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PURPOSE Antitumor activity in preclinical models and a phase I study of patients with dedifferentiated liposarcoma (DD-LPS) was observed with selinexor. We evaluated the clinical benefit of selinexor in patients with previously treated DD-LPS whose sarcoma progressed on approved agents. METHODS SEAL was a phase II-III, multicenter, randomized, double-blind, placebo-controlled study. Patients age 12 years or older with advanced DD-LPS who had received two-five lines of therapy were randomly assigned (2:1) to selinexor (60 mg) or placebo twice weekly in 6-week cycles (crossover permitted). The primary end point was progression-free survival (PFS). Patients who received at least one dose of study treatment were included for safety analysis (ClinicalTrials.gov identifier: NCT02606461 ). RESULTS Two hundred eighty-five patients were enrolled (selinexor, n = 188; placebo, n = 97). PFS was significantly longer with selinexor versus placebo: hazard ratio (HR) 0.70 (95% CI, 0.52 to 0.95; one-sided P = .011; medians 2.8 v 2.1 months), as was time to next treatment: HR 0.50 (95% CI, 0.37 to 0.66; one-sided P < .0001; medians 5.8 v 3.2 months). With crossover, no difference was observed in overall survival. The most common treatment-emergent adverse events of any grade versus grade 3 or 4 with selinexor were nausea (151 [80.7%] v 11 [5.9]), decreased appetite (113 [60.4%] v 14 [7.5%]), and fatigue (96 [51.3%] v 12 [6.4%]). Four (2.1%) and three (3.1%) patients died in the selinexor and placebo arms, respectively. Exploratory RNA sequencing analysis identified that the absence of CALB1 expression was associated with longer PFS with selinexor compared with placebo (median 6.9 v 2.2 months; HR, 0.19; P = .001). CONCLUSION Patients with advanced, refractory DD-LPS showed improved PFS and time to next treatment with selinexor compared with placebo. Supportive care and dose reductions mitigated side effects of selinexor. Prospective validation of CALB1 expression as a predictive biomarker for selinexor in DD-LPS is warranted.

Background: Artificial intelligence (AI)-based analysis of the electrocardiogram (ECG) can estimate the probability of having atrial fibrillation (AF) while in sinus rhythm. We evaluated the association between the probability of AF using AI-ECG and incident ischemic stroke (I-Stroke) and cardiovascular (CV) mortality. Methods: We evaluated a community-based cohort of consecutive patients that sought primary care in Olmsted County, MN, from the years 1998 to 2000 followed-up through March 2016. Inclusion criteria were ages 40-79, an electrocardiogram within 2 years and complete CHA2DS2-VASc data. We excluded those with known atherosclerotic CV disease or AF. The AI network was developed in 180,000 patients (649,931 ECG). The primary endpoint was incident I-Stroke; secondary endpoint was I-Stroke or CV mortality; validated in duplicate. Multivariate cox models adjusted for age, sex, history of hypertension, diabetes, heart failure, and in CHA2DS2-VASc Score subgroups (categorized as low=0, moderate=1, and high risk ≥2). Results: We included 14,601 patients, mean age 53.2±13.08 (51% women, 95% whites), CHA2DS2-VASc score 1.3±1.2, median follow-up of 16.3 years, IQR 14.5-17.5, 727 (4.9%) had an I-Stroke and 1309 (8.9%) had I-Stroke or died from CV causes. When comparing the 5 th vs the 1 st quintile, those with high AI-ECG AF probability had increased risk for I-Stroke (HR 3.25, 95%CI 2.55-4.14, P-for trend <.0001 Figure-A ) and ischemic stroke or CV mortality (HR 5.73, 95%CI 4.69-6.99, P-for trend <.0001 Figure-B ). This remained significant after adjustments. Furthermore, AI-ECG AF probability was associated with I-Stroke across CHA2DS2-VASc risk levels (HR 2.43 for high, 2.33 for moderate, 1.98 for low CHA2DS2-VASc risk, all P<0.01). Conclusion: The AI-ECG AF algorithm predicts long-term I-Stroke and CV mortality and may refine CHA2DS2-VASc. These results highlight the utility of the AI enabled ECG when estimating I-Stroke or CV mortality risk.

Abstract Stroke represents a considerable burden of disease for both men and women. However, a growing body of literature suggests clinically relevant sex differences in the underlying causes, presentations and outcomes of acute ischaemic stroke. In a recent study, we reported sex divergences in lesion topographies: specific to women, acute stroke severity was linked to lesions in the left-hemispheric posterior circulation. We here determined whether these sex-specific brain manifestations also affect long-term outcomes. We relied on 822 acute ischaemic patients [age: 64.7 (15.0) years, 39% women] originating from the multi-centre MRI-GENIE study to model unfavourable outcomes (modified Rankin Scale &gt;2) based on acute neuroimaging data in a Bayesian hierarchical framework. Lesions encompassing bilateral subcortical nuclei and left-lateralized regions in proximity to the insula explained outcomes across men and women (area under the curve = 0.81). A pattern of left-hemispheric posterior circulation brain regions, combining left hippocampus, precuneus, fusiform and lingual gyrus, occipital pole and latero-occipital cortex, showed a substantially higher relevance in explaining functional outcomes in women compared to men [mean difference of Bayesian posterior distributions (men – women) = −0.295 (90% highest posterior density interval = −0.556 to −0.068)]. Once validated in prospective studies, our findings may motivate a sex-specific approach to clinical stroke management and hold the promise of enhancing outcomes on a population level.

