Complicated anorectal sepsis: Validation of scoring system for predicting anorectal sepsis severity

Abstract

Anorectal sepsis is a common and potentially serious medical condition characterized by infection and inflammation of the anal canal and surrounding tissues. However, the lack of standardized and comprehensive scoring systems specifically tailored for predicting the severity of anorectal sepsis poses challenges in clinical practice. This study aimed to develop and validate a scoring system for predicting the severity of anorectal sepsis by incorporating relevant patient factors. A retrospective cohort study was conducted at Mansoura University Hospital, a tertiary care center, over a period of 5 years. The study population consisted of 330 patients diagnosed with anorectal sepsis during the study period. A scoring system was developed using multiple variables, with each variable assigned a specific score based on its clinical significance and weight in predicting disease severity. The developed scoring system’s predictive performance was evaluated using receiver operating characteristic (ROC) analysis, calculating the area under the ROC curve to assess discriminative ability. Descriptive statistics were used to summarize the demographic and clinical characteristics of the study population. Chi-square tests or t tests were performed to assess differences between non-severe and severe anal sepsis groups. The scoring system consisted of 12 variables, with a maximum total score of 18. The logistic regression analysis revealed significant associations between localized swelling, presentation within 72 hours, multiple drainage sessions, and severe anorectal sepsis. The ROC analysis showed an area under the curve of 0.85, indicating good discriminative ability of the scoring system. The scoring system was developed and validated in a single center, which may limit its generalizability to other settings. The scoring system demonstrated good predictive performance and can be a valuable tool for clinicians in assessing disease severity, guiding treatment decisions, and identifying high-risk patients.