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.

Is barbed better? Evaluation of triclosan-coated barbed suture on wound complications following emergency laparotomy

“…Some of these factors include emergency surgery and elevated BMI; the patients included in this analysis were already at a higher risk for dehiscence, as every case was performed in an emergent setting in patients with an average BMI of >28 kg/m 2 . 16 The presence of SSI has also been shown to be a predictor of fascial dehiscence. 16,17 This study simultaneously evaluated the effect of TCB on dehiscence and SSI in high risk surgical patients.…”

“…16 The presence of SSI has also been shown to be a predictor of fascial dehiscence. 16,17 This study simultaneously evaluated the effect of TCB on dehiscence and SSI in high risk surgical patients.…”

“…Dehiscence is a known morbidity following laparotomy, with known associated risk factors. Some of these factors include emergency surgery and elevated BMI; the patients included in this analysis were already at a higher risk for dehiscence, as every case was performed in an emergent setting in patients with an average BMI of >28 kg/m 2 16 . The presence of SSI has also been shown to be a predictor of fascial dehiscence 16,17 .…”

Occurrence and prevention of incisional hernia following laparoscopic colorectal surgery

“…They stratified IH patients using the HERNIA score = 4 (laparotomy) + 3 (hand-assisted laparoscopy) + 1 (chronic obstructive pulmonary disease) + 1 (BMI ≥ 25). Basta et al [ 4 , 76 ] has been dedicated to establishing and refining IH models[ 4 , 76 , 77 ]. Ultimately incorporating smoking and relevant medical history as risk factors, they used beta coefficients to weight 16 variables, resulting in the Penn Hernia Calculator, a specific IH prediction tool with excellent risk discrimination ability ( C -statistic = 0.76-0.89)[ 4 ].…”

“…Currently, the majority of prediction models lack independent clinical validation and statistical data elaboration, leading to limited calibration, discrimination, and feasibility[ 77 ]. It is crucial to broaden the incorporation of high-risk factors or biomarkers, and enhance and improve the clinical implementation and verification of these models.…”