Cost-analysis of real time RT-PCR test performed for COVID-19 diagnosis at India's national reference laboratory during the early stages of pandemic mitigation

SARS-CoV-2 RT-LAMP in saliva: enhancing the results via a combination of cooling and specimen dilution procedure

Colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) is a potential and relatively simple rapid diagnostics method for COVID-19 detection. This study aims to evaluate and optimize the RT-LAMP performance on saliva specimens based on a commercially available kit.Modifications on an established protocol (Protocol A) were used, including Proteinase K supplementation (Protocol B); pre-treatment using nuclease-free water and proteinase K (Protocol C); Saliva cooling (Protocol D); saliva dilution after pre-treatment (Protocol E); lastly a combination of saliva cooling and dilution (Protocol F). Protocol performances were evaluated by comparing success rates (SR), diagnostic accuracy (DA), sensitivity, specificity, and predictive values. Additionally, a correlation between the Ct value by RT-qPCR and RT-LAMP performance was analyzed.. A total of 106 specimens were used in this study. Protocols B and C showed 100% unreadable results, therefore were paused. Protocol F showed the highest SR (87.65%) compared to other protocols, with a slight compromise to DA (81.69%), sensitivity (57.14%), specificity (97.67%), PPV (94.12%), and NPV (77.78%). In the sub-analysis of the low Ct value group (Ct < 30), Protocol F demonstrated a higher success rate (86.57%) compared to protocol A (64.18%); increased 3.08% sensitivity and 2.42% NPV; comparable DA; minor reduction in specificity (A = 100%; F = 97.67%) and PPV (A = 100%; F = 92.31%). A combination of saliva cooling-dilution substantially increased the tested kit's success rate, despite a slight decrease in specificity and PPV. Findings confirmed the saliva cooling-dilution procedure was beneficial to the test's SR, sensitivity, and NPV in the low Ct value group.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13337-024-00870-1.

Evaluation of Multiplex Rapid Antigen Tests for the Simultaneous Detection of SARS-CoV-2 and Influenza A/B Viruses

Single-target rapid antigen tests (RATs) are commonly used to detect highly transmissible respiratory viruses (RVs), such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses. The simultaneous detection of RVs presenting overlapping symptoms is vital in making appropriate decisions about treatment, isolation, and resource utilization; however, few studies have evaluated multiplex RATs for SARS-CoV-2 and other RVs. We assessed the diagnostic performance of multiplex RATs targeting both the SARS-CoV-2 and influenza A/B viruses with the GenBody Influenza/COVID-19 Ag Triple, InstaView COVID-19/Flu Ag Combo (InstaView), STANDARDTM Q COVID-19 Ag Test, and STANDARDTM Q Influenza A/B Test kits using 974 nasopharyngeal swab samples. The cycle threshold values obtained from the real-time reverse transcription polymerase chain reaction results showed higher sensitivity (72.7-100%) when the values were below, rather than above, the cut-off values. The InstaView kit exhibited significantly higher positivity rates (80.21% for SARS-CoV-2, 61.75% for influenza A, and 46.15% for influenza B) and cut-off values (25.57 for SARS-CoV-2, 21.19 for influenza A, and 22.35 for influenza B) than the other two kits, and was able to detect SARS-CoV-2 Omicron subvariants. Therefore, the InstaView kit is the best choice for routine screening for both SARS-CoV-2 and influenza A/B in local communities.

Cost estimation of COVID Kavach test developed in 2020: A National Reference Laboratory initiative during early phase of Indian pandemic mitigation

BACKGROUND OBJECTIVES

A new indigenously developed technology, coronavirus disease (COVID) Kavach, an IgG immunoglobulin-based enzyme-linked immunosorbent assay (ELISA) kit, was developed in 2020 by the Indian Council of Medical Research-National Institute of Virology (ICMR-NIV), Pune, India. The primary objective of this study was to determine the total cost of development of COVID Kavach IgG ELISA and estimate the unit cost (UC) as well.

METHODS

The total development cost (TDC) of COVID Kavach and its UC during the early phase of pandemic mitigation were estimated through a micro-costing approach from provider's perspective. An activity-based bottom-up costing approach was used to facilitate data collection from all resources, and analysis was performed using Microsoft Excel version 2016. The micro-costing data were utilized to interpret the breakdown of cost across all inputs and different levels of activity.

RESULTS

The TDC of COVID Kavach was estimated to be JOURNAL/ijmer/04.03/02223309-202310000-00007/363FF04/v/2023-11-25T134903Z/r/image-tiff 2,884,032 (US$ 38,265). The UC of providing test results for exposure to severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) was estimated to be JOURNAL/ijmer/04.03/02223309-202310000-00007/363FF04/v/2023-11-25T134903Z/r/image-tiff 300 (US$ 4) during July 2020. The capital and recurrent cost were incurred around 5-10 per cent and 90-95 per cent, respectively, in both the development and UC of COVID Kavach. The major portion of funds (70-80%) was utilized for procurement of laboratory consumables, followed by human resources (8-12%) in the development as well as for UC of COVID Kavach.

INTERPRETATION CONCLUSIONS

The estimates from this study can be useful for conducting economic evaluations, which will help in deciding upon the subsidy in government health facilities. The data may be useful to set up laboratory facilities analogous to the National Reference Laboratory located at the ICMR-NIV, Pune and for allotting sufficient budget to develop such assays in government-funded laboratories.

Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms

In order to limit the spread of the novel betacoronavirus (SARS-CoV-2), it is necessary to detect positive cases as soon as possible and isolate them. For this purpose, machine-learning algorithms, as a field of artificial intelligence, have been recognized as a promising tool. The aim of this study was to assess the utility of the most common machine-learning algorithms in the rapid triage of children with suspected COVID-19 using easily accessible and inexpensive laboratory parameters. A cross-sectional study was conducted on 566 children treated for respiratory diseases: 280 children with PCR-confirmed SARS-CoV-2 infection and 286 children with respiratory symptoms who were SARS-CoV-2 PCR-negative (control group). Six machine-learning algorithms, based on the blood laboratory data, were tested: random forest, support vector machine, linear discriminant analysis, artificial neural network, k-nearest neighbors, and decision tree. The training set was validated through stratified cross-validation, while the performance of each algorithm was confirmed by an independent test set. Random forest and support vector machine models demonstrated the highest accuracy of 85% and 82.1%, respectively. The models demonstrated better sensitivity than specificity and better negative predictive value than positive predictive value. The F1 score was higher for the random forest than for the support vector machine model, 85.2% and 82.3%, respectively. This study might have significant clinical applications, helping healthcare providers identify children with COVID-19 in the early stage, prior to PCR and/or antigen testing. Additionally, machine-learning algorithms could improve overall testing efficiency with no extra costs for the healthcare facility.