Xin-Yi Xu1, Jin-Ling Lu1, Qin Xu2, Hong-Xia Hua1, Le Xu3, Li Chen4. 1. School of Nursing, Nanjing Medical University, Nanjing, China. 2. School of Nursing, Nanjing Medical University, Nanjing, China. 248629512@qq.com. 3. School of Health Policy & Management, Nanjing Medical University, Nanjing, China. 4. Department of general surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Abstract
BACKGROUND: Postoperative fatigue (POF) is a common complication after gastrointestinal tumor surgery, and it also brings negative effect on prognosis and life quality. However, there are no prediction models for POF, and studies of risk factors are not comprehensive. Therefore, the aim of this study is to investigate the risk factors and pick out the best prediction model for POF and to validate it. METHODS: A prospective study was conducted for patients undergoing elective gastrointestinal tumor surgery. Physiological, psychological, and socioeconomic factors were collected. Logistic regression, back-propagation artificial neural networks (BP-ANNs), and classification and regression tree (CART) were constructed and compared. RESULTS: A total of 598 patients consisting of 463 derivation sample and 135 validation sample were included. The incidence of POF in derivation sample, validation sample, and total were 58.3%, 57.0%, and 58.7%, respectively. Logistic regression results showed age, higher degree of education, lower personal monthly income, advanced cancer, hypoproteinemia, preoperative anxiety or depression, and limited social support were risk factors for POF. Receiver operating characteristic curve (ROC) and performance indices were used to test three models. BP-ANN was the best by the comparison of models, and its strong predictive performance was also validated. CONCLUSIONS: More attention should be paid on specific patients after gastrointestinal tumor surgery. BP-ANN is a powerful mathematical tool that could predict POF exactly. It may be used as a noninvasive screening tool to guide clinicians for early identification of high-risk patients and grading interventions.
BACKGROUND: Postoperative fatigue (POF) is a common complication after gastrointestinal tumor surgery, and it also brings negative effect on prognosis and life quality. However, there are no prediction models for POF, and studies of risk factors are not comprehensive. Therefore, the aim of this study is to investigate the risk factors and pick out the best prediction model for POF and to validate it. METHODS: A prospective study was conducted for patients undergoing elective gastrointestinal tumor surgery. Physiological, psychological, and socioeconomic factors were collected. Logistic regression, back-propagation artificial neural networks (BP-ANNs), and classification and regression tree (CART) were constructed and compared. RESULTS: A total of 598 patients consisting of 463 derivation sample and 135 validation sample were included. The incidence of POF in derivation sample, validation sample, and total were 58.3%, 57.0%, and 58.7%, respectively. Logistic regression results showed age, higher degree of education, lower personal monthly income, advanced cancer, hypoproteinemia, preoperative anxiety or depression, and limited social support were risk factors for POF. Receiver operating characteristic curve (ROC) and performance indices were used to test three models. BP-ANN was the best by the comparison of models, and its strong predictive performance was also validated. CONCLUSIONS: More attention should be paid on specific patients after gastrointestinal tumor surgery. BP-ANN is a powerful mathematical tool that could predict POF exactly. It may be used as a noninvasive screening tool to guide clinicians for early identification of high-risk patients and grading interventions.
Authors: V Pergialiotis; A Pouliakis; C Parthenis; V Damaskou; C Chrelias; N Papantoniou; I Panayiotides Journal: Public Health Date: 2018-08-24 Impact factor: 2.427
Authors: Johanna S Paddison; Roger J Booth; Linda D Cameron; Elizabeth Robinson; Frank A Frizelle; Andrew G Hill Journal: J Surg Res Date: 2008-02-29 Impact factor: 2.192