SAA联合CRP、血常规检测在新型冠状病毒肺炎中的诊断价值

目的探讨血清淀粉样蛋白A(SAA)联合血常规、C反应蛋白(CRP)检测在新型冠状病毒肺炎(COVID-19)诊断中的价值。方法选取襄阳市中心医院30例COVID-19住院患者(COVID-19组),以33名体检健康者作为对照组,收集并分析所有研究对象的血常规、CRP、SAA检测数据以及临床资料。结果与对照组比较,COVID-19组体温、中性粒细胞百分比(NEUT%)、CRP、SAA显著升高,淋巴细胞百分比(LYMPH%)显著降低(P0.01),但白细胞(WBC)计数差异无统计学意义(P0.05)。COVID-19组5个血液学指标中SAA的阳性率最高(93.3%)。受试者工作特征(ROC)曲线分析结果显示,SAA诊断COVID-19的曲线下面积(AUC)为0.994,最佳临界值为6.43 mg/L,敏感性和特异性分别为100%和91.9%。不同电子计算机断层扫描(CT)分期患者比较,SAA的差异有统计学意义(P0.01),SAA判断CT分期的AUC为0.851,敏感性和特异性分别为82.4%和76.9%。结论血液学指标,尤其是SAA,可快速、准确地辅助诊断COVID-19,为临床及时诊治提供参考。     

SAA、CRP在新型冠状病毒肺炎诊断中的价值

目的:探讨血清淀粉样蛋白A(SAA)、C反应蛋白(CRP)在新型冠状病毒肺炎(COVID-19)诊断中的价值。方法:回顾性分析2020年1月~6月本院COVID-19患者的临床资料,包括基本人口学特征、实验室检查结果和影像学检查结果,并与甲流组、疑似组、健康组进行比较分析。结果:COVID-19组患者SAA和CRP明显高于甲流组和疑似组(均P<0.05);COVID-19组SAA和CRP的阳性率显著高于疑似组(均P<0.01),且SAA的阳性率高于CRP;重型和危重型COVID-19患者的年龄、SAA、CRP、IL-6明显高于普通型(均P<0.05)。结论:COVID-19患者中升高的SAA和CRP,可作为COVID-19的早期诊断和辅助诊断的重要参考指标,值得临床进一步推广应用。     

新型冠状病毒IgM和IgG抗体不同检测方法在新型冠状病毒感染中的临床应用评价

目的探讨新型冠状病毒(SARS-CoV-2)IgM和IgG抗体不同检测方法在新型冠状病毒肺炎(COVID-19)中的应用。方法选取25例COVID-19患者,以同期20例排除SARS-CoV-2感染的患者作为对照组,分别采用磁微粒化学发光法和胶体金法检测所有对象的血清SARS-CoV-2 IgM和IgG抗体。同时检测COVID-19患者血清降钙素原(PCT)、铁蛋白及C反应蛋白(CRP)。结果化学发光法检测血清SARSCoV-2 IgM和IgG抗体的敏感性分别为48%和56%,特异性均为100%,胶体金法检测血清SARS-CoV-2 IgM和IgG抗体的敏感性分别为88%和76%,临床特异性均为100%。2种方法检测血清SARS-CoV-2 IgM和IgG抗体总符合率分别为68.9%和73.3%。25例COVID-19患者中有36%的患者血清PCT升高、72%的患者血清CRP升高、84%的患者血清铁蛋白水平升高。结论SARS-CoV-2 IgM和IgG抗体不同检测方法之间差异较大,用于COVID-19患者的临床诊断时应综合考虑。     

