Literature DB >> 21059414

Risk-adjusted scoring systems in colorectal surgery.

Edmund Leung1, Kirsten McArdle, Ling S Wong.   

Abstract

Consequent to recent advances in surgical techniques and management, survival rate has increased substantially over the last 25 years, particularly in colorectal cancer patients. However, post-operative morbidity and mortality from colorectal cancer vary widely across the country. Therefore, standardised outcome measures are emphasised not only for professional accountability, but also for comparison between treatment units and regions. In a heterogeneous population, the use of crude mortality as an outcome measure for patients undergoing surgery is simply misleading. Meaningful comparisons, however, require accurate risk stratification of patients being analysed before conclusions can be reached regarding the outcomes recorded. Sub-specialised colorectal surgical units usually dedicated to more complex and high-risk operations. The need for accurate risk prediction is necessary in these units as both mortality and morbidity often are tools to justify the practice of high-risk surgery. The Acute Physiology And Chronic Health Evaluation (APACHE) is a system for classifying patients in the intensive care unit. However, APACHE score was considered too complex for general surgical use. The American Society of Anaesthesiologists (ASA) grade has been considered useful as an adjunct to informed consent and for monitoring surgical performance through time. ASA grade is simple but too subjective. The Physiological & Operative Severity Score for the enUmeration of Mortality and morbidity (POSSUM) and its variant Portsmouth POSSUM (P-POSSUM) were devised to predict outcomes in surgical patients in general, taking into account of the variables in the case-mix. POSSUM has two parts, which include assessment of physiological parameters and operative scores. There are 12 physiological parameters and 6 operative measures. The physiological parameters are taken at the time of surgery. Each physiological parameter or operative variable is sub-divided into three or four levels with an exponentially increasing score. However, POSSUM and P-POSSUM over-predict mortality in patients who have had colorectal surgery. Discrepancies in these models have led to the introduction of a specialty-specific POSSUM: the ColoRectal POSSUM (CR-POSSUM). CR-POSSUM only uses six physiological parameters and four operative measures for prediction of mortality. It is much simplified to allow ease of use.
Copyright © 2010 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.

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Year:  2010        PMID: 21059414     DOI: 10.1016/j.ijsu.2010.10.016

Source DB:  PubMed          Journal:  Int J Surg        ISSN: 1743-9159            Impact factor:   6.071


  15 in total

1.  Risk factors for anastomotic leak and postoperative morbidity and mortality after elective right colectomy for cancer: results from a prospective, multicentric study of 1102 patients.

Authors:  Matteo Frasson; Pablo Granero-Castro; José Luis Ramos Rodríguez; Blas Flor-Lorente; Mariela Braithwaite; Eva Martí Martínez; Jose Antonio Álvarez Pérez; Antonio Codina Cazador; Alejandro Espí; Eduardo Garcia-Granero
Journal:  Int J Colorectal Dis       Date:  2015-08-28       Impact factor: 2.571

Review 2.  Predicting and Preventing Postoperative Outcomes.

Authors:  Sung Gon Lee; Andrew Russ
Journal:  Clin Colon Rectal Surg       Date:  2019-04-02

3.  Comorbidity and the risk of anastomotic leak in Chinese patients with colorectal cancer undergoing colorectal surgery.

Authors:  Yaohua Tian; Beibei Xu; Guopei Yu; Yan Li; Hui Liu
Journal:  Int J Colorectal Dis       Date:  2017-03-23       Impact factor: 2.571

4.  P-POSSUM for onco-surgeries: Does one suit fits all!

Authors:  Rakesh Garg; Kanika Rustagi
Journal:  J Anaesthesiol Clin Pharmacol       Date:  2022-04-25

5.  Impact of socioeconomic deprivation on short-term outcomes and long-term overall survival after colorectal resection for cancer.

Authors:  Chintamani Godbole; Aneel Bhangu; Douglas M Bowley; Thejasvi Subramanian; Sivesh K Kamarajah; Sharad Karandikar
Journal:  Int J Colorectal Dis       Date:  2019-11-12       Impact factor: 2.571

6.  A non-linear ensemble model-based surgical risk calculator for mixed data from multiple surgical fields.

Authors:  Ruoyu Liu; Xin Lai; Jiayin Wang; Xuanping Zhang; Xiaoyan Zhu; Paul B S Lai; Ci-Ren Guo
Journal:  BMC Med Inform Decis Mak       Date:  2021-07-30       Impact factor: 2.796

7.  Development of a prognostic score using the complete blood cell count for survival prediction in unselected critically ill patients.

Authors:  Fang Chongliang; Li Yuzhong; Shi Qian; Liu Xiliang; Liu Hui
Journal:  Biomed Res Int       Date:  2013-02-28       Impact factor: 3.411

8.  Nonspecific changes in clinical laboratory indicators in unselected terminally ill patients and a model to predict survival time based on a prospective observational study.

Authors:  Liu Hui; Liu Qigui; Ren Sashuang; Liu Xiliang; Luan Guihong
Journal:  J Transl Med       Date:  2014-03-22       Impact factor: 5.531

9.  Validity of the CR-POSSUM model in surgery for colorectal cancer in Spain (CCR-CARESS study) and comparison with other models to predict operative mortality.

Authors:  Marisa Baré; Manuel Jesús Alcantara; Maria José Gil; Pablo Collera; Marina Pont; Antonio Escobar; Cristina Sarasqueta; Maximino Redondo; Eduardo Briones; Paula Dujovne; Jose Maria Quintana
Journal:  BMC Health Serv Res       Date:  2018-01-29       Impact factor: 2.655

10.  Age-adjusted Charlson comorbidity index score as predictor of survival of patients with digestive system cancer who have undergone surgical resection.

Authors:  Yaohua Tian; Zhong Jian; Beibei Xu; Hui Liu
Journal:  Oncotarget       Date:  2017-06-07
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