Construction and evaluation of networks among multiple postoperative complications. (April 2023)

• Record Type: Journal Article
• Authors: Shen, Yubing; Zhang, Luwen; Wu, Peng; Huang, Yuguang; Xin, Shijie; Zhang, Qiang; Zhao, Shengxiu; Sun, Hong; Lei, Guanghua; Zhang, Taiping; Han, Wei; Wang, Zixing; Jiang, Jingmei; Yu, Xiaochu
• Is Part Of: Computer methods and programs in biomedicine. Volume 232(2023)
• Publication Date: 2023-04
• Subjects: Postoperative complications -- Bayes' theorem -- Complication grading -- Network analysis -- Probability inference; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2023.107439
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The present network visualized the complex associations among multiple complications. Our new grading approach can classify complication severity using the items. Quantified associations among complications included cascade, cluster, and synergy. Updating probability with new information supports prevention and clinical decision. Abstract: Background and objective: Postoperative complications confer an increased risk of reoperation, prolonged length of hospital stay, and increased mortality. Many studies have attempted to identify the complex associations among complications to preemptively interrupt their progression, but few studies have looked at complications as a whole to reveal and quantify their possible trajectories of progression. The main objective of this study was to construct and quantify the association network among multiple postoperative complications from a comprehensive perspective to elucidate the possible evolution trajectories. Methods: In this study, a Bayesian network model was proposed to analyze the associations among 15 complications. Prior evidence and score-based hill-climbing algorithms were used to build the structure. The severity of complications was graded according to their connection to death, with the association between them quantified using conditional probabilities. The data of surgical inpatients used in this study were collected from four regionally representative academic/teaching hospitals in a prospective cohort study in China. Results: In the network obtained, 15 nodes represented complications or death, and 35 arcs with arrows represented the directly dependent relationship between them. With three grades classified on that basis, the correlation coefficients of complications within grades increased with increased grade, ranging from −0.11 to −0.06, 0.16, and 0.21 to 0.4 in grade 1 to grade 3, respectively. Moreover, the probability of each complication in the network increased with the occurrence of any other complication, even mild complications. Most seriously, once cardiac arrest requiring cardiopulmonary resuscitation occurs, the probability of death will be up to 88.1%. Conclusions: The present evolving network can facilitate the identification of strong associations among specific complications and provides a basis for the development of targeted measures to prevent further deterioration in high-risk patients.