Sensitivity of ID NOW and RT-PCR for detection of SARS-CoV-2 in an ambulatory population
- Everett Clinic, United States
- James J Peters Veterans Affairs Medical Center, United States
- Maryland School of Medicine, United States
Abstract
Diagnosis of SARS-CoV-2 (COVID-19) requires confirmation by Reverse-Transcription Polymerase Chain Reaction (RT-PCR). Abbott ID NOW provides fast results but has been criticized for low sensitivity. Here we determine the sensitivity of ID NOW in an ambulatory population presenting for testing. The study enrolled 785 symptomatic patients, 21 of whom were positive by both ID NOW and RT-PCR, and 2 only by RT-PCR. All 189 asymptomatic patients tested negative. The positive percent agreement between the ID NOW assay and the RT-PCR assay was 91.3%, and negative percent agreement was 100%. The results from the current study were included into a larger systematic review of literature where at least 20 subjects were simultaneously tested using ID NOW and RT-PCR. The overall sensitivity for ID NOW assay was calculated at 84% (95% CI 55- 96%) and had the highest correlation to RT-PCR at viral loads most likely to be associated with transmissible infections.
Data availability
All data used for analysis has been included in the figures, tables and two appendices.
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Author details
Funding
NIH Clinical Center (U19 AG60917)
- Jameel Iqbal
NIH Clinical Center (R01 DK113627)
- Jameel Iqbal
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: The human ethics review and IRB for these studies was approved by the United Health Group Office of Human Research Affairs (OHRA), Federal wide Assurance #: FWA00028881, OHRP Registration #: IORG0010356.
Copyright
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
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Sensitivity of ID NOW and RT-PCR for detection of SARS-CoV-2 in an ambulatory population
eLife 10:e65726.
https://doi.org/10.7554/eLife.65726
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Background:
Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes.
Methods:
This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites.
Results:
During a median follow-up of 13.6 years, 2525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine-learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver-operating characteristic curve, 1 year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5 years: 0.830 vs 0.798, 10 years: 0.801 vs 0.776, all p < 0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes.
Conclusions:
Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions.
Funding:
Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500).
