Value of E-PASS and mE-PASS in predicting morbidity and mortality of gastric cancer surgery.
- Author:
Ningbo LIU
1
;
Jiangong CUI
1
;
Zengqiang ZHANG
1
;
Zhicheng ZHAO
1
;
Weidong LI
1
;
Weihua FU
2
;
Email: FUWEIHUA@TIJMU.EDU.CN.
Author Information
- Publication Type:Journal Article
- MeSH: Age Factors; Area Under Curve; Diabetes Complications; Elective Surgical Procedures; Homeostasis; Humans; Lung Diseases; complications; Operative Time; Postoperative Complications; etiology; mortality; Postoperative Period; Predictive Value of Tests; Regression Analysis; Retrospective Studies; Risk Assessment; methods; Risk Factors; Stomach Neoplasms; mortality; physiopathology; surgery; Stress, Physiological
- From: Chinese Journal of Oncology 2015;37(10):753-758
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVETo investigate the clinical value of Physiologic Ability and Surgical Stress (E-PASS) and modified Estimation of Physiologic Ability and Surgical Stress (mE-PASS) scoring systems in predicting the mortality and surgical risk of gastric cancer patients, and to analyze the relationship between the parameters of E-PASS and early postoperative complications.
METHODSClinical data of 778 gastric cancer patients who underwent elective surgical resection in Tianjin Medical University General Hospital from Jan. 2010 to Jan. 2014 were analyzed retrospectively. E-PASS and mE-PASS scoring systems were used to predict the mortality of gastric cancer patients, respectively. Univariate and unconditioned logistic regression analyses were performed to assess the relationships between nine parameters of E-PASS system and early postoperative complications.
RESULTSE-PASS and mE-PASS systems were used to predict the mortality in the death group and non-death group. The Z value was -5.067 and -4.492, respectively, showing a significant difference between the two groups (P<0.05). AUCs of mortality predicted by E-PASS and mE-PASS were 0.926 and 0.878 (P>0.05), and the prediction calibration of postoperative mortality showed statistically non-significant difference (P>0.05) between the E-PASS and mE-PASS prediction and actual mortality. Univariate analysis showed that age, operation time, severe heart disease, severe lung disease, diabetes mellitus, physical state index and ASA classification score are related to postoperative complications (P<0.05 for all). Unconditioned logistic regression analysis showed that severe lung disease, diabetes mellitus, ASA classification score and operation time are risk factors for early postoperative complications (P<0.05 for all).
CONCLUSIONSBoth mE-PASS and E-PASS scoring system have good consistency in the predicting postoperative mortality and actual mortality, and both are suitable for clinical application. Moreover, the mE-PASS scoring system is clinically more simple and convenient than E-PASS scoring system. Preoperative severe lung disease, diabetes mellitus, ASA classification score and operation time are independent factors affecting the early postoperative complications.
