Cargando…

Identifying adults with acute rhinosinusitis in primary care that benefit most from antibiotics: protocol of an individual patient data meta-analysis using multivariable risk prediction modelling

INTRODUCTION: Acute rhinosinusitis (ARS) is a prime reason for doctor visits and among the conditions with highest antibiotic overprescribing rates in adults. To reduce inappropriate prescribing, we aim to predict the absolute benefit of antibiotic treatment for individual adult patients with ARS by...

Descripción completa

Detalles Bibliográficos
Autores principales: Venekamp, Roderick P, Hoogland, Jeroen, van Smeden, Maarten, Rovers, Maroeska M, De Sutter, An I, Merenstein, Daniel, van Essen, Gerrit A, Kaiser, Laurent, Liira, Helena, Little, Paul, Bucher, Heiner CC, Reitsma, Johannes B
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8252877/
https://www.ncbi.nlm.nih.gov/pubmed/34210729
http://dx.doi.org/10.1136/bmjopen-2020-047186
_version_ 1783717392025649152
author Venekamp, Roderick P
Hoogland, Jeroen
van Smeden, Maarten
Rovers, Maroeska M
De Sutter, An I
Merenstein, Daniel
van Essen, Gerrit A
Kaiser, Laurent
Liira, Helena
Little, Paul
Bucher, Heiner CC
Reitsma, Johannes B
author_facet Venekamp, Roderick P
Hoogland, Jeroen
van Smeden, Maarten
Rovers, Maroeska M
De Sutter, An I
Merenstein, Daniel
van Essen, Gerrit A
Kaiser, Laurent
Liira, Helena
Little, Paul
Bucher, Heiner CC
Reitsma, Johannes B
author_sort Venekamp, Roderick P
collection PubMed
description INTRODUCTION: Acute rhinosinusitis (ARS) is a prime reason for doctor visits and among the conditions with highest antibiotic overprescribing rates in adults. To reduce inappropriate prescribing, we aim to predict the absolute benefit of antibiotic treatment for individual adult patients with ARS by applying multivariable risk prediction methods to individual patient data (IPD) of multiple randomised placebo-controlled trials. METHODS AND ANALYSIS: This is an update and re-analysis of a 2008 IPD meta-analysis on antibiotics for adults with clinically diagnosed ARS. First, the reference list of the 2018 Cochrane review on antibiotics for ARS will be reviewed for relevant studies published since 2008. Next, the systematic searches of CENTRAL, MEDLINE and Embase of the Cochrane review will be updated to 1 September 2020. Methodological quality of eligible studies will be assessed using the Cochrane Risk of Bias 2 tool. The primary outcome is cure at 8–15 days. Regression-based methods will be used to model the risk of being cured based on relevant predictors and treatment, while accounting for clustering. Such model allows for risk predictions as a function of treatment and individual patient characteristics and hence gives insight into individualised absolute benefit. Candidate predictors will be based on literature, clinical reasoning and availability. Calibration and discrimination will be evaluated to assess model performance. Resampling techniques will be used to assess internal validation. In addition, internal–external cross-validation procedures will be used to inform on between-study differences and estimate out-of-sample model performance. Secondarily, we will study possible heterogeneity of treatment effect as a function of outcome risk. ETHICS AND DISSEMINATION: In this study, no identifiable patient data will be used. As such, the Medical Research Involving Humans Subject Act (WMO) does not apply and official ethical approval is not required. Results will be submitted for publication in international peer-reviewed journals. PROSPERO REGISTRATION NUMBER: CRD42020220108.
format Online
Article
Text
id pubmed-8252877
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BMJ Publishing Group
record_format MEDLINE/PubMed
spelling pubmed-82528772021-07-23 Identifying adults with acute rhinosinusitis in primary care that benefit most from antibiotics: protocol of an individual patient data meta-analysis using multivariable risk prediction modelling Venekamp, Roderick P Hoogland, Jeroen van Smeden, Maarten Rovers, Maroeska M De Sutter, An I Merenstein, Daniel van Essen, Gerrit A Kaiser, Laurent Liira, Helena Little, Paul Bucher, Heiner CC Reitsma, Johannes B BMJ Open General practice / Family practice INTRODUCTION: Acute rhinosinusitis (ARS) is a prime reason for doctor visits and among the conditions with highest antibiotic overprescribing rates in adults. To reduce inappropriate prescribing, we aim to predict the absolute benefit of antibiotic treatment for individual adult patients with ARS by applying multivariable risk prediction methods to individual patient data (IPD) of multiple randomised placebo-controlled trials. METHODS AND ANALYSIS: This is an update and re-analysis of a 2008 IPD meta-analysis on antibiotics for adults with clinically diagnosed ARS. First, the reference list of the 2018 Cochrane review on antibiotics for ARS will be reviewed for relevant studies published since 2008. Next, the systematic searches of CENTRAL, MEDLINE and Embase of the Cochrane review will be updated to 1 September 2020. Methodological quality of eligible studies will be assessed using the Cochrane Risk of Bias 2 tool. The primary outcome is cure at 8–15 days. Regression-based methods will be used to model the risk of being cured based on relevant predictors and treatment, while accounting for clustering. Such model allows for risk predictions as a