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

doi:10.1136/bmjopen-2020-042893

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

^*Corresponding author for this work

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Abstract

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

Design A modelling study.

Setting England and Wales.

Participants Individuals accessing sentinel health services.

Intervention Developing 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 measures Estimated 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.

Results Between 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.

Conclusions Using 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.

Original language	English
Article number	e042893
Journal	BMJ Open
Volume	11
Issue number	6
DOIs	https://doi.org/10.1136/bmjopen-2020-042893
Publication status	Published - 25 Jun 2021

Bibliographical note

Funding Information: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors. Disclaimer The views expressed are those of the author and not necessarily those of the NIHR, the Department of Health and Social Care, or PHE. JR reports personal fees from GSK Pharma, Mycovia and Nabriva Therapeutics as well as ownership of shares in GSK Pharma and AstraZeneca Pharma; and is author of the UK and European Guidelines on Pelvic Inflammatory Disease; is a Member of the European Sexually Transmitted Infections Guidelines Editorial Board; is a Member of the National Institute for Health Research Funding Committee (Health Technology Assessment programme). He is an NIHR Journals Editor and associate editor of Sexually Transmitted Infections journal. He is an officer of the International Union against Sexually Transmitted Infections (treasurer) and a charity trustee of the Sexually Transmitted Infections Research Foundation. KMET has received grant funding from GlaxoSmithKline and consultancy fees from Aquarius Population Health for work outside this project.
We acknowledge Public Health England for permitting and facilitating access to GRASP data. LF, MG and KMET acknowledge support from the NIHR Health Protection Research Unit in Behavioural Science and Evaluation at University of Bristol. KMET acknowledges support from Health Data Research UK via the Better Care Partnership Southwest (HDR CF0129).

Open Access: http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

Publisher Copyright:© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Citation: Findlater L, Mohammed H, Gobin M, et al. Developing a model to predict individualised treatment for gonorrhoea: a modelling study. BMJ Open 2021;11:e042893.

DOI: 10.1136/bmjopen-2020-042893

Keywords

Epidemiology
Genitourinary medicine
Public health
Sexual medicine

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1136/bmjopen-2020-042893Licence: CC BY-NC

Findlater2021DevelopingAModelToPredictIndividualTreatmentForGonorrhoeaBMJOpenFinal published version, 1.74 MBLicence: CC BY-NC

Cite this

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title = "Developing a model to predict individualised treatment for gonorrhoea: A modelling study",

abstract = "Objective To develop a tool predicting individualised treatment for gonorrhoea, enabling treatment with previously recommended antibiotics, to reduce use of last-line treatment ceftriaxone. Design A modelling study. Setting England and Wales. Participants Individuals accessing sentinel health services. Intervention Developing 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 measures Estimated 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. Results Between 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. Conclusions Using 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. ",

keywords = "Epidemiology, Genitourinary medicine, Public health, Sexual medicine",

author = "Lucy Findlater and Hamish Mohammed and Maya Gobin and Helen Fifer and Jonathan Ross and {Geffen Obregon}, Oliver and Turner, {Katy M.E.}",

note = "Funding Information: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors. Disclaimer The views expressed are those of the author and not necessarily those of the NIHR, the Department of Health and Social Care, or PHE. JR reports personal fees from GSK Pharma, Mycovia and Nabriva Therapeutics as well as ownership of shares in GSK Pharma and AstraZeneca Pharma; and is author of the UK and European Guidelines on Pelvic Inflammatory Disease; is a Member of the European Sexually Transmitted Infections Guidelines Editorial Board; is a Member of the National Institute for Health Research Funding Committee (Health Technology Assessment programme). He is an NIHR Journals Editor and associate editor of Sexually Transmitted Infections journal. He is an officer of the International Union against Sexually Transmitted Infections (treasurer) and a charity trustee of the Sexually Transmitted Infections Research Foundation. KMET has received grant funding from GlaxoSmithKline and consultancy fees from Aquarius Population Health for work outside this project. We acknowledge Public Health England for permitting and facilitating access to GRASP data. LF, MG and KMET acknowledge support from the NIHR Health Protection Research Unit in Behavioural Science and Evaluation at University of Bristol. KMET acknowledges support from Health Data Research UK via the Better Care Partnership Southwest (HDR CF0129). Open Access: http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. Publisher Copyright:{\textcopyright} Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. Citation: Findlater L, Mohammed H, Gobin M, et al. Developing a model to predict individualised treatment for gonorrhoea: a modelling study. BMJ Open 2021;11:e042893. DOI: 10.1136/bmjopen-2020-042893 ",

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T1 - Developing a model to predict individualised treatment for gonorrhoea

T2 - A modelling study

AU - Findlater, Lucy

AU - Mohammed, Hamish

AU - Gobin, Maya

AU - Fifer, Helen

AU - Ross, Jonathan

AU - Geffen Obregon, Oliver

AU - Turner, Katy M.E.

N1 - Funding Information: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors. Disclaimer The views expressed are those of the author and not necessarily those of the NIHR, the Department of Health and Social Care, or PHE. JR reports personal fees from GSK Pharma, Mycovia and Nabriva Therapeutics as well as ownership of shares in GSK Pharma and AstraZeneca Pharma; and is author of the UK and European Guidelines on Pelvic Inflammatory Disease; is a Member of the European Sexually Transmitted Infections Guidelines Editorial Board; is a Member of the National Institute for Health Research Funding Committee (Health Technology Assessment programme). He is an NIHR Journals Editor and associate editor of Sexually Transmitted Infections journal. He is an officer of the International Union against Sexually Transmitted Infections (treasurer) and a charity trustee of the Sexually Transmitted Infections Research Foundation. KMET has received grant funding from GlaxoSmithKline and consultancy fees from Aquarius Population Health for work outside this project. We acknowledge Public Health England for permitting and facilitating access to GRASP data. LF, MG and KMET acknowledge support from the NIHR Health Protection Research Unit in Behavioural Science and Evaluation at University of Bristol. KMET acknowledges support from Health Data Research UK via the Better Care Partnership Southwest (HDR CF0129). Open Access: http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. Publisher Copyright:© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. Citation: Findlater L, Mohammed H, Gobin M, et al. Developing a model to predict individualised treatment for gonorrhoea: a modelling study. BMJ Open 2021;11:e042893. DOI: 10.1136/bmjopen-2020-042893

PY - 2021/6/25

Y1 - 2021/6/25

N2 - Objective To develop a tool predicting individualised treatment for gonorrhoea, enabling treatment with previously recommended antibiotics, to reduce use of last-line treatment ceftriaxone. Design A modelling study. Setting England and Wales. Participants Individuals accessing sentinel health services. Intervention Developing 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 measures Estimated 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. Results Between 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. Conclusions Using 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.

AB - Objective To develop a tool predicting individualised treatment for gonorrhoea, enabling treatment with previously recommended antibiotics, to reduce use of last-line treatment ceftriaxone. Design A modelling study. Setting England and Wales. Participants Individuals accessing sentinel health services. Intervention Developing 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 measures Estimated 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. Results Between 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. Conclusions Using 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.

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

Abstract

Bibliographical note

Keywords

UN SDGs

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