Conventional compared to network meta-analysis to evaluate antibiotic prophylaxis in patients with cancer and haematopoietic stem cell transplantation recipients.


Journal

BMJ evidence-based medicine
ISSN: 2515-4478
Titre abrégé: BMJ Evid Based Med
Pays: England
ID NLM: 101719009

Informations de publication

Date de publication:
12 2021
Historique:
accepted: 20 07 2020
pubmed: 2 9 2020
medline: 15 12 2021
entrez: 2 9 2020
Statut: ppublish

Résumé

Our purpose was to compare conventional meta-analysis and network meta-analysis to evaluate the efficacy of different prophylactic systemic antibiotic classes in patients undergoing chemotherapy or haematopoietic stem cell transplant (HSCT). We included randomised trials if patients had cancer or were HSCT recipients and the intervention was systemic antibacterial prophylaxis. Three types of control groups were used: (1) placebo, no antibiotic and non-absorbable antibiotic separately; (2) placebo and no antibiotic combined; and (3) all three combined. These gave different network geometries. Strategies synthesised were fluoroquinolone, trimethoprim-sulfamethoxazole, cephalosporin and parenteral glycopeptide versus control groups. In total 113 trials met the eligibility criteria. Where treatment effects could be estimated with both conventional and network meta-analysis, values were generally similar. However, where events were sparse, network meta-analysis could be more precise. For example, trimethoprim-sulfamethoxazole versus placebo for infection-related mortality showed a relative risk ratio (RR) of 0.55, 95% CI (0.21 to 1.44) with conventional, and RR 0.43, 95% credible region (0.20 to 0.82) with network meta-analysis. Cephalosporin versus fluoroquinolone was comparable only indirectly using the network approach and yielded RR 0.59, 95% credible region (0.28 to 1.20) to reduce bacteraemia. Incoherence (difference between direct and indirect estimates raising concerns about network meta-analysis validity) was observed with network geometry where control groups were separated, but not where control groups were combined. In this situation, conventional and network meta-analysis yielded similar results in general. Network meta-analysis results could be more precise when events were rare. Some analysis could only be performed with the network approach. These results identify scenarios in which network meta-analysis may be advantageous.

Identifiants

pubmed: 32868288
pii: bmjebm-2020-111362
doi: 10.1136/bmjebm-2020-111362
doi:

Substances chimiques

Anti-Bacterial Agents 0

Types de publication

Journal Article Meta-Analysis

Langues

eng

Sous-ensembles de citation

IM

Pagination

320-326

Subventions

Organisme : Medical Research Council
ID : G0800472
Pays : United Kingdom

Informations de copyright

© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: BF’s institution receives research funding from Pfizer and Merck, and he serves on a Data Safety Monitoring Board for Astellas. TL has received research grants from Gilead Sciences, is a consultant to Astellas, Basilea, Gilead Sciences and Merck/MSD, and served at the speaker's bureau of Astellas, Gilead Sciences, Merck/MSD and Pfizer.

Auteurs

Juan Pablo Diaz Martinez (JPD)

Biostatistic Research Unit, Toronto General Hospital, Toronto, Ontario, Canada.
Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.

Paula D Robinson (PD)

Pediatric Oncology Group of Ontario, Toronto, Ontario, Canada.

Bob Phillips (B)

Leeds General Infirmary, Leeds Teaching Hospitals, NHS Trust, Leeds, United Kingdom and Centre for Reviews and Dissemination, Leeds Children's Hospital, Leeds, UK.

Thomas Lehrnbecher (T)

Pediatric Hematology and Oncology, Johann Wolfgang Goethe, Universität Frankfurt am Main, Frankfurt am Main, Hessen, Germany.

Christa Koenig (C)

Division of Pediatric Hematology/Oncology, Department of Pediatrics, Inselspital University Hospital Bern, Bern, Switzerland.

Brian Fisher (B)

Division of Infectious Diseases, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.

Grace Egan (G)

Haematology/Oncology, Hospital for Sick Children, Toronto, Ontario, Canada.

L Lee Dupuis (LL)

Department of Pharmacy and Research Institute, Hospital for Sick Children, Toronto, Ontario, Canada.
Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada.

Roland A Ammann (RA)

Division of Pediatric Hematology/Oncology, Department of Pediatrics, Inselspital University Hospital Bern, Bern, Switzerland.

Sarah Alexander (S)

Haematology/Oncology, Hospital for Sick Children, Toronto, Ontario, Canada.

Sandra Cabral (S)

Pediatric Oncology Group of Ontario, Toronto, Ontario, Canada.

George Tomlinson (G)

Biostatistic Research Unit, Toronto General Hospital, Toronto, Ontario, Canada.
Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.

Lillian Sung (L)

Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada lillian.sung@sickkids.ca.
Haematology/Oncology, Hospital for Sick Children, Toronto, Ontario, Canada.

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Classifications MeSH