Dataset Information

An Automated Dashboard to Improve Laboratory COVID-19 Diagnostics Management.

ABSTRACT: Background: In response to the COVID-19 pandemic, our microbial diagnostic laboratory located in a university hospital has implemented several distinct SARS-CoV-2 RT-PCR systems in a very short time. More than 148,000 tests have been performed over 12 months, which represents about 405 tests per day, with peaks to more than 1,500 tests per days during the second wave. This was only possible thanks to automation and digitalization, to allow high throughput, acceptable time to results and to maintain test reliability. An automated dashboard was developed to give access to Key Performance Indicators (KPIs) to improve laboratory operational management. Methods: RT-PCR data extraction of four respiratory viruses-SARS-CoV-2, influenza A and B and RSV-from our laboratory information system (LIS), was automated. This included age, gender, test result, RT-PCR instrument, sample type, reception time, requester, and hospitalization status etc. Important KPIs were identified and the visualization was achieved using an in-house dashboard based on the open-source language R (Shiny). Results: The dashboard is organized into three main parts. The "Filter" page presents all the KPIs, divided into five sections: (i) general and gender-related indicators, (ii) number of tests and positivity rate, (iii) cycle threshold and viral load, (iv) test durations, and (v) not valid results. Filtering allows to select a given period, a dedicated instrument, a given specimen, an age range or a requester. The "Comparison" page allows a custom charting of all the available variables, which represents more than 182 combination. The "Data" page, gives the user an access to the raw data in tables format, with possibility of filtering, allowing for a deeper analysis and data download. Informations are updated every 4 h. Conclusions: By giving a rapid access to a huge number of up-to-date information, represented using the most relevant visualization types, without the burden of timely data extraction and analysis, the dashboard represents a reliable and user-friendly tool for operational laboratory management. The dashboard represents a reliable and user-friendly tool improving the decision-making process, resource planning and quality management.

SUBMITTER: Maury E

PROVIDER: S-EPMC8685224 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveTo extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management.Materials and methodsAll initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans.ResultsWhile demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79-0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77-0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85-0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66-0.88).ConclusionChest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.

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.

Project description:The coronavirus infection 2019 (COVID-19) pandemic, caused by the highly contagious SARS-CoV-2 virus, has provoked a global healthcare and economic crisis. The control over the spread of the disease requires an efficient and scalable laboratory-based strategy for testing the population based on multiple platforms to provide rapid and accurate diagnosis. With the onset of the pandemic, the reverse transcription polymerase chain reaction (RT-PCR) method has become a standard diagnostic tool, which has received wide clinical use. In large-scale and repeated examinations, these tests can identify infected patients with COVID-19, with their accuracy, however, dependent on many factors, while the entire process takes up to 6-8 h. Here we also describe a number of serological systems for detecting antibodies against SARS-CoV-2. These are used to assess the level of population immunity in various categories of people, as well as for retrospective diagnosis of asymptomatic and mild COVID-19 in patients. However, the widespread use of traditional diagnostic tools in the context of the rapid spread of COVID-19 is hampered by a number of limitations. Therefore, the sharp increase in the number of patients with COVID-19 necessitates creation of new rapid, inexpensive, sensitive, and specific tests. In this regard, we focus on new laboratory technologies such as loop mediated isothermal amplification (LAMP) and lateral flow immunoassay (LFIA), which have proven to work well in the COVID-19 diagnostics and can become a worthy alternative to traditional laboratory-based diagnostics resources. To cope with the COVID-19 pandemic, the healthcare system requires a combination of various types of laboratory diagnostic testing techniques, whodse sensitivity and specificity increases with the progress in the SARS-CoV-2 research. The testing strategy should be designed in such a way to provide, depending on the timing of examination and the severity of the infection in patients, large-scale and repeated examinations based on the principle: screening-monitoring-control. The search and development of new methods for rapid diagnostics of COVID-19 in laboratory, based on new analytical platforms, is still a highly important and urgent healthcare issue. In the final part of the review, special emphasis is made on the relevance of the concept of personalized medicine to combat the COVID-19 pandemic in the light of the recent studies carried out to identify the causes of variation in individual susceptibility to SARS-CoV-2 and increase the efficiency and cost-effectiveness of treatment.

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.

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An Automated Dashboard to Improve Laboratory COVID-19 Diagnostics Management.

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