Key Findings

  • Extreme humidity (95th percentile, 90% RH) increased VT/VF odds over 7 days (aOR 1.23; 95% CI 1.00–1.51).
  • Greatest humidity-related risk among males, age 67–75, and those with CAD, HF, diabetes, hypertension, or prior MI.
  • Communities with high socioeconomic deprivation and income inequality, and urban areas with less greenspace, had higher risk.
  • High temperatures were not associated with VT/VF events.

Clinical Implications

Implement humidity-aware risk alerts and remote monitoring for high-risk device patients during humid spells; tailor counseling for patients with CAD/HF/diabetes; public health and urban planning (greenspace) may mitigate vulnerability.

Why It Matters

This is among the first large-scale analyses linking extreme humidity to ventricular arrhythmias, integrating clinical, environmental, and community-level exposures with advanced time-series modeling.

Limitations

  • Observational design; residual confounding (e.g., indoor climate, medications) possible.
  • Generalizability limited to North Carolina device population; exposure misclassification cannot be fully excluded.

Future Directions

Prospective multicenter studies integrating personal humidity exposure, physiologic monitoring, and intervention trials (e.g., cooling/dehumidification, adaptive pacing or alerting) to reduce arrhythmic events.

Study Information

Study Type
Cohort
Research Domain
Prognosis/Prevention
Evidence Level
III - Observational time-series analysis linking exposures to outcomes
Study Design
OTHER