Association of air pollution with ventricular arrhythmias and physical activity: A natural experiment from US wildfires.

BACKGROUND

The association between particulate matter (PM) air pollution and ventricular arrhythmias is not well established. In patients with cardiac implantable electronic devices (CIEDs), publicly available day-level air pollution data provide a unique opportunity to study acute and subacute effects of PM pollution.

OBJECTIVE

The purpose of this study was to evaluate the association of air pollution with ventricular arrhythmias, physical activity, and CIED markers of heart failure.

METHODS

We performed a retrospective cohort study using the CERTITUDE database (Biotronik SE & Co. KG, Berlin, Germany) of patients with CIEDs. The primary predictors were Air Quality Index (AQI), PM < 10 μm in diameter, and PM < 2.5 μm in diameter (PM2.5). We cross-linked day-level air pollutant levels with patient zip codes. We determined the association of air pollution with CIED parameters using (1) a case-crossover analysis using a conditional logistic regression and (2) a time-varying exposure analysis with the Andersen-Gill model.

RESULTS

The study cohort included 28,349 patients (9062 [32%] female; mean age 72.8±11.9 years), of whom 17,448 (61.6%) had pacemakers and 9079 (32%) had defibrillators. AQI and PM2.5 were associated with significant changes in physical activity, heart rate, and thoracic impedance. When limiting to the 8687 patients living in Western US Fire States (California, Oregon, Washington, Arizona, Utah, Nevada, New Mexico, and Colorado), there was a strong association between PM2.5 and premature ventricular contraction burden, with an odds ratio of 7.72 (95% confidence interval 7.48-7.96; P < .0001) for PM2.5 ≥ 13.7. Multiple sensitivity analyses demonstrated the stability of our findings.

CONCLUSION

In a large cohort of patients with CIEDs, AQI and PM2.5 had significant associations with premature ventricular contraction burden, physical activity, and heart rate. These data also demonstrate the feasibility of linking environmental data with patient sensor data to evaluate exposure-outcome relationships.