Prediction Model of Delayed Hemothorax in Patients with Traumatic Occult Hemothorax Using a Novel Nomogram.

Q4 Medicine
Junepill Seok, Su Young Yoon, Jonghee Han, Yook Kim, Jong-Myeon Hong
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引用次数: 0

Abstract

Background: Delayed hemothorax (dHTX) can occur unexpectedly, even in patients who initially present without signs of hemothorax (HTX), potentially leading to death. We aimed to develop a predictive model for dHTX requiring intervention, specifically targeting those with no or occult HTX.

Methods: This retrospective study was conducted at a level 1 trauma center. The primary outcome was the occurrence of dHTX requiring intervention in patients who had no HTX or occult HTX and did not undergo closed thoracostomy post-injury. To minimize overfitting, we employed the least absolute shrinkage and selection operator (LASSO) logistic regression model for feature selection. Thereafter, we developed a multivariable logistic regression (MLR) model and a nomogram.

Results: In total, 688 patients were included in the study, with 64 cases of dHTX (9.3%). The LASSO and MLR analyses revealed that the depth of HTX (adjusted odds ratio [aOR], 3.79; 95% confidence interval [CI], 2.10-6.85; p<0.001) and the number of totally displaced rib fractures (RFX) (aOR, 1.90; 95% CI, 1.56-2.32; p<0.001) were significant predictors. Based on these parameters, we developed a nomogram to predict dHTX, with a sensitivity of 78.1%, a specificity of 76.0%, a positive predictive value of 25.0%, and a negative predictive value of 97.1% at the optimal cut-off value. The area under the receiver operating characteristic curve was 0.832.

Conclusion: The depth of HTX on initial chest computed tomography and the number of totally displaced RFX emerged as significant risk factors for dHTX. We propose a novel nomogram that is easily applicable in clinical settings.

利用新颖的提名图预测外伤性隐匿性血气胸患者的延迟性血气胸模型
背景:延迟性血气胸(dHTX)可意外发生,即使患者最初没有血气胸(HTX)症状,也可能导致死亡。我们的目标是建立一个需要干预的 dHTX 预测模型,特别是针对那些没有或隐性 HTX 的患者:这项回顾性研究在一家一级创伤中心进行。主要结果是在无 HTX 或隐性 HTX 且在受伤后未接受闭式胸腔造口术的患者中发生需要干预的 dHTX。为了尽量减少过拟合,我们采用了最小绝对收缩和选择算子(LASSO)逻辑回归模型进行特征选择。之后,我们建立了一个多变量逻辑回归(MLR)模型和一个提名图:研究共纳入 688 例患者,其中 64 例为 dHTX(9.3%)。LASSO和MLR分析显示,HTX深度(调整后的几率比[aOR],3.79;95%置信区间[CI],2.10-6.85;pConclusion:初始胸部计算机断层扫描显示的 HTX 深度和完全移位的 RFX 数量是导致 dHTX 的重要风险因素。我们提出了一种易于应用于临床的新型提名图。
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来源期刊
Journal of Chest Surgery
Journal of Chest Surgery Medicine-Surgery
CiteScore
0.80
自引率
0.00%
发文量
76
审稿时长
7 weeks
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