Survival after primary breast cancer surgery following propofol or sevoflurane general anesthesia—A retrospective, multicenter, database analysis of 6305 Swedish patients

2020 ◽  
Vol 64 (8) ◽  
pp. 1048-1054 ◽  
Author(s):  
Mats Enlund ◽  
Anders Berglund ◽  
Rebecca Ahlstrand ◽  
Jakob Walldén ◽  
Johan Lundberg ◽  
...  
Keyword(s):  
Breast Cancer ◽  
General Anesthesia ◽  
Primary Breast Cancer ◽  
Cancer Surgery ◽  
Breast Cancer Surgery ◽  
Database Analysis

scholarly journals Prediction of total hospital expenses of patients undergoing breast cancer surgery in Shanghai, China by comparing three models

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Minjie Chen ◽  
Xiaopin Wu ◽  
Jidong Zhang ◽  
Enhong Dong
Keyword(s):  
Breast Cancer ◽  
Minimally Invasive ◽  
General Anesthesia ◽  
Medical Insurance ◽  
Cancer Surgery ◽  
Classification And Regression Tree ◽  
Breast Cancer Surgery ◽  
Total Hospital ◽  
Hospital Managers ◽  
Hospital Expenses

Abstract Background Breast cancer imposes a considerable burden on both the health care system and society, and becomes increasingly severe among women in China. To reduce the economic burden of this disease is crucial for patients undergoing the breast cancer surgery, hospital managers, and medical insurance providers. However, few studies have evidenced the prediction of the total hospital expenses (THE) for breast cancer surgery. The aim of the study is to predict THE for breast cancer surgery and identify the main influencing factors. Methods Data were retrieved from the first page of medical records of 3699 patients undergoing breast cancer surgery in one tertiary hospital from 2017 to 2018. Multiple liner regression (MLR), artificial neural networks (ANNs), and classification and regression tree (CART) were constructed and compared. Results The dataset from 3699 patients were randomly divided into training and test sets at a 70:30 ratio (2599 and 1100 records, respectively). The average total hospital expenses were 12520.54 ± 7844.88 ¥ (US$ 1929.20 ± 1208.11). MLR results revealed six factors to be significantly associated with THE: age, LOS, type of disease, having medical insurance, minimally invasive surgery, and receiving general anesthesia. After comparing three models, ANNs was the best model to predict THEs in patients undergoing breast cancer surgery, and its strong predictive performance was also validated. Conclusions To reduce the THEs, more attention should be paid to related factors of LOS, major and minimally invasive surgeries, and general anesthesia for these patient groups undergoing breast cancer surgery. This may reduce the information asymmetry between doctors and patients and provide more reliable cost, practical inpatient medical consumption standards and reimbursement standards reference for patients, hospital managers, and medical insurance providers ,respectively.

Predictors of return to work ten months after primary breast cancer surgery

2009 ◽  
Vol 48 (1) ◽  
pp. 93-98 ◽  
Author(s):  
Aina Johnsson ◽  
Tommy Fornander ◽  
Lars-Erik Rutqvist ◽  
Marjan Vaez ◽  
Kristina Alexanderson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Return To Work ◽  
Primary Breast Cancer ◽  
Cancer Surgery ◽  
Breast Cancer Surgery

Breast Cancer Surgery Under General Anesthesia: Is it Really Worth it?

2017 ◽  
Vol 23 (4) ◽  
pp. 492-494
Author(s):  
Antonello Accurso ◽  
Nicola Rocco ◽  
Gianni Antonio Della Corte ◽  
Giuseppe Accardo ◽  
Paola Reale ◽  
...  
Keyword(s):  
Breast Cancer ◽  
General Anesthesia ◽  
Cancer Surgery ◽  
Breast Cancer Surgery

Anesthetic Technique and the Cytokine and Matrix Metalloproteinase Response to Primary Breast Cancer Surgery

2010 ◽  
Vol 35 (6) ◽  
pp. 490-495 ◽  
Author(s):  
Catherine A. Deegan ◽  
David Murray ◽  
Peter Doran ◽  
Denis C. Moriarty ◽  
Daniel I. Sessler ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Matrix Metalloproteinase ◽  
Primary Breast Cancer ◽  
Cancer Surgery ◽  
Breast Cancer Surgery ◽  
Anesthetic Technique
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