Can Predictive Modeling Identify Head and Neck Oncology Patients at Risk for Readmission?. (22nd May 2018)

• Record Type: Journal Article
• Authors: Manning, Amy M.; Casper, Keith A.; Peter, Kay St.; Wilson, Keith M.; Mark, Jonathan R.; Collar, Ryan M.
• Is Part Of: Otolaryngology--head and neck surgery. Volume 159:Number 4(2018)
• Page Start: 669
• Page End: 674
• Publication Date: 2018-05-22
• Subjects: readmission -- 30‐day readmission -- quality improvement -- predictive models -- head and neck cancer; Head -- Surgery -- Periodicals; Neck -- Surgery -- Periodicals; Otolaryngology -- Periodicals; 617.51
• DOI: 10.1177/0194599818775938
• Languages: English
• ISSNs: 0194-5998

Abstract

Abstract : Objective: Unplanned readmission within 30 days is a contributor to health care costs in the United States. The use of predictive modeling during hospitalization to identify patients at risk for readmission offers a novel approach to quality improvement and cost reduction. Study Design: Two‐phase study including retrospective analysis of prospectively collected data followed by prospective longitudinal study. Setting: Tertiary academic medical center. Subjects and Methods: Prospectively collected data for patients undergoing surgical treatment for head and neck cancer from January 2013 to January 2015 were used to build predictive models for readmission within 30 days of discharge using logistic regression, classification and regression tree (CART) analysis, and random forests. One model (logistic regression) was then placed prospectively into the discharge workflow from March 2016 to May 2016 to determine the model's ability to predict which patients would be readmitted within 30 days. Results: In total, 174 admissions had descriptive data. Thirty‐two were excluded due to incomplete data. Logistic regression, CART, and random forest predictive models were constructed using the remaining 142 admissions. When applied to 106 consecutive prospective head and neck oncology patients at the time of discharge, the logistic regression model predicted readmissions with a specificity of 94%, a sensitivity of 47%, a negative predictive value of 90%, and a positive predictive value of 62% (odds ratio, 14.9; 95% confidence interval, 4.02‐55.45). Conclusion: Prospectively collected head and neck cancer databases can be used to develop predictive models that can accurately predict which patients will be readmitted. This offers valuable support for quality improvement initiatives and readmission‐related cost reduction in head and neck cancer care.