Bressem Keno K, Adams Lisa C, Albrecht Jakob, Petersen Antonie, Thieß Hans-Martin, Niehues Alexandra, Niehues Stefan M, Vahldiek Janis L
Charité - Universitätsmedizin Berlin, Berlin, Germany.
Pol J Radiol. 2020 Oct 30;85:e600-e606. doi: 10.5114/pjr.2020.100788. eCollection 2020.
Emphysema and chronic obstructive lung disease were previously identified as major risk factors for severe disease progression in COVID-19. Computed tomography (CT)-based lung-density analysis offers a fast, reliable, and quantitative assessment of lung density. Therefore, we aimed to assess the benefit of CT-based lung density measurements to predict possible severe disease progression in COVID-19.
Thirty COVID-19-positive patients were included in this retrospective study. Lung density was quantified based on routinely acquired chest CTs. Presence of COVID-19 was confirmed by reverse transcription polymerase chain reaction (RT-PCR). Wilcoxon test was used to compare two groups of patients. A multivariate regression analysis, adjusted for age and sex, was employed to model the relative increase of risk for severe disease, depending on the measured densities.
Intensive care unit (ICU) patients or patients requiring mechanical ventilation showed a lower proportion of medium- and low-density lung volume compared to patients on the normal ward, but a significantly larger volume of high-density lung volume (12.26 dl IQR 4.65 dl vs. 7.51 dl vs. IQR 5.39 dl, = 0.039). In multivariate regression analysis, high-density lung volume was identified as a significant predictor of severe disease.
The amount of high-density lung tissue showed a significant association with severe COVID-19, with odds ratios of 1.42 (95% CI: 1.09-2.00) and 1.37 (95% CI: 1.03-2.11) for requiring intensive care and mechanical ventilation, respectively. Acknowledging our small sample size as an important limitation; our study might thus suggest that high-density lung tissue could serve as a possible predictor of severe COVID-19.
肺气肿和慢性阻塞性肺疾病先前被确定为新冠病毒病(COVID-19)严重疾病进展的主要风险因素。基于计算机断层扫描(CT)的肺密度分析可对肺密度进行快速、可靠且定量的评估。因此,我们旨在评估基于CT的肺密度测量对预测COVID-19可能的严重疾病进展的益处。
本回顾性研究纳入了30例COVID-19阳性患者。基于常规获取的胸部CT对肺密度进行量化。通过逆转录聚合酶链反应(RT-PCR)确认COVID-19的存在。采用Wilcoxon检验比较两组患者。进行多变量回归分析,并对年龄和性别进行校正,以根据测量的密度对严重疾病风险的相对增加进行建模。
与普通病房的患者相比,重症监护病房(ICU)患者或需要机械通气的患者中低密度和中密度肺体积的比例较低,但高密度肺体积明显更大(12.26 dl,四分位间距4.65 dl,相比7.51 dl,四分位间距5.39 dl,P = 0.039)。在多变量回归分析中,高密度肺体积被确定为严重疾病的显著预测指标。
高密度肺组织的量与严重COVID-19显著相关,需要重症监护和机械通气的比值比分别为1.42(95%置信区间:1.09 - 2.00)和1.37(95%置信区间:1.03 - 2.11)。认识到我们的小样本量是一个重要局限性;因此我们的研究可能表明高密度肺组织可作为严重COVID-19的一个可能预测指标。