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A Multi-Institutional Study Benchmarking Cycle Threshold Values for Major Clinical SARS-CoV-2 RT-PCR Assays
Preprint
in En
| PREPRINT-MEDRXIV
| ID: ppmedrxiv-22276072
ABSTRACT
Real-time, reverse transcriptase PCR assays are a pervasive technology used for diagnosis of SARS-CoV-2 infection. These assays produce a cycle threshold value (Ct) corresponding to the first amplification cycle in which reliable amplification is detected. (1)Such Ct values have been used by clinicians and in public health settings to guide treatment, monitor disease progression, assess prognosis, and inform isolation practices. To understanding the risk of reporting out uncalibrated Ct values and potential for instead reporting out calibrated viral load values, we performed a multi-institutional study to benchmark major clinical platforms against a calibrated standard. We found that for any given Ct value, corresponding viral loads varied up to 1000-fold among the different tests. In contrast, when these different assays were calibrated against a common standard and then used to test unknown de-identified specimens at several dilutions, viral load values showed high precision between methods (standard deviation and range of 0.36 and 1.1 log10 genome copies) and high accuracy compared with droplet digital PCR (ddPCR) determinations (difference between mean CDC N2 and Sarbeco E ddPCR determinations and mean determinations by calibrated RT-PCR assays examined in our study of 0.044 log10 genome copies). We, therefore, find strong support for calibration of SARS-CoV-2 RT-PCR tests to allow conversion of cycle thresholds to accurate and precise viral load values that are reproducible across major clinical systems. Implementation of calibrated assays will provide more reliable information for clinical decision making and allow more rigorous interpretation of SARS-CoV-2 laboratory data in clinical and laboratory investigation.
cc_by_nc_nd
Full text:
1
Collection:
09-preprints
Database:
PREPRINT-MEDRXIV
Type of study:
Prognostic_studies
Language:
En
Year:
2022
Type:
Preprint