Efficacy of embolotherapy for the treatment of pelvic congestion syndrome: A systematic review.
Joseph HannaJoshua BruinsmaHugo C TemperlyDhanushke FernandoNiall O'SullivanMark HannaIan BrennanStefan PonoshPublished in: Irish journal of medical science (2024)
Pelvic congestion syndrome (PCS) poses a significant health, diagnostic, and economic challenges. Transcatheter embolisation has emerged as a promising treatment for PCS. A systematic review was performed in order to assess the safety and efficacy of transcatheter embolisation in the treatment of PCS. A systematic search of electronic databases was performed using 'PubMed', 'Embase', 'Medline (OVID)', and 'Web of Science', for articles pertaining to efficacy of embolotherapy for the treatment of pelvic congestion syndrome. A total of 25 studies were included in this systematic review with a combined total of 2038 patients. All patients included were female with a mean average age of 37.65 (31-51). Of the 25 studies, 18/25 studies reported pre- and post-procedural pelvic pain outcomes using a visual analogue scale (VAS). All studies showed a reduction in VAS post-procedure. Transcatheter embolisation had a high technical success rate (94%) and an overall complication rate of 9.0%, of which 10.4% were major and 89.6% were minor. Fifteen out of 19 (78.9%) major complications required a subsequent intervention. Transcatheter embolisation using various techniques is effective and safe in treating PCS. A low quality of evidence limits the currently available literature; however, embolisation has shown to improve symptoms in the majority of patients with low complication rates and recurrence rates.
Keyphrases
- systematic review
- end stage renal disease
- public health
- randomized controlled trial
- ejection fraction
- chronic pain
- chronic kidney disease
- rectal cancer
- peritoneal dialysis
- mental health
- adipose tissue
- risk factors
- minimally invasive
- physical activity
- insulin resistance
- artificial intelligence
- depressive symptoms
- sleep quality
- big data
- weight loss