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Risk modelling of outcome after general and trauma surgery (the IRIS score)
B Liebman1, R P Strating, W van Wieringen
1Department of Surgery, Zaans Medical Centre, Zaandam, The Netherlands.
Background:
A practical, easy to use model was developed to stratify risk groups in surgical patients: the Identification of Risk In Surgical patients (IRIS) score.
Methods:
Over 15 years an extensive database was constructed in a general surgery unit, containing all patients who underwent general or trauma surgery. A logistic regression model was developed to predict mortality. This model was simplified to the IRIS score to enhance practicality. Receiver operating characteristic (ROC) curve analysis was performed.
Results:
The database contained a consecutive series of 33 224 patients undergoing surgery. Logistic regression analysis gave the following formula for the probability of mortality: P (mortality) = A/(1 + A), where A = exp (-4.58 + (0.26 x acute admission) + (0.63 x acute operation) + (0.044 x age) + (0.34 x severity of surgery)). The area under the ROC curve (AUC) was 0.92. The IRIS score also included age (divided into quartiles, 0-3 points), acute admission, acute operation and grade of surgery. The AUC predicting postoperative mortality was 0.90.
Conclusion:
The IRIS score accurately predicted mortality after general or trauma surgery.