Simplified Machine Learning Models Can Accurately Identify High-Need High-Cost Patients With Inflammatory Bowel Disease.

INTRODUCTION: Hospitalization is the primary driver of inflammatory bowel disease (IBD)-related healthcare costs and morbidity. Traditional prediction models have poor performance at identifying patients at highest risk of unplanned healthcare utilization. Identification of patients who are high-need and high-cost (HNHC) could reduce unplanned healthcare utilization and healthcare costs. METHODS: We conducted a retrospective cohort study in adult patients hospitalized with IBD using the Nationwide Readmissions Database (model derivation in the 2013 Nationwide Readmission Database and validation in the 2017 Nationwide Readmission Database). We built 2 tree-based algorithms (decision tree classifier and decision tree using gradient boosting framework [XGBoost]) and compared traditional logistic regression to identify patients at risk for becoming HNHC (patients in the highest decile of total days spent in hospital in a calendar year). RESULTS: Of 47,402 adult patients hospitalized with IBD, we identified 4,717 HNHC patients. The decision tree classifier model (length of stay, Charlson Comorbidity Index, procedure, Frailty Risk Score, and age) had a mean area under the receiver operating characteristic curve (AUC) of 0.78 ± 0.01 in the derivation data set and 0.78 ± 0.02 in the validation data set. XGBoost (length of stay, procedure, chronic pain, drug abuse, and diabetic complication) had a mean AUC of 0.79 ± 0.01 and 0.75 ± 0.02 in the derivation and validation data sets, respectively, compared with AUC 0.55 ± 0.01 and 0.56 ± 0.01 with traditional logistic regression (peptic ulcer disease, paresthesia, admission for osteomyelitis, renal failure, and lymphoma) in derivation and validation data sets, respectively. DISCUSSION: In hospitalized patients with IBD, simplified tree-based machine learning algorithms using administrative claims data can accurately predict patients at risk of progressing to HNHC.

Impact of Comorbid Psychiatric Disorders on Healthcare Utilization in Patients with Inflammatory Bowel Disease: A Nationally Representative Cohort Study.

BACKGROUND AND AIMS: Patients with inflammatory bowel disease (IBD) frequently experience comorbid psychiatric disorders, which negatively impact quality of life. We characterized the longitudinal burden of hospitalization-related healthcare utilization in adults with IBD with and without comorbid anxiety, depression, or bipolar disorder. METHODS: In the 2017 Nationwide Readmissions Database (NRD), we identified 40,177 patients with IBD who were hospitalized between January 1, 2017 and June 30, 2017 and who were followed until December 31, 2017. In this cohort, we compared the annual burden (i.e., total days spent in hospital), costs, risk of readmission, inpatient mortality, and IBD-related surgery in patients with and without comorbid psychiatric disorders (anxiety, depression, or bipolar disorder). RESULTS: Of the 40,177 adults who were hospitalized for IBD, 25.7% had comorbid psychiatric disorders. Over a 10 month-long period of follow-up, patients with comorbid psychiatric disorders spent more days in the hospital (median, 7 days vs. 5 days, p < 0.01), experienced higher 30-day (31.3 vs. 25.4%; p < 0.01) and 90-day (42.6 vs. 35.3%, p < 0.01) readmission rates, and had higher hospitalization-related costs (median, $41,418 vs. $39,242, p < 0.01). However, they were less likely to undergo IBD-related procedures or surgeries. There were no differences in risk of mortality. On Cox proportional hazard analysis, the presence of comorbid psychiatric disorders was associated with a 16% higher risk of readmission (HR, 1.16; 95% CI, 1.13-1.20) and a 13% higher risk of severe IBD-related hospitalization (HR, 1.13; 95% CI, 1.08-1.16). CONCLUSIONS: In adults with IBD, comorbid psychiatric disorders were independently associated with a higher burden and cost of hospitalization, without an increase in the risk of IBD-related surgery or procedures. Population-based interventions aimed at treating psychiatric comorbidities may decrease the risk of unplanned healthcare utilization.

Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review.

BACKGROUND AND AIMS: There is increasing interest in machine learning-based prediction models in inflammatory bowel diseases [IBD]. We synthesised and critically appraised studies comparing machine learning vs traditional statistical models, using routinely available clinical data for risk prediction in IBD. METHODS: Through a systematic review till January 1, 2021, we identified cohort studies that derived and/or validated machine learning models, based on routinely collected clinical data in patients with IBD, to predict the risk of harbouring or developing adverse clinical outcomes, and reported its predictive performance against a traditional statistical model for the same outcome. We appraised the risk of bias in these studies using the Prediction model Risk of Bias ASsessment [PROBAST] tool. RESULTS: We included 13 studies on machine learning-based prediction models in IBD, encompassing themes of predicting treatment response to biologics and thiopurines and predicting longitudinal disease activity and complications and outcomes in patients with acute severe ulcerative colitis. The most common machine learning models used were tree-based algorithms, which are classification approaches achieved through supervised learning. Machine learning models outperformed traditional statistical models in risk prediction. However, most models were at high risk of bias, and only one was externally validated. CONCLUSIONS: Machine learning-based prediction models based on routinely collected data generally perform better than traditional statistical models in risk prediction in IBD, though frequently have high risk of bias. Future studies examining these approaches are warranted, with special focus on external validation and clinical applicability.