Clinical audit in gynecological cancer surgery: Development of a risk scoring system to predict adverse events

Kondalsamy-Chennakesavan, Srinivas, Bouman, Chantal, De Jong, Suzanne, Sanday, Karen, Nicklin, Jim, Land, Russell and Obermair, Andreas (2010) Clinical audit in gynecological cancer surgery: Development of a risk scoring system to predict adverse events. Obstetrical and Gynecological Survey, 65 2: 93-94. doi:10.1097/01.ogx.0000368144.18384.fa

Author Kondalsamy-Chennakesavan, Srinivas
Bouman, Chantal
De Jong, Suzanne
Sanday, Karen
Nicklin, Jim
Land, Russell
Obermair, Andreas
Title Clinical audit in gynecological cancer surgery: Development of a risk scoring system to predict adverse events
Journal name Obstetrical and Gynecological Survey   Check publisher's open access policy
ISSN 0029-7828
Publication date 2010-02
Year available 2009
Sub-type Editorial
DOI 10.1097/01.ogx.0000368144.18384.fa
Volume 65
Issue 2
Start page 93
End page 94
Total pages 2
Place of publication Philadelphia, PA, U.S.A.
Publisher Lippincott Williams & Wilkins
Collection year 2010
Language eng
Subject C1
920114 Reproductive System and Disorders
111402 Obstetrics and Gynaecology
1114 Paediatrics and Reproductive Medicine
Abstract Major risk factors in the general surgical population for morbidity and mortality have been identified using a number of risk scoring systems. Such risk factors include older age, length of surgery, anemia, and patients' comorbidities. A US Veteran's Affairs hospital program evaluated measures to reduce adverse events (AEs) and improve intra- and postoperative outcome variables in the over one million major operations occurring in Veteran's Affairs hospitals over a 10-year period (1991-2001). The data showed a 27% and 45% reduction in postoperative mortality and morbidity respectively. This program has been a model for developing a validated risk adjusted model for morbidity and mortality for different surgical specialties. There is no validated risk factor model for gynecological cancer. The aim of this study was to determine the incidence of AEs in patients with suspected or proven gynecological cancer, and to develop a clinical risk scoring system to predict the development of AEs. Between 2007 and 2008, 369 patients with suspected gynecological malignancies underwent a laparotomy or a laparoscopic procedure at a tertiary referral center for gynecological cancer. Surgical AEs were prospectively recorded and information was extracted from the medical records on potential patient risk factors, demographics, clinical values and histopathological findings. Multivariate logistic regression analysis was used to determine independent risk factors predictive for any surgical AE. The overall risk score (RS) for each variable was determined as follows: The coefficients from the multivariate model were scaled by a factor of 2; rounding the values obtained to the nearest integer provided the risk points. The sum of the risk points was the overall RS. Of the 369 patients, 95 (25.8%) developed at least one intra- and postoperative AE. Twenty-nine (8%) patients had intraoperative ARs and 77 (21%) experienced postoperative AEs; 11 patients (3.0%) developed both. Independent risk factors predictive of surgical AEs were surgical complexity, elevated SGOT (serum glutamic oxaloacetic transaminase, >=35 U/liter), elevated body mass index, and higher ASA (American Society of Anesthesiology) scores. The overall RS for an AR ranged from 0 to 14. The probability of having an AR was determined with the following formula: Risk (%) for an AR = 100/(1 + e(3.697 - (risk score/2)). These findings demonstrate that quantification of surgical risks in gynecological cancer surgery based on this model system may allow cross comparison of AE rates with other studies and has the potential to help reduce adverse events. Obesity is the only independent risk factor identified that is generally modifiable. Copyright (c) 2000-2010 Ovid Technologies, Inc.
Q-Index Code CX
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Editorial
Collections: 2010 Higher Education Research Data Collection
School of Medicine Publications
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Created: Sun, 21 Feb 2010, 00:02:23 EST