Clinical Relevance of Vaginal Cuff Dehiscence after Minimally Invasive versus Open Hysterectomy.
Kyung Jin EohYoung Joo LeeEun Ji NamHye In JungYoung-Tae KimPublished in: Journal of clinical medicine (2023)
This study aimed to evaluate the clinical relevance of vaginal cuff dehiscence following a hysterectomy. Data were prospectively collected from all patients who underwent hysterectomies at a tertiary academic medical center between 2014 and 2018. The incidence and clinical factors of vaginal cuff dehiscence after minimally invasive versus open hysterectomy were compared. Vaginal cuff dehiscence occurred in 1.0% (95% confidence interval [95% CI], 0.7-1.3%) of women who underwent either form of hysterectomy. Among those who underwent open (n = 1458), laparoscopic (n = 3191), and robot-assisted (n = 423) hysterectomies, vaginal cuff dehiscence occurred in 15 (1.0%), 33 (1.0%), and 3 (0.7%) cases, respectively. No significant differences in cuff dehiscence occurrence were identified in patients who underwent various modes of hysterectomies. A multivariate logistic regression model was created using the variables indication for surgery and body mass index. Both variables were identified as independent risk factors for vaginal cuff dehiscence (odds ratio [OR]: 2.74; 95% CI, 1.51-4.98 and OR: 2.20; 95% CI, 1.09-4.41, respectively). The incidence of vaginal cuff dehiscence was exceedingly low in patients who underwent various modes of hysterectomies. The risk of cuff dehiscence was predominantly influenced by surgical indications and obesity. Thus, the different modes of hysterectomy do not influence the risk of vaginal cuff dehiscence.
Keyphrases
- minimally invasive
- robot assisted
- ejection fraction
- body mass index
- newly diagnosed
- adipose tissue
- metabolic syndrome
- prognostic factors
- pregnant women
- atrial fibrillation
- physical activity
- coronary artery disease
- risk factors
- high fat diet induced
- patient reported outcomes
- patient reported
- deep learning
- weight loss
- percutaneous coronary intervention
- big data