S108 Predictor Model to Identify High-Risk Groups for Readmission in Patients With Ulcerative Colitis

Abstract

Background: Ulcerative colitis is characterized by recurring episodes of inflammation limited to the colon's mucosal layer. It commonly involves the rectum and may extend proximally and continuously to affect other parts of the colon. Risk factors for readmission over the first few weeks may differ from those that influence re-hospitalization at later time points. We investigated 30-day readmission rates, causes, risk factors, and interventions to reduce hospital readmission in patients who received medical treatment for ulcerative colitis. Methods: The Nationwide Readmission Database (HCUP) was queried from 2019-2022. Data on hospital readmissions of 1,048,576 adults who were readmitted within 30 days was collected. Our study first applied standard logistic regression and decision trees to obtain influential variables and derive practically meaningful decision rules. We then stratified the original data set and applied logistic regression to each data stratum. Finally, using the area under the curve and odds ratio, we further explored the risk and accuracy of interacting variables in the logistic regression modeling. Results: A total of 1,048,576 patients were readmitted between 2019-2022. Of these, 13,358 (Mean age 52.4 ± 10.4, 52% women) patients were included after the propensity score matching. 6215 (46%) patients were ulcerative colitis positive. Multiple logistic regression of the independent variable showed a readmission probability of 6.4% in the bottom quartile income group (P < 0.01), 5.3% in HLD on medication (P < 0.01), 1.3% in sepsis (P < 0.01), 4.7% in heart failure (P < 0.01), and 8.4% in insured patients (P < 0.01). The odds of readmission were increased in patients with a history of HLD requiring medication (OR 1.203, P < 0.01), heart failure (OR 1.303, P < 0.01), sepsis (OR 2.021, P < 0.01), bottom quartile of income (OR 1.45, P < 0.01), and insured status (OR 1.33, P < 0.01). In addition, the female sex was associated with higher odds of readmission (OR 1.21, P < 0.01). Accuracy of median income was also significant for AU ROC (0.632, P < 0.01) and insured status AU ROC (0.511, P < 0.01) as compared to the logistic regression. Conclusion(s): Our results suggest that patients with comorbid medical conditions, insured status, and bottom quartile of income at increased risk for readmission. Research is needed to determine if targeted interventions for high-risk patients decrease readmissions.