Cargando…

Clinical outcomes of antimicrobial resistance in cancer patients: a systematic review of multivariable models

BACKGROUND: Infections are major causes of disease in cancer patients and pose a major obstacle to the success of cancer care. The global rise of antimicrobial resistance threatens to make these obstacles even greater and hinder continuing progress in cancer care. To prevent and handle such infectio...

Descripción completa

Detalles Bibliográficos
Autores principales: Danielsen, Anders Skyrud, Franconeri, Léa, Page, Samantha, Myhre, Anders Eivind, Tornes, Ragnhild Agathe, Kacelnik, Oliver, Bjørnholt, Jørgen Vildershøj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10114324/
https://www.ncbi.nlm.nih.gov/pubmed/37072711
http://dx.doi.org/10.1186/s12879-023-08182-3
_version_ 1785027991411097600
author Danielsen, Anders Skyrud
Franconeri, Léa
Page, Samantha
Myhre, Anders Eivind
Tornes, Ragnhild Agathe
Kacelnik, Oliver
Bjørnholt, Jørgen Vildershøj
author_facet Danielsen, Anders Skyrud
Franconeri, Léa
Page, Samantha
Myhre, Anders Eivind
Tornes, Ragnhild Agathe
Kacelnik, Oliver
Bjørnholt, Jørgen Vildershøj
author_sort Danielsen, Anders Skyrud
collection PubMed
description BACKGROUND: Infections are major causes of disease in cancer patients and pose a major obstacle to the success of cancer care. The global rise of antimicrobial resistance threatens to make these obstacles even greater and hinder continuing progress in cancer care. To prevent and handle such infections, better models of clinical outcomes building on current knowledge are needed. This internally funded systematic review (PROSPERO registration: CRD42021282769) aimed to review multivariable models of resistant infections/colonisations and corresponding mortality, what risk factors have been investigated, and with what methodological approaches. METHODS: We employed two broad searches of antimicrobial resistance in cancer patients, using terms associated with antimicrobial resistance, in MEDLINE and Embase through Ovid, in addition to Cinahl through EBSCOhost and Web of Science Core Collection. Primary, observational studies in English from January 2015 to November 2021 on human cancer patients that explicitly modelled infection/colonisation or mortality associated with antimicrobial resistance in a multivariable model were included. We extracted data on the study populations and their malignancies, risk factors, microbial aetiology, and methods for variable selection, and assessed the risk of bias using the NHLBI Study Quality Assessment Tools. RESULTS: Two searches yielded a total of 27,151 unique records, of which 144 studies were included after screening and reading. Of the outcomes studied, mortality was the most common (68/144, 47%). Forty-five per cent (65/144) of the studies focused on haemato-oncological patients, and 27% (39/144) studied several bacteria or fungi. Studies included a median of 200 patients and 46 events. One-hundred-and-three (72%) studies used a p-value-based variable selection. Studies included a median of seven variables in the final (and largest) model, which yielded a median of 7 events per variable. An in-depth example of vancomycin-resistant enterococci was reported. CONCLUSIONS: We found the current research to be heterogeneous in the approaches to studying this topic. Methodological choices resulting in very diverse models made it difficult or even impossible to draw statistical inferences and summarise what risk factors were of clinical relevance. The development and adherence to more standardised protocols that build on existing literature are urgent. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-023-08182-3.
format Online
Article
Text
id pubmed-10114324
