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Establishing diagnostic algorithms for SARS-CoV-2 nucleic acid testing in clinical practice
Hsiang-Ling Ho1,2, Yen-Yu Lin1, Fang-Yu Wang1
1Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
Journal of the Chinese Medical Association : JCMA
|November 12, 2020
Summary
This study evaluated COVID-19 diagnostic assays, finding that combining manual methods like the T-CDC and TaqPath kits can achieve high accuracy. Proposed algorithms help clinical labs optimize SARS-CoV-2 testing strategies for efficiency and resource management.
Area of Science:
- Clinical diagnostics
- Molecular biology
- Virology
Background:
- The COVID-19 pandemic necessitates rapid and accurate SARS-CoV-2 detection.
- Initial use of the Taiwan CDC (T-CDC) method for SARS-CoV-2 identification.
- Evaluation of commercial diagnostic assays for clinical laboratory implementation.
Purpose of the Study:
- To evaluate the performance of three commercially available SARS-CoV-2 diagnostic assays.
- To establish diagnostic algorithms for clinical laboratories based on assay performance.
- To compare manual and automated testing strategies for COVID-19 diagnosis.
Main Methods:
- Analysis of 790 clinical specimens from suspected or confirmed COVID-19 patients.
- Testing specimens using the T-CDC method, TaqPath COVID-19 Combo kit, cobas SARS-CoV-2 test, and Rendu 2019-nCoV Assay kit.
- Performance variance analysis between the evaluated SARS-CoV-2 assays.
Main Results:
- The T-CDC and TaqPath kits showed similar sensitivity but T-CDC had higher false-positive rates.
- A manual testing strategy combining T-CDC and TaqPath demonstrated >99% concordance with automated systems.
- The cobas and Rendu automated assays achieved 100% concordance.
Conclusions:
- Two diagnostic algorithms (manual and automated) for SARS-CoV-2 testing were proposed.
- Results offer guidance for clinical laboratories to select optimal diagnostic strategies.
- Considerations for implementation include test volume, turnaround time, and resource availability.
