AI-luminating Artificial Intelligence in Inflammatory Bowel Diseases: A Narrative Review on the Role of AI in Endoscopy, Histology, and Imaging for IBD.

Endoscopy, histology, and cross-sectional imaging serve as fundamental pillars in the detection, monitoring, and prognostication of inflammatory bowel disease (IBD). However, interpretation of these studies often relies on subjective human judgment, which can lead to delays, intra- and interobserver variability, and potential diagnostic discrepancies. With the rising incidence of IBD globally coupled with the exponential digitization of these data, there is a growing demand for innovative approaches to streamline diagnosis and elevate clinical decision-making. In this context, artificial intelligence (AI) technologies emerge as a timely solution to address the evolving challenges in IBD. Early studies using deep learning and radiomics approaches for endoscopy, histology, and imaging in IBD have demonstrated promising results for using AI to detect, diagnose, characterize, phenotype, and prognosticate IBD. Nonetheless, the available literature has inherent limitations and knowledge gaps that need to be addressed before AI can transition into a mainstream clinical tool for IBD. To better understand the potential value of integrating AI in IBD, we review the available literature to summarize our current understanding and identify gaps in knowledge to inform future investigations.

Digital Health Interventions for Heart Failure Management in Underserved Rural Areas of the United States: A Systematic Review of Randomized Trials.

BACKGROUND: Heart failure disproportionately affects individuals residing in rural areas, leading to worse health outcomes. Digital health interventions have been proposed as a promising approach for improving heart failure management. This systematic review aims to identify randomized trials of digital health interventions for individuals living in underserved rural areas with heart failure. METHODS AND RESULTS: We conducted a systematic review by searching 6 databases (CINAHL, EMBASE, MEDLINE, Web of Science, Scopus, and PubMed; 2000-2023). A total of 30 426 articles were identified and screened. Inclusion criteria consisted of digital health randomized trials that were conducted in underserved rural areas of the United States based on the US Census Bureau's classification. Two independent reviewers screened the studies using the National Heart, Lung, and Blood Institute tool to evaluate the risk of bias. The review included 5 trials from 6 US states, involving 870 participants (42.9% female). Each of the 5 studies employed telemedicine, 2 studies used remote monitoring, and 1 study used mobile health technology. The studies reported improvement in self-care behaviors in 4 trials, increased knowledge in 2, and decreased cardiovascular mortality in 1 study. However, 3 trials revealed no change or an increase in health care resource use, 2 showed no change in cardiac biomarkers, and 2 demonstrated an increase in anxiety. CONCLUSIONS: The results suggest that digital health interventions have the potential to enhance self-care and knowledge of patients with heart failure living in underserved rural areas. However, further research is necessary to evaluate their impact on clinical outcomes, biomarkers, and health care resource use. REGISTRATION: URL: https://www.crd.york.ac.uk/prospero/; Unique identifier: CRD42022366923.

Design and Implementation of an Electronic Health Record-Integrated Hypertension Management Application.

BACKGROUND: High blood pressure affects approximately 116 million adults in the United States. It is the leading risk factor for death and disability across the world. Unfortunately, over the past decade, hypertension control rates have decreased across the United States. Prediction models and clinical studies have shown that reducing clinician inertia alone is sufficient to reach the target of ≥80% blood pressure control. Digital health tools containing evidence-based algorithms that are able to reduce clinician inertia are a good fit for turning the tide in blood pressure control, but careful consideration should be taken in the design process to integrate digital health interventions into the clinical workflow. METHODS: We describe the development of a provider-facing hypertension management platform. We enumerate key steps of the development process, including needs finding, clinical workflow analysis, treatment algorithm creation, platform design and electronic health record integration. We interviewed and surveyed 5 Stanford clinicians from primary care, cardiology, and their clinical care team members (including nurses, advanced practice providers, medical assistants) to identify needs and break down the steps of clinician workflow analysis. The application design and development stage were aided by a team of approximately 15 specialists in the fields of primary care, hypertension, bioinformatics, and software development. CONCLUSIONS: Digital monitoring holds immense potential for revolutionizing chronic disease management. Our team developed a hypertension management platform at an academic medical center to address some of the top barriers to adoption and achieving clinical outcomes. The frameworks and processes described in this article may be used for the development of a diverse range of digital health tools in the cardiovascular space.

Comparative arrhythmia patterns among patients on tyrosine kinase inhibitors.

