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Published on: February 16, 2024
Development and Validation of a Prediction Model for Admission After Endoscopic Retrograde Cholangiopancreatography
Gregory A Coté1, Sheryl Lynch2, Jeffery J Easler2
1Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana; Division of Gastroenterology and Hepatology, Medical University of South Carolina, Charleston, South Carolina.
Background & Aims:
In outpatients undergoing endoscopic retrograde cholangiopancreatography (ERCP) with anesthesia, rates of and risk factors for admission are unclear. We aimed to develop a model that would allow physicians to predict hospitalization of patients during postanesthesia recovery.
Methods:
We conducted a retrospective study of data from ERCPs performed on outpatients from May 2012 through October 2013 at the Indiana University School of Medicine. Medical records were abstracted for preanesthesia, intra-anesthesia, and early (within the first hour) postanesthesia characteristics potentially associated with admission. Significant factors associated with admission were incorporated into a logistic regression model to identify subgroups with low, moderate, or high probabilities for admission. The population was divided into training (first 12 months) and validation (last 6 months) sets to develop and test the model.
Results:
We identified 3424 ERCPs during the study period; 10.7% of patients were admitted to the hospital, and 3.7% developed post-ERCP pancreatitis. Postanesthesia recovery times were significantly longer for patients requiring admission (362.6 ± 213.0 minutes vs 218.4 ± 71.8 minutes for patients not admitted; P < .0001). A higher proportion of admitted patients had high-risk indications. Admitted patients also had more severe comorbidities, higher baseline levels of pain, longer procedure times, performance of sphincter of Oddi manometry, higher pain during the first hour after anesthesia, and greater use of opiates or anxiolytics. A multivariate regression model identified patients who were admitted with a high level of accuracy in the training set (area under the curve, 0.83) and fair accuracy in the validation set (area under the curve, 0.78). On the basis of this model, nearly 50% of patients could be classified as low risk for admission.
Conclusion:
By using factors that can be assessed through the first hour after ERCP, we developed a model that accurately predicts which patients are likely to be admitted to the hospital. Rates of admission after outpatient ERCP are low, so a policy of prolonged observation might be unnecessary.
