Efficacy of pharmacological agents for the management of treatment-resistant schizophrenia: a network meta-analysis

ABSTRACT

Objective

The present network meta-analysis (NMA) was conducted to compare and generate evidence for the most efficacious treatment among available pharmacological interventions for treatment-resistant schizophrenia (TRS).

Methods

Reviewers extracted data from 47 studies screened from PubMed/MEDLINE, Embase, Cochrane databases and clinical trial registries fulfilling the eligibility criteria. Random effects Bayesian NMA was done with non-informative priors. Network geometry was visualized, and node splitting was done for the closed triangles. Standardized mean difference and 95% credible interval(95%CrI) were reported for the reduction in symptom severity scores. The probability of each intervention for each rank was plotted. Meta-regression was done for the duration of the therapy.

Results

Augmentation of antipsychotics with escitalopram (SMD: −1.7[95%CrI: −2.8, −0.70]), glycine (SMD: −1.2 [95%CrI: −2.2, −0.28]) and Yokukansan (SMD: −1.3 [95%CrI: −2.4, −0.24]) shows a statistically significant reduction in symptom severity when compared to clozapine. As per surface under cumulative ranking curve analysis, escitalopram in combination with antipsychotics appeared to be the best intervention with moderate certainty of evidence. There was no significant effect of the duration of therapy on the treatment effects.

Conclusion

Escitalopram augmentation of antipsychotics appears to be the most efficacious treatment with moderate certainty of evidence among the available pharmacological interventions.

PROSPERO Registration

CRD42022380292

KEYWORDS:

• Treatment-resistant schizophrenia
• clozapine
• pharmacotherapy
• network meta-analysis
• escitalopram

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

Concept and design: R Maiti, A Srinivasan; Literature search: A Mishra; Study screening and selection: All authors; Data extraction and management: All authors; Data analysis and interpretation of data: A Mishra; Drafting of the manuscript: A Mishra, R Maiti; Critical revision of the manuscript for important intellectual content: BR Mishra, A Srinivasan; Final approval of the manuscript: All authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request. The codes used for running the network meta-analysis have been shared at Harvard Dataverse. DOI for R codes in Harvard dataverse: https://doi.org/10.7910/DVN/AFKQE5

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/17512433.2024.2310715

Additional information

Funding

This paper was not funded.