BACKGROUND: Tyrosine kinase inhibitors (TKIs) are widely used in the treatment of hematologic malignancies. Limited studies have shown an association between treatment-limiting arrhythmias and TKI, particularly ibrutinib, a Bruton's tyrosine kinase (BTK) inhibitor. We sought to comprehensively assess the arrhythmia burden in patients receiving ibrutinib vs non-BTK TKI vs non-TKI therapies. METHODS: We performed a retrospective analysis of consecutive patients who received long-term cardiac event monitors while on ibrutinib, non-BTK TKIs, or non-TKI therapy for a hematologic malignancy between 2014 and 2022. RESULTS: One hundred ninety-three patients with hematologic malignancies were included (ibrutinib = 72, non-BTK TKI = 46, non-TKI therapy = 75). The average duration of TKI therapy was 32 months in the ibrutinib group vs 64 months in the non-BTK TKI group (p = 0.003). The ibrutinib group had a higher prevalence of atrial fibrillation (n = 32 [44%]) compared to the non-BTK TKI (n = 7 [15%], p = 0.001) and non-TKI (n = 15 [20%], p = 0.002) groups. Similarly, the prevalence of non-sustained ventricular tachycardia was higher in the ibrutinib group (n = 31, 43%) than the non-BTK TKI (n = 8 [17%], p = 0.004) and non-TKI groups (n = 20 [27%], p = 0.04). TKI therapy was held in 25% (n = 18) of patients on ibrutinib vs 4% (n = 2) on non-BTK TKIs (p = 0.005) secondary to arrhythmias. CONCLUSIONS: In this large retrospective analysis of patients with hematologic malignancies, patients receiving ibrutinib had a higher prevalence of atrial and ventricular arrhythmias compared to those receiving other TKI, with a higher rate of treatment interruption due to arrhythmias.

Opportunities to Increase Science of Diversity and Inclusion in Clinical Trials: Equity and a Lack of a Control.

The United States witnessed a nearly 4-fold increase in personal health care expenditures between 1980 and 2010. Despite innovations and obvious benefits to health, participants enrolled in clinical trials still do not accurately represent the racial and ethnic composition of patients nationally or globally. This lack of diversity in cohorts limits the generalizability and significance of results among all populations and has deep repercussions for patient equity. To advance diversity in clinical trials, robust evidence for the most effective strategies for recruitment of diverse participants is needed. A major limitation of previous literature on clinical trial diversity is the lack of control or comparator groups for different strategies. To date, interventions have focused primarily on (1) community-based interventions, (2) institutional practices, and (3) digital health systems. This review article outlines prior intervention strategies across these 3 categories and considers health policy and ethical incentives for substantiation before US Food and Drug Administration approval. There are no current studies that comprehensively compare these interventions against one another. The American Heart Association Strategically Focused Research Network on the Science of Diversity in Clinical Trials represents a multicenter, collaborative network between Stanford School of Medicine and Morehouse School of Medicine created to understand the barriers to diversity in clinical trials by contemporaneous head-to-head interventional strategies accessing digital, institutional, and community-based recruitment strategies to produce informed recruitment strategies targeted to improve underrepresented patient representation in clinical trials.

Electrophysiological mapping of the epicardium via 3D-printed flexible arrays.

Cardiac electrophysiology mapping and ablation are widely used to treat heart rhythm disorders such as atrial fibrillation (AF) and ventricular tachycardia (VT). Here, we describe an approach for rapid production of three dimensional (3D)-printed mapping devices derived from magnetic resonance imaging. The mapping devices are equipped with flexible electronic arrays that are shaped to match the epicardial contours of the atria and ventricle and allow for epicardial electrical mapping procedures. We validate that these flexible arrays provide high-resolution mapping of epicardial signals in vivo using porcine models of AF and myocardial infarction. Specifically, global coverage of the epicardial surface allows for mapping and ablation of myocardial substrate and the capture of premature ventricular complexes with precise spatial-temporal resolution. We further show, as proof-of-concept, the localization of sites of VT by means of beat-to-beat whole-chamber ventricular mapping of ex vivo Langendorff-perfused human hearts.

Development of a compact NMR system to measure pO2 in a tissue-engineered graft.

