Risk factors for multidrug-resistant bacterial infections in perianal abscesses and the construction of a nomogram prediction model

WANG Dawei; ZHENG Zheng; SHENG Yuxiang; LI Zhiran; HUANGFU Shao-hua; JIANG Bin

doi:10.3969/j.issn.1006-5725.2025.24.014

Abstract：Objective To identify risk factors for multidrug-resistant organism(MDRO)infections in patients with perianal abscesses and develop a nomogram-based predictive model to guide individualized clinical management..Methods A total of 757 patients with perianal abscesses admitted to the Nanjing Hospital of Tradi-tional Chinese Medicine between January 2022 and December 2023 were included in this study.Clinical data were systematically collected,and potential risk factors were identified through univariate and multivariate logistic regression analyses.Patients were randomly assigned to a training set(n=530,70%)or a validation set(n=227,30%)to develop and validate the nomogram prediction model.Results Among the 757 patients,137(18.1%)had MDRO infections,while 620(81.9%)had non-MDRO infections.A total of 927 pathogenic strains were isolated,of which 298(32.15%)were classified as MDROs.The predominant MDROs included Escherichia coli(149 strains,50.00%),extended-spectrum β-lactamase(ESBL)-producing organisms(68 strains,22.82%),and Streptococcus intermedius(27 strains,9.06%).Both univariate and multivariate logistic regression analyses revealed that a history of alcohol consumption,hypertension,and diabetes was significantly associated with MDRO infections(P<0.05).The nomogram model exhibited good discriminative ability,and the calibration curve demonstrated strong agree-ment between predicted probabilities and observed outcomes.Conclusions MDRO infections are commonly observed in patients with perianal abscess and are primarily attributed to Gram-negative bacteria.By incorporating key clinical indicators-such as a history of alcohol use,hypertension,and diabetes-the nomogram model developed in this study demonstrates strong predictive accuracy for identifying the risk of multidrug-resistant organism infec-tion in these patients.This robust model can serve as a reliable quantitative tool to guide antimicrobial therapy and support clinical decision-making.