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-101143242023-04-20 Clinical outcomes of antimicrobial resistance in cancer patients: a systematic review of multivariable models Danielsen, Anders Skyrud Franconeri, Léa Page, Samantha Myhre, Anders Eivind Tornes, Ragnhild Agathe Kacelnik, Oliver Bjørnholt, Jørgen Vildershøj BMC Infect Dis Research Article BACKGROUND: Infections are major causes of disease in cancer patients and pose a major obstacle to the success of cancer care. The global rise of antimicrobial resistance threatens to make these obstacles even greater and hinder continuing progress in cancer care. To prevent and handle such infections, better models of clinical outcomes building on current knowledge are needed. This internally funded systematic review (PROSPERO registration: CRD42021282769) aimed to review multivariable models of resistant infections/colonisations and corresponding mortality, what risk factors have been investigated, and with what methodological approaches. METHODS: We employed two broad searches of antimicrobial resistance in cancer patients, using terms associated with antimicrobial resistance, in MEDLINE and Embase through Ovid, in addition to Cinahl through EBSCOhost and Web of Science Core Collection. Primary, observational studies in English from January 2015 to November 2021 on human cancer patients that explicitly modelled infection/colonisation or mortality associated with antimicrobial resistance in a multivariable model were included. We extracted data on the study populations and their malignancies, risk factors, microbial aetiology, and methods for variable selection, and assessed the risk of bias using the NHLBI Study Quality Assessment Tools. RESULTS: Two searches yielded a total of 27,151 unique records, of which 144 studies were included after screening and reading. Of the outcomes studied, mortality was the most common (68/144, 47%). Forty-five per cent (65/144) of the studies focused on haemato-oncological patients, and 27% (39/144) studied several bacteria or fungi. Studies included a median of 200 patients and 46 events. One-hundred-and-three (72%) studies used a p-value-based variable selection. Studies included a median of seven variables in the final (and largest) model, which yielded a median of 7 events per variable. An in-depth example of vancomycin-resistant enterococci was reported. CONCLUSIONS: We found the current research to be heterogeneous in the approaches to studying this topic. Methodological choices resulting in very diverse models made it difficult or even impossible to draw statistical inferences and summarise what risk factors were of clinical relevance. The development and adherence to more standardised protocols that build on existing literature are urgent. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-023-08182-3. BioMed Central 2023-04-18 /pmc/articles/PMC10114324/ /pubmed/37072711 http://dx.doi.org/10.1186/s12879-023-08182-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Danielsen, Anders Skyrud
Franconeri, Léa
Page, Samantha
Myhre, Anders Eivind
Tornes, Ragnhild Agathe
Kacelnik, Oliver
Bjørnholt, Jørgen Vildershøj
Clinical outcomes of antimicrobial resistance in cancer patients: a systematic review of multivariable models
title Clinical outcomes of antimicrobial resistance in cancer patients: a systematic review of multivariable models
title_full Clinical outcomes of antimicrobial resistance in cancer patients: a systematic review of multivariable models
title_fullStr Clinical outcomes of antimicrobial resistance in cancer patients: a systematic review of multivariable models
title_full_unstemmed Clinical outcomes of antimicrobial resistance in cancer patients: a systematic review of multivariable models
title_short Clinical outcomes of antimicrobial resistance in cancer patients: a systematic review of multivariable models
title_sort clinical outcomes of antimicrobial resistance in cancer patients: a systematic review of multivariable models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10114324/
https://www.ncbi.nlm.nih.gov/pubmed/37072711
http://dx.doi.org/10.1186/s12879-023-08182-3
work_keys_str_mv AT danielsenandersskyrud clinicaloutcomesofantimicrobialresistanceincancerpatientsasystematicreviewofmultivariablemodels
AT franconerilea clinicaloutcomesofantimicrobialresistanceincancerpatientsasystematicreviewofmultivariablemodels
AT pagesamantha clinicaloutcomesofantimicrobialresistanceincancerpatientsasystematicreviewofmultivariablemodels
AT myhreanderseivind clinicaloutcomesofantimicrobialresistanceincancerpatientsasystematicreviewofmultivariablemodels
AT tornesragnhildagathe clinicaloutcomesofantimicrobialresistanceincancerpatientsasystematicreviewofmultivariablemodels
AT kacelnikoliver clinicaloutcomesofantimicrobialresistanceincancerpatientsasystematicreviewofmultivariablemodels
AT bjørnholtjørgenvildershøj clinicaloutcomesofantimicrobialresistanceincancerpatientsasystematicreviewofmultivariablemodels