Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation
Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation
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DOI：
10.1007/s00330-020-07013-2
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发表时间：
2020-06-26
影响因子：
5.9
通讯作者：
Balzarini, Luca
中科院分区：
文献类型：
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作者：
Lanza, Ezio;Muglia, Riccardo;Balzarini, Luca
目的意大利伦巴第是2020年3月新冠肺炎大流行的震中。医疗保健系统受到ICU床位和氧合支持设备短缺的影响。在我们研究所，大多数患者在入院时都接受了胸部CT检查，只是视觉上的解释。鉴于定量CT分析(QCT)在急性呼吸窘迫综合征(ARDS)中被证明的价值，我们测试了QCT作为新冠肺炎预后预测因子。方法采用单中心回顾性研究方法，对2020年1月25日至2020年4月28日收治的因呼吸困难或低饱和度等呼吸道症状入院时行CT检查的新冠肺炎患者进行研究。QCT采用半自动方法(3D-SLICER)。肺按Hounsfield单位间隔进行划分。折中肺容量(%CL)是充气不良和非充气容量之和(-500,100HU)。我们收集了患者的临床数据，包括整个住院期间的氧合支持。结果222例患者(男性163例，中位年龄66岁，智商54~6岁)，75%接受氧合支持(插管率20%)。肺容量受损是最准确的预后预测因素(Logistic回归，p<；0.001)。%CL值在6-23%范围内会增加氧合支持的风险；超过23%值则有插管的风险。%CL与PaO2/FiO(2)比值呈负相关(p<；0.001)，是住院死亡率的危险因素(p<；0.001)。结论QCT为新冠肺炎提供了新的指标。肺容量受损在预测是否需要氧合支持和插管方面是准确的，并且是院内死亡的重要危险因素。QCT可作为新冠肺炎分类过程的一种工具。
Objective Lombardy (Italy) was the epicentre of the COVID-19 pandemic in March 2020. The healthcare system suffered from a shortage of ICU beds and oxygenation support devices. In our Institution, most patients received chest CT at admission, only interpreted visually. Given the proven value of quantitative CT analysis (QCT) in the setting of ARDS, we tested QCT as an outcome predictor for COVID-19. Methods We performed a single-centre retrospective study on COVID-19 patients hospitalised from January 25, 2020, to April 28, 2020, who received CT at admission prompted by respiratory symptoms such as dyspnea or desaturation. QCT was performed using a semi-automated method (3D Slicer). Lungs were divided by Hounsfield unit intervals. Compromised lung (%CL) volume was the sum of poorly and non-aerated volumes (- 500, 100 HU). We collected patient's clinical data including oxygenation support throughout hospitalisation. Results Two hundred twenty-two patients (163 males, median age 66, IQR 54-6) were included; 75% received oxygenation support (20% intubation rate). Compromised lung volume was the most accurate outcome predictor (logistic regression,p < 0.001). %CL values in the 6-23% range increased risk of oxygenation support; values above 23% were at risk for intubation. %CL showed a negative correlation with PaO2/FiO(2)ratio (p < 0.001) and was a risk factor for in-hospital mortality (p < 0.001). Conclusions QCT provides new metrics of COVID-19. The compromised lung volume is accurate in predicting the need for oxygenation support and intubation and is a significant risk factor for in-hospital death. QCT may serve as a tool for the triaging process of COVID-19.