第 1 组和第 3 组肺动脉高压患者正常肺容积在计算机断层扫描定量分析中的意义

Tadasu Okaya MD. PhD , Ayako Shigeta MD. PhD , Nobuhiro Tanabe MD. PhD , Koichiro Tatsumi MD. PhD , Hajime Yokota MD. PhD , Akira Nishiyama MD. PhD , Akira Naito MD. PhD , Ayumi Sekine , Toshihiko Sugiura MD. PhD , Seiichiro Sakao MD. PhD , Takuji Suzuki MD. PhD
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引用次数: 0

摘要

背景:并发肺动脉高压(PH)和肺实质疾病的患者有很高的死亡率。然而,他们的结果是否与胸部高分辨率CT (HRCT)成像中肺实质疾病导致的低正常肺容量(NLV)有关尚不清楚。研究问题:定量HRCT成像的NLV是否会影响PH患者的疾病行为?研究设计和方法本回顾性观察性研究评估了1,471例接受右心导管置入的经医生诊断为1组和3组PH的患者。采用HRCT成像三维图像分析系统,计算NLV (-950 ~ -600 Hounsfield单位)占全肺的百分比(%NLV)。采用受试者工作特征(ROC)曲线检测预测生存率的最佳临界点%NLV。采用Kaplan-Meier法和Cox比例风险回归检测%NLV与预后的关系。采用多变量logistic回归来检验%NLV与肺血管扩张剂疗效的关系。结果157例患者(平均年龄53.1±17.6岁;纳入性别,n = 111例(70.7%)女性患者。ROC曲线分析显示,NLV %预测生存率的最佳截止值为83.2%。%NLV≥83.2%的患者5年生存率显著高于%NLV≥83.2%的患者;83.2% (81.7% vs 36.6%;P & lt;。)。多变量logistic回归分析显示%NLV <;83.2%为独立预后因素(风险比2.49 [95% CI, 1.14-5.44];P = .022)。应答者的NLV %明显高于无应答者(90.0±5.1% vs 84.7±9.2%;P = 0.0002)。多变量回归分析显示,只有高NLV %预测疗效(OR, 1.12 [95% CI, 1.01-1.23];P = .016)。定量CT成像分析可能允许对ph患者的肺部状况和血管扩张剂可治疗区域进行数值量化,而不是主观视觉评估。%NLV可能是这些患者预后和治疗反应的一种新的预测指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Significance of Normal Lung Volume on Quantitative CT Imaging Analysis in Group 1 and Group 3 Pulmonary Hypertension

Background

Patients with concurrent pulmonary hypertension (PH) and parenchymal lung diseases have a high risk of mortality. However, whether their outcomes are related to low normal lung volume (NLV) resulting from parenchymal lung diseases on chest high-resolution CT (HRCT) imaging remains unknown.

Research Question

Would NLV on quantitative HRCT imaging affect disease behavior in patients with PH?

Study Design and Methods

This retrospective observational study evaluated patients with physician-diagnosed group 1 and group 3 PH among 1,471 patients who underwent right heart catheterization. Using a 3-dimensional image analysis system for HRCT imaging, the percentage of NLV (–950 to –600 Hounsfield units) to the whole lungs (%NLV) was calculated. The optimal cutoff point of %NLV for predicting survival was examined using the receiver operating characteristic (ROC) curve. The Kaplan-Meier method and Cox proportional hazards regression were used to detect the association between %NLV and prognosis. Multivariable logistic regression was performed to examine the association of %NLV with response to pulmonary vasodilators.

Results

Overall, 157 patients (mean age, 53.1 ± 17.6 years; sex, n = 111 [70.7%] female patients) were included. ROC curve analysis showed that the optimal cutoff of %NLV for predicting survival was 83.2%. The patients with %NLV of ≥ 83.2% showed significantly higher 5-year survival than that of those with %NLV of < 83.2% (81.7% vs 36.6%; P < .0001). Multivariable logistic regression analysis revealed %NLV of < 83.2% as an independent prognostic factor (hazard ratio, 2.49 [95% CI, 1.14–5.44]; P = .022). Responders showed significantly higher %NLV than nonresponders (90.0 ± 5.1% vs 84.7 ± 9.2%; P = .0002). Multivariable regression analysis showed that only high %NLV predicted response (OR, 1.12 [95% CI, 1.01–1.23]; P = .016).

Interpretation

Quantitative CT imaging analysis might allow numerical quantification of the lung condition and vasodilator-treatable area beyond subjective visual assessment in patients with PH. The %NLV could be a novel predictor of prognosis and treatment response in these patients.
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