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  11. Construction And Validation Of Nomogram To Predict Surgical Site Infection After Hysterectomy: A Retrospective Study.

Construction and validation of nomogram to predict surgical site infection after hysterectomy: a retrospective study.

Hui Shao1, Xiujuan Wang1, Lili Feng2

  • 1Department of Infectology, Shaoxing Maternity and ChildHealth Care Hospital, Shaoxing, China.

Scientific Reports
|September 4, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

A new nomogram predicts surgical site infections (SSI) after hysterectomy, identifying high-risk patients for early intervention. This tool aids in reducing infection rates and improving patient outcomes in gynecological surgery.

Area of Science:

  • Gynecology and Surgical Oncology
  • Infectious Disease Epidemiology
  • Biostatistics and Predictive Modeling

Background:

  • Surgical site infections (SSI) are a significant complication following hysterectomy.
  • Effective prediction and prevention strategies are crucial for improving patient safety and reducing healthcare burdens.

Purpose of the Study:

  • To develop and validate a predictive nomogram for identifying patients at high risk of SSI after hysterectomy.
  • To establish a tool for personalized risk assessment and targeted prevention strategies.

Main Methods:

  • Retrospective analysis of hysterectomy patients (January 2018 - December 2023) at a tertiary hospital.
  • LASSO regression for risk factor identification (2018-2022 training set).
  • Nomogram construction and validation using ROC, calibration curves, DCA, and CIC (2023 validation set).
Keywords:
Hospital-acquired infectionHysterectomyLASSO regressionSurgical site infection

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Main Results:

  • Six independent risk factors for SSI identified: BMI ≥ 24 kg/m², hypoproteinemia, postoperative antibiotics ≥ 3 days, prior abdominal surgery, hospital stay ≥ 10 days, and malignant pathological type.
  • The nomogram demonstrated good calibration and discrimination in both training and validation sets.
  • Clinical decision and impact curve analyses confirmed the nomogram's practical utility.

Conclusions:

  • The developed nomogram effectively predicts SSI risk in hysterectomy patients.
  • Personalized risk assessment enables timely interventions to reduce SSI incidence.
  • This tool supports enhanced prevention and control strategies for surgical site infections.