Cellular macroencapsulation devices, known as tissue engineered grafts (TEGs), enable the transplantation of allogeneic cells without the need for life-long systemic immunosuppression. Islet containing TEGs offer promise as a potential functional cure for type 1 diabetes. Previous research has indicated sustained functionality of implanted islets at high density in a TEG requires external supplementary oxygen delivery and an effective tool to monitor TEG oxygen levels. A proven oxygen-measurement approach employs a 19F oxygen probe molecule (a perfluorocarbon) implanted alongside therapeutic cells to enable oxygen- and temperature- dependent NMR relaxometry. Although the approach has proved effective, the clinical translation of 19F oxygen relaxometry for TEG monitoring will be limited by the current inaccessibility and high cost of MRI. Here, we report the development of an affordable, compact, and tabletop 19F NMR relaxometry system for monitoring TEG oxygenation. The system uses a 0.5 T Halbach magnet with a bore diameter (19 cm) capable of accommodating the human arm, a potential site of future TEG implantation. 19F NMR relaxometry was performed while controlling the temperature and oxygenation levels of a TEG using a custom-built perfusion setup. Despite the magnet's nonuniform field, a pulse sequence of broadband adiabatic full-passage pulses enabled accurate 19F longitudinal relaxation rate (R1) measurements in times as short as ∼2 min (R1 vs oxygen partial pressure and temperature (R2 > 0.98)). The estimated sensitivity of R1 to oxygen changes at 0.5 T was 1.62-fold larger than the sensitivity previously reported for 16.4 T. We conclude that TEG oxygenation monitoring with a compact, tabletop 19F NMR relaxometry system appears feasible.

Somatic mutational landscape of hereditary hematopoietic malignancies caused by germline variants in RUNX1, GATA2, and DDX41.

Individuals with germ line variants associated with hereditary hematopoietic malignancies (HHMs) have a highly variable risk for leukemogenesis. Gaps in our understanding of premalignant states in HHMs have hampered efforts to design effective clinical surveillance programs, provide personalized preemptive treatments, and inform appropriate counseling for patients. We used the largest known comparative international cohort of germline RUNX1, GATA2, or DDX41 variant carriers without and with hematopoietic malignancies (HMs) to identify patterns of genetic drivers that are unique to each HHM syndrome before and after leukemogenesis. These patterns included striking heterogeneity in rates of early-onset clonal hematopoiesis (CH), with a high prevalence of CH in RUNX1 and GATA2 variant carriers who did not have malignancies (carriers-without HM). We observed a paucity of CH in DDX41 carriers-without HM. In RUNX1 carriers-without HM with CH, we detected variants in TET2, PHF6, and, most frequently, BCOR. These genes were recurrently mutated in RUNX1-driven malignancies, suggesting CH is a direct precursor to malignancy in RUNX1-driven HHMs. Leukemogenesis in RUNX1 and DDX41 carriers was often driven by second hits in RUNX1 and DDX41, respectively. This study may inform the development of HHM-specific clinical trials and gene-specific approaches to clinical monitoring. For example, trials investigating the potential benefits of monitoring DDX41 carriers-without HM for low-frequency second hits in DDX41 may now be beneficial. Similarly, trials monitoring carriers-without HM with RUNX1 germ line variants for the acquisition of somatic variants in BCOR, PHF6, and TET2 and second hits in RUNX1 are warranted.

Phrenic Relocation by Endoscopy, Intentional Pneumothorax Using Carbon Dioxide, and Single Lung Ventilation (PHRENICS) Technique.

Strategies to prevent right phrenic nerve (PN) injury during catheter ablation can be difficult to employ, ineffective, and risky. A novel PN-sparing technique involving single lung ventilation followed by "intentional pneumothorax" was prospectively evaluated in patients with multidrug refractory periphrenic atrial tachycardia (AT). This hybrid technique, termed PHRENICS (Phrenic Relocation by Endoscopy & Intentional Pneumothorax using Carbon Dioxide & Single Lung Ventilation), resulted in effective PN relocation away from the target site in all cases, allowing successful catheter ablation of AT without procedural complication or arrhythmia recurrence. The PHRENICS hybrid ablation technique can effectively mobilize the PN, avoiding unnecessary invasion of the pericardium, and can expand the safety of catheter ablation for periphrenic AT.

Atrial fibrillation ablation outcome prediction with a machine learning fusion framework incorporating cardiac computed tomography.