新型冠状病毒IgM/IgG抗体在病程监测中的临床意义

目的评价新型冠状病毒(SARS-CoV-2)IgM/IgG抗体检测(化学发光法)在新型冠状病毒肺炎(COVID-19)病程监测和转归方面的临床意义。方法回顾性研究。收集2020年1月20日至2020年3月1日在天津市疾病预防控制中心核酸检测阳性的COVID-19确诊患者88例(115例血清样本)作为疾病组,排除COVID-19(核酸检测阴性)的其他疾病患者245例(245例血清样本)作为对照组。用化学发光法检测SARS-CoV-2 IgM和IgG抗体。结果 SARS-CoV-2 IgM抗体检测的敏感性、特异性分别为90.91%、100%;SARS-CoV-2 IgG抗体检测的敏感性、特异性分别为88.64%、100%;IgM和IgG抗体联合检测敏感性显著提高至96.59%,特异性为100%,与核酸检测的总符合率高达99.10%。SARS-CoV-2 IgM抗体水平在COVID-19病程中呈现先升高后降低的趋势,IgG抗体水平随着病程的进展逐渐升高。结论 SARS-CoV-2 IgM和IgG抗体化学发光法联合检测敏感性高,可作为COVID-19一种有效的辅助诊断,在病程监测和转归方面有一定的意义。     

272例新型冠状病毒肺炎患者多项炎性免疫指标分析

目的探讨新型冠状病毒肺炎(COVID-19)患者多项炎性免疫指标在血液中的表达水平,为临床诊断提供更多的理论依据。方法选取武汉市第三医院2020年1月27日至2月29日收治的272例COVID-19确诊患者作为病例组,按照病情分为普通型、重型和危重型3组,另选取54例同时期已排除感染的患者作为对照组,回顾性分析各组白细胞(WBC)计数、淋巴细胞(LYM)计数、C反应蛋白(CRP)、血清淀粉样蛋白A(SAA)、降钙素原(PCT)、血清补体C3水平,并进行比较;采用受试者工作特征曲线(ROC曲线)评价以上6项指标单独及联合诊断COVID-19的价值。结果与对照组相比,病例组WBC计数、LYM计数均显著降低(P<0.05),CRP、SAA和PCT水平均显著增加(P<0.05);并且随着病情的加重,CRP、SAA和PCT水平逐渐增加(P<0.05),但LYM计数和补体C3水平均逐渐降低(P<0.05);WBC计数、LYM计数、CRP、SAA、PCT和补体C3联合检测诊断COVID-19时,ROC曲线下面积为0.984,灵敏度为90.0%,特异度为100.0%;6项指标联合检测鉴别COVID-19患者重型和危重型时,ROC曲线下面积为0.911,灵敏度为86.0%,特异度为85.0%。结论COVID-19患者体内WBC计数、LYM计数、CRP、SAA、PCT及补体C3等多项炎性免疫指标的联合检测,有助于疾病的临床诊断、分型。     

SARS-CoV-2抗体动态监测在COVID-19诊断中的价值

目的探讨严重急性呼吸综合征冠状病毒2(SARS-CoV-2)免疫球蛋白(Ig)M和IgG抗体检测在新型冠状病毒肺炎(COVID-19)诊断中的价值。方法选取COVID-19患者80例,以排除COVID-19的患者20例作为对照组。采用胶体金法检测SARS-CoV-2 IgM抗体和IgG抗体。收集23例COVID-19患者不同时间点采集的样本,并进行SARS-CoV-2抗体检测。结果 SARS-CoV-2 IgM抗体、IgG抗体和IgM+IgG抗体联用的特异性均为100%,敏感性分别为76.3%、81.3%、90.0%,总体符合率分别为81.0%、85.0%、92.0%。在发病7 d内,SARS-CoV-2 IgM抗体、IgG抗体和IgM+IgG抗体联用的敏感性分别为65.0%、70.0%、70.0%;在发病8～14 d,三者的敏感性分别为72.7%、95.5%、95.5%;在发病15 d后,三者的敏感性分别为84.2%、97.4%、97.4%。SARS-CoV-2IgM抗体在发病后第5～13天出现转阳趋势,在发病第18天后可观察到转阴趋势;SARS-CoV-2IgG抗体在第5天出现转阳趋势,之后持续阳性。结论血清SARS-CoV-2 IgM抗体和IgG抗体可作为COVID-19的辅助诊断指标,对其连续监测有助于判断疾病进程。     

