학술논문
Risk modelling of outcome after general and trauma surgery (the IRIS score).
Document Type
Academic Journal
Author
Liebman B; Department of Surgery, Zaans Medical Centre, Zaandam, The Netherlands.; Strating RP; van Wieringen W; Mulder W; Oomen JL; Engel AF
Source
Publisher: Oxford University Press on behalf of BJS Foundation Ltd Country of Publication: England NLM ID: 0372553 Publication Model: Print Cited Medium: Internet ISSN: 1365-2168 (Electronic) Linking ISSN: 00071323 NLM ISO Abbreviation: Br J Surg Subsets: MEDLINE
Subject
Language
English
Abstract
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.
(Copyright 2010 British Journal of Surgery Society Ltd. Published by John Wiley & Sons, Ltd.)
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.
(Copyright 2010 British Journal of Surgery Society Ltd. Published by John Wiley & Sons, Ltd.)