Abstract：
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.
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).
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.
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).
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.
摘要：
目的：术前胸片（CR）对预测术后肺炎的作用尚不明确。我们旨在开发和验证术后肺炎的预测模型，其中包括基于深度学习的计算机辅助检测（DL-CAD）系统评估的术前CR结果材料和方法：这项回顾性研究包括2019年1月至2020年3月期间连续接受手术的患者，并分为发展（2019年手术）和验证（2020年1月至3月手术）队列。在手术前1个月内获得的术前CR使用商业化的DL-CAD进行分析，该DL-CAD提供了CR中存在10种不同异常的概率值。使用临床变量（临床模型）建立预测术后肺炎的Logistic回归模型，术前CRs的临床变量和DL-CAD结果（DL-CAD模型）。通过接收器工作特性曲线下的面积评估模型的判别性能。
结果：在发展队列中（n=19,349；平均年龄，57岁；11,392名男子），肺结节的DL-CAD结果（比值比[OR，概率值增加1%]，1.007;p=0.021),合并(或，1.019;p<0.001)，和心脏肥大(或，1.013;p<0.001）是术后肺炎的独立预测因子，并包括在DL-CAD模型中。在验证队列中(n=4957；平均年龄，56岁；2848名男性），DL-CAD模型表现出比临床模型更高的AUROC(0.843vs.0.815；p=0.012）。
结论：通过DL-CAD评估的术前CRs异常是术后肺炎的独立危险因素。使用术前CR的DL-CAD结果改善了术后肺炎的预测。
结果：在发展队列中（n=19,349；平均年龄，57岁；11,392名男子），肺结节的DL-CAD结果（比值比[OR，概率值增加1%]，1.007;p=0.021),合并(或，1.019;p<0.001)，和心脏肥大(或，1.013;p<0.001）是术后肺炎的独立预测因子，并包括在DL-CAD模型中。在验证队列中(n=4957；平均年龄，56岁；2848名男性），DL-CAD模型表现出比临床模型更高的AUROC(0.843vs.0.815；p=0.012）。
结论：通过DL-CAD评估的术前CRs异常是术后肺炎的独立危险因素。使用术前CR的DL-CAD结果改善了术后肺炎的预测。
