Tongyu Liu1, Shao-Wei Lin2, Su Lin3, Lin Yang1, Haizhou Ji1, Jianping Zou1, Rong Xie1. 1. Department of Gynecology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou 350014, China. 2. Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou 350004, China. 3. Liver Research Center, First Affiliated Hospital of Fujian Medical University, Fuzhou 350004, China.
Abstract
BACKGROUND: The aim of this study was to find the high-risk factors of patients undergoing postoperative anti-thrombotic therapy that had a pulmonary embolism (PE). METHODS: In this retrospective analysis, a total of 35 patients with gynecologic oncological surgery were included from April 3, 2013, to January 12, 2018. Variable characteristic selection (Boruta's method), binary logistic regression (Fifth's method), and elastic network analysis were used to establish models and screen the high-risk factors. The decision curve analysis (DCA) was used to discriminate differences on these models as well. RESULTS: Three models were established, and the clinical efficiency of these three models was similar. Within the risk range of 0.1-0.3, the elastic network model was shown to have a good net benefit rate. The preoperative cholesterol levels were selected in all three models. The high-risk factors that were selected in both models were operative time, preoperative fibrinogen level, crystal rehydration on the day of surgery in the ward, a preoperative low-density lipoprotein (LDL) level, deep venous catheters, and presence of colloids. Other factors included crystal fluid intake during surgery, age, and chemotherapy before surgery. CONCLUSIONS: Positive anti-thrombotic therapy may be required among the patients with increased risk and further research might be needed to verify high-risk factors.
BACKGROUND: The aim of this study was to find the high-risk factors of patients undergoing postoperative anti-thrombotic therapy that had a pulmonary embolism (PE). METHODS: In this retrospective analysis, a total of 35 patients with gynecologic oncological surgery were included from April 3, 2013, to January 12, 2018. Variable characteristic selection (Boruta's method), binary logistic regression (Fifth's method), and elastic network analysis were used to establish models and screen the high-risk factors. The decision curve analysis (DCA) was used to discriminate differences on these models as well. RESULTS: Three models were established, and the clinical efficiency of these three models was similar. Within the risk range of 0.1-0.3, the elastic network model was shown to have a good net benefit rate. The preoperative cholesterol levels were selected in all three models. The high-risk factors that were selected in both models were operative time, preoperative fibrinogen level, crystal rehydration on the day of surgery in the ward, a preoperative low-density lipoprotein (LDL) level, deep venous catheters, and presence of colloids. Other factors included crystal fluid intake during surgery, age, and chemotherapy before surgery. CONCLUSIONS: Positive anti-thrombotic therapy may be required among the patients with increased risk and further research might be needed to verify high-risk factors.