A Clinical Risk Model for Surgical Site Infection Following Pediatric Spine Deformity Surgery

Hiroko Matsumoto1,2, Elaine L Larson2,3, Shay I Warren4

  • 1Department of Orthopaedic Surgery, Columbia University Irving Medical Center, New York, NY.

Insights

This study developed a validated risk calculator to predict surgical site infections (SSI) in pediatric spinal deformity surgery. The tool aids in informed decision-making and targeted resource allocation for high-risk patients.

Area of Science:

  • Orthopedics
  • Pediatric Surgery
  • Infectious Disease Epidemiology

Background:

  • Surgical site infection (SSI) remains a significant concern in pediatric spinal deformity surgery.
  • Previous studies had limitations in analyzing multiple risk factors simultaneously.
  • This study aimed to identify and model key predictors of SSI in this patient population.

Purpose of the Study:

  • To evaluate a wide range of preoperative and intraoperative factors for predicting SSI.
  • To develop and validate a predictive model for individual SSI risk assessment.
  • To create a practical tool for clinical use in pediatric spinal deformity surgery.

Main Methods:

  • Analysis of 3,092 pediatric spinal deformity surgeries from seven institutions.
  • Utilized logistic regression and cross-validation to develop and validate a predictive model.
  • Included patient, surgical, and hospital factors, defining SSI as per CDC criteria within 90 days.

Main Results:

  • Identified key predictors: nonambulatory status, neuromuscular etiology, pelvic instrumentation, long procedure time (≥7 hours), high ASA grade (>2), revision surgery, low hospital volume (<100 spine cases/year), abnormal hemoglobin, and BMI (overweight/obese).
  • The final model demonstrated good discrimination (AUC 0.76) and calibration.
  • Developed a risk probability calculator and mobile application.

Conclusions:

  • The validated risk calculator accurately predicts 90-day SSI probability in pediatric spinal deformity surgery.
  • The tool enhances informed consent and shared decision-making processes.
  • Facilitates selective deployment of resources for high-risk individuals.
Abstract