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Authors Acher AW, Squires MH, Fields RC, Poultsides GA, Schmidt C, Votanopoulos KI, Pawlik TM, Jin LX, Ejaz A, Kooby DA, Bloomston M, Worhunsky D, Levine EA, Saunders N, Winslow E, Cho CS, Meredith K, Leverson G, Maithel SK, Weber SM
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Journal J. Gastrointest. Surg. Volume: 19 Issue: 2 Pages: 207-16
Publish Date 2015 Feb
PubMed ID 25373704

There are no validated methods to preoperatively identify patients with increased risk of discharge to skilled nursing facilities following resection of gastric cancer. We sought to identify preoperative predictors of non-home discharge to optimize transition of care to skilled nursing facility.Patients who underwent resection of gastric cancer from 2000 to 2012 from seven participating institutions of the US Gastric Cancer Collaborative were analyzed. Fisher’s exact tests, Student t tests, and logistic regression analyses identified preoperative variables associated with non-home discharge. A prediction tool was created and validated through c-indices. Survival analysis was conducted according to the methods of Kaplan and Meier.Out of the 918 patients identified, 93 (10 %) were discharged to nonhome location. Univariate analysis identified advancing age, American Society of Anesthesiology (ASA) score, hypertension, decreasing preoperative albumin, and lack of neoadjuvant chemotherapy as risk factors for non-home discharge (NHD). Multivariable analysis identified advanced age (odds ratio (OR) = 1.07, 95 % confidence interval (CI) = 1.04-1.10, p < 0.0001), depressed preoperative albumin (OR = 2.17, 95 % CI = 1.47-3.19, p = 0.0001), and total gastrectomy (OR = 2.56, 95 % CI = 1.53-4.3, p = 0.0003) as risk factors for NHD. The c-index of the model and the validation population were 0.76 and 0.8, respectively. Additionally, there was an association of decreased overall survival in patients discharged to nonhome location (35.5 months, home discharge, vs 12 months, NHD, p < 0.0001).Older patients with compromised nutritional status have greater risk of NHD following resection of gastric cancer. The prediction tool can augment preoperative planning to optimize transition of care to skilled nursing facility. Copyright © 2016 The Board of Regents of the University of Wisconsin System