POSSUM and P-POSSUM overpredict mortality for carotid endarterectomy

G Kuhan1, A F Abidia, L D Wijesinghe

  • 1Academic Vascular Unit, Hull Royal Infirmary, Hull, UK.

Insights

The Physiological and Operative Severity Score for the enUmerged Surgical Mortality (POSSUM) and its Portsmouth modification (P-POSSUM) overpredict mortality for Carotid Endarterectomy (CEA) patients. These models are unsuitable for audit, suggesting a need for CEA-specific prediction tools.

Area of Science:

  • Vascular Surgery
  • Surgical Outcomes Research
  • Health Services Research

Background:

  • Carotid Endarterectomy (CEA) is a common procedure to prevent stroke.
  • Accurate mortality prediction is crucial for surgical audit and patient risk stratification.
  • Existing scoring systems like POSSUM and P-POSSUM are widely used but their applicability to specific procedures like CEA requires validation.

Purpose of the Study:

  • To evaluate the predictive accuracy of the POSSUM and P-POSSUM scoring systems for 30-day mortality in patients undergoing CEA.
  • To determine if these general surgical risk models are suitable for comparative audits of CEA procedures.

Main Methods:

  • A retrospective and prospective study involving 499 CEAs performed between 1992 and 1999.
  • Collection of patient physiological and operative parameters, and 30-day mortality data.
  • Calculation of predicted mortality using POSSUM and P-POSSUM, followed by chi-squared analysis to compare observed versus predicted deaths.

Main Results:

  • The observed 30-day mortality rate was 1.8% (9 out of 499 patients).
  • POSSUM and P-POSSUM predicted significantly higher mortality rates (49 and 25, respectively) compared to the observed nine deaths.
  • Statistical analysis revealed a significant lack of fit for both models (p<0.05), indicating poor predictive accuracy.

Conclusions:

  • Both POSSUM and P-POSSUM demonstrate a tendency to overpredict mortality in patients undergoing CEA.
  • These models are not suitable for comparative audit purposes in the context of CEA.
  • Development of specific risk prediction models tailored for CEA procedures is recommended for improved accuracy.
Abstract

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