A Validated Risk Prediction Model For Readmission After Colorectal Surgery
Harnsberger CR, Wyman A, Hoang CM, Davids JS, Sturrock PR, Maykel JA, Alavi K
Readmission is increasingly scrutinized, with CMS penalizing hospitals with excess readmissions. Additionally, minimizing adverse circumstances leading to readmission improves patient care. Our aim was to create a model predicting risk of readmission following elective colon and rectal surgery on the day of discharge.
Using a 60% random sample of the ACS NSQIP database (2012-2014), we fit a multivariable logistic regression predicting 30-day readmission among adult patients undergoing elective abdominal colon or rectal resection based on CPT codes. Covariates included pre-hospital and perioperative factors, and postoperative occurrences during the index admission. Clinically relevant and statistically significant (p<0.0001) factors were included in the final risk prediction model; patients were divided into low, moderate, and high risk by tertile. The model was validated using the remaining 40%.
51,058 patients were used to create the model; the overall readmission rate was 11.1%. Risk factors were identified in the pre-hospital, perioperative, and postoperative setting, with corresponding calculated risk scores (Figure 1A). The c-statistic for the model was 0.75, and the observed readmission rate according to risk score correlated to that which was predicted (Figure 1B). The model was successfully validated on the remaining 40% sample.
This model can be used to predict readmission following elective colorectal surgery to identify high-risk patients that may benefit from targeted intervention of modifiable risk factors, extending inpatient treatment, or intensive post-discharge follow-up. Expected readmission can be used to avoid undue reimbursement penalties incurred from treating high-risk patients. Creation of an online risk calculator and institutional validation are in process.
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