function of treatment and individual patient characteristics and hence gives insight into individualised absolute benefit. Candidate predictors will be based on literature, clinical reasoning and availability. Calibration and discrimination will be evaluated to assess model performance. Resampling techniques will be used to assess internal validation. In addition, internal–external cross-validation procedures will be used to inform on between-study differences and estimate out-of-sample model performance. Secondarily, we will study possible heterogeneity of treatment effect as a function of outcome risk. ETHICS AND DISSEMINATION: In this study, no identifiable patient data will be used. As such, the Medical Research Involving Humans Subject Act (WMO) does not apply and official ethical approval is not required. Results will be submitted for publication in international peer-reviewed journals. PROSPERO REGISTRATION NUMBER: CRD42020220108. BMJ Publishing Group 2021-07-01 /pmc/articles/PMC8252877/ /pubmed/34210729 http://dx.doi.org/10.1136/bmjopen-2020-047186 Text en © 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. https://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/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle General practice / Family practice
Venekamp, Roderick P
Hoogland, Jeroen
van Smeden, Maarten
Rovers, Maroeska M
De Sutter, An I
Merenstein, Daniel
van Essen, Gerrit A
Kaiser, Laurent
Liira, Helena
Little, Paul
Bucher, Heiner CC
Reitsma, Johannes B
Identifying adults with acute rhinosinusitis in primary care that benefit most from antibiotics: protocol of an individual patient data meta-analysis using multivariable risk prediction modelling
title Identifying adults with acute rhinosinusitis in primary care that benefit most from antibiotics: protocol of an individual patient data meta-analysis using multivariable risk prediction modelling
title_full Identifying adults with acute rhinosinusitis in primary care that benefit most from antibiotics: protocol of an individual patient data meta-analysis using multivariable risk prediction modelling
title_fullStr Identifying adults with acute rhinosinusitis in primary care that benefit most from antibiotics: protocol of an individual patient data meta-analysis using multivariable risk prediction modelling
title_full_unstemmed Identifying adults with acute rhinosinusitis in primary care that benefit most from antibiotics: protocol of an individual patient data meta-analysis using multivariable risk prediction modelling
title_short Identifying adults with acute rhinosinusitis in primary care that benefit most from antibiotics: protocol of an individual patient data meta-analysis using multivariable risk prediction modelling
title_sort identifying adults with acute rhinosinusitis in primary care that benefit most from antibiotics: protocol of an individual patient data meta-analysis using multivariable risk prediction modelling
topic General practice / Family practice
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8252877/
https://www.ncbi.nlm.nih.gov/pubmed/34210729
http://dx.doi.org/10.1136/bmjopen-2020-047186
work_keys_str_mv AT venekamproderickp identifyingadultswithacuterhinosinusitisinprimarycarethatbenefitmostfromantibioticsprotocolofanindividualpatientdatametaanalysisusingmultivariableriskpredictionmodelling
AT hooglandjeroen identifyingadultswithacuterhinosinusitisinprimarycarethatbenefitmostfromantibioticsprotocolofanindividualpatientdatametaanalysisusingmultivariableriskpredictionmodelling
AT vansmedenmaarten identifyingadultswithacuterhinosinusitisinprimarycarethatbenefitmostfromantibioticsprotocolofanindividualpatientdatametaanalysisusingmultivariableriskpredictionmodelling
AT roversmaroeskam identifyingadultswithacuterhinosinusitisinprimarycarethatbenefitmostfromantibioticsprotocolofanindividualpatientdatametaanalysisusingmultivariableriskpredictionmodelling
AT desutterani identifyingadultswithacuterhinosinusitisinprimarycarethatbenefitmostfromantibioticsprotocolofanindividualpatientdatametaanalysisusingmultivariableriskpredictionmodelling
AT merensteindaniel identifyingadultswithacuterhinosinusitisinprimarycarethatbenefitmostfromantibioticsprotocolofanindividualpatientdatametaanalysisusingmultivariableriskpredictionmodelling
AT vanessengerrita identifyingadultswithacuterhinosinusitisinprimarycarethatbenefitmostfromantibioticsprotocolofanindividualpatientdatametaanalysisusingmultivariableriskpredictionmodelling
AT kaiserlaurent identifyingadultswithacuterhinosinusitisinprimarycarethatbenefitmostfromantibioticsprotocolofanindividualpatientdatametaanalysisusingmultivariableriskpredictionmodelling
AT liirahelena identifyingadultswithacuterhinosinusitisinprimarycarethatbenefitmostfromantibioticsprotocolofanindividualpatientdatametaanalysisusingmultivariableriskpredictionmodelling
AT littlepaul identifyingadultswithacuterhinosinusitisinprimarycarethatbenefitmostfromantibioticsprotocolofanindividualpatientdatametaanalysisusingmultivariableriskpredictionmodelling
AT bucherheinercc identifyingadultswithacuterhinosinusitisinprimarycarethatbenefitmostfromantibioticsprotocolofanindividualpatientdatametaanalysisusingmultivariableriskpredictionmodelling
AT reitsmajohannesb identifyingadultswithacuterhinosinusitisinprimarycarethatbenefitmostfromantibioticsprotocolofanindividualpatientdatametaanalysisusingmultivariableriskpredictionmodelling