Risk factors for acute pain and its persistence following breast cancer surgery

Pain ◽  
2005 ◽  
Vol 119 (1-3) ◽  
pp. 16-25 ◽  
Author(s):  
Jennifer Katz ◽  
Ellen L. Poleshuck ◽  
Carl H. Andrus ◽  
Laura A. Hogan ◽  
Beth F. Jung ◽  
...  
2006 ◽  
Vol 7 (9) ◽  
pp. 626-634 ◽  
Author(s):  
Ellen L. Poleshuck ◽  
Jennifer Katz ◽  
Carl H. Andrus ◽  
Laura A. Hogan ◽  
Beth F. Jung ◽  
...  

2019 ◽  
Vol 26 (4) ◽  
pp. 825-828 ◽  
Author(s):  
Chul‐Hyun Cho ◽  
Kyoung‐Lak Lee ◽  
Jihyoung Cho ◽  
Duhan Kim

2012 ◽  
Vol 107 (9) ◽  
pp. 1459-1466 ◽  
Author(s):  
R Sipilä ◽  
A-M Estlander ◽  
T Tasmuth ◽  
M Kataja ◽  
E Kalso

Biology ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 47
Author(s):  
Shi-Jer Lou ◽  
Ming-Feng Hou ◽  
Hong-Tai Chang ◽  
Hao-Hsien Lee ◽  
Chong-Chi Chiu ◽  
...  

Machine learning algorithms have proven to be effective for predicting survival after surgery, but their use for predicting 10-year survival after breast cancer surgery has not yet been discussed. This study compares the accuracy of predicting 10-year survival after breast cancer surgery in the following five models: a deep neural network (DNN), K nearest neighbor (KNN), support vector machine (SVM), naive Bayes classifier (NBC) and Cox regression (COX), and to optimize the weighting of significant predictors. The subjects recruited for this study were breast cancer patients who had received breast cancer surgery (ICD-9 cm 174–174.9) at one of three southern Taiwan medical centers during the 3-year period from June 2007, to June 2010. The registry data for the patients were randomly allocated to three datasets, one for training (n = 824), one for testing (n = 177), and one for validation (n = 177). Prediction performance comparisons revealed that all performance indices for the DNN model were significantly (p < 0.001) higher than in the other forecasting models. Notably, the best predictor of 10-year survival after breast cancer surgery was the preoperative Physical Component Summary score on the SF-36. The next best predictors were the preoperative Mental Component Summary score on the SF-36, postoperative recurrence, and tumor stage. The deep-learning DNN model is the most clinically useful method to predict and to identify risk factors for 10-year survival after breast cancer surgery. Future research should explore designs for two-level or multi-level models that provide information on the contextual effects of the risk factors on breast cancer survival.


2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Fumimasa Amaya ◽  
Toyoshi Hosokawa ◽  
Akiko Okamoto ◽  
Megumi Matsuda ◽  
Yosuke Yamaguchi ◽  
...  

Regional analgesia, opioids, and several oral analgesics are commonly used for the treatment of acute pain after breast cancer surgery. While all of these treatments can suppress the acute postsurgical pain, there is growing evidence that suggests that the postsurgical comorbidity will differ in accordance with the type of analgesic used during the surgery. Our current study reviewed the effect of analgesics used for acute pain treatments on the major comorbidities that occur after breast cancer surgery. A considerable number of clinical studies have been performed to investigate the relationship between the acute analgesic regimen and common comorbidities, including inadequate quality of recovery after the surgery, persistent postsurgical pain, and cancer recurrence. Previous studies have shown that the choice of the analgesic modality does affect the postsurgical comorbidity. In general, the use of regional analgesics has a beneficial effect on the occurrence of comorbidity. In order to determine the best analgesic choice after breast cancer surgery, prospective studies that are based on a clear definition of the comorbidity state will need to be undertaken in the future.


2012 ◽  
Vol 48 ◽  
pp. S157
Author(s):  
A. Bergmann ◽  
B.A. Silva ◽  
R.A. Dias ◽  
E.A.N. Fabro ◽  
M.A. Bello ◽  
...  

2012 ◽  
Vol 13 (12) ◽  
pp. 1172-1187 ◽  
Author(s):  
Christine Miaskowski ◽  
Bruce Cooper ◽  
Steven M. Paul ◽  
Claudia West ◽  
Dale Langford ◽  
...  

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