Savushkina Olga I, Muraveva Elena S, Zhitareva Irina V, Nekludova Galina V, Mustafina Malika Kh, Avdeev Sergey N
Pulmonology Research Institute under Federal Medical and Biological Agency of Russia, Moscow, Russia.
Pirogov Russian National Research Medical University (Pirogov Medical University), Moscow, Russia.
J Thorac Dis. 2024 Nov 30;16(11):7282-7289. doi: 10.21037/jtd-24-1118. Epub 2024 Nov 18.
Patients surviving the coronavirus disease 2019 (COVID-19) are reported to explore pulmonary sequelae. It is challenging to provide pulmonary function tests (PFTs) during the pandemic of this contagious diseases because of the difficulty related to infection control risks. This study aims to identify important predictors of lung diffusion capacity impairment in COVID-19 survivors after hospital discharge.
The retrospective cohort study included 341 patients after COVID-19. The parameters of spirometry, body plethysmography, lung diffusion capacity for carbon monoxide (DLco), and the worst chest computed tomography (CT) scan in the acute phase of COVID-19 (CT, %) were assessed. Multivariable logistic regression analysis for exploring risk factors associated with lung diffusion capacity impairment was used. The receiver operating characteristic (ROC) curve of multivariate observation and the area under the curve (AUC) were used to assess the performance of a model.
At the time of the analysis, 64.8% (221/341) patients participated in follow-up visits on 90 days, 23.5% (80/341) on 90-180 days, and 11.7% (40/341) on more than 180 days after the onset of COVID-19 symptoms. The median CT was 50% (50% of the lung area was involved in a pathological process according to a semi-quantitative CT score). Abnormal DLco (<80% of predicted) was recorded in 60.4% cases. The predictors such as age, gender, body mass index (BMI), CT, and the time interval between the COVID-19 symptoms onset and follow-up PFTs were encapsulated in the logistic regression analysis to explore the prediction of reduced DLco. Backward stepwise regression was applied to eliminate insignificant predictors. It was found that CT was important predictor of impaired DLco. AUC value was 0.780 [95% confidential interval (CI): 0.723-0.837, P<0.001]. The sensitivity and specificity in the training group were 80% and 67%, respectively. The odds ratio (OR) showed that CT =45% and more in the acute phase of COVID-19 was significantly associated with reduced DLco during 6 months after COVID-19 (OR 1.21, 95% CI: 1.095-1.334; P<0.05).
Pulmonary interstitial damage caused by severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) definitely contributes to reduced DLco after hospital discharge. This indicates that analysis of CT scans during the acute phase of COVID-19 may have prognostic relevance for abnormal DLco.
据报道,2019年冠状病毒病(COVID-19)幸存者会出现肺部后遗症。在这种传染病大流行期间,由于存在感染控制风险方面的困难,进行肺功能测试(PFTs)具有挑战性。本研究旨在确定COVID-19幸存者出院后肺弥散功能受损的重要预测因素。
这项回顾性队列研究纳入了341例COVID-19康复患者。评估了肺活量测定、体容积描记法、一氧化碳肺弥散量(DLco)以及COVID-19急性期胸部计算机断层扫描(CT)最差结果(CT,%)的参数。采用多变量逻辑回归分析来探索与肺弥散功能受损相关的危险因素。使用多变量观察的受试者工作特征(ROC)曲线和曲线下面积(AUC)来评估模型的性能。
在分析时,64.8%(221/341)的患者在COVID-19症状出现后90天进行了随访,23.5%(80/341)在90 - 180天进行了随访,11.7%(40/341)在180天以上进行了随访。CT的中位数为50%(根据半定量CT评分,50%的肺面积参与了病理过程)。60.4%的病例记录到DLco异常(<预测值的80%)。在逻辑回归分析中纳入了年龄、性别、体重指数(BMI)、CT以及COVID-19症状出现至随访PFTs的时间间隔等预测因素,以探索DLco降低的预测情况。采用向后逐步回归法剔除无显著意义的预测因素。发现CT是DLco受损的重要预测因素。AUC值为0.780 [95%置信区间(CI):0.723 - 0.837,P<0.001]。训练组的敏感性和特异性分别为80%和67%。优势比(OR)显示,COVID-19急性期CT =45%及以上与COVID-19后6个月内DLco降低显著相关(OR 1.21,95% CI:1.095 - 1.334;P<0.05)。
严重急性呼吸综合征冠状病毒2(SARS-CoV-2)引起的肺间质损伤肯定会导致出院后DLco降低。这表明COVID-19急性期的CT扫描分析可能对DLco异常具有预后意义。