scholarly journals Risk Prediction Model of Postoperative Venous Thrombosis of Ovarian Cancer

2021 ◽  
Author(s):  
Xue Wang ◽  
Xiao-hui Wang
Keyword(s):  
Ovarian Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Prediction Model ◽  
Risk Prediction ◽  
Roc Curve ◽  
Cancer Surgery ◽  
Risk Prediction Model ◽  
Prediction Ability ◽  
Figo Staging ◽  
D Dimer

Abstract Objective To investigate the influencing factors of venous thromboembolism (VTE) after ovarian cancer surgery, and construct its prediction model. Methods A total of 67 patients with ovarian cancer who developed VTE after surgery were selected from October 2008 to June 2020 in the Department of Obstetrics and Gynecology, First Hospital of Lanzhou University, and conducted a retrospective study with 100 patients without VTE after the operation who were confirmed by imaging during the same period. The clinical data of two groups of patients were analyzed and compared, and the risk prediction model was established. The ROC curve was drawn to evaluate the prediction effect of the model. Results Univariate analysis showed that there were statistically significant differences in age, menopausal status, hypertension, neoadjuvant chemotherapy, FIGO staging, lymph node metastasis, operation time, preoperative plasma FIB and D-dimer between the thrombosis group and the non-thrombosis group;The results of multivariate analysis showed that old age, neoadjuvant chemotherapy, late FIGO staging, high levels of plasma FIB and D-dimer before surgery are independent risk factors for VTE after ovarian cancer surgery. Construct a prediction model based on the results of multivariate regression analysis: Logit(P) = 0.053 × age + 0.917 × neoadjuvant chemotherapy + 0.956 × tumor FIGO staging + 0.398 × preoperative plasma FIB + 0.531 × preoperative D-dimer -7.679 ( Neoadjuvant chemotherapy, yes=1, no=0; tumor FIGO stage Ⅰ+Ⅱ=1, Ⅲ+Ⅳ=2; age, preoperative plasma FIB and D-dimer are actual values). The ROC curve analysis shows that the AUC value of the model is 0.773, the sensitivity is 74.6%, the specificity is 71.0%, and the total prediction accuracy rate is (78+39)/167=0.701. Conclusions Age, neoadjuvant chemotherapy, tumor FIGO staging, preoperative plasma FIB and D-dimer can be used as reliable indicators to predict the occurrence of postoperative VTE in patients with ovarian cancer. The constructed prediction model has good risk prediction ability, It has certain clinical application value.

Risk-prediction model of severe postoperative complications after primary debulking surgery for advanced ovarian cancer

2016 ◽  
Vol 140 (1) ◽  
pp. 15-21 ◽  
Author(s):  
Amanika Kumar ◽  
Jo Marie Janco ◽  
Andrea Mariani ◽  
Jamie N. Bakkum-Gamez ◽  
Carrie L. Langstraat ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Postoperative Complications ◽  
Prediction Model ◽  
Risk Prediction ◽  
Advanced Ovarian Cancer ◽  
Risk Prediction Model ◽  
Debulking Surgery ◽  
Primary Debulking Surgery

scholarly journals Australasian ACPGBI risk prediction model for 30‐day mortality after colorectal cancer surgery

BJS Open ◽  
2020 ◽  
Vol 4 (6) ◽  
pp. 1208-1216
Author(s):  
S. Wilkins ◽  
K. Oliva ◽  
E. Chowdhury ◽  
B. Ruggiero ◽  
A. Bennett ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Prediction Model ◽  
Risk Prediction ◽  
Cancer Surgery ◽  
Risk Prediction Model ◽  
Colorectal Cancer Surgery

Risk prediction model for surgical site infections in patients undergoing gynecologic cancer surgery

2016 ◽  
Vol 141 ◽  
pp. 166
Author(s):  
O. Zivanovic ◽  
J. Yan ◽  
S. Usiak ◽  
M. Lilavois ◽  
S. Ogden ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Gynecologic Cancer ◽  
Surgical Site Infections ◽  
Cancer Surgery ◽  
Risk Prediction Model

scholarly journals Construction of a Risk Prediction Model for Hospital-Acquired Pulmonary Embolism in Hospitalized Patients

2021 ◽  
Vol 27 ◽  
pp. 107602962110408
Author(s):  
Lengchen Hou ◽  
Longjun Hu ◽  
Wenxue Gao ◽  
Wenbo Sheng ◽  
Zedong Hao ◽  
...  
Keyword(s):  
Growth Rate ◽  
Pulmonary Embolism ◽  
Prediction Model ◽  
Risk Prediction ◽  
Predictive Performance ◽  
Hospitalized Patients ◽  
Risk Prediction Model ◽  
Gradient Boosting ◽  
Support Vector ◽  
D Dimer

The purpose of this study is to establish a novel pulmonary embolism (PE) risk prediction model based on machine learning (ML) methods and to evaluate the predictive performance of the model and the contribution of variables to the predictive performance. We conducted a retrospective study at the Shanghai Tenth People's Hospital and collected the clinical data of in-patients that received pulmonary computed tomography imaging between January 1, 2014 and December 31, 2018. We trained several ML models, including logistic regression (LR), support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT), compared the models with representative baseline algorithms, and investigated their predictability and feature interpretation. A total of 3619 patients were included in the study. We discovered that the GBDT model demonstrated the best prediction with an area under the curve value of 0.799, whereas those of the RF, LR, and SVM models were 0.791, 0.716, and 0.743, respectively. The sensibilities of the GBDT, LR, RF, and SVM models were 63.9%, 68.1%, 71.5%, and 75%, respectively; the specificities were 81.1%, 66.1, 72.7%, and 65.1%, respectively; and the accuracies were 77.8%, 66.5%, 72.5%, and 67%, respectively. We discovered that the maximum D-dimer level contributed the most to the outcome prediction, followed by the extreme growth rate of the plasma fibrinogen level, in-hospital duration, and extreme growth rate of the D-dimer level. The study demonstrates the superiority of the GBDT model in predicting the risk of PE in hospitalized patients. However, in order to be applied in clinical practice and provide support for clinical decision-making, the predictive performance of the model needs to be prospectively verified.

scholarly journals An epidemiologic risk prediction model for ovarian cancer in Europe: the EPIC study

2015 ◽  
Vol 112 (7) ◽  
pp. 1257-1265 ◽  
Author(s):  
K Li ◽  
A Hüsing ◽  
R T Fortner ◽  
A Tjønneland ◽  
L Hansen ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Epidemiologic Risk

Development of Neurobehavioral Deterioration Risk Prediction Model for Welder: A Proposed Study

2018 ◽  
Vol 10 (5) ◽  
Author(s):  
Nuur Azreen Paiman ◽  
◽  
Azian Hariri ◽  
Ibrahim Masood ◽  
Arma Noor ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model
Sign in / Sign up

Export Citation Format

Share Document