Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study.
Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study.
复制标题
人工智能匹配肺炎的主观严重程度评估以预测患者预后及机械通气需求：一项队列研究
DOI：
10.1038/s41598-020-79470-0
复制
发表时间：
2021-01-13
影响因子：
4.6
通讯作者：
Kalra MK
中科院分区：
文献类型：
--
作者：
Ebrahimian S;Homayounieh F;Rockenbach MABC;Putha P;Raj T;Dayan I;Bizzo BC;Buch V;Wu D;Kim K;Li Q;Digumarthy SR;Kalra MK
目的：比较人工智能(AI)和胸部X线胸片(CXR)的肺水肿评分(RALE)对新冠肺炎肺炎患者预后和机械通气需求的预测作用。我们的IRB批准的研究包括来自美国(A点)和韩国(B点)两个地点的405名成年患者(平均年龄65 ± 16岁)的1367例连续CXR。我们记录了与患者人口统计数据(年龄、性别)、吸烟史、合并疾病(如癌症、心血管和其他疾病)、生命体征(温度、血氧饱和度)以及可用实验室数据(如白细胞计数和C反应蛋白)有关的信息。两位胸科放射科医生根据评估肺部受累严重程度的Rale评分对所有CXR进行了定性评估。所有CXR用商业AI算法处理，获得与新冠肺炎相关的发现的肺百分比(AI评分)。除使用曲线下面积(AUC)作为输出的多元Logistic回归外，还使用独立的t检验和卡方检验来预测疾病结局和是否需要机械通气。Rale和AI得分在每个站点的CXR中有很强的正相关(R2 = 0.79-0.86；p <； 0.0001)。死亡或接受机械通气者的罗音和AI评分显著高于恢复或不需要机械通气者(p <； 0.001)。基线和最大罗音评分以及AI评分差异越大的患者死亡和机械通气的发生率越高(p <； 0.001)。增加患者的年龄、性别、白细胞计数和外周血氧饱和度，罗音评分的预后预测从0.87增加到0.94(95%可信区间0.90-0.97)，AI评分的预后预测从0.82增加到0.91(95%可信区间0.87-0.95)。AI算法与新冠肺炎肺炎患者的主观罗音评分一样，是预测患者不良结果(死亡或需要机械通气)的可靠指标。
To compare the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema (RALE) scores from frontal chest radiographs (CXRs) for predicting patient outcomes and the need for mechanical ventilation in COVID-19 pneumonia. Our IRB-approved study included 1367 serial CXRs from 405 adult patients (mean age 65 ± 16 years) from two sites in the US (Site A) and South Korea (Site B). We recorded information pertaining to patient demographics (age, gender), smoking history, comorbid conditions (such as cancer, cardiovascular and other diseases), vital signs (temperature, oxygen saturation), and available laboratory data (such as WBC count and CRP). Two thoracic radiologists performed the qualitative assessment of all CXRs based on the RALE score for assessing the severity of lung involvement. All CXRs were processed with a commercial AI algorithm to obtain the percentage of the lung affected with findings related to COVID-19 (AI score). Independent t- and chi-square tests were used in addition to multiple logistic regression with Area Under the Curve (AUC) as output for predicting disease outcome and the need for mechanical ventilation. The RALE and AI scores had a strong positive correlation in CXRs from each site (r2 = 0.79–0.86; p < 0.0001). Patients who died or received mechanical ventilation had significantly higher RALE and AI scores than those with recovery or without the need for mechanical ventilation (p < 0.001). Patients with a more substantial difference in baseline and maximum RALE scores and AI scores had a higher prevalence of death and mechanical ventilation (p < 0.001). The addition of patients’ age, gender, WBC count, and peripheral oxygen saturation increased the outcome prediction from 0.87 to 0.94 (95% CI 0.90–0.97) for RALE scores and from 0.82 to 0.91 (95% CI 0.87–0.95) for the AI scores. AI algorithm is as robust a predictor of adverse patient outcome (death or need for mechanical ventilation) as subjective RALE scores in patients with COVID-19 pneumonia.
登录
查看更多内容
影响因子：
5
作者：
Dane, Bari;Brusca-Augello, Geraldine;Katz, Douglas S.
通讯作者：
Katz, Douglas S.
影响因子：
8.9
作者：
Borghesi, Andrea;Maroldi, Roberto
通讯作者：
Maroldi, Roberto
DOI：
10.1148/ryct.2020200034
发表时间：
2020-02-01
期刊：
RADIOLOGY-CARDIOTHORACIC IMAGING
影响因子：
--
作者：
Ng, Ming-Yen;Lee, Elaine Y. P.;Kuo, Michael D.
通讯作者：
Kuo, Michael D.
影响因子：
10
作者：
Warren MA;Zhao Z;Koyama T;Bastarache JA;Shaver CM;Semler MW;Rice TW;Matthay MA;Calfee CS;Ware LB
通讯作者：
Ware LB
影响因子：
10
作者：
Ing, Alvin J.;Cocks, Christine;Green, Jeffery Peter
通讯作者：
Green, Jeffery Peter