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Setting-Up a Rapid SARS-CoV-2 Genome Assessment by Next-Generation Sequencing in an Academic Hospital Center (LPCE, Louis Pasteur Hospital, Nice, France).

ABSTRACT: Introduction: Aside from the reverse transcription-PCR tests for the diagnosis of the COVID-19 in routine clinical care and population-scale screening, there is an urgent need to increase the number and the efficiency for full viral genome sequencing to detect the variants of SARS-CoV-2. SARS-CoV-2 variants assessment should be easily, rapidly, and routinely available in any academic hospital. Materials and Methods: SARS-CoV-2 full genome sequencing was performed retrospectively in a single laboratory (LPCE, Louis Pasteur Hospital, Nice, France) in 103 SARS-CoV-2 positive individuals. An automated workflow used the Ion Ampliseq SARS-CoV-2 panel on the Genexus Sequencer. The analyses were made from nasopharyngeal swab (NSP) (n = 64) and/or saliva (n = 39) samples. All samples were collected in the metropolitan area of the Nice city (France) from September 2020 to March 2021. Results: The mean turnaround time between RNA extraction and result reports was 30 h for each run of 15 samples. A strong correlation was noted for the results obtained between NSP and saliva paired samples, regardless of low viral load and high (>28) Ct values. After repeated sequencing runs, complete failure of obtaining a valid sequencing result was observed in 4% of samples. Besides the European strain (B.1.160), various variants were identified, including one variant of concern (B.1.1.7), and different variants under monitoring. Discussion: Our data highlight the current feasibility of developing the SARS-CoV-2 next-generation sequencing approach in a single hospital center. Moreover, these data showed that using the Ion Ampliseq SARS-CoV-2 Assay, the SARS-CoV-2 genome sequencing is rapid and efficient not only in NSP but also in saliva samples with a low viral load. The advantages and limitations of this setup are discussed.

SUBMITTER: Hofman P

PROVIDER: S-EPMC8787061 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe United States, and especially West Virginia, have a tremendous burden of coronary artery disease (CAD). Undiagnosed familial hypercholesterolemia (FH) is an important factor for CAD in the U.S. Identification of a CAD phenotype is an initial step to find families with FH.ObjectiveWe hypothesized that a CAD phenotype detection algorithm that uses discrete data elements from electronic health records (EHRs) can be validated from EHR information housed in a data repository.MethodsWe developed an algorithm to detect a CAD phenotype which searched through discrete data elements, such as diagnosis, problem lists, medical history, billing, and procedure (International Classification of Diseases [ICD]-9/10 and Current Procedural Terminology [CPT]) codes. The algorithm was applied to two cohorts of 500 patients, each with varying characteristics. The second (younger) cohort consisted of parents from a school child screening program. We then determined which patients had CAD by systematic, blinded review of EHRs. Following this, we revised the algorithm by refining the acceptable diagnoses and procedures. We ran the second algorithm on the same cohorts and determined the accuracy of the modification.ResultsCAD phenotype Algorithm I was 89.6% accurate, 94.6% sensitive, and 85.6% specific for group 1. After revising the algorithm (denoted CAD Algorithm II) and applying it to the same groups 1 and 2, sensitivity 98.2%, specificity 87.8%, and accuracy 92.4; accuracy 93% for group 2. Group 1 F1 score was 92.4%. Specific ICD-10 and CPT codes such as "coronary angiography through a vein graft" were more useful than generic terms.ConclusionWe have created an algorithm, CAD Algorithm II, that detects CAD on a large scale with high accuracy and sensitivity (recall). It has proven useful among varied patient populations. Use of this algorithm can extend to monitor a registry of patients in an EHR and/or to identify a group such as those with likely FH.

Dataset Information

Setting-Up a Rapid SARS-CoV-2 Genome Assessment by Next-Generation Sequencing in an Academic Hospital Center (LPCE, Louis Pasteur Hospital, Nice, France).

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