Fascial Dehiscence and Incisional Hernia Prediction Models: A Systematic Review and Meta-analysis

Abstract

Background: Fascial dehiscence (FD) and incisional hernia (IH) pose considerable risks to patients who undergo abdominal surgery, and many preventive strategies have been applied to reduce this risk. An accurate predictive model could aid identification of high-risk patients, who could be targeted for particular care. This study aims to systematically review existing FD and IH prediction models.

Methods: Prediction models were identified using pre-specified search terms on SCOPUS, PubMed, and Web of Science. Eligible studies included those conducted in adult patients who underwent any kind of abdominal surgery, and reported model performance. Data from the eligible studies were extracted, and the risk of bias (RoB) was assessed using the PROBAST tool. Pooling of C-statistics was performed using a random-effect meta-analysis. [Registration: PROSPERO (CRD42021282463)].

Results: Twelve studies were eligible for review; five were FD prediction model studies. Most included studies had high RoB, especially in the analysis domain. The C-statistics of the FD and IH prediction models ranged from 0.69 to 0.92, but most have yet to be externally validated. Pooled C-statistics (95% CI) were 0.80 (0.74, 0.86) and 0.81 (0.75, 0.86) for the FD (external-validation) and IH prediction model, respectively. Some predictive factors such as body mass index, smoking, emergency operation, and surgical site infection were associated with FD or IH occurrence and were included in multiple models.

Conclusions: Several models have been developed as an aid for FD and IH prediction, mostly with modest performance and lacking independent validation. New models for specific patient groups may offer clinical utility.

Fig. 1

PRISMA flow of study selection

Fig. 2

C-statistics of fascial dehiscence prediction models, where a higher C-statistic means a better discrimination performance. Dash line indicates the overall pooled C-statistic value. (VAMC Veteran Affairs Medical Centre, ML Machine Learning, REML restricted maximum likelihood)

Fig. 3

C-statistics of incisional hernia prediction models, where a higher C-statistic means a better discrimination performance. Dash line indicates the overall pooled C-statistic value. (REML restricted maximum likelihood)