BACKGROUND: Predicting the severity of complications after esophagectomy may supply important information for both patient and surgeon. The aim of the present study was to develop a nomogram based on preoperative risk factors to predict the severity of complications in patients who undergo esophagectomy for cancer. METHODS: A consecutive series of 663 patients who underwent esophagectomy between January 1993 and August 2005 was used to develop a prognostic model. The model was validated in a second group of patients who were operated between August 2005 and November 2006. Ordinal logistic regression analysis was performed to predict the severity of complications. Diverse simple and conventional preoperative risk factors were evaluated. A nomogram was developed to enhance clinical applicability. RESULTS: Patients were divided into three complication categories: those who suffered from no complications (n = 197); minor complications (n = 354); and major complications (n = 112). The following predictors remained in the model after multivariate analysis: higher age (p = 0.014); cerebrovascular accident/transient ischemic attack (CVA/TIA) (p = 0.009) or myocardial infarction in the medical history (p = 0.066); lower forced expiratory volume in the first second of expiration (FEV(1)) (p = 0.030); presence of electrocardiogram-changes (p = 0.008); and more extensive surgery (p < 0.001). A nomogram based on these variables was constructed. Overall agreement between the predicted probabilities and the observed frequencies was good in the development and the validation set. CONCLUSIONS: The nomogram predicts the severity of complications for individual patients and may help in informing the patient before undergoing esophagectomy for cancer and in choosing the optimal extent of surgery. When externally validated, the nomogram may play a role in risk-adjusted audit of morbidity after esophagectomy.
BACKGROUND: Predicting the severity of complications after esophagectomy may supply important information for both patient and surgeon. The aim of the present study was to develop a nomogram based on preoperative risk factors to predict the severity of complications in patients who undergo esophagectomy for cancer. METHODS: A consecutive series of 663 patients who underwent esophagectomy between January 1993 and August 2005 was used to develop a prognostic model. The model was validated in a second group of patients who were operated between August 2005 and November 2006. Ordinal logistic regression analysis was performed to predict the severity of complications. Diverse simple and conventional preoperative risk factors were evaluated. A nomogram was developed to enhance clinical applicability. RESULTS:Patients were divided into three complication categories: those who suffered from no complications (n = 197); minor complications (n = 354); and major complications (n = 112). The following predictors remained in the model after multivariate analysis: higher age (p = 0.014); cerebrovascular accident/transient ischemic attack (CVA/TIA) (p = 0.009) or myocardial infarction in the medical history (p = 0.066); lower forced expiratory volume in the first second of expiration (FEV(1)) (p = 0.030); presence of electrocardiogram-changes (p = 0.008); and more extensive surgery (p < 0.001). A nomogram based on these variables was constructed. Overall agreement between the predicted probabilities and the observed frequencies was good in the development and the validation set. CONCLUSIONS: The nomogram predicts the severity of complications for individual patients and may help in informing the patient before undergoing esophagectomy for cancer and in choosing the optimal extent of surgery. When externally validated, the nomogram may play a role in risk-adjusted audit of morbidity after esophagectomy.
Authors: John M Findlay; Richard S Gillies; Bruno Sgromo; Robert E K Marshall; Mark R Middleton; Nicholas D Maynard Journal: J Gastrointest Surg Date: 2014-04-24 Impact factor: 3.452
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Authors: Sumanta Dutta; Grant M Fullarton; Matthew J Forshaw; Paul G Horgan; Donald C McMillan Journal: World J Surg Date: 2011-05 Impact factor: 3.352
Authors: Onkar V Khullar; Renjian Jiang; Seth D Force; Allan Pickens; Manu S Sancheti; Kevin Ward; Theresa Gillespie; Felix G Fernandez Journal: J Surg Oncol Date: 2015-09-16 Impact factor: 3.454
Authors: K H Sheetz; L Zhao; S A Holcombe; S C Wang; R M Reddy; J Lin; M B Orringer; A C Chang Journal: Dis Esophagus Date: 2013-01-25 Impact factor: 3.429