Citations

ABSTRACT Background Patients with gastric dysmotility exhibit abnormal transit and reduced contractility, for which quantification is limited with current methodologies. The Atmo Capsule is a wireless motility capsule that measures regional gastrointestinal transit. We tested its technical capabilities to evaluate gastric contractility by detecting changes in capsule acceleration during transit. Methods Data from 91 healthy controls and 182 patients with suspected dysmotility who ingested the Atmo Capsule following a standardized protocol were included. Gastric emptying times (GET) ≥ 5 h were considered delayed. Contraction frequencies (cycles per minute [cpm]) and amplitudes (as arbitrary units [au]) were identified following Fast Fourier Transformation of accelerometer data using 5‐min windows and were assessed in 30‐min windows post‐ingestion (early gastric phase) and before gastric emptying (late gastric phase). Data were compared via ANOVA, using Bonferroni corrections, between healthy controls, patients with normal gastric emptying, and patients with delayed gastric emptying. Key Results Gastric emptying was delayed in 53/182 (29%) patients. Three cpm contraction frequencies were identified across both gastric phases in controls and patients. Contraction amplitudes did not differ between groups in the early gastric phase. In the late phase, mean amplitudes were lower in patients with delayed gastric emptying (6.4 au) compared with subjects with normal gastric emptying (controls, 9.7 au, p < 0.0001; patients, 8.6 au, p < 0.0001). Conclusions & Inference The Atmo Capsule enables precise quantification of gastric contraction profiles, permitting differentiation of normal from pathological motor patterns. Findings of this technical assessment support its potential for evaluating gastric physiology and detecting pathophysiological patterns of gastric dysmotility.

Abstract The phase 1/2 Intergroup study E4412 investigated checkpoint blockade with nivolumab (Nivo) and ipilimumab (Ipi) combined with the CD30 targeting antibody-drug conjugate brentuximab vedotin (BV) in relapsed/refractory classic Hodgkin lymphoma. A total of 147 patients aged ≥12 years were randomized between BV/Nivo and BV/Ipi/Nivo; 132 patients were included in the primary efficacy analysis. The complete response (CR) rate, was 64.7% (52.2, 75.9) for BV/Nivo and 70.3% (57.6, 81.1) for BV/Ipi/Nivo (1-sided P = .29). The median survival follow-up was 38.0 months (interquartile range, 32.6-48.1). Progression-free survival (PFS) did not significantly differ between the 2 arms (hazard ratio [HR], 0.78, confidence interval [CI], 0.39-1.57; 1-sided P = .24). Treatment-related grade 3+ toxicities in the adult cohort, excluding rash, were similar between both arms (38.5% BV/Nivo and 39.3% BV/Ipi/Nivo); there was a higher frequency of grade 3 rash with BV/Ipi/Nivo (24.6%) compared with BV/Nivo (9.2%). We compared PFS by stem cell transplantation (SCT) status in a planned before hoc comparison; 58 patients received SCT, and 36-month PFS (from SCT) was &gt;90% for both arms. Sixty-six patients were progression free after the first scan and did not undergo SCT. The 36-month PFS was 73.0% (54.5, 85.0) for BV/Ipi/Nivo compared with 45.8% (26.3, 63.4) for BV/Nivo (HR, 0.45; CI, 0.19-1.08; 1-sided P = .03). The study did not meet its primary end point of superior CR rate for the triplet, but it supports the use of checkpoint antibody-drug conjugate induction prior to auto SCT, and there is an intriguing signal of disease control for patients wishing to defer or avoid SCT with the triplet of BV/Ipi/Nivo. This trial was registered at www.clinicaltrials.gov as NCT01896999.

The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Vaginal Cancer outline the recommended diagnostic workup, staging considerations, and treatment options for this rare malignancy. Because vaginal cancer is uncommon and shares many biologic and clinical characteristics with cervical cancer, several management recommendations, particularly systemic therapy, are extrapolated from evidence and practices established for cervical cancer. This guideline excerpt summarizes key components of management, including diagnostic evaluation and workup, principles of staging, pathology considerations, radiation therapy principles, and primary treatment recommendations for both early-stage and advanced disease. It also details approaches to manage relapses, including locoregional recurrence and distant metastases, and provides an in-depth overview of systemic therapy recommendations for vaginal cancer.

