Postoperative 30-day Readmission: Time to Focus on What Happens Outside the Hospital. Issue 4 (October 2016)

• Record Type: Journal Article
• Authors: Morris, Melanie S.; Graham, Laura A.; Richman, Joshua S.; Hollis, Robert H.; Jones, Caroline E.; Wahl, Tyler; Itani, Kamal M. F.; Mull, Hillary J.; Rosen, Amy K.; Copeland, Laurel; Burns, Edith; Telford, Gordon; Whittle, Jeffery; Wilson, Mark; Knight, Sara J.; Hawn, Mary T.
• Is Part Of: Annals of surgery. Volume 264:Issue 4(2016:Oct.)
• Page Start: 621
• Page End: 631
• Publication Date: 2016-10
• Subjects: improvement -- outcome assessment -- postoperative complications -- quality -- surgical readmission -- veterans; Surgery -- Periodicals; 617.005
• DOI: 10.1097/SLA.0000000000001855
• Languages: English
• ISSNs: 0003-4932

Abstract

Abstract : Objective: The aim of this study is to understand the relative contribution of preoperative patient factors, operative characteristics, and postoperative hospital course on 30-day postoperative readmissions. Background: Determining the risk of readmission after surgery is difficult. Understanding the most important contributing factors is important to improving prediction of and reducing postoperative readmission risk. Methods: National Veterans Affairs Surgical Quality Improvement Program data on inpatient general, vascular, and orthopedic surgery from 2008 to 2014 were merged with laboratory, vital signs, prior healthcare utilization, and postoperative complications data. Variables were categorized as preoperative, operative, postoperative/predischarge, and postdischarge. Logistic models predicting 30-day readmission were compared using adjusted R 2 and c-statistics with cross-validation to estimate predictive discrimination. Results: Our study sample included 237, 441 surgeries: 43% orthopedic, 39% general, and 18% vascular. Overall 30-day unplanned readmission rate was 11.1%, differing by surgical specialty (vascular 15.4%, general 12.9%, and orthopedic 7.6%, P < 0.001). Most common readmission reasons were wound complications (30.7%), gastrointestinal (16.1%), bleeding (4.9%), and fluid/electrolyte (7.5%) complications. Models using information available at the time of discharge explained 10.4% of the variability in readmissions. Of these, preoperative patient-level factors contributed the most to predictive models ( R 2 7.0% [c-statistic 0.67]); prediction was improved by inclusion of intraoperative ( R 2 9.0%, c-statistic 0.69) and postoperative variables ( R 2 10.4%, c-statistic 0.71). Including postdischarge complications improved predictive ability, explaining 19.6% of the variation ( R 2 19.6%, c-statistic 0.76). Conclusions: Postoperative readmissions are difficult to predict at the time of discharge, and of information available at that time, preoperative factors are the most important.