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Do risk calculators accurately predict surgical site occurrences?

, Thomas O Mitchell1, Julie L Holihan1

  • 1Department of Surgery, University of Texas Health Science Center at Houston, Houston, Texas.

The Journal of Surgical Research
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PubMed
Summary

Existing surgical risk models show limited accuracy for predicting surgical site occurrences (SSO) and infections (SSI) after open ventral hernia repair (VHR). The Ventral Hernia Risk Score (VHRS) and ACS-NSQIP models show modest success for SSI prediction, indicating a need for further refinement.

Keywords:
NSQIPRisk calculatorSurgical riskSurgical site infectionSurgical site occurrenceVentral hernia

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Area of Science:

  • Surgical outcomes research
  • Predictive modeling in healthcare
  • Hernia surgery complications

Background:

  • Current risk assessment models for surgical site occurrence (SSO) and surgical site infection (SSI) after open ventral hernia repair (VHR) lack robust external validation.
  • Accurate prediction of SSO and SSI is crucial for optimizing patient care and outcomes in VHR.

Purpose of the Study:

  • To evaluate the risk stratification capabilities of existing models for SSO and SSI after open VHR.
  • To identify the most accurate predictive model for SSO and SSI in this patient population.

Main Methods:

  • Utilized two datasets: a retrospective multicenter database (Ventral Hernia Outcomes Collaborative) and a single-center prospective database (Prospective).
  • Assessed five models: Ventral Hernia Risk Score (VHRS), Ventral Hernia Working Group (VHWG), Centers for Disease Control and Prevention Wound Class, Hernia Wound Risk Assessment Tool (HW-RAT), and American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP).
  • Compared predictive accuracy using area under the receiver operating characteristic curve (AUC) and assessed risk stratification via Pearson's chi-square test.

Main Results:

  • Existing models demonstrated limited predictive accuracy (low AUCs) for SSO and SSI across both databases.
  • VHRS and HW-RAT showed significant risk stratification for SSO, while VHWG and HW-RAT stratified for SSI in the first database.
  • ACS-NSQIP stratified for SSO, and VHRS and ACS-NSQIP for SSI in the second database.
  • VHRS, VHWG, and CDC overestimated risk; HW-RAT and ACS-NSQIP underestimated risk.

Conclusions:

  • All evaluated models possess limited ability to accurately risk-stratify patients for SSO after open VHR.
  • VHRS and ACS-NSQIP models show moderate success in identifying patients at risk for SSI.
  • Further refinement of predictive models, particularly VHRS and ACS-NSQIP, is necessary to improve SSO and SSI prediction and patient outcomes.