Chest CT scan predictors of intensive care unit admission in hospitalized pregnant women with COVID-19: a case–control study

To investigate the role of chest computed tomography (CT) scan in the prediction of admission of pregnant women with COVID-19 into intensive care unit (ICU). This was a single-center retrospective case–control study. We included pregnant women diagnosed with COVID-19 by reverse transcriptase polymerase chain reaction between February 2020 and July 2021, requiring hospital admission due to symptoms, who also had a CT chest scan at presentation. Patients admitted to the ICU (case group) were compared with patients who did not require ICU admission (control group). The CT scans were reported by an experienced radiologist, blinded to the patient’s course and outcome, aided by an artificial intelligence software. Total CT scan score, chest CT severity score (CT-SS), total lung volume (TLV), infected lung volume (ILV), and infected-to-total lung volume ratio (ILV/TLV) were calculated. Receiver operating characteristic curves were constructed to test the sensitivity and specificity of each parameter. 8/28 patients (28.6%) required ICU admission. These also had lower TLV, higher ILV, and ILV/TLV. The area under the curve (AUC) for these three parameters was 0.789, 0.775, and 0.763, respectively. TLV, ILV, and ILV/TLV had good sensitivity (62.5%, 87.5%, and 87.5%, respectively) and specificity (84.2%, 70%, and 73.7%, respectively) for predicting ICU admission at the following selected thresholds: 2255 mL, 319 mL, and 14%, respectively. The performance of CT-SS, CT scan score, and ILV/TLV in predicting ICU admission was comparable. TLV, ILV, and ILV/TLV as measured by an artificial intelligence software on chest CT, may predict ICU admission in hospitalized pregnant women, symptomatic for COVID-19.

本研究旨在探讨胸部计算机断层扫描（CT）在预测新型冠状病毒肺炎（COVID-19）孕妇入住重症监护病房（ICU）风险中的作用。本研究为单中心回顾性病例对照研究。纳入2020年2月至2021年7月期间，经逆转录聚合酶链反应确诊为COVID-19、因症状需住院治疗且就诊时行胸部CT扫描的孕妇。将入住ICU的患者（病例组）与无需ICU收治的患者（对照组）进行比较。CT扫描由经验丰富的放射科医师在人工智能软件辅助下完成阅片，该医师对患者的病程及转归不知情。本研究计算了CT总评分、胸部CT严重程度评分（CT-SS）、肺总容积（TLV）、受累肺容积（ILV）及受累肺容积与肺总容积比值（ILV/TLV）。通过构建受试者工作特征曲线（ROC曲线），检验各参数的灵敏度与特异度。本研究共纳入28例患者，其中8例（28.6%）需ICU收治。该组患者的肺总容积更低，受累肺容积及受累肺容积与肺总容积比值更高。上述三项参数的曲线下面积（AUC）分别为0.789、0.775及0.763。在选定的阈值（2255mL、319mL及14%）下，肺总容积、受累肺容积及受累肺容积与肺总容积比值预测ICU收治的灵敏度分别为62.5%、87.5%及87.5%，特异度分别为84.2%、70%及73.7%。胸部CT严重程度评分、CT总评分及受累肺容积与肺总容积比值的ICU收治预测性能相当。通过人工智能软件对胸部CT进行测量得到的肺总容积、受累肺容积及受累肺容积与肺总容积比值，或可用于预测有COVID-19症状的住院孕妇的ICU收治风险。