Impacts of different treatment methods on patients with COVID-related olfactory dysfunction: a systematic review and network meta-analysis
Abstract
Background: Olfactory Dysfunction (OD) is a prevalent neurological sequela of Coronavirus Disease 2019 (COVID-19) and substantially impairs quality of life. Although multiple pharmaceutical and non-pharmaceutical interventions have been proposed, their relative efficacy remains controversial. This study aimed to compare the efficacy of existing interventions for post-COVID-19 OD.
Methods: PubMed, Embase, the Cochrane Library, and Web of Science databases were systematically searched up to July 31, 2025. Randomized Controlled Trials (RCTs), cohort studies, and non-RCT studies evaluating interventions for post-COVID-19 OD were included. Study quality was assessed using the National Institutes of Health (NIH) scale and the Newcastle-Ottawa Scale (NOS). Data were analyzed using RStudio and Stata. The interventions were ranked by Surface Under the Cumulative Ranking (SUCRA) values.
Results: A total of 44 studies involving 3,516 patients and 26 interventions were included, with overall moderate to high quality. Chelators (Standardized Mean Difference [SMD = 3.61], 95% Credible Interval [CrI]: 2.77, 4.55) significantly improved total olfactory scores and their subdomains (with SUCRA values of 98.2%). Platelet-rich plasma (SMD = 1.33, 95% CrI: 0.92, 1.74) markedly improved the results of the University of Pennsylvania Smell Identification Test (UPSIT). Corticosteroids substantially improved olfactory function, as revealed by the Visual Analogue Scale (VAS) (SMD = 1.99, 95% CrI:…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 3,516 participants or records.
Read the paper
Related studies
- Calcium Ions in the Physiology and Pathology of the Central Nervous System
- AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare
- In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene
- In Silico drug repurposing pipeline using deep learning and structure based approaches in epilepsy
- Targeting serotonin receptors with phytochemicals – an in-silico study
- In silico exploration of anti-prostate cancer compounds from differential expressed genes
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.