Evidence map›Paper›PMID 34172543›Full record

ArticleBMJ open2021

Developing a model to predict individualised treatment for gonorrhoea: a modelling study.

Lucy Findlater, Hamish Mohammed, Maya Gobin, Helen Fifer, Jonathan Ross, Oliver Geffen Obregon, Katy M E Turner

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.8field-weighted citation impact, top 15% 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

2 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. 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

7 authors at 3 institutions in 1 country.

Lucy FindlaterNational Infection Service, Public Health England, Bristol, UK lucy.findlater@phe.gov.uk.ORCID 0000-0003-1736-7661
Hamish MohammedHIV & STI Department, Public Health England, London, UK.
Maya GobinNational Infection Service, Public Health England, Bristol, UK.
Helen FiferReference Microbiology, Public Health England, London, UK.ORCID 0000-0001-7756-403X
Jonathan RossInstitute of Microbiology and Infection, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
Oliver Geffen ObregonHIV & STI Department, Public Health England, London, UK.
Katy M E TurnerBristol Veterinary School, University of Bristol, Bristol, UK.ORCID 0000-0002-8152-6017
Public Health England · GBUniversity Hospitals Birmingham NHS Foundation Trust · GBUniversity of Bristol · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo develop a tool predicting individualised treatment for gonorrhoea, enabling treatment with previously recommended antibiotics, to reduce use of last-line treatment ceftriaxone.

designA modelling study.

settingEngland and Wales.

participantsIndividuals accessing sentinel health services.

interventionDeveloping an Excel model which uses participants' demographic, behavioural and clinical characteristics to predict susceptibility to legacy antibiotics. Model parameters were calculated using data for 2015-2017 from the Gonococcal Resistance to Antimicrobials Surveillance Programme.

main outcome measuresEstimated number of doses of ceftriaxone saved, and number of people delayed effective treatment, by model use in clinical practice. Model outputs are the predicted risk of resistance to ciprofloxacin, azithromycin, penicillin and cefixime, in groups of individuals with different combinations of characteristics (gender, sexual orientation, number of recent sexual partners, age, ethnicity), and a treatment recommendation.

resultsBetween 2015 and 2017, 8013 isolates were collected: 64% from men who have sex with men, 18% from heterosexual men and 18% from women. Across participant subgroups, stratified by all predictors, resistance prevalence was high for ciprofloxacin (range: 11%-51%) and penicillin (range: 6%-33%). Resistance prevalence for azithromycin and cefixime ranged from 0% to 13% and for ceftriaxone it was 0%. Simulating model use, 88% of individuals could be given cefixime and 10% azithromycin, saving 97% of ceftriaxone doses, with 1% of individuals delayed effective treatment.

conclusionsUsing demographic and behavioural characteristics, we could not reliably identify a participant subset in which ciprofloxacin or penicillin would be effective. Cefixime resistance was almost universally low; however, substituting ceftriaxone for near-uniform treatment with cefixime risks re-emergence of resistance to cefixime and ceftriaxone. Several subgroups had low azithromycin resistance, but widespread azithromycin monotherapy risks resistance at population level. However, this dataset had limitations; further exploration of individual characteristics to predict resistance to a wider range of legacy antibiotics may still be appropriate.

Indexed as

GonorrheaSexual and Gender MinoritiesAnti-Bacterial AgentsAzithromycinCeftriaxoneCiprofloxacinDrug Resistance, BacterialEnglandFemaleHomosexuality, MaleHumansMaleMicrobial Sensitivity TestsNeisseria gonorrhoeaeWalesAnti-Bacterial AgentsAzithromycinCeftriaxoneCiprofloxacinepidemiologygenitourinary medicinepublic healthsexual medicine

Identifiers

PMID34172543
PMCPMC8237724
OpenAlexW3174744093

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.