Efficacy and safety of antibiotic therapy for post-Lyme disease? A systematic review and network meta-analysis.
Antibiotics
Curative effect
Meta-analysis
Post-treatment Lyme disease
Treatment
Journal
BMC infectious diseases
ISSN: 1471-2334
Titre abrégé: BMC Infect Dis
Pays: England
ID NLM: 100968551
Informations de publication
Date de publication:
12 Jan 2023
12 Jan 2023
Historique:
received:
25
07
2022
accepted:
06
01
2023
entrez:
12
1
2023
pubmed:
13
1
2023
medline:
17
1
2023
Statut:
epublish
Résumé
At present, the pathogenesis of post-treatment Lyme disease (PTLDS) is not clear, so the treatment scheme of PTLDS, especially antibiotic treatment, is still controversial. This study aims to evaluate the efficacy of antibiotics in the treatment of PTLDS using network meta-analysis (NMA). Following PRISMA guidelines, a systematic literature search was conducted on randomized controlled trials in PubMed, EMBASE, Web of Science and Cochrane Library (the literature was published from database inception through December 16, 2022). Using random effect model and fixed effect model. STATA17.0 software was used to evaluate the quality and heterogeneity of the included research literature. The system included 4 randomized controlled trials (485 subjects). The network meta-analysis showed that ceftriaxone had better results than placebo [Mean = 0.87, 95% CI (0.02, 1.71)] and doxycycline [Mean = 1.01, 95% CI (0.03, 1.98)] in FSS scale scores. There was no statistical difference in FSS scale scores of other drugs after treatment. In terms of FSS score results, Ceftriaxone was the best intervention according to the SUCRA value of each treatment (97.7). The analysis of outcome indicators such as Beck Depression Inventory (BDI), Mental-health Scale and Physical-functioning scale showed that there was no statistically significant difference between the antibiotic group and placebo group. Ceftriaxone treatment may be the best choice for antibiotic treatment of PTLD, which provides useful guidance for antibiotic treatment of PTLD in the future.
Sections du résumé
BACKGROUND
BACKGROUND
At present, the pathogenesis of post-treatment Lyme disease (PTLDS) is not clear, so the treatment scheme of PTLDS, especially antibiotic treatment, is still controversial. This study aims to evaluate the efficacy of antibiotics in the treatment of PTLDS using network meta-analysis (NMA).
METHODS
METHODS
Following PRISMA guidelines, a systematic literature search was conducted on randomized controlled trials in PubMed, EMBASE, Web of Science and Cochrane Library (the literature was published from database inception through December 16, 2022). Using random effect model and fixed effect model. STATA17.0 software was used to evaluate the quality and heterogeneity of the included research literature.
RESULTS
RESULTS
The system included 4 randomized controlled trials (485 subjects). The network meta-analysis showed that ceftriaxone had better results than placebo [Mean = 0.87, 95% CI (0.02, 1.71)] and doxycycline [Mean = 1.01, 95% CI (0.03, 1.98)] in FSS scale scores. There was no statistical difference in FSS scale scores of other drugs after treatment. In terms of FSS score results, Ceftriaxone was the best intervention according to the SUCRA value of each treatment (97.7). The analysis of outcome indicators such as Beck Depression Inventory (BDI), Mental-health Scale and Physical-functioning scale showed that there was no statistically significant difference between the antibiotic group and placebo group.
CONCLUSION
CONCLUSIONS
Ceftriaxone treatment may be the best choice for antibiotic treatment of PTLD, which provides useful guidance for antibiotic treatment of PTLD in the future.
Identifiants
pubmed: 36635681
doi: 10.1186/s12879-023-07989-4
pii: 10.1186/s12879-023-07989-4
pmc: PMC9838005
doi:
Substances chimiques
Anti-Bacterial Agents
0
Ceftriaxone
75J73V1629
Doxycycline
N12000U13O
Types de publication
Meta-Analysis
Systematic Review
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
22Subventions
Organisme : National Natural Science Foundation of China
ID : 32060180
Organisme : Natural Science Foundation of Yunnan Province
ID : 2017FE467-001
Informations de copyright
© 2023. The Author(s).
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