Evidence map›Paper›PMID 33136068›Full record

ArticleAging2020

Evaluation of droplet digital PCR for quantification of SARS-CoV-2 Virus in discharged COVID-19 patients.

Chong Liu, Qingxin Shi, Mingfei Peng, Ruyue Lu, Haohao Li, Yingying Cai, Jiaxi Chen, Jiaqin Xu, Bo Shen

Open access · hybridAbstract read
In one paragraph

Article in Aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 2 pooled it
2.8field-weighted citation impact, top 9% 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

26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Droplet Digital PCR Improves Detection ofInternational journal of molecular sciences · 2025
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  5. Establishing methods to monitor H5N1 influenza virus in dairy cattle milk.medRxiv : the preprint server for health sciences · 2024
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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

9 authors at 2 institutions in 1 country.

Chong LiuZhejiang Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China.
Qingxin ShiZhejiang Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China.
Mingfei PengZhejiang Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China.
Ruyue LuZhejiang Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China.
Haohao LiZhejiang Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China.
Yingying CaiZhejiang Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China.
Jiaxi ChenZhejiang Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China.
Jiaqin XuZhejiang Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China.
Bo ShenZhejiang Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China.
Wenzhou Medical University · CNZhejiang Taizhou Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The worldwide severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak has led to the rapid spread of coronavirus disease (COVID-19). The quantitative real time PCR (qPCR) is widely used as the gold standard for clinical detection of SARS-CoV-2. However, more and more infected patients are relapsing after discharge, which suggests qPCR may fail to detect the virus in some cases. In this study, we selected 74 clinical samples from 43 recovering inpatients for qPCR and Droplet Digital PCR (ddPCR) synchronous blind detection, and established a cutoff value for ddPCR diagnosis of COVID-19. The results showed that at a cutoff value of 0.04 copies/μL, the ddPCR sensitivity and specificity are 97.6% and 100%, respectively. In addition, we also analyzed 18 retained samples from 9 discharged patients who relapsed. Although qPCR showed all 18 samples to be negative, ddPCR showed 12 to be positive, and there was only one patient with two negative samples; the other eight patients had at least one positive sample. These results indicate that ddPCR could significantly improve the accuracy of COVID-19 diagnosis, especially for discharged patients with a low viral load, and help to reduce misdiagnosis during recovery.

Indexed as

COVID-19COVID-19 Nucleic Acid TestingChinaHumansPatient DischargeQuality ImprovementSARS-CoV-2Secondary PreventionSensitivity and SpecificityViral LoadCOVID-19discharged criteriadroplet digital PCRSARS-CoV-2

Identifiers

PMID33136068
PMCPMC7695381
OpenAlexW3094860797

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.