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Laboratory testing for the diagnosis of COVID-19.

ABSTRACT: Rapid and accurate laboratory diagnosis of active COVID-19 infection is one of the cornerstones of pandemic control. With the myriad of tests available in the market, the use of correct specimen type and laboratory-testing technique in the right clinical scenario could be challenging for non-specialists. In this mini-review, we will discuss the difference in diagnostic performance for different upper and lower respiratory tract specimens, and the role of blood and fecal specimens. We will analyze the performance characteristics of laboratory testing techniques of nucleic acid amplification tests, antigen detection tests, antibody detection tests, and point-of-care tests. Finally, the dynamics of viral replication and antibody production, and laboratory results interpretation in conjunction with clinical scenarios will be discussed.

SUBMITTER: Lai CKC

PROVIDER: S-EPMC7598306 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Laboratory testing for the diagnosis of COVID-19.

Lai Christopher K C CKC Lam Wilson W

Biochemical and biophysical research communications 20201028

Rapid and accurate laboratory diagnosis of active COVID-19 infection is one of the cornerstones of pandemic control. With the myriad of tests available in the market, the use of correct specimen type and laboratory-testing technique in the right clinical scenario could be challenging for non-specialists. In this mini-review, we will discuss the difference in diagnostic performance for different upper and lower respiratory tract specimens, and the role of blood and fecal specimens. We will analyz ...[more]

PMID: 33139015

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Project description:BackgroundCOVID-19 disease has had a profound impact worldwide since it was discovered in Wuhan, China, in December 2019. Laboratory testing is crucial to prompt identification of positive cases, initiation of treatment and management strategies. However, medical scientists are vulnerable to infection due to the risk of exposure in the laboratory and the community. This study sought to determine the awareness of laboratory safety measures, assess the personal efforts of medical scientists in creating a safe laboratory environment for testing and examine the laboratory safety enabling factors.MethodsThe data used for the study were generated among medical scientists in Nigeria through an internet-broadcasted questionnaire and were analyzed using IBM SPSS Statistics (version 25).ResultsThe majority of the respondents had a high awareness of laboratory safety measures (60.3%) and demonstrated good personal efforts in creating a safe laboratory testing environment (63%). The level of awareness of laboratory safety measures was significantly associated with respondents' level of education (χ2 = 6.143; p = 0.046) and influences respondents' efforts in creating a safe laboratory testing environment (p = 0.007). However, just a few respondents could convincingly attest to the availability of adequate and appropriate PPE with proper utilization training (45.1%), adequate rest and other welfare packages (45.8%) as well as access to appropriate Biological Safety Cabinets (BSCs) and other essential equipment in their laboratories (48.8%). Furthermore, a significant association existed between the availability of laboratory safety enabling factors and respondents' efforts in creating a safe environment for testing with the p-value ranging between < 0.0001 and 0.003.ConclusionThis study revealed that despite the high awareness of safety measures and good personal efforts of the study participants in creating a safe laboratory-testing environment, there was poor availability of safety facilities, equipment, support and welfare packages required to enhance their safety. It is, therefore, crucial to provide necessary laboratory biosafety equipment and PPE in order not to compromise medical scientists' safety as they perform their duties in COVID-19 pandemic response.

Project description:Objective: To distinguish COVID-19 patients and non-COVID-19 viral pneumonia patients and classify COVID-19 patients into low-risk and high-risk at admission by laboratory indicators. Materials and methods: In this retrospective cohort, a total of 3,563 COVID-19 patients and 118 non-COVID-19 pneumonia patients were included. There are two cohorts of COVID-19 patients, including 548 patients in the training dataset, and 3,015 patients in the testing dataset. Laboratory indicators were measured during hospitalization for all patients. Based on laboratory indicators, we used the support vector machine and joint random sampling to risk stratification for COVID-19 patients at admission. Based on laboratory indicators detected within the 1st week after admission, we used logistic regression and joint random sampling to develop the survival mode. The laboratory indicators of COVID-10 and non-COVID-19 were also compared. Results: We first identified the significant laboratory indicators related to the severity of COVID-19 in the training dataset. Neutrophils percentage, lymphocytes percentage, creatinine, and blood urea nitrogen with AUC >0.7 were included in the model. These indicators were further used to build a support vector machine model to classify patients into low-risk and high-risk at admission in the testing dataset. Results showed that this model could stratify the patients in the testing dataset effectively (AUC = 0.89). Our model still has good performance at different times (Mean AUC: 0.71, 0.72, 0.72, respectively for 3, 5, and 7 days after admission). Moreover, laboratory indicators detected within the 1st week after admission were able to estimate the probability of death (AUC = 0.95). We identified six indicators with permutation p < 0.05, including eosinophil percentage (p = 0.007), white blood cell count (p = 0.045), albumin (p = 0.041), aspartate transaminase (p = 0.043), lactate dehydrogenase (p = 0.002), and hemoglobin (p = 0.031). We could diagnose COVID-19 and differentiate it from other kinds of viral pneumonia based on these laboratory indicators. Conclusions: Our risk-stratification model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage of COVID-19. In addition, laboratory findings could be used to distinguish COVID-19 and non-COVID-19.

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Laboratory testing for the diagnosis of COVID-19.

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Laboratory testing for the diagnosis of COVID-19.

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