Evidence map›Paper›PMID 36223658›Full record

ArticleJournal of clinical virology : the official publication of the Pan American Society for Clinical Virology2022

Clinical scoring system to predict viable viral shedding in patients with COVID-19.

Sung Woon Kang, Heedo Park, Ji Yeun Kim, Sunghee Park, So Yun Lim, Sohyun Lee, Joon-Yong Bae, Jeonghun Kim, Seongman Bae, Jiwon Jung and 8 more

Open access · greenAbstract read
In one paragraph

Article in Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.0field-weighted citation impact, top 23% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 10 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors at 3 institutions in 1 country.

Sung Woon KangDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Heedo ParkDepartment of Microbiology, Vaccine Innovation Center, Biosafety Center, College of Medicine, Korea University, 73 Goryeodae-ro, Seongbuk-gu, Seoul, 02841, South Korea.
Ji Yeun KimDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Sunghee ParkDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea; Division of Infectious Diseases, Department of Internal Medicine, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, South Korea.
So Yun LimDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Sohyun LeeDepartment of Microbiology, Vaccine Innovation Center, Biosafety Center, College of Medicine, Korea University, 73 Goryeodae-ro, Seongbuk-gu, Seoul, 02841, South Korea.
Joon-Yong BaeDepartment of Microbiology, Vaccine Innovation Center, Biosafety Center, College of Medicine, Korea University, 73 Goryeodae-ro, Seongbuk-gu, Seoul, 02841, South Korea.
Jeonghun KimDepartment of Microbiology, Vaccine Innovation Center, Biosafety Center, College of Medicine, Korea University, 73 Goryeodae-ro, Seongbuk-gu, Seoul, 02841, South Korea.
Seongman BaeDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Jiwon JungDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Min Jae KimDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Yong Pil ChongDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Sang-Oh LeeDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Sang-Ho ChoiDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Yang Soo KimDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Sung-Cheol YunDepartment of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Man-Seong ParkDepartment of Microbiology, Vaccine Innovation Center, Biosafety Center, College of Medicine, Korea University, 73 Goryeodae-ro, Seongbuk-gu, Seoul, 02841, South Korea. Electronic address: manseong.park@gmail.com.
Sung-Han KimDepartment of Infectious Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea. Electronic address: shkimmd@amc.seoul.kr.
Asan Medical Center · KRUniversity of Ulsan · KRUlsan College · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Centers for Disease Control and Prevention (CDC) recommends 5-10 days of isolation for patients with COVID-19, depending on symptom duration and severity. However, in clinical practice, an individualized approach is required. We thus developed a clinical scoring system to predict viable viral shedding.

methodsWe prospectively enrolled adult patients with SARS-CoV-2 infection admitted to a hospital or community isolation facility between February 2020 and January 2022. Daily dense respiratory samples were obtained, and genomic RNA viral load assessment and viral culture were performed. Clinical predictors of negative viral culture results were identified using survival analysis and multivariable analysis.

resultsAmong 612 samples from 121 patients including 11 immunocompromised patients (5 organ transplant recipients, 5 with hematologic malignancy, and 1 receiving immunosuppressive agents) with varying severity, 154 (25%) revealed positive viral culture results. Multivariable analysis identified symptom onset day, viral copy number, disease severity, organ transplant recipient, and vaccination status as independent predictors of culture-negative rate. We developed a 4-factor predictive model based on viral copy number (-3 to 3 points), disease severity (1 point for moderate to critical disease), organ transplant recipient (2 points), and vaccination status (-2 points for fully vaccinated). Predicted culture-negative rates were calculated through the symptom onset day and the score of the day the sample was collected.

conclusionsOur clinical scoring system can provide the objective probability of a culture-negative state in a patient with COVID-19 and is potentially useful for implementing personalized de-isolation policies beyond the simple symptom-based isolation strategy.

Indexed as

COVID-19AdultCOVID-19 TestingHumansSARS-CoV-2United StatesViral LoadVirus SheddingCOVID-19Isolation policyPredictive modelViral shedding

Identifiers

PMID36223658
PMCPMC9529675
OpenAlexW4300860365

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.