The impact of enhanced recovery after surgery on inflammatory indicators and prognosis related to complex appendicitis in children

Purpose This study aims to explore the value of multiple indicators in the evaluation of risk factors for intestinal adhesion (IA) after appendectomy in children with acute appendicitis (AA). Methods A retrospective study was conducted on 608 patients who underwent appendectomy in the Department of Pediatric Surgery, Children’s Medical Center of Jilin Province from January 2017 to April 2023, with a one-year follow-up period to record the occurrence of IA after appendectomy. Univariate and multivariate analysis were used to screen the risk factors of postoperative IA, and a prediction model was established to predict postoperative IA. Results There were 527 patients in the non-IA group and 81 patients in the IA group. Binary Logistic regression was used to determine the strength of correlation with postoperative intestinal adhesion. The risk factors identified were as follows: DS ≥ 43 h (OR = 3.903, 5points), CRP ≥ 65 mg/L (OR = 3.424, 4.5points), PCT ≥ 0.9 µg/L (OR = 8.683, 8points), Surgical duration ≥ 100 min (OR = 6.457, 7points), Appendiceal perforation (OR = 6.073, 6.5points), Postoperative exhaust time ≥ 55 h (OR = 14.483, 10points). After test, the nomogram drawn based on binary logistic regression can obtain good prediction efficiency. In the training set, the area under the curve was 0.960, the sensitivity was 0.898, and the specificity was 0.905. In the test set, the area under the curve was 0.957, the sensitivity was 0.864, and the specificity was 0.906. Conclusion Postoperative exhaust time ≥ 55 h has a high risk of IA after appendicitis surgery in children. Early recovery of intestinal peristalsis function is essential. This scoring model is a novel and promising method for predicting postoperative IA.

show abstract

Decoding Pediatric Appendicitis: Unraveling Complexity with Artificial Intelligence and Evolving Management Insights

Shikha,

Kasem

2024

Appendicitis - Current Insights [Working Title]

View full text Add to dashboard Cite

Pediatric appendicitis poses unique diagnostic and management challenges, necessitating a precise approach that considers the complexities of developing anatomy and subtle clinical presentations. This chapter addresses these challenges by integrating clinical expertise with advanced technology, showcasing the potential of artificial intelligence models such as the artificial intelligence pediatric appendicitis decision-tree (AiPAD) to streamline diagnostic accuracy. In addition to improving decision-making, this chapter provides a comprehensive overview of the latest management strategies, offering valuable insights into optimizing outcomes and reducing complications. By bridging innovative AI techniques with evolving management principles, this chapter serves as a crucial resource for clinicians aiming to enhance their understanding and approach to pediatric appendicitis.

show abstract

The impact of enhanced recovery after surgery on inflammatory indicators and prognosis related to complex appendicitis in children

Cited by 3 publications

References 19 publications

Effect of enhanced recovery after surgery on postoperative outcomes in children undergoing robot-assisted laparoscopic pyeloplasty

Effect of enhanced recovery after surgery on postoperative outcomes in children undergoing robot-assisted laparoscopic pyeloplasty

Establishment and validation of a nomogram for predicting postoperative intestinal adhesion in children with acute appendicitis

Decoding Pediatric Appendicitis: Unraveling Complexity with Artificial Intelligence and Evolving Management Insights

Contact Info

Product

Resources

About