Gresser Eva, Reich Jakob, Sabel Bastian O, Kunz Wolfgang G, Fabritius Matthias P, Rübenthaler Johannes, Ingrisch Michael, Wassilowsky Dietmar, Irlbeck Michael, Ricke Jens, Puhr-Westerheide Daniel
Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany.
Department of Anesthesiology, University Hospital, LMU Munich, 81377 Munich, Germany.
Diagnostics (Basel). 2021 Jun 3;11(6):1029. doi: 10.3390/diagnostics11061029.
(1) Background: Extracorporeal membrane oxygenation (ECMO) therapy in intensive care units (ICUs) remains the last treatment option for Coronavirus disease 2019 (COVID-19) patients with severely affected lungs but is highly resource demanding. Early risk stratification for the need of ECMO therapy upon admission to the hospital using artificial intelligence (AI)-based computed tomography (CT) assessment and clinical scores is beneficial for patient assessment and resource management; (2) Methods: Retrospective single-center study with 95 confirmed COVID-19 patients admitted to the participating ICUs. Patients requiring ECMO therapy ( = 14) during ICU stay versus patients without ECMO treatment ( = 81) were evaluated for discriminative clinical prediction parameters and AI-based CT imaging features and their diagnostic potential to predict ECMO therapy. Reported patient data include clinical scores, AI-based CT findings and patient outcomes; (3) Results: Patients subsequently allocated to ECMO therapy had significantly higher sequential organ failure (SOFA) scores ( < 0.001) and significantly lower oxygenation indices on admission ( = 0.009) than patients with standard ICU therapy. The median time from hospital admission to ECMO placement was 1.4 days (IQR 0.2-4.0). The percentage of lung involvement on AI-based CT assessment on admission to the hospital was significantly higher in ECMO patients ( < 0.001). In binary logistic regression analyses for ECMO prediction including age, sex, body mass index (BMI), SOFA score on admission, lactate on admission and percentage of lung involvement on admission CTs, only SOFA score (OR 1.32, 95% CI 1.08-1.62) and lung involvement (OR 1.06, 95% CI 1.01-1.11) were significantly associated with subsequent ECMO allocation. Receiver operating characteristic (ROC) curves showed an area under the curve (AUC) of 0.83 (95% CI 0.73-0.94) for lung involvement on admission CT and 0.82 (95% CI 0.72-0.91) for SOFA scores on ICU admission. A combined parameter of SOFA on ICU admission and lung involvement on admission CT yielded an AUC of 0.91 (0.84-0.97) with a sensitivity of 0.93 and a specificity of 0.84 for ECMO prediction; (4) Conclusions: AI-based assessment of lung involvement on CT scans on admission to the hospital and SOFA scoring, especially if combined, can be used as risk stratification tools for subsequent requirement for ECMO therapy in patients with severe COVID-19 disease to improve resource management in ICU settings.
（1）背景：重症监护病房（ICU）中的体外膜肺氧合（ECMO）治疗仍然是新型冠状病毒肺炎（COVID-19）肺部严重受累患者的最后治疗选择，但对资源需求很高。入院时使用基于人工智能（AI）的计算机断层扫描（CT）评估和临床评分对ECMO治疗需求进行早期风险分层，有利于患者评估和资源管理；（2）方法：对参与研究的ICU收治的95例确诊COVID-19患者进行回顾性单中心研究。对ICU住院期间需要ECMO治疗的患者（n = 14）与未接受ECMO治疗的患者（n = 81）进行鉴别性临床预测参数、基于AI的CT成像特征及其预测ECMO治疗的诊断潜力评估。报告的患者数据包括临床评分、基于AI的CT检查结果和患者预后；（3）结果：随后接受ECMO治疗的患者与接受标准ICU治疗的患者相比，序贯器官衰竭评估（SOFA）评分显著更高（P < 0.001），入院时氧合指数显著更低（P = 0.009）。从入院到放置ECMO的中位时间为1.4天（IQR 0.2 - 4.0）。入院时基于AI的CT评估显示，ECMO患者的肺部受累百分比显著更高（P < 0.001）。在包括年龄、性别、体重指数（BMI）、入院时SOFA评分、入院时乳酸水平和入院CT肺部受累百分比的ECMO预测二元逻辑回归分析中，只有SOFA评分（OR 1.32，95%CI 1.08 - 1.62）和肺部受累（OR 1.06，95%CI 1.01 - 1.11）与随后的ECMO分配显著相关。受试者工作特征（ROC）曲线显示，入院CT肺部受累的曲线下面积（AUC）为0.83（95%CI 0.73 - 0.94），ICU入院时SOFA评分的AUC为0.82（95%CI 0.72 - 0.91）。ICU入院时SOFA评分和入院CT肺部受累的联合参数对ECMO预测的AUC为0.91（0.84 - 0.97），敏感性为0.93，特异性为0.84；（4）结论：入院时基于AI的CT扫描肺部受累评估和SOFA评分，尤其是两者结合，可作为重症COVID-19疾病患者后续ECMO治疗需求的风险分层工具，以改善ICU环境中的资源管理。
