Evidence map›Paper›PMID 28727773›Full record

ArticlePLoS neglected tropical diseases2017

The tradition algorithm approach underestimates the prevalence of serodiagnosis of syphilis in HIV-infected individuals.

Bin Chen, Xiuming Peng, Tiansheng Xie, Changzhong Jin, Fumin Liu, Nanping Wu

Open access · goldAbstract readComparative StudyEvaluation Study
In one paragraph

Article in PLoS neglected tropical diseases, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.6field-weighted citation impact, top 31% 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

6 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. A Model for Syphilis Screening in the Emergency Department.Public health reports (Washington, D.C. : 1974)
    Article
  5. Review
  6. Article
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

6 authors at 1 institution in 1 country.

Bin ChenState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.ORCID http://orcid.org/0000-0003-1553-2803
Xiuming PengState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Tiansheng XieState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Changzhong JinState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Fumin LiuState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Nanping WuState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
State Key Laboratory of Diagnosis and Treatment of Infectious Diseases

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Currently, there are three algorithms for screening of syphilis: traditional algorithm, reverse algorithm and European Centre for Disease Prevention and Control (ECDC) algorithm. To date, there is not a generally recognized diagnostic algorithm. When syphilis meets HIV, the situation is even more complex. To evaluate their screening performance and impact on the seroprevalence of syphilis in HIV-infected individuals, we conducted a cross-sectional study included 865 serum samples from HIV-infected patients in a tertiary hospital. Every sample (one per patient) was tested with toluidine red unheated serum test (TRUST), T. pallidum particle agglutination assay (TPPA), and Treponema pallidum enzyme immunoassay (TP-EIA) according to the manufacturer's instructions. The results of syphilis serological testing were interpreted following different algorithms respectively. We directly compared the traditional syphilis screening algorithm with the reverse syphilis screening algorithm in this unique population. The reverse algorithm achieved remarkable higher seroprevalence of syphilis than the traditional algorithm (24.9% vs. 14.2%, p < 0.0001). Compared to the reverse algorithm, the traditional algorithm also had a missed serodiagnosis rate of 42.8%. The total percentages of agreement and corresponding kappa values of tradition and ECDC algorithm compared with those of reverse algorithm were as follows: 89.4%,0.668; 99.8%, 0.994. There was a very good strength of agreement between the reverse and the ECDC algorithm. Our results supported the reverse (or ECDC) algorithm in screening of syphilis in HIV-infected populations. In addition, our study demonstrated that screening of HIV-populations using different algorithms may result in a statistically different seroprevalence of syphilis.

Indexed as

AlgorithmsAdolescentAdultAgedAged, 80 and overCross-Sectional StudiesHIV InfectionsHumansMaleMass ScreeningMiddle AgedSeroepidemiologic StudiesSerologic TestsSyphilisTertiary Care CentersTreponema pallidum

Identifiers

PMID28727773
PMCPMC5538742
OpenAlexW2739004063

What OpenQuestion holds

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LicenceCC BY
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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.