Dataset Information

Development and Validation of a Prediction Model for Admission After Endoscopic Retrograde Cholangiopancreatography.

ABSTRACT: 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.

SUBMITTER: Cote GA

PROVIDER: S-EPMC4655134 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Development and Validation of a Prediction Model for Admission After Endoscopic Retrograde Cholangiopancreatography.

Coté Gregory A GA Lynch Sheryl S Easler Jeffery J JJ Keen Alyson A Vassell Patricia A PA Sherman Stuart S Hui Siu S Xu Huiping H

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association 20150627 13

<h4>Background & aims</h4>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.<h4>Methods</h4>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 a ...[more]

PMID: 26122761

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Project description:Background and aimsNoninvasive predictors of choledocholithiasis have generally exhibited marginal performance characteristics. We aimed to identify noninvasive independent predictors of endoscopic retrograde cholangiopancreatography (ERCP)-confirmed choledocholithiasis and accordingly developed predictive machine learning models (MLMs).MethodsClinical data of consecutive patients undergoing first-ever ERCP for suspected choledocholithiasis from 2015-2019 were abstracted from a prospectively-maintained database. Multiple logistic regression was used to identify predictors of ERCP-confirmed choledocholithiasis. MLMs were then trained to predict ERCP-confirmed choledocholithiasis using pre-ERCP ultrasound (US) imaging only and separately using all available noninvasive imaging (US/CT/magnetic resonance cholangiopancreatography). The diagnostic performance of American Society for Gastrointestinal Endoscopy (ASGE) "high-likelihood" criteria was compared to MLMs.ResultsWe identified 270 patients (mean age 46 years, 62.2% female, 73.7% Hispanic/Latino, 59% with noninvasive imaging positive for choledocholithiasis) with native papilla who underwent ERCP for suspected choledocholithiasis, of whom 230 (85.2%) were found to have ERCP-confirmed choledocholithiasis. Logistic regression identified choledocholithiasis on noninvasive imaging (odds ratio (OR) = 3.045, P = 0.004) and common bile duct (CBD) diameter on noninvasive imaging (OR=1.157, P = 0.011) as predictors of ERCP-confirmed choledocholithiasis. Among the various MLMs trained, the random forest-based MLM performed best; sensitivity was 61.4% and 77.3% and specificity was 100% and 75.0%, using US-only and using all available imaging, respectively. ASGE high-likelihood criteria demonstrated sensitivity of 90.9% and specificity of 25.0%; using cut-points achieving this specificity, MLMs achieved sensitivity up to 97.7%.ConclusionsMLMs using age, sex, race, presence of diabetes, fever, body mass index (BMI), total bilirubin, maximum CBD diameter, and choledocholithiasis on pre-ERCP noninvasive imaging predict ERCP-confirmed choledocholithiasis with good sensitivity and specificity and outperform the ASGE criteria for patients with suspected choledocholithiasis.

Dataset Information

Development and Validation of a Prediction Model for Admission After Endoscopic Retrograde Cholangiopancreatography.

Publications

Development and Validation of a Prediction Model for Admission After Endoscopic Retrograde Cholangiopancreatography.

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