SARS-CoV-2抗体检测在新型冠状病毒肺炎诊断中的应用价值

目的探讨血清严重急性呼吸综合征冠状病毒(SARS-CoV-2)IgM和IgG抗体检测在新型冠状病毒肺炎(COVID-19)诊断中的价值。方法选取COVID-19患者173例(COVID-19组),其中140例SARSCoV-2首次核酸检测阳性,33例多次检测后呈阳性。以101例排除COVID-19的患者作为对照组。采用胶体金免疫层析法(CGIA)检测SARS-CoV-2 IgM和IgG抗体,采用荧光定量聚合酶链反应(PCR)检测SARS-CoV-2核酸。以临床诊断为金标准,分析SARS-CoV-2 IgM和IgG抗体诊断COVID-19的效能。结果 SARS-CoV-2 IgM和IgG抗体的敏感性分别为76.9%、74.6%,特异性分别为94.0%、95.1%,临床总符合率分别为83.2%、82.1%,与临床诊断的一致性均较好(kappa值分别为0.663、0.644)。受试者工作特征(ROC)曲线分析结果显示,SARS-CoV-2 IgM和IgG抗体诊断COVID-19的曲线下面积(AUC)均为0.85。结论 SARS-CoV-2抗体检测具有较高的敏感性、特异性和临床总符合率,可用于COVID-19的辅助诊断。     

妊娠合并新型冠状病毒肺炎临床特征分析

目的:探讨妊娠合并新型冠状病毒肺炎(COVID-19)患者的流行病学特点、临床特征及诊断。方法:回顾性分析2020年1月15日至2020年2月15日在华中科技大学同济医学院附属同济医院收治的22例妊娠合并COVID-19患者的临床资料,分析其流行病学特点、临床及放射学特征和实验室数据。结果:22例患者临床表现中有发热10例(45.45%)、咳嗽5例(22.73%)、呼吸急促1例(4.55%)和腹泻1例(4.55%)。21例(95.45%)为普通型,1例(4.55%)为重型。实验室检查中,淋巴细胞降低14例(63.64%),D-二聚体增高22例(100%)。胸部CT检查均示典型的COVID-19表现,病原学核酸检测阳性率40.91%(9/22)。结论:孕妇患COVID-19的临床特征和实验室检查与非妊娠成人患者相似,相对于病原学检查,胸部CT检查快速安全且敏感性高,更适合COVID-19流行地区产科急诊住院患者的初筛,同时能监测病情进展,有助于COVID-19孕妇的筛查、诊断及监测。     

NLR、CRP、PCT、DD、NT-proBNP在重症COVID-19中的诊断价值

目的探讨中性粒细胞/淋巴细胞比值(NLR)、C反应蛋白(CRP)、降钙素原(PCT)、D-二聚体(DD)、氨基末端B型钠尿肽原(NT-proBNP)对重症新型冠状病毒肺炎(COVID-19)的诊断价值。方法选取105例COVID-19患者,根据COVID-19诊断标准将患者分成轻症组(83例)、重症组(22例)。以50名体检健康者作为健康对照组。比较3组之间的年龄、性别及白细胞(WBC)计数、中性粒细胞绝对数(NEUT#)、淋巴细胞绝对数(LYMPH#)、NLR、CRP、PCT、DD、NT-proBNP水平。结果与健康对照组相比,轻症组、重症组年龄大,且以男性为主。与轻症组相比,重症组年龄更大(P<0.05)。与轻症组相比,重症组WBC计数、NEUT#、NLR、CRP、PCT、DD、NT-proBNP水平明显增高(P<0.05),LYMPH#水平明显降低(P<0.05);与健康对照组相比,轻症组NLR、CRP、DD、NT-proBNP水平较高(P<0.05),WBC计数、LYMPH#水平较低(P<0.05)。5种诊断指标中NT-proBNP对重症COVID-19的诊断价值较高,联合检测优于单项检测。NT-proBNP诊断COVID-19的敏感性为95.45%,特异性为93.98%,阳性预测值80.77%、阴性预测值98.73%。结论NT-proBNP对重症COVID-19的诊断价值较高,当与NLR、CRP、PCT、DD时联合检测时对重症COVID-19的诊断价值更高。     