ABSTRACT Objectives While maxillary sinus fungal balls (MSFB) can be associated with odontogenic conditions (MSFBO), MSFBO clinical and dental features have not been compared to odontogenic sinusitis (ODS). This multicenter study aimed to compare characteristics of MSFB, MSFBO, and ODS. Methods A multicenter international retrospective cohort study was conducted on adults with MSFBs and ODS who underwent sinus surgery. First in MSFBs, it was determined whether different dental conditions were more likely in FB versus non‐FB sides. Second, clinical features and dental pathologies were compared between MSFBO and ODS. For analyses, dental conditions were considered individually and as two groups: infectious pathologies and dental/oral procedures with indwelling metallic materials. Results After exclusions, there were 203 MSFBs and 163 ODS. Among MSFBs, 141 were MSFBOs. FB sides were associated with sinus protrusion of root canal treatment (RCT) materials ( p = 0.040) and dental implants ( p = 0.040). Compared to MSFBO, ODS patients were younger, more likely to have MS purulence (OR = 40.9, p < 0.010), more likely associated with apical periodontitis (OR = 2.59, p = 0.010) and oroantral fistulas (OR = 6.94, p = 0.020), and less likely associated with extruded RCT materials (OR = 0.01, p = 0.010) and protruded midface screws (OR < 0.01, p = 0.010). Comparing purulent MSFBO and ODS, ODS was more associated with infectious dental pathologies ( p < 0.009). Conclusion Compared to MSFBs, MSFBOs were associated with RCT extrusion and implant protrusion. Compared to MSFBOs, ODS was more likely purulent and associated with infectious dental pathologies. While ODS is often distinct from MSFBO, the two conditions can coexist, and surgeons must determine whether patients have infectious dental pathology requiring treatment with both conditions. Level of Evidence 4.

Abstract Aims Artificial intelligence electrocardiography (AI-ECG) algorithms are emerging tools for identifying individuals at risk of atrial fibrillation (AF). We evaluated the predictive performance of a validated AI-ECG algorithm for incident AF in UK Biobank participants with AF risk factors, irrespective of prevalent cardiovascular disease, and its incremental value when added to clinical predictors. Methods and results The AI-ECG tool was applied to sinus rhythm ECGs from UK-Biobank participants with risk factors for AF but no AF. Model performance was evaluated using time-dependent ROC-AUC and Harrell’s C-index. Multivariable Cox regression was used to identify clinical risk factors associated with incident AF and to quantify the contribution of AI-ECG. A total of 21 842 participants (56% male) were included. The median follow-up time was 3.7 years (IQR 0.5–5.4) The ECG-AI tool achieved a ROC-AUC of 0.73 (95% CI 0.68–0.78) at 1 and 0.69 (95% CI 0.66–0.72) at 3 years. A multivariable Cox regression model using clinical parameters achieved a ROC-AUC of 0.71 (95% CI 0.66–0.75) at 1 and 0.71 (95% CI 0.68–0.74) at 3 years. By adding ECG-AI to the clinical Cox regression model, the ROC-AUC increased to 0.75 (95%CI 0.71–0.80) at 1 and 0.74 (95% CI 0.71–0.77) at 3 years. AI-ECG showed a hazard ratio of 1.23 per decile increase (95% CI 1.18–1.27). Conclusion An AI-ECG algorithm improved the prediction of incident AF when added to a clinical parameter-based model over a median follow-up time of 3.7 years among individuals with comorbidities predisposing to AF who may benefit from targeted screening and preventive strategies.

Abstract: Autosomal dominant polycystic kidney disease (ADPKD) is a common, inherited disorder characterized by the progressive enlargement of fluid-filled cysts in the kidneys and liver. Since the cystic fibrosis transmembrane conductance regulator (CFTR) Cl– channel may mediate the secretion of Cl– and fluid into the cysts, it is conceivable that coexisting cystic fibrosis (CF) in patients with ADPKD could attenuate their development. We previously reported that two patients with ADPKD coexisting with cystic fibrosis (CF) had a milder cystic phenotype than that of kindred without CFTR mutations. A subsequent report failed to confirm this protective effect. We now have identified another family with coexisting type 1 ADPKD and CF. The kidney volumes and the number and size of renal and hepatic cysts were markedly less in a member of this family with ADPKD (PKD1 mutation C508R) and CF (homozygous ΔF508 mutation) than in her sister with ADPKD alone at comparable ages.

