Bayesian Analysis of Postoperative Complication Risk Associated With Preoperative Exposure to Fine Particulate...

Area of Science:

• Environmental Epidemiology
• Bayesian Statistics
• Perioperative Medicine

Background:

• Air pollution, particularly fine particulate matter (PM2.5), is an emerging perioperative risk factor.
• Current methods for modeling environmental exposures in surgical cohorts are limited.
• A Bayesian hierarchical framework offers a flexible approach to quantify these associations.

Purpose of the Study:

• To quantify the probabilistic association between preoperative PM2.5 exposure and postoperative complications.
• To demonstrate the utility of a Bayesian hierarchical framework in clinical environmental epidemiology.
• To highlight the interpretability and flexibility of this modeling approach.

Main Methods:

• Retrospective cohort study of 49,615 surgical patients (2016-2018).
• Geocoded patient addresses linked to census-tract level PM2.5 estimates.
• Hierarchical Bayesian regression model used to assess the association between 7-day preoperative PM2.5 exposure and a composite outcome of postoperative complications, adjusting for covariates.

Main Results:

• A dose-dependent relationship was observed between PM2.5 exposure and postoperative complications.
• An 8.2% increase in the odds of complications was associated with every 10 μg/m³ increase in daily PM2.5.
• Increased PM2.5 exposure from 1 to 30 μg/m³ elevated complication odds by over 27%.

Conclusions:

• Hierarchical Bayesian modeling effectively quantifies the probabilistic association between PM2.5 exposure and postoperative complications.
• This approach provides transparent risk estimation and uncertainty characterization.
• Findings can inform future multicenter perioperative environmental studies.