凝血指标对COVID-19病情发展的预测价值

目的探讨凝血酶原时间(PT)、活化部分凝血活酶时间(APTT)、纤维蛋白原(Fib)、凝血酶时间(TT)及D-二聚体(DD)对新型冠状病毒肺炎(COVID-19)病情发展的预测和诊断的价值。方法选取2020年1—2月阜阳市第二人民医院就诊的COVID-19患者120例,分为重症组(17例)和轻症组(103例)。比较2组PT、APTT、Fib、TT和DD 5项指标的差异,用Logistic回归分析建立预测模型,采用受试者工作特征(ROC)曲线进行评价。结果重症组TT和DD均高于轻症组,差异均有统计学意义(P<0.01)。重症组Fib高于轻症组,差异有统计学意义(P<0.05)。DD和TT是COVID-19患者重症化的独立危险因素,建立预测模型Logit(P)=-12.460+1.760DD+0.601TT,预测模型拟合优度检验结果提示模型拟合度良好。ROC曲线预测效能分析结果显示,DD、TT以及联合检测的曲线下面积(AUC)分别为0.854、0.817、0.875,敏感性分别为76.47%、70.59%、70.59%,特异性分别为83.50%、86.41%、98.06%。DD、TT预测COVID-19患者重症化的最佳临界值分别为0.48μg/L、16 s。结论DD>0.48μg/L、TT>16 s以及二者联合能有效预测和诊断COVID-19患者病情重症化的发展。     

Lung Ultrasound Severity Index: Development and Usefulness in Patients with Suspected SARS-Cov-2 Pneumonia—A Prospective Study

Coronavirus disease 2019 (COVID-19) has spread across the world with a strong impact on populations and health systems. Lung ultrasound is increasingly employed in clinical practice but a standard approach and data on the accuracy of lung ultrasound are still needed. Our study's objective was to evaluate lung ultrasound diagnostic and prognostic characteristics in patients with suspected COVID-19. We conducted a monocentric, prospective, observational study. Patients with respiratory distress and suspected COVID-19 consecutively admitted to the Emergency Medicine Unit were enrolled. Lung ultrasound examinations were performed blindly to clinical data. Outcomes were diagnosis of COVID-19 pneumonia and in-hospital mortality. One hundred fifty-nine patients were included in our study; 66% were males and 63.5% had a final diagnosis of COVID-19. COVID-19 patients had a higher mortality rate (18.8% vs. 6.9%, p = 0.04) and Lung Ultrasound Severity Index (16.14 [8.71] vs. 10.08 [8.92], p < 0.001) compared with non-COVID-19 patients. This model proved able to distinguish between positive and negative cases with an area under the receiver operating characteristic (AUROC) equal to 0.72 (95% confidence interval [CI]: 0.64–0.78) and to predict in-hospital mortality with an AUROC equal to 0.81 (95% CI: 0.74–0.86) in the whole population and an AUROC equal to 0.76 (95% CI: 0.66–0.84) in COVID-19 patients. The Lung Ultrasound Severity Index can be a useful tool in diagnosing COVID-19 in patients with a high pretest probability of having the disease and to identify, among them, those with a worse prognosis.     

Accuracy and Acceptance of a Self-Collection Model for Respiratory Tract Infection Diagnostics: A Concise Clinical Literature Review

BackgroundNurses are the primary clinicians who collect specimens for respiratory tract infection testing. The specimen collection procedure is time and resource-consuming, but more importantly, it places nurses at risk for potential infection. The practice of allowing patients to self-collect their diagnostic specimens may provide an alternative testing model for the current COVID-19 outbreaks. The objective of this paper was to evaluate the accuracy and patient perception of self-collected specimens for respiratory tract infection diagnostics.MethodsA concise clinical review of the recently published literature was conducted.ResultsA total of 11 articles were included the review synthesis. The concept of self-collected specimens has a high patient acceptance rate of 83-99%. Self-collected nasal-swab specimens demonstrated strong diagnostic fidelity for respiratory tract infections with a sensitivity between 80-100%, this is higher than the 76% sensitivity observed with self-collected throat specimens. In a comparative study evaluating a professionally collected to a self-collected specimen for COVID-19 testing, a high degree of agreement (k = 0.89) was observed between the two methods.ConclusionAs we continue to explore for testing models to combat the COVID-19 pandemic, self-collected specimens is a practical alternative to nurse specimen collection.     

