Immunity and coagulation and fibrinolytic processes may reduce the risk of severe illness in pregnant women with coronavirus disease 2019.
Immunity and coagulation and fibrinolytic processes may reduce the risk of severe illness in pregnant women with coronavirus disease 2019.
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免疫、凝血和纤溶过程可能会降低患有 2019 年冠状病毒病的孕妇患严重疾病的风险
DOI：
10.1016/j.ajog.2020.10.032
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发表时间：
2021-04
影响因子：
9.8
通讯作者：
Zhao X
中科院分区：
文献类型：
--
作者：
Zhong Y;Cao Y;Zhong X;Peng Z;Jiang S;Tang T;Chen H;Li X;Xia Y;Cheng Y;Zhao X
孕妇在免疫和凝血方面存在特定的生理特征，这些生理特征可能在2019冠状病毒病的发展中发挥重要作用。本研究旨在确定2019冠状病毒病患者病情恶化的关键因素，以及2019冠状病毒病孕妇临床特征的差异性，以干扰2019冠状病毒病的进展。对539例中国成年汉族新冠肺炎患者进行回顾性研究，其中孕妇36例。此外，还招募了36名未感染2019冠状病毒病的孕妇作为对照。利用机器学习算法分析2019冠状病毒病患者重症和危重症与轻、中度疾病的区别特征。此外，探讨2019冠状病毒病孕妇与年龄匹配的2019冠状病毒病重症或危重型非妊娠妇女的主要差异，并与未患2019冠状病毒病的孕妇配对，以确定2019冠状病毒病孕妇的特定生理特征。重症、危重症患者淋巴细胞、CD3+、CD4+、CD8+、CD19+、CD16+CD56+细胞计数明显低于轻、中度疾病患者，白细胞计数、中性粒细胞计数、中性粒细胞/淋巴细胞比值明显高于轻、中度疾病患者（P< 0.001）。危重患者血浆白细胞介素-6、白细胞介素-10、白细胞介素-6 /白细胞介素-10比值明显高于轻、中、重度患者（P< 0.001）。上述免疫共簇在受试者工作特征曲线下的面积为0.801(95%可信区间0.764-0.838)，其与凝血和纤溶指标（凝血酶原时间、d -二聚体）联合模型采用随机森林回归模型预测重症或危重症时，在受试者工作特征曲线下的面积为0.815（95%可信区间0.779-0.851）。在感染2019冠状病毒病的孕妇中，没有人先前存在疾病。与未怀孕的轻、中度2019冠状病毒病孕妇相比，2019冠状病毒病孕妇白细胞计数、中性粒细胞计数、中性粒细胞与淋巴细胞比值、d -二聚体和纤维蛋白原水平升高，淋巴细胞和白细胞介素-4水平降低（P< 0.05）。虽然两组淋巴细胞免疫指标变化相似；白细胞计数；neutrophil-to-lymphocyte比率;CD3+、CD4+、CD8+、CD16+CD56+细胞计数；白细胞介素-6与白细胞介素-10的比值，与未怀孕的2019冠状病毒病重症、危重型孕妇相比，2019冠状病毒病重症、危重型孕妇均未恶化为重症、危重型。2019冠状病毒病孕妇与非2019冠状病毒病孕妇白细胞计数、淋巴细胞计数、中性粒细胞计数、中性粒细胞与淋巴细胞比值、免疫标志物、凝血和纤溶标志物均无显著差异。对于2019冠状病毒病孕妇与2019冠状病毒病重症、危重型非孕妇病理生理特征差异，免疫指标在受体工作特征曲线下的面积为0.875(95%可信区间0.773-0.977)，其与凝血、纤溶指标联合模型在受体工作特征曲线下的面积为0.931（95%可信区间0.850-1.000）。免疫失调被认为是2019年冠状病毒病患者发展为严重或危重疾病的一个关键特征，2019年冠状病毒病孕妇表现出类似的免疫反应，但严重或危重疾病的发生率较低。免疫失调与恶化为严重或危重疾病的风险有关。妊娠期特定的凝血和纤溶系统可降低无既往疾病的2019冠状病毒感染孕妇发展为严重疾病的风险。
There are specific physiological features regarding the immunity and coagulation among pregnant women, which may play important roles in the development of coronavirus disease 2019. This study aimed to determine the key factors associated with the deterioration of patients with coronavirus disease 2019 and the differentiating clinical characteristics of pregnant women with coronavirus disease 2019 to interfere with the progression of coronavirus disease 2019. A retrospective study of 539 Chinese Han adult patients with coronavirus disease 2019 was conducted, of which 36 cases were pregnant women. In addition, 36 pregnant women without coronavirus disease 2019 were recruited as the control. The characteristics of severe and critical illnesses, which were differentiated from mild and moderate illnesses in patients with coronavirus disease 2019, were analyzed using a machine learning algorithm. In addition, major differences between pregnant women with coronavirus disease 2019 and age-matched nonpregnant women with severe or critical coronavirus disease 2019, paired with pregnant women without coronavirus disease 2019, were explored to identify specific physiological features of pregnant women with coronavirus disease 2019. For the total patient population, the lymphocyte, CD3+, CD4+, CD8+, CD19+, and CD16+CD56+ cell counts were significantly lower, and white blood cell count, neutrophil count, and neutrophil-to-lymphocyte ratio were higher in those with severe or critical illness than those with mild or moderate illness (P<.001). The plasma levels of interleukin-6, interleukin-10, and interleukin-6–to–interleukin-10 ratio were significantly