Acta Anaesthesiol Scand. 2026 Jul;70(6):e70235. doi: 10.1111/aas.70235.
ABSTRACT
BACKGROUND: Air pollution, especially particle pollution, is increasingly recognized as a potential perioperative risk factor, yet modeling environmental exposures in surgical cohorts remains methodologically underdeveloped. We demonstrate a Bayesian hierarchical framework to quantify probabilistic associations between preoperative fine particulate matter (PM2.5) exposure and postoperative complications, highlighting its interpretability and flexibility for clinical environmental epidemiology.
METHODS: We conducted a single center, retrospective cohort study using data from 49,615 surgical patients in Utah who underwent elective surgical procedures from 2016 to 2018. Patients’ addresses were geocoded and linked to daily Census-tract level PM2.5 estimates. The exposure variable was defined as the maximum PM2.5 concentrations in the 7 days prior to surgery. The binary outcome was a composite of postoperative complications: pneumonia, surgical site infection, urinary tract infection, sepsis, stroke, myocardial infarction, or thromboembolic event. A hierarchical Bayesians regression model with weakly informative priors was used adjusting for age, sex, season, neighborhood disadvantage, and the Elixhauser index of comorbidities with census tract as a group (random) effect. We present posterior estimates with credible intervals, highlight model transparency and sensitivity, and discuss contrasts with standard frequentist methods.
RESULTS: Postoperative complications were associated in a dose-dependent manner with higher concentrations of PM2.5 exposure. We found a relative increase of 8.2% in the odds of complications (OR = 1.082) for every 10.ug/m3 increase in the highest single-day 24-h PM2.5 exposure during the 7 days prior to surgery. For an increase in PM2.5 from 1 to 30 ug/m3, the odds of complication rose to over 27% (95% CI: 4%-55%). The results were robust across prior choices and model specifications. We report full posterior distributions and highlight advantages of Bayesian modeling for uncertainty quantification and clinical interpretability.
CONCLUSIONS: 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.
EDITORIAL COMMENT: Using Bayesian statistical analysis, the authors demonstrate a dose-dependent risk for postoperative complications in patients exposed to air polluted with fine particulate matter with a size of less than 2.5 μm.
PMID:42036603 | DOI:10.1111/aas.70235