基于CT影像的COVID-19智能辅助诊断方法

目的结合COVID-19患者肺部CT影像学特征,探讨深度学习技术在COVID-19辅助诊断上的价值。方法搜集武汉大学中南医院和华中科技大学同济医学院确诊为COVID-19患者的部分CT影像资料构建小样本COVID-19数据集,将VGG-16具有提取高层抽象特征部分与设计的全连接层共同构成初步的基于迁移学习的COVID-19智能辅助诊断模型,使用COVID-19训练集迭代训练诊断模型,不断优化全连接层网络参数,最后训练出一个基于VGG-16卷积神经网络迁移学习的COVID-19智能辅助诊断模型。结果在COVID-19测试集中早期、进展期和重症期3个类别的样本上,COVID-19智能辅助诊断模型测试的敏感度分别为0.95、0.93和0.96,F1 Score分别为0.98、0.95和0.92,综合的诊断准确率达到94.59%。结论小样本数据集上采用迁移学习技术训练的COVID-19辅助诊断模型具有较高的可靠性,在防控疫情的关键时期,能快速地为医生提供诊断的参考意见,提高医生的工作效率。      

Clinical and inflammatory features based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study

ObjectivesTo appraise effective predictors for COVID-19 mortality in a retrospective cohort study.MethodsA total of 1270 COVID-19 patients, including 984 admitted in Sino French New City Branch (training and internal validation sets randomly split at 7:3 ratio) and 286 admitted in Optical Valley Branch (external validation set) of Wuhan Tongji hospital, were included in this study. Forty-eight clinical and laboratory features were screened with LASSO method. Further multi-tree extreme gradient boosting (XGBoost) machine learning-based model was used to rank importance of features selected from LASSO and subsequently constructed death risk prediction model with simple-tree XGBoost model. Performances of models were evaluated by AUC, prediction accuracy, precision, and F1 scores.ResultsSix features, including disease severity, age, levels of high-sensitivity C-reactive protein (hs-CRP), lactate dehydrogenase (LDH), ferritin, and interleukin-10 (IL-10), were selected as predictors for COVID-19 mortality. Simple-tree XGBoost model conducted by these features can predict death risk accurately with >90% precision and >85% sensitivity, as well as F1 scores >0.90 in training and validation sets.ConclusionWe proposed the disease severity, age, serum levels of hs-CRP, LDH, ferritin, and IL-10 as significant predictors for death risk of COVID-19, which may help to identify the high-risk COVID-19 cases.

KEY MESSAGES

1. A machine learning method is used to build death risk model for COVID-19 patients.
2. Disease severity, age, hs-CRP, LDH, ferritin, and IL-10 are death risk factors.
3. These findings may help to identify the high-risk COVID-19 cases.

     

A nomogram predicting severe COVID-19 based on a large study cohort from China

BackgroundThe use of accurate prediction tools and early intervention are important for addressing severe coronavirus disease 2019 (COVID-19). However, the prediction models for severe COVID-19 available to date are subject to various biases. This study aimed to construct a nomogram to provide accurate, personalized predictions of the risk of severe COVID-19.MethodsThis study was based on a large, multicenter retrospective derivation cohort and a validation cohort. The derivation cohort consisted of 496 patients from Jiangsu Province, China, between January 10, 2020, and March 15, 2020, and the validation cohort contained 105 patients from Huangshi, Hunan Province, China, between January 21, 2020, and February 29, 2020. A nomogram was developed with the selected predictors of severe COVID-19, which were identified by univariate and multivariate logistic regression analyses. We evaluated the discrimination of the nomogram with the area under the receiver operating characteristic curve (AUC) and the calibration of the nomogram with calibration plots and Hosmer-Lemeshow tests.ResultsThree predictors, namely, age, lymphocyte count, and pulmonary opacity score, were selected to develop the nomogram. The nomogram exhibited good discrimination (AUC 0.93, 95% confidence interval [CI] 0.90–0.96 in the derivation cohort; AUC 0.85, 95% CI 0.76–0.93 in the validation cohort) and satisfactory agreement.ConclusionsThe nomogram was a reliable tool for assessing the probability of severe COVID-19 and may facilitate clinicians stratifying patients and providing early and optimal therapies.     

