Rationale and objectives
The role of preoperative chest radiography (CR) for prediction of postoperative pneumonia
remains uncertain. We aimed to develop and validate a prediction model for postoperative
pneumonia incorporating findings of preoperative CRs evaluated by a deep learning-based
computer-aided detection (DL-CAD) system
Materials and methods
This retrospective study included consecutive patients who underwent surgery between
January 2019 and March 2020 and divided into development (surgery in 2019) and validation
(surgery between January and March 2020) cohorts. Preoperative CRs obtained within
1-month before surgery were analyzed with a commercialized DL-CAD that provided probability
values for the presence of 10 different abnormalities in CRs. Logistic regression
models to predict postoperative pneumonia were built using clinical variables (clinical
model), and both clinical variables and DL-CAD results for preoperative CRs (DL-CAD
model). The discriminative performances of the models were evaluated by area under
the receiver operating characteristic curves.
Results
In development cohort (n = 19,349; mean age, 57 years; 11,392 men), DL-CAD results for pulmonary nodules (odds
ratio [OR, for 1% increase in probability value], 1.007; p = 0.021), consolidation (OR, 1.019; p < 0.001), and cardiomegaly (OR, 1.013; p < 0.001) were independent predictors of postoperative pneumonia and were included
in the DL-CAD model. In validation cohort (n = 4957; mean age, 56 years; 2848 men), the DL-CAD model exhibited a higher AUROC
than the clinical model (0.843 vs. 0.815; p = 0.012).
Conclusion
Abnormalities in preoperative CRs evaluated by a DL-CAD were independent risk factors
for postoperative pneumonia. Using DL-CAD results for preoperative CRs led to an improved
prediction of postoperative pneumonia.
Key Words
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Article info
Publication history
Published online: March 15, 2023
Accepted:
February 17,
2023
Received in revised form:
February 10,
2023
Received:
January 10,
2023
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
