Machine Learning to Predict Fascial Dehiscence after Exploratory Laparotomy Surgery

Abstract

Background: Fascial dehiscence following exploratory laparotomy is associated with significant morbidity and increased mortality. Previously published risk prediction models for fascial dehiscence are dated and limit a surgeon's ability to perform reliable risk assessment intraoperatively. We sought to determine if machine learning can predict fascial dehiscence after exploratory laparotomy.

Materials and methods: A retrospective cohort study was conducted of 93,024 patients undergoing exploratory laparotomy from the 2011-2018 ACS NSQIP data files. Data were divided into training (2011-2016, n = 69,969) and temporal validation (2017-2018, n = 23,055) cohorts. A clinical decision support tool was developed using the model generated via machine learning techniques.

Results: 1,332 (1.9%) patients in the training cohort and 390 (1.7%) patients in the temporal validation cohort developed fascial dehiscence. The area under the receiver operating characteristic curve was 0.69 (95% CI 0.66 to 0.72) in the validation cohort. Model predictions demonstrated excellent probability calibration. Decision curve analysis calculates net clinical benefit within a threshold range of 0.8%-4.5%. Operative time, surgical site and deep space infections, and body mass index were among the most important features for model predictions. Finally, operative time, sodium level, and hematocrit demonstrated non-linear relationships with predicted risk.

Conclusion: A clinical decision support tool for predicting fascial dehiscence after exploratory laparotomy was created and validated on a contemporary, national patient cohort using machine learning. The tool calculates net clinical benefit and can be used at the point of care. Some identified risk factor relationships were found to be complex and non-linear, highlighting the ability of some machine learning applications to capture nuanced, patient-specific risk profiles.

Keywords: Artificial intelligence; Clinical risk prediction; Exploratory laparotomy; Fascial dehiscence; General surgery; Machine learning.