Katelyn F Flick1, C Max Schmidt2,3,4,5,6, Cameron L Colgate7, Michele T Yip-Schneider1,8,9,10, Chris M Sublette11, Thomas K Maatman1, Mazhar Soufi1, Eugene P Ceppa1,10, Michael G House1, Nicholas J Zyromski1, Attila Nakeeb1. 1. Department of Surgery, Indiana University Simon Cancer Center, Indianapolis, IN, USA. 2. Department of Surgery, Indiana University Simon Cancer Center, Indianapolis, IN, USA. maxschmi@iupui.edu. 3. Department of Biochemistry/Molecular Biology, Indiana University Simon Cancer Center, Indianapolis, IN, USA. maxschmi@iupui.edu. 4. Walther Oncology Center, Indianapolis, IN, USA. maxschmi@iupui.edu. 5. Indiana University Simon Comprehensive Cancer Center, Indianapolis, IN, USA. maxschmi@iupui.edu. 6. Indiana University Health Pancreatic Cyst and Cancer Early Detection Center, Indianapolis, IN, USA. maxschmi@iupui.edu. 7. Center for Outcomes Research in Surgery, Indiana University School of Medicine, Indianapolis, IN, USA. 8. Walther Oncology Center, Indianapolis, IN, USA. 9. Indiana University Simon Comprehensive Cancer Center, Indianapolis, IN, USA. 10. Indiana University Health Pancreatic Cyst and Cancer Early Detection Center, Indianapolis, IN, USA. 11. Indiana University School of Medicine, Indianapolis, IN, USA.
Abstract
BACKGROUND: In patients undergoing pancreatoduodenectomy, non-home discharge is common and often results in an unnecessary delay in hospital discharge. This study aimed to develop and validate a preoperative prediction model to identify patients with a high likelihood of non-home discharge following pancreatoduodenectomy. METHODS: Patients undergoing pancreatoduodenectomy from 2013 to 2018 were identified using an institutional database. Patients were categorized according to discharge location (home vs. non-home). Preoperative risk factors, including social determinants of health associated with non-home discharge, were identified using Pearson's chi-squared test and then included in a multiple logistic regression model. A training cohort composed of 80% of the sampled patients was used to create the prediction model, and validation carried out using the remaining 20%. Statistical significance was defined as P < 0.05. RESULTS: Seven hundred sixty-six pancreatoduodenectomy patients met the study criteria for inclusion in the analysis (non-home, 126; home, 640). Independent predictors of non-home discharge on multivariable analysis were age, marital status, mental health diagnosis, functional health status, dyspnea, and chronic obstructive pulmonary disease. The prediction model was then used to generate a nomogram to predict likelihood of non-home discharge. The training and validation cohorts demonstrated comparable performances with an identical area under the curve (0.81) and an accuracy of 84%. CONCLUSION: A prediction model to reliably assess the likelihood of non-home discharge after pancreatoduodenectomy was developed and validated in the present study.
BACKGROUND: In patients undergoing pancreatoduodenectomy, non-home discharge is common and often results in an unnecessary delay in hospital discharge. This study aimed to develop and validate a preoperative prediction model to identify patients with a high likelihood of non-home discharge following pancreatoduodenectomy. METHODS:Patients undergoing pancreatoduodenectomy from 2013 to 2018 were identified using an institutional database. Patients were categorized according to discharge location (home vs. non-home). Preoperative risk factors, including social determinants of health associated with non-home discharge, were identified using Pearson's chi-squared test and then included in a multiple logistic regression model. A training cohort composed of 80% of the sampled patients was used to create the prediction model, and validation carried out using the remaining 20%. Statistical significance was defined as P < 0.05. RESULTS: Seven hundred sixty-six pancreatoduodenectomy patients met the study criteria for inclusion in the analysis (non-home, 126; home, 640). Independent predictors of non-home discharge on multivariable analysis were age, marital status, mental health diagnosis, functional health status, dyspnea, and chronic obstructive pulmonary disease. The prediction model was then used to generate a nomogram to predict likelihood of non-home discharge. The training and validation cohorts demonstrated comparable performances with an identical area under the curve (0.81) and an accuracy of 84%. CONCLUSION: A prediction model to reliably assess the likelihood of non-home discharge after pancreatoduodenectomy was developed and validated in the present study.
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