BACKGROUND: Structural changes in the left atrium (LA) modestly predict outcomes in patients undergoing catheter ablation for atrial fibrillation (AF). Machine learning (ML) is a promising approach to personalize AF management strategies and improve predictive risk models after catheter ablation by integrating atrial geometry from cardiac computed tomography (CT) scans and patient-specific clinical data. We hypothesized that ML approaches based on a patient's specific data can identify responders to AF ablation. METHODS: Consecutive patients undergoing AF ablation, who had preprocedural CT scans, demographics, and 1-year follow-up data, were included in the study for a retrospective analysis. The inputs of models were CT-derived morphological features from left atrial segmentation (including the shape, volume of the LA, LA appendage, and pulmonary vein ostia) along with deep features learned directly from raw CT images, and clinical data. These were merged intelligently in a framework to learn their individual importance and produce the optimal classification. RESULTS: Three hundred twenty-one patients (64.2 ± 10.6 years, 69% male, 40% paroxysmal AF) were analyzed. Post 10-fold nested cross-validation, the model trained to intelligently merge and learn appropriate weights for clinical, morphological, and imaging data (AUC 0.821) outperformed those trained solely on clinical data (AUC 0.626), morphological (AUC 0.659), or imaging data (AUC 0.764). CONCLUSION: Our ML approach provides an end-to-end automated technique to predict AF ablation outcomes using deep learning from CT images, derived structural properties of LA, augmented by incorporation of clinical data in a merged ML framework. This can help develop personalized strategies for patient selection in invasive management of AF.

Quantifying a spectrum of clinical response in atrial tachyarrhythmias using spatiotemporal synchronization of electrograms.

AIMS: There is a clinical spectrum for atrial tachyarrhythmias wherein most patients with atrial tachycardia (AT) and some with atrial fibrillation (AF) respond to ablation, while others do not. It is undefined if this clinical spectrum has pathophysiological signatures. This study aims to test the hypothesis that the size of spatial regions showing repetitive synchronized electrogram (EGM) shapes over time reveals a spectrum from AT, to AF patients who respond acutely to ablation, to AF patients without acute response. METHODS AND RESULTS: We studied n = 160 patients (35% women, 65.0 ± 10.4 years) of whom (i) n = 75 had AF terminated by ablation propensity matched to (ii) n = 75 without AF termination and (iii) n = 10 with AT. All patients had mapping by 64-pole baskets to identify areas of repetitive activity (REACT) to correlate unipolar EGMs in shape over time. Synchronized regions (REACT) were largest in AT, smaller in AF termination, and smallest in non-termination cohorts (0.63 ± 0.15, 0.37 ± 0.22, and 0.22 ± 0.18, P < 0.001). Area under the curve for predicting AF termination in hold-out cohorts was 0.72 ± 0.03. Simulations showed that lower REACT represented greater variability in clinical EGM timing and shape. Unsupervised machine learning of REACT and extensive (50) clinical variables yielded four clusters of increasing risk for AF termination (P < 0.01, χ2), which were more predictive than clinical profiles alone (P < 0.001). CONCLUSION: The area of synchronized EGMs within the atrium reveals a spectrum of clinical response in atrial tachyarrhythmias. These fundamental EGM properties, which do not reflect any predetermined mechanism or mapping technology, predict outcome and offer a platform to compare mapping tools and mechanisms between AF patient groups.

Impact of additional genetic abnormalities at diagnosis of chronic myeloid leukemia for first-line imatinib-treated patients receiving proactive treatment intervention.

The BCR::ABL1 gene fusion initiates chronic myeloid leukemia (CML); however, evidence has accumulated from studies of highly selected cohorts that variants in other cancer-related genes are associated with treatment failure. Nevertheless, the true incidence and impact of additional genetic abnormalities (AGA) at diagnosis of chronic phase (CP)-CML is unknown. We sought to determine whether AGA at diagnosis in a consecutive imatinib-treated cohort of 210 patients enrolled in the TIDEL-II trial influenced outcome despite a highly proactive treatment intervention strategy. Survival outcomes including overall survival, progression-free survival, failure-free survival, and BCR::ABL1 kinase domain mutation acquisition were evaluated. Molecular outcomes were measured at a central laboratory and included major molecular response (MMR, BCR::ABL1 ≤0.1%IS), MR4 (BCR::ABL1 ≤0.01%IS), and MR4.5 (BCR::ABL1 ≤0.0032%IS). AGA included variants in known cancer genes and novel rearrangements involving the formation of the Philadelphia chromosome. Clinical outcomes and molecular response were assessed based on the patient's genetic profile and other baseline factors. AGA were identified in 31% of patients. Potentially pathogenic variants in cancer-related genes were detected in 16% of patients at diagnosis (including gene fusions and deletions) and structural rearrangements involving the Philadelphia chromosome (Ph-associated rearrangements) were detected in 18%. Multivariable analysis demonstrated that the combined genetic abnormalities plus the EUTOS long-term survival clinical risk score were independent predictors of lower molecular response rates and higher treatment failure. Despite a highly proactive treatment intervention strategy, first-line imatinib-treated patients with AGA had poorer response rates. These data provide evidence for the incorporation of genomically-based risk assessment for CML.