Evidence map›Paper›PMID 42036603›Full record

ArticleActa anaesthesiologica Scandinavica2026

Bayesian Analysis of Postoperative Complication Risk Associated With Preoperative Exposure to Fine Particulate Matter: A Single-Center Cohort Study.

John F Pearson, Cameron K Jacobson, Calvin S Riss, Matthew J Strickland, Longyin Lee, Neng Wan, Tabitha M Benney, Nathan L Pace, Ben K Goodrich, Jonah S Gabry and 4 more

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Article in Acta anaesthesiologica Scandinavica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

John F PearsonDepartment of Anesthesiology, University of Utah School of Medicine, Salt Lake City, Utah, USA.ORCID 0000-0003-1759-9518
Cameron K JacobsonDepartment of Anesthesiology, University of Utah School of Medicine, Salt Lake City, Utah, USA.
Calvin S RissDepartment of Biostatistics, Epidemiology and Environmental Health, University of Nevada Reno School of Public Health, Reno, Nevada, USA.
Matthew J StricklandDepartment of Biostatistics, Epidemiology and Environmental Health, University of Nevada Reno School of Public Health, Reno, Nevada, USA.
Longyin LeeSchool of Environment, Society and Sustainability, University of Utah, Salt Lake City, Utah, USA.
Neng WanSchool of Environment, Society and Sustainability, University of Utah, Salt Lake City, Utah, USA.
Tabitha M BenneySchool of Public Affairs, University of Utah, Salt Lake City, Utah, USA.
Nathan L PaceDepartment of Anesthesiology, University of Utah School of Medicine, Salt Lake City, Utah, USA.
Ben K GoodrichApplied Statistics Center, Columbia University, New York City, New York, USA.
Jonah S GabryApplied Statistics Center, Columbia University, New York City, New York, USA.
John V PhamDepartment of Anesthesiology, University of Utah School of Medicine, Salt Lake City, Utah, USA.
Cade K KartchnerDepartment of Anesthesiology, University of Utah School of Medicine, Salt Lake City, Utah, USA.
Jacob A WoodIndiana University School of Medicine, Indianapolis, Indiana, USA.
Michael H AndreaeDepartment of Anesthesiology, University of Utah School of Medicine, Salt Lake City, Utah, USA.

Funding

National Science Foundation 2051246National Science Foundation 2153019NCI NIH HHS 5R37CA276365-02NIEHS NIH HHS 5R01ES029528-05
6 · The paper itself

Abstract

backgroundAir 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 (PM

methodsWe 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 PM

resultsPostoperative complications were associated in a dose-dependent manner with higher concentrations of PM

conclusionsThis case study demonstrates the application of hierarchical Bayesian modeling to quantify the probabilistic associations between preoperative PM 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.

Indexed as

Environmental ExposureParticulate MatterPostoperative ComplicationsAdultAgedBayes TheoremCohort StudiesFemaleHumansMaleMiddle AgedRetrospective StudiesRisk FactorsUtahParticulate Matter

Identifiers

PMID42036603
PMCPMC13111187

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