BACKGROUND: Bicuspid aortic valve (BAV) is a frequent congenital heart defect with a high heritability. Despite this, only a limited number of genes have been associated with the disease, and the molecular mechanisms remain unexplained in most cases. This study aimed to further understand the genetic architecture of BAV. METHODS: A genome-wide association study meta-analysis including 9631 cases among 65 677 participants was performed. Genes were prioritized using transcriptomic analyses based on RNA sequencing in relevant tissues, including human fetal and adult aortic valves. The impact of the knockdown or knockout of 4 candidate genes on cardiac development was verified in zebrafish. A polygenic risk score was developed, its association with BAV was evaluated in an independent cohort, and its association with a wide range of phenotypes (n=976) was evaluated in UK Biobank (n=355 618 individuals). RESULTS: Thirty-six genomic loci were identified, including 32 that were not described previously. Among the prioritized genes, KANK2 and ERBB4 were identified as potentially causal through transcriptomic analyses, colocalization, and Mendelian randomization based on gene expression in human aortic valves (n=484), whereas PRDM6 and STRN were prioritized using similar analyses from aortic (n=326) and left ventricular tissues (n=326), respectively. Targeting 4 candidate genes ( WNT4 , LEF1 , STRN , and KANK2 ) in zebrafish led to disruption in cardiac development. A polygenic risk score was associated with an odds ratio of 2.07 (95% CI, 1.90–2.25; P =5.43×10 -62 ) per SD for BAV and significantly associated with thoracic aortic aneurysm and atrial fibrillation in UK Biobank. CONCLUSIONS: This study supports a significant polygenic contribution to BAV, where the combination of multiple common variants in genes involved in heart morphogenesis disrupts aortic valve development.

Abstract Introduction Atrial fibrillation (AF) is a major contributor to stroke risk, yet many cases remain undiagnosed due to its silent nature. Deep learning-based electrocardiogram (DL-ECG) analysis can streamline AF risk stratification, providing a more reliable and accessible method to stratify risk using only a standard resting ECG. However, this tool has been validated only on AF detected clinically or with short ECG event monitors. Purpose This study aimed to validate the predictive accuracy of a DL-ECG model for identifying AF in normal sinus rhythm (NSR) ECGs among patients undergoing continuous cardiac monitoring with implantable loop recorders (ILRs). Methods We conducted a historical cohort study across three major U.S. academic medical centers, enrolling patients with an ILR and no prior diagnosis or documented ECG evidence of AF. Pre-implantation 10-second, 12-lead ECGs in NSR were analyzed using the DL-ECG model. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with the CHARGE-AF score. Kaplan-Meier survival analysis and Cox proportional hazards models assessed the association between DL-ECG risk categories and incident AF, defined as any episode lasting ≥6 minutes. Results We included 2,523 patients (mean age 66 ± 17 years, 52% male). AF was detected in 658 (26%) over a median follow-up of 19 months (IQR 8-37), Table 1. The DL-ECG model classified 1,634 patients (65%) as at low-risk and 889 (35%) as at high-risk. AF developed in 242 (14.8%) of the low-risk group and 416 (46.8%) of the high-risk group, yielding an AUC of 0.73 (95% CI 0.70-0.75), surpassing the CHARGE-AF AUC of 0.69 (95% CI 0.66-0.71). A combined DL-ECG and CHARGE-AF model yielded an AUC of 0.74 (95% CI 0.72-0.76), Figure 1 presents the AUC, Net Reclassification Index, and DeLong test comparisons among the three models. High-risk patients had a significantly higher incidence of AF, which remained significant after adjusting for CHARGE-AF variables, Figure 2. AF incidence increased as DL-ECG quartiles increased, with 61% of patients at the top quartile developing AF during follow-up, Figure 3. False negatives (patients initially classified as low-risk but in whom later AF was detected) had a longer median time to AF detection (127 days [IQR 28-425] vs. 101 days [IQR 20-318] for true positives, p = 0.01), and where found to have at least one positive ECG during follow-up in 36%, compared to 32% of true negatives, indicating eventual risk reclassification. Conclusions Among patients undergoing ILR monitoring, the DL-ECG model demonstrated superior predictive performance compared to CHARGE-AF, offering a promising and accessible alternative for early detection of silent AF. This approach could aid early AF detection, particularly in resource-limited settings where access to specialized rhythm monitoring services is limited.Table 1 and Figure 1 Figure 2 and 3

