Using insurance claims to predict and improve hospitalizations and biologics use in members with inflammatory bowel diseases

Predicting ICU Readmission from Electronic Health Records via BERTopic with Long Short Term Memory Network Approach

Background: The increasing rate of intensive care unit (ICU) readmissions poses significant challenges in healthcare, impacting both costs and patient outcomes. Predicting patient readmission after discharge is crucial for improving medical quality and reducing expenses. Traditional analyses of electronic health record (EHR) data have primarily focused on numerical data, often neglecting valuable text data. Methods: This study employs a hybrid model combining BERTopic and Long Short-Term Memory (LSTM) networks to predict ICU readmissions. Leveraging the MIMIC-III database, we utilize both quantitative and text data to enhance predictive capabilities. Our approach integrates the strengths of unsupervised topic modeling with supervised deep learning, extracting potential topics from patient records and transforming discharge summaries into topic vectors for more interpretable and personalized predictions. Results: Utilizing a comprehensive dataset of 36,232 ICU patient records, our model achieved an AUROC score of 0.80, thereby surpassing the performance of traditional machine learning models. The implementation of BERTopic facilitated effective utilization of unstructured data, generating themes that effectively guide the selection of relevant predictive factors for patient readmission prognosis. This significantly enhanced the model's interpretative accuracy and predictive capability. Additionally, the integration of importance ranking methods into our machine learning framework allowed for an in-depth analysis of the significance of various variables. This approach provided crucial insights into how different input variables interact and impact predictions of patient readmission across various clinical contexts. Conclusions: The practical application of BERTopic technology in our hybrid model contributes to more efficient patient management and serves as a valuable tool for developing tailored treatment strategies and resource optimization. This study highlights the significance of integrating unstructured text data with traditional quantitative data to develop more accurate and interpretable predictive models in healthcare, emphasizing the importance of individualized care and cost-effective healthcare paradigms.

Corticosteroids but not Anti-TNF Are Associated With Increased COVID-19 Complications in Patients With Inflammatory Bowel Disease

BACKGROUND AND AIMS

Immunosuppressed individuals are at higher risk for COVID-19 complications, yet data in patients with inflammatory bowel disease (IBD) are limited. We evaluated the risk of COVID-19- severe sequelae by medication utilization in a large cohort of patients with IBD.

METHODS

We conducted a retrospective cohort study utilizing insurance claims data between August 31, 2019, and August 31, 2021.We included IBD patients identified by diagnosis and treatment codes. Use of IBD medications was defined in the 90 days prior to cohort entry. Study outcomes included COVID-19 hospitalization, mechanical ventilation, and inpatient death. Patients were followed until the outcome of interest, outpatient death, disenrollment, or end of study period. Due to the aggregate nature of available data, we were unable to perform multivariate analyses.

RESULTS

We included 102 986 patients (48 728 CD, 47 592 UC) with a mean age of 53 years; 55% were female. Overall, 412 (0.4%) patients were hospitalized with COVID-19. The incidence of hospitalization was higher in those on corticosteroids (0.6% vs 0.3%; P < .0001; 13.6 per 1000 person-years; 95% confidence interval [CI], 10.8-16.9) and lower in those receiving anti-tumor necrosis factor α therapy (0.2% vs 0.5%; P < .0001; 3.9 per 1000 person-years; 95% CI, 2.7-5.4). Older age was associated with increased hospitalization with COVID-19. Overall, 71 (0.07%) patients required mechanical ventilation and 52 (0.05%) died at the hospital with COVID-19. The proportion requiring mechanical ventilation (1.9% vs 0.05%; P < .0001; 3.9 per 1000 person-years; 95% CI, 2.5-5.9) was higher among users of corticosteroids.

CONCLUSIONS

Among patients with IBD, those on corticosteroids had more hospitalizations and mechanical ventilation with COVID-19. Anti-tumor necrosis factor α therapy was associated with a decreased risk of hospitalization. These findings reinforce previous guidance to taper and/or discontinue corticosteroids in IBD.

Creation of an Inflammatory Bowel Disease Referral Pathway for Identifying Patients Who Would Benefit From Inflammatory Bowel Disease Specialist Consultation

BACKGROUND

Recommendations regarding signs and symptoms that should prompt referral of patients with inflammatory bowel disease (IBD) to an IBD specialist for a consultation could serve to improve the quality of care for these patients. Our aim was to develop a consult care pathway consisting of clinical features related to IBD that should prompt appropriate consultation.

METHODS

A scoping literature review was performed to identify clinical features that should prompt consultation with an IBD specialist. A panel of 11 experts was convened over 4 meetings to develop a consult care pathway using the RAND/UCLA Appropriateness Method. Items identified via scoping review were ranked and were divided into major and minor criteria. Additionally, a literature and panel review was conducted assessing potential barriers and facilitators to implementing the consult care pathway.

RESULTS

Of 43 features assessed, 13 were included in the care pathway as major criteria and 15 were included as minor criteria. Experts agreed that stratification into major criteria and minor criteria was appropriate and that 1 major or 2 or more minor criteria should be required to consider consultation. The greatest barrier to implementation was considered to be organizational resource allocation, while endorsements by national gastroenterology and general medicine societies were considered to be the strongest facilitator.

CONCLUSIONS

This novel referral care pathway identifies key criteria that could be used to triage patients with IBD who would benefit from IBD specialist consultation. Future research will be required to validate these findings and assess the impact of implementing this pathway in routine IBD-related care.

Artificial Intelligence for Inflammatory Bowel Diseases (IBD); Accurately Predicting Adverse Outcomes Using Machine Learning

BACKGROUND

Inflammatory Bowel Diseases with its complexity and heterogeneity could benefit from the increased application of Artificial Intelligence in clinical management.

AIM

To accurately predict adverse outcomes in patients with IBD using advanced computational models in a nationally representative dataset for potential use in clinical practice.

METHODS

We built a training model cohort and validated our result in a separate cohort. We used LASSO and Ridge regressions, Support Vector Machines, Random Forests and Neural Networks to balance between complexity and interpretability and analyzed their relative performances and reported the strongest predictors to the respective models. The participants in our study were patients with IBD selected from The OptumLabs® Data Warehouse (OLDW), a longitudinal, real-world data asset with de-identified administrative claims and electronic health record (EHR) data.

RESULTS

We included 72,178 and 69,165 patients in the training and validation set, respectively. In total, 4.1% of patients in the validation set were hospitalized, 2.9% needed IBD-related surgeries, 17% used long-term steroids and 13% of patients were initiated with biological therapy. Of the AI models we tested, the Random Forest and LASSO resulted in high accuracies (AUCs 0.70-0.92). Our artificial neural network performed similarly well in most of the models (AUCs 0.61-0.90).

CONCLUSIONS

This study demonstrates feasibility of accurately predicting adverse outcomes using complex and novel AI models on large longitudinal data sets of patients with IBD. These models could be applied for risk stratification and implementation of preemptive measures to avoid adverse outcomes in a clinical setting.