Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Jan 14, 2021
Date Accepted: Jun 16, 2021
Prediction Model of Anastomotic Leakage Among Esophageal Cancer Patients After Receiving Esophagectomy: Machine Learning Approach
ABSTRACT
Background:
Minimally invasive esophagectomy (MIE) is an important surgical approach for esophageal cancer patients while anastomotic leakage (AL) is a severe complication after MIE (5%-30%).
Objective:
The aim of this study is to use machine learning techniques to develop and validate a simple risk prediction panel to screen patients with emerging risk factors.
Methods:
In this machine learning risk prediction model, we used data from a retrospective study. We randomly split (9:1) the dataset using a computer algorithm into a training dataset of 639 patients and a testing dataset of 71 patients. We identified independently important diagnostic features of AL using the feature importance and correlation-based feature selection. We assessed multiple classification tools to create a multivariate risk prediction model. Internal validation of the model using the testing dataset was followed.
Results:
Final risk panel included 36 independent risk features. Of those, 10 features were significantly identified by logistic model, including aortic calcification (OR=2.77, 95% CI 1.32-5.81), abdominal dry calcification (OR=2.79, 95% CI 1.20-6.48) , forced expiratory volume (FEV) 1% (OR=0.51, 95% CI 0.30-0.89); TLCO (OR=0.56, 95% CI 0.27-1.18), peripheral vascular disease (OR=4.97, 95% CI 1.44-17.07), laparoscope (OR=3.92, 95% CI 1.23-12.51), post-operative hospital length of stay (OR=1.17, 95% CI 1.13-1.21), vascular permeability activity (OR=0.46, 95% CI 1.14-1.48), incisions fat colliquation or infections (OR=4.36, 95% CI 1.86-10.21). Logistic regression offered the highest prediction quality with accuracy of 87% in the training dataset, same performance was also archived by the testing model.
Conclusions:
Our diagnostic model offers valid predictions of diagnosis of AL, assisting in anastomotic leakage prevention and treatment. It suggests screening patients’ arteriosclerosis related risks factors and continuous monitoring of perioperative hemodynamics. Our risk prediction panel will need further calibration and validation in a prospective study in multi-center settings.
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