Hypergraph learning for identification of COVID-19 with CT imaging

The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used as a complementary tool to assist early screening, especially for the rapid identification of COVID-19 cases from community acquired pneumonia (CAP) cases. The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features. To tackle this challenge, we propose an Uncertainty Vertex-weighted Hypergraph Learning (UVHL) method to identify COVID-19 from CAP using CT images. In particular, multiple types of features (including regional features and radiomics features) are first extracted from CT image for each case. Then, the relationship among different cases is formulated by a hypergraph structure, with each case represented as a vertex in the hypergraph. The uncertainty of each vertex is further computed with an uncertainty score measurement and used as a weight in the hypergraph. Finally, a learning process of the vertex-weighted hypergraph is used to predict whether a new testing case belongs to COVID-19 or not. Experiments on a large multi-center pneumonia dataset, consisting of 2148 COVID-19 cases and 1182 CAP cases from five hospitals, are conducted to evaluate the prediction accuracy of the proposed method. Results demonstrate the effectiveness and robustness of our proposed method on the identification of COVID-19 in comparison to state-of-the-art methods.     

A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning

How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world. Currently, the chest CT is regarded as a popular and informative imaging tool for COVID-19 diagnosis. However, we observe that there are two issues – weak annotation and insufficient data that may obstruct automatic COVID-19 severity assessment with CT images. To address these challenges, we propose a novel three-component method, i.e., 1) a deep multiple instance learning component with instance-level attention to jointly classify the bag and also weigh the instances, 2) a bag-level data augmentation component to generate virtual bags by reorganizing high confidential instances, and 3) a self-supervised pretext component to aid the learning process. We have systematically evaluated our method on the CT images of 229 COVID-19 cases, including 50 severe and 179 non-severe cases. Our method could obtain an average accuracy of 95.8%, with 93.6% sensitivity and 96.4% specificity, which outperformed previous works.     

临床医师对新型冠状病毒肺炎患者凝血相关事件认知的调查

目的评估疫情初期一线医师对新型冠状病毒肺炎（COVID-19）患者凝血相关事件的认知情况。 方法通过网络调查问卷进行数据收集,分别对受访者一般情况、凝血系统的关注与评估,以及深静脉血栓（DVT）和急性肺栓塞（APE）的评估、诊断和治疗情况进行统计分析。 结果在回收的70份调查问卷中,所有受访者均认为关注COVID-19患者的凝血系统改变是非常必要的,且大多数受访者认为危重型和重型患者应常规进行凝血指标的检测,分别为100.00%、92.86%。75.71%的受访者进行了弥散性血管内凝血（DIC）评分,其中大部分受访者倾向应用2017中国DIC评分标准（34.29%）。大多数受访者所收治的COVID-19患者DVT和APE发生率均＜5%,分别为84.29%、82.86%。分别有92.86%和82.86%的受访者进行了DVT的评估、筛查,大多数受访者更易选择机械联合药物的方式预防DVT（60.00%）,应用最多的预防药物是低分子量肝素（54.29%）。 结论临床医师已经注意到监测COVID-19患者的凝血系统变化是非常重要的,并且对于DVT和APE的评估与治疗相对规范。多数受访者所收治的患者DVT和APE的发生率很低,但大家对DIC诊断标准的选择差异很大。D-二聚体对于评估凝血系统改变是一项非常重要的指标,但纤溶过程在感染性疾病凝血功能障碍中的作用应重新审视。