Abstract Background Left ventricular diastolic function (LVDF) and left bundle branch block (LBBB) are both linked with increased risk of LV systolic dysfunction, heart failure, and mortality. Assessing LVDF, which is typically done through echocardiography, becomes challenging in the presence of LBBB due to the intraventricular conduction delay that disrupts the reliability of tissue Doppler imaging. Recently, we validated an artificial intelligence-enabled electrocardiogram (AI-ECG) model to assess LVDF and filling pressures. Methods We performed a retrospective study among patients with ECG-confirmed LBBB who also underwent comprehensive echocardiography within 14 days of the index ECG between September 2001 and June 2022. Using multivariable survival models, we assessed the risk of all-cause mortality across AI-ECG-derived LVDF grades. Results Of 2,554 patients with LBBB (mean age 72.0+12.6, 54.6% women), 252 (9.8%), 656 (25.7%), and 1,436 (56.2%), and 210 (8.2%) had normal LVDF, G1 LVDF, G2 LVDF, and G3 LVDF, respectively. The median LV ejection fraction (LVEF) was 54% and 49.3% of the patients had heart failure. Worse LVDF was associated with older age, lower LVEF, and higher rates of diabetes, hypertension, chronic pulmonary disease, renal failure, cardiovascular disease, and heart failure (p-of-trend&lt;0.001 for all). During a median follow-up of 2.6 years (interquartile range 0.5-4.0 years), 847 (33.2%) patients died. In multivariable survival analysis, adjusted to multiple risk factors including baseline LVEF and heart failure, worse LVDF was independently associated with a higher risk of death [G1 LVDF: adjusted hazard ratio (aHR) 2.15 (95% confidence interval (95%CI) 0.75-1.77), G2 LVDF: aHR 2.25 (95%CI 1.50-3.38) G3 LVDF: aHR 2.96 (95%CI 1.88-4.65); p&lt;0.001 for both)]. These findings were consistent also in a subgroup of patients with baseline LVEF &gt; 50%. Conclusion Among patients with LBBB, worse LVDF, as derived by a dedicated AI-ECG algorithm is independently associated with all-cause mortality beyond baseline LVEF and clinical heart failure.survival across AI-ECG LVDF grades

Abstract Background Artificial intelligence (AI) models trained on 12-lead ECGs effectively detect left ventricular systolic dysfunction (LVSD; left ventricular ejection fraction [LVEF] &lt;=40%). Continuous ECG monitoring via Holter recordings provides an opportunity for opportunistic screening for structural heart disease beyond rhythm disorders. We hypothesized that a lead-invariant version of the 12-lead AI model would enable a Holter monitor to screen for both arrhythmias and ventricular dysfunction. Methods We retrospectively analyzed continuous Holter ECGs from 17,665 patients who underwent a Holter and transthoracic echocardiogram (TTE) within 30 days of each other at Mayo Clinic. From each Holter, a random 20-minute of valid (non-flatline/lead disconnect) ECG segment was extracted and analyzed for LVSD detection using the adapted lead-invariant AI model. To evaluate stability, we examined model performance across different time points of the day, presenting results as area under the receiver operating characteristic curve (AUC) over time. Moreover, we illustrated the model’s robustness to noisy data by comparing its performance on raw ECG signals with that on bandpass-filtered inputs. Results Among 17,665 patients (mean age 59 years, 48.57% female), 4.96% had an LVEF &lt;=40%. The AI model demonstrated strong predictive performance (20-minute segment AUC 0.90, mean prediction of 24-hour AUC 0.92). Analysis of results over time (Figure) revealed temporal patterns in predictive accuracy, with specific time periods showing greater stability. Despite modest variability, model performance remained consistently high throughout the day, confirming robustness across different physiological states. The predictions remained robust with noisy input. We did not observe performance improvement when the baseline wander and high frequency noise are removed by the bandpass filter. Conclusion Applying a 12-lead AI ECG model with a lead-invariant framework to a continuous Holter ECG enables effective screening for left ventricular dysfunction. This suggests that AI-based analysis of Holter-monitors can facilitate opportunistic screening of ventricular dysfunction and may enable assessment of an arrhythmia’s impact on LVEF, as well as the relationship between arrhythmia burden and LVEF.Figure 1.Mean prediction AUC of the day Figure 2.AUC for different time point