Patient Care and Quality Improvement: Utilizing a Novel Risk Calculator to Predict Plastic Surgery Postoperative Readmission

Purpose: We have previously identified patient risk factors contributing to unanticipated postoperative readmission, a quality-directed metric that will be linked to CMS reimbursement. Building on this knowledge, the aim of this study is to create a risk index calculator to quantifiably predict the likelihood of 60-day postoperative readmissions.

Methods: An IRB approved retrospective review was done evaluating 671 randomly-selected patients undergoing procedures with the Plastic & Reconstructive Surgery service at our institution between January 1, 2013 and December 31, 2014. 60-day postoperative readmissions were identified for 58 (8.6%) patients. Logistical regression and backward variable selection with an inclusive p-value <0.30 yielded patient age, BMI, same-site procedure, renal disease, thyroid disease, CAD, COPD, and history of malignancy as variables associated with readmission.

Results: We used a linear combination rule with the associated variables to create the following risk index calculator equation: 

(Age x 0.0127) + (BMI x 0.041) + (Same Site (yes = 1, no = 0) x 0.4427) + (Renal Disease (1, 0) x 0.499) + (Thyroid Disease (1, 0) x 0.392) + (CAD (1, 0) x 0.3725) + (COPD (1, 0) x 0.4644) + (History of Malignancy (1, 0) x 0.4644) = Risk Index Value, RVI.

The product of the risk calculator yields a Risk Index Value (RIV) corresponding to a Predicted Percentage (PP) representing the probability of 60-day readmission. The RVI scale and associated PP of readmission are:

RIV less than -2.2 = PP 0-10%; -2.2 to -1.39 = 11-20%; -1.39 to -0.85 = 21-30%; -0.85 to -0.41 = 31-40%; -0.41 to 0 = 41-50%; 0 to 0.41 = 51-60%; 0.41 to 0.85 = 61-70%; 0.85 to 1.39 = 71-80%; 1.39 to 2.2 = 81-90%; greater than 2.2 = 90+%.

Conclusion: For the first time, a statistically-valid risk calculator to predict postoperative readmission after plastic surgery has been created. With this information, practitioners can provide appropriate resources for patients at increased risk of readmission, ultimately satisfying quality-directed metrics and optimizing the delivery of patient care.