increased in patients with critical illness compared with patients with mild, moderate, and severe illnesses (P<.001). The above immunologic coclusters achieved an area under the receiver operating characteristic curve of 0.801 (95% confidence interval, 0.764–0.838), and its combined model with the coagulation and fibrinolysis indices (prothrombin time, D-dimer) achieved an area under the receiver operating characteristic curve of 0.815 (95% confidence interval, 0.779–0.851) using the random forest regression model to predict severe or critical illness. For pregnant women with coronavirus disease 2019, none had preexisting diseases. Compared with nonpregnant women with mild or moderate coronavirus disease 2019, pregnant women with coronavirus disease 2019 displayed increased white blood cell count, neutrophil count, neutrophil-to-lymphocyte ratio, and levels of D-dimer and fibrinogen, along with decreased lymphocyte and interleukin-4 levels (P<.05). Although they presented similar changes of immunologic markers of lymphocyte; white blood cell count; neutrophil-to-lymphocyte ratio; CD3+, CD4+, CD8+, and CD16+CD56+ cell counts; and interleukin-6–to–interleukin-10 ratio, compared with nonpregnant women with severe or critical coronavirus disease 2019, none of the pregnant women with coronavirus disease 2019 deteriorated into severe or critical illness. There was no significant difference in white blood cell count, lymphocyte count, neutrophil count, neutrophil-to-lymphocyte ratio, immunologic markers, or coagulation and fibrinolysis markers between pregnant women with coronavirus disease 2019 and pregnant women without coronavirus disease 2019. As for the discrepancy of pathophysiological features between pregnant women with coronavirus disease 2019 and nonpregnant women with severe or critical coronavirus disease 2019, the immunologic markers achieved an area under the receiver operating characteristic curve of 0.875 (95% confidence interval, 0.773–0.977), and its combined model with coagulation and fibrinolysis indices achieved an area under the receiver operating characteristic curve of 0.931 (95% confidence interval, 0.850–1.000). Immune dysregulation was identified as a crucial feature of patients with coronavirus disease 2019, which developed severe or critical illness, and pregnant women with coronavirus disease 2019 presented with similar immune responses but rarer incidences of severe or critical illness. Immune dysregulation is related to the risks of deterioration into severe or critical illness. The specific coagulation and fibrinolysis systems of pregnancy may reduce the risk of pregnant women with coronavirus disease 2019 without preexisting disease from developing severe illness.
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影响因子：
56.9
作者：
Kissler, Stephen M.;Tedijanto, Christine;Lipsitch, Marc
通讯作者：
Lipsitch, Marc
影响因子：
11.8
作者：
Qin, Chuan;Zhou, Luoqi;Tian, Dai-Shi
通讯作者：
Tian, Dai-Shi
影响因子：
158.5
作者：
Ackermann, Maximilian;Verleden, Stijn E.;Jonigk, Danny
通讯作者：
Jonigk, Danny
DOI：
10.1111/jth.14817
发表时间：
2020-05
期刊：
Journal of thrombosis and haemostasis : JTH
影响因子：
--
作者：
通讯作者：
--
影响因子：
82.9
作者：
Del Valle DM;Kim-Schulze S;Huang HH;Beckmann ND;Nirenberg S;Wang B;Lavin Y;Swartz TH;Madduri D;Stock A;Marron TU;Xie H;Patel M;Tuballes K;Van Oekelen O;Rahman A;Kovatch P;Aberg JA;Schadt E;Jagannath S;Mazumdar M;Charney AW;Firpo-Betancourt A;Mendu DR;Jhang J;Reich D;Sigel K;Cordon-Cardo C;Feldmann M;Parekh S;Merad M;Gnjatic S
通讯作者：
Gnjatic S