European journal of obstetrics, gynecology, and reproductive biology · 2025
Value of multi-parameter assessment in predicting antibiotic treatment failure and surgical intervention in pelvic abscess.
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Xu Yike, Yang Bing, Nie Hong, Zou Xiaofeng, Yang Danhe, Wang Zhiliang
Аннотация
OBJECTIVE: We aimed to develop a predictive model for the failure of antibiotic therapy in patients with pelvic abscesses and to determine the need for surgical intervention.
METHODS: We conducted a retrospective analysis of 368 female patients with pelvic abscesses in the Affiliated Hospital of Zunyi Medical University between 2014 and 2023. Participants were stratified into two distinct cohorts predicated on their therapeutic responses: those who achieved treatment success following medical intervention, while the second cohort included those who underwent surgical intervention subsequent to medical intervention failure.We evaluated demographic characteristics, clinical symptoms, laboratory findings, and imaging data. A multivariate analysis was performed on parameters including age, abscess dimensions, WBC, NLR, PLR, MLR, SII, and SIRI.
RESULTS: Among the 368 patients, 80 (80/368, 21.74%) were treated successfully with antibiotics, while 288 (288/368, 78.26%) needed surgery. The multivariate analysis revealed that age, abscess size, WBC, and PLR were significant predictors of the requirement for surgery following antibiotic failure. The combined model showed superior discriminative power (AUC 0.85) to any single parameter, though abscess size demonstrated the strongest univariate association (AUC 0.776). A column-line graph prediction model for predicting the failure of drug anti-infective therapy for patients with pelvic abscess was developed using R software based on the four independent predictors affecting the failure of drug therapy for pelvic abscess. This model had an AUC of 0.850 (95% CI: 0.801-0.899), a corresponding sensitivity of 0.76, and a specificity of 0.80.The calibration curve of the model basically coincides with the trend of the ideal curve, indicating that the prediction is in good agreement with the actual clinical observations and has good calibration.
CONCLUSION: Our findings present a novel predictive model that identifies key predictors of antibiotic failure in patients with pelvic abscesses. This model could facilitate the early identification of patients likely to fail antibiotic therapy, aiding in the timely adjustment of treatment strategies and potentially enhancing clinical efficacy.
METHODS: We conducted a retrospective analysis of 368 female patients with pelvic abscesses in the Affiliated Hospital of Zunyi Medical University between 2014 and 2023. Participants were stratified into two distinct cohorts predicated on their therapeutic responses: those who achieved treatment success following medical intervention, while the second cohort included those who underwent surgical intervention subsequent to medical intervention failure.We evaluated demographic characteristics, clinical symptoms, laboratory findings, and imaging data. A multivariate analysis was performed on parameters including age, abscess dimensions, WBC, NLR, PLR, MLR, SII, and SIRI.
RESULTS: Among the 368 patients, 80 (80/368, 21.74%) were treated successfully with antibiotics, while 288 (288/368, 78.26%) needed surgery. The multivariate analysis revealed that age, abscess size, WBC, and PLR were significant predictors of the requirement for surgery following antibiotic failure. The combined model showed superior discriminative power (AUC 0.85) to any single parameter, though abscess size demonstrated the strongest univariate association (AUC 0.776). A column-line graph prediction model for predicting the failure of drug anti-infective therapy for patients with pelvic abscess was developed using R software based on the four independent predictors affecting the failure of drug therapy for pelvic abscess. This model had an AUC of 0.850 (95% CI: 0.801-0.899), a corresponding sensitivity of 0.76, and a specificity of 0.80.The calibration curve of the model basically coincides with the trend of the ideal curve, indicating that the prediction is in good agreement with the actual clinical observations and has good calibration.
CONCLUSION: Our findings present a novel predictive model that identifies key predictors of antibiotic failure in patients with pelvic abscesses. This model could facilitate the early identification of patients likely to fail antibiotic therapy, aiding in the timely adjustment of treatment strategies and potentially enhancing clinical efficacy.