Prediction of complications associated with general surgery using a Bayesian network.

Background: Numerous attempts have been made to identify risk factors for surgery complications, but few studies have identified accurate methods of predicting complex outcomes involving multiple complications.
Methods: We performed a prospective cohort study of general surgical inpatients who attended 4 regionally representative hospitals in China from January to June 2015 and January to June 2016. The risk factors were identified using logistic regression. A Bayesian network model, consisting of directed arcs and nodes, was used to analyze the relationships between risk factors and complications. Probability ratios for complications for a given node state relative to the baseline probability were calculated to quantify the potential effects of risk factors on complications or of complications on other complications.
Results: We recruited 19,223 participants and identified 21 nodes, representing 9 risk factors and 12 complications, and 55 direct relationships between these. Respiratory failure was at the center of the network, directly affected by 5 risk factors, and directly affected 7 complications. Cardiopulmonary resuscitation and sepsis or septic shock also directly affected death. The area under the receiver operating characteristic curve for the ability of the network to predict complications was >0.7. Notably, the probability of other severe complications or death significantly increased when a severe complication occurred. Most importantly, there was a 141-fold higher risk of death when cardiopulmonary resuscitation was required.
Conclusion: We have created a Bayesian network that displays how risk factors affect complications and their interrelationships and permits the accurate prediction of complications and the creation of appropriate preventive guidelines.
(Copyright © 2023. Published by Elsevier Inc.)