Personalizing therapy selection in inflammatory bowel disease.

INTRODUCTION: Inflammatory bowel disease (IBD) is a complex disease, caused by aberrant immune responses to environmental stimuli where genetic, metabolomic, and environmental variables interact to cause mucosal inflammation. This review sheds light on the different drug and patient related factors that affect personalization of biologics in IBD treatment. AREAS COVERED: We utilized the online research database PubMed to carry out literature search pertaining to therapies in IBD. We incorporated a combination of primary literature as well as review articles and meta-analyses in writing this clinical review. In this paper, we discuss the mechanisms of action for different biologics, the genotype and phenotype of patients, and pharmacokinetics/pharmacodynamics of drugs, as factors that influence response rates. We also touch upon the role of artificial intelligence in treatment personalization. EXPERT OPINION: The future of IBD therapeutics is one of precision medicine, based on the identification of aberrant signaling pathways unique to individual patients as well as exploring the exposome, diet, viruses, and epithelial cell dysfunction as part of disease pathogenesis. We need global cooperation for pragmatic study designs as well as equitable access to machine learning/artificial intelligence technology to reach the unfulfilled potential of IBD care.

A Multicenter Study of Left Ventricular Assist Device-Related Gastrointestinal Bleeding.

INTRODUCTION: Continuous left ventricular assist devices (LVADs) offer hemodynamic support in advanced and decompensated heart failure but are often complicated by gastrointestinal bleeding (GIB) in medically fragile patients. METHODS: We performed a retrospective analysis of 475 consecutive patients who underwent LVAD implantation at the Massachusetts General Hospital and Tufts Medical Center from 2008 to 2019 and identified 128 patients with clinically significant GIB. Clinical characteristics of each bleeding event, including procedures and interventions, were recorded. We examined LVAD patients with overt and occult presentations to determine diagnostic endoscopic yield and analyzed predictors of recurrent GIB. RESULTS: We identified 128 unique patients with LVAD implantation complicated by GIB. No significant difference was observed based on study center, underlying cardiomyopathy, race/ethnicity, serum indices, and medications used. Overt bleeders presented more commonly during LVAD implantation admission ( P = 0.001) than occult bleeders. Occult bleed presentations had only 1 lower and no middle GI bleed source identified, despite similar workups to overt bleeds. Destination therapy (e.g., among nontransplant candidates) LVAD implantation (odds ratio 2.38, 95% confidence interval 1.05-5.58) and a history of GIB (odds ratio 3.85, 95% confidence interval 1.29-12.7) were independently associated with an increased risk of recurrent GIB-related hospitalization. DISCUSSION: Our findings confirm a high rate of GIB, especially in destination LVAD patients, and show a low diagnostic yield for colonoscopy and middle GI bleed assessments in LVAD patients with occult bleeds. Overt bleeding was more common and associated with vascular malformations. Although endoscopic interventions stopped active hemorrhage, GIB often recurred.

Emerging use of artificial intelligence in inflammatory bowel disease.

Inflammatory bowel disease (IBD) is a complex, immune-mediated gastrointestinal disorder with ill-defined etiology, multifaceted diagnostic criteria, and unpredictable treatment response. Innovations in IBD diagnostics, including developments in genomic sequencing and molecular analytics, have generated tremendous interest in leveraging these large data platforms into clinically meaningful tools. Artificial intelligence, through machine learning facilitates the interpretation of large arrays of data, and may provide insight to improving IBD outcomes. While potential applications of machine learning models are vast, further research is needed to generate standardized models that can be adapted to target IBD populations.