Perioperative medicine & outcomes
Bayesian Analysis of Postoperative Complication Risk Associated With Preoperative Exposure to Fine Particulate Matter: A Single‐Center Cohort Study
Authors' conclusion
This case study demonstrates the application of hierarchical Bayesian modeling to quantify the probabilistic associations between preoperative PM2.5 exposure and postoperative complications, highlighting transparent risk estimation and uncertainty characterization that may inform the design of future multicenter perioperative environmental studies.
Study context
- Population
- Patients undergoing elective surgical procedures in Utah, 2016–2018
- Participants
- 49,615 patients
- Exposure
- Higher vs lower maximum PM2.5 exposure in the 7 days before surgery
- Outcome
- Composite of postoperative complications:
- Pneumonia
- Surgical site infection
- Urinary tract infection
- Sepsis
- Stroke
- MI
- Thromboembolic event
Methodological detailssingle-centre, hierarchical Bayesian regression, weakly informative priors, Addresses geocoded and linked to daily Census-tract level PM2.5 estimates
- Single-centre
- Hierarchical Bayesian regression
- Weakly informative priors
- Addresses geocoded and linked to daily Census-tract level PM2.5 estimates
Main finding
As reported in the abstract
OR per 10 ug/m3 increase in highest single-day 24-h PM2.5 in 7 days before surgery (8.2% relative increase in odds)
1.082
Increase in odds of complication for PM2.5 increase from 1 to 30 ug/m3 (95% CI: 4%-55%)
over 27%
Observational data: associations, not effects.
Original publication
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