Predictive Modeling Analysis to Predict Complications after Laparotomy: A PRISMA-Based Systematic Review

Abstract

Background
Postoperative complications following laparotomy remain a major contributor to surgical morbidity, mortality, prolonged hospitalization, and increased healthcare burden. Traditional perioperative risk assessment tools demonstrate limited predictive accuracy at the individual patient level, prompting increasing interest in predictive modeling approaches, including machine learning–based techniques, to enhance risk stratification and clinical decision-making.

Objective
To systematically evaluate predictive modeling approaches used to forecast postoperative complications after laparotomy and to analyze their methodological characteristics, predictive performance, and clinical applicability.

Methods
A PRISMA-based systematic review was conducted to identify studies evaluating predictive models developed to estimate postoperative complications following laparotomy or major abdominal surgery. Electronic databases were systematically searched for relevant studies published within the defined time frame. Eligible studies included those employing traditional statistical models or machine learning–based algorithms for postoperative risk prediction. Data extraction focused on study design, patient population, predictors used, modeling techniques, performance metrics, validation methods, and reported clinical outcomes. Risk of bias and methodological quality were evaluated in accordance with established systematic review and prediction modeling reporting standards.

Results
The included studies demonstrated increasing utilization of machine learning–based approaches alongside traditional regression models for predicting postoperative complications. Predictive performance across studies showed moderate to good discrimination, commonly reported using AUROC values ranging from approximately 0.70 to 0.85. Models incorporating dynamic perioperative variables including intraoperative parameters and early postoperative physiological data demonstrated improved predictive performance compared with models relying solely on static preoperative predictors. However, external validation remained limited, and calibration reporting was inconsistent.

Conclusions
Predictive modeling represents a promising strategy for improving early identification of high-risk patients undergoing laparotomy. Machine learning approaches offer potential advantages in handling complex perioperative data; however, their superiority over traditional statistical models appears context-dependent. Greater emphasis on external validation, methodological transparency, and integration into clinical decision-support systems is essential for translating predictive models into routine surgical practice.