Dataset related to article "Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation "

This record contains raw data related to article “Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation" <strong>Objective: </strong> 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. <strong>Methods: </strong> 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. <strong>Results: </strong> 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 &lt; 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/FiO₂ ratio (p &lt; 0.001) and was a risk factor for in-hospital mortality (p &lt; 0.001). <strong>Conclusions: </strong> 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. <strong>Key points: </strong> • Quantitative computer-aided analysis of chest CT (QCT) provides new metrics of COVID-19. • The compromised lung volume measured in the - 500, 100 HU interval predicts oxygenation support and intubation and is a risk factor for in-hospital death. • Compromised lung values in the 6-23% range prompt oxygenation therapy; values above 23% increase the need for intubation.本数据集包含与论文《新型冠状病毒肺炎胸部CT定量分析预测氧疗与插管需求》相关的原始数据。**研究目的**：2020年3月，意大利伦巴第大区（Lombardy）成为新型冠状病毒肺炎（COVID-19）全球大流行的震中区域，当地医疗系统遭遇重症监护病房（Intensive Care Unit, ICU）床位与氧疗支持设备严重短缺的危机。本研究机构内，多数患者于入院时接受胸部CT检查，仅通过人工视觉方式完成影像解读。鉴于定量CT分析（quantitative CT analysis, QCT）在急性呼吸窘迫综合征（Acute Respiratory Distress Syndrome, ARDS）诊疗领域已被证实的应用价值，我们将QCT用于COVID-19患者的预后预测研究。**研究方法**：本研究为单中心回顾性研究，纳入2020年1月25日至2020年4月28日期间住院的COVID-19患者，所有患者均因呼吸困难、血氧饱和度下降等呼吸道症状入院时接受CT检查。采用半自动化工具3D Slicer完成QCT分析：按照亨氏单位（Hounsfield unit, HU）区间对肺部进行分区，将通气不良与无通气肺组织的体积之和定义为受损肺容积百分比（%CL），对应的HU区间为-500至100 HU。同时收集患者住院期间的临床数据，包括全程氧疗支持情况。**研究结果**：本研究共纳入222例患者，其中男性163例，中位年龄66岁，四分位间距为54-6；75%的患者接受了氧疗支持，气管插管率为20%。受损肺容积是最精准的预后预测指标（logistic回归分析，p<0.001）。当%CL处于6%~23%区间时，患者接受氧疗支持的风险升高；当%CL高于23%时，患者出现气管插管需求的风险显著增加。%CL与动脉血氧分压/吸入氧分数（PaO₂/FiO₂）比值呈负相关（p<0.001），同时也是住院期间死亡的独立危险因素（p<0.001）。**研究结论**：定量CT分析（QCT）可为COVID-19诊疗提供全新的量化指标。受损肺容积可精准预测患者的氧疗与插管需求，同时是住院死亡的显著危险因素。QCT可作为COVID-19患者分诊流程的有效工具。**核心要点**：• 胸部CT定量计算机辅助分析（quantitative computer-aided analysis of chest CT, QCT）可为COVID-19诊疗提供全新量化指标。• 以-500~100 HU区间测量的受损肺容积，可预测患者的氧疗与插管需求，同时是住院死亡的危险因素。• 受损肺容积百分比处于6%~23%区间时，提示需启动氧疗；当该百分比高于23%时，患者的气管插管需求显著升高。