Background: Medicare and Medicaid have implemented penalties for hospitals with high readmission rates. While this does not yet apply to post-operative readmissions, there is concern that it soon will. Algorithms for predicting readmission have been developed for medical patients; however, to date, no such tool has been developed for post-operative patients.
Study design: Retrospective review and prospective validation of a predictive nomogram.
Setting: Single academic hospital.
Synopsis: Using the American College of Surgeons’ National Surgical Quality Improvement Program (ACS NSQIP) and hospital billing data, a retrospective analysis of 2,799 patients who had elective surgery between 2006 and 2011 was performed in order to develop a predictive nomogram for post-operative readmissions. Pre-operative, operative, and post-operative variables associated with readmission were evaluated, and the following variables were found to be independently associated with readmission:
- Bleeding disorder;
- Prolonged procedure length;
- In-hospital complications;
- Dependent functional status; and/or
- Higher care at discharge.
Using a linear regression model, a nomogram was developed that was prospectively validated in 255 patients from a single center. The nomogram accurately predicted the risk of post-operative readmission (C statistic=0.756) in the prospective analysis.
The nomogram has limited generalizability given the fact that it included patients from a single institution; it would benefit from external validation before widespread use.
Bottom line: The use of this predictive nomogram could aid in identifying patients at high risk of readmission.
Citation: Tevis SE, Weber SM, Kent KC, Kennedy GD. Nomogram to predict postoperative readmission in patients who undergo general surgery. JAMA Surg. 2015;150(6):505-510. doi: 10.1001/jamasurg.2014.4043.