359881 Background: Sunitinib is approved for advanced gastrointestinal stromal tumor (GIST) after imatinib failure. Ripretinib, a broad-spectrum KIT and PDGFRA switch-control tyrosine kinase inhibitor (TKI), is indicated for the treatment of adult patients (pts) with GIST who received prior treatment with 3 or more TKIs, including imatinib. We compared the efficacy and safety of ripretinib vs sunitinib in pts with advanced GIST who progressed on or were intolerant to imatinib. Methods: This multicenter, global, randomized, open-label phase 3 study (NCT03673501) enrolled adult pts with GIST who progressed on or had intolerance to imatinib. Pts were randomized 1:1 to ripretinib 150 mg once daily (QD) or sunitinib 50 mg QD (4 wks on/2 wks off). Randomization was stratified by KIT mutational status and imatinib intolerance. The primary endpoint was progression-free survival (PFS) by independent radiologic review (IRR) using modified RECIST version 1.1. Key secondary endpoints were objective response rate (ORR) by IRR and overall survival (OS). Hierarchical testing was performed for primary and key secondary endpoints in a prespecified sequence; testing pts with a KIT exon 11 primary mutation (Ex11 intention-to-treat [ITT] population) preceded the all-patient (AP) ITT population. Data cutoff was 1 Sep 2021; final analyses of PFS and ORR and the first interim analysis of OS were conducted. Results: A total of 453 pts were randomized to ripretinib (n = 226; Ex11 ITT, n = 163) or sunitinib (n = 227; Ex11 ITT, n = 164). Median age was 60 yrs (range 18–88) and most pts were white (66.2%) males (62.0%). PFS was not statistically different between ripretinib and sunitinib in the Ex11 ITT (hazard ratio [HR] 0.88, 95% CI 0.66, 1.16; P = 0.36; median 8.3 vs 7.0 mos) or in the AP populations (HR 1.05, 95% CI 0.82, 1.33; P = 0.72; median 8.0 vs 8.3 mos). ORR was numerically higher for ripretinib vs sunitinib in the Ex11 ITT (23.9% vs 14.6%; difference 9.3%, 95% CI 0.7, 17.8; nominal P = 0.03) and AP ITT populations (21.7% vs 17.6%; difference 4.2%, 95% CI −3.2, 11.5; nominal P = 0.27). OS data was highly immature; median OS was not reached in either arm. Fewer pts in the ripretinib arm experienced Grade 3-4 (G3-4) treatment-emergent adverse events (TEAEs) vs sunitinib (41.3% vs 65.6%). Among G3-4 TEAEs with a difference ≥5% between arms, ripretinib had fewer events vs sunitinib (hypertension [8.5% vs 26.7%], palmar-plantar erythrodysesthesia [1.3% vs 10.0%], neutropenia [0% vs 6.3%], and neutrophil count decreased [0% vs 7.2%]). Conclusions: The PFS in both arms was longer than PFS achieved by sunitinib in its pivotal phase 3 trial. While the PFS for ripretinib did not meet the primary endpoint of superiority vs sunitinib, meaningful clinical activity and fewer G3-4 TEAEs were observed in pts with advanced GIST treated with ripretinib after imatinib failure. Clinical trial information: NCT03673501.

OBJECTIVE Stereotactic radiosurgery (SRS) has been increasingly employed in recent years to treat intracranial metastatic lesions. However, there is still a need for optimization of treatment paradigms to provide better local control and prevent progressive intracranial disease. In the current study, the authors utilized a national collaborative registry to investigate the outcomes of patients with intracranial metastatic disease who underwent SRS and to determine factors associated with lesion treatment response, overall progression, and mortality. METHODS The NeuroPoint Alliance SRS registry was queried for all patients with intracranial metastatic lesions undergoing single- or multifraction SRS at participating institutions between 2016 and 2020. The main outcomes of interest included lesion response (lesion-level analysis), progression using Response Assessment for Neuro-Oncology criteria, and mortality (patient-level analysis). Kaplan-Meier analysis was used to report time to progression and overall survival, and multivariable Cox proportional hazards analysis was used to investigate factors associated with lesion response, progression, and mortality. RESULTS A total of 501 patients (1447 intracranial metastatic lesions) who underwent SRS and had available follow-up were included in the current analyses. The most common primary tumor was lung cancer (49.5%, n = 248), followed by breast (15.4%, n = 77) and melanoma (12.2%, n = 61). Most patients had a single lesion (44.9%, n = 225), 29.3% (n = 147) had 2 or 3 lesions, and 25.7% (n = 129) had > 3 lesions. The mean sum of baseline measurements of the lesions according to Response Evaluation Criteria in Solid Tumors (RECIST) was 35.54 mm (SD 25.94). At follow-up, 671 lesions (46.4%) had a complete response, 631 (43.6%) had a partial response (≥ 30% decrease in longest diameter) or were stable (< 30% decrease but < 20% increase), and 145 (10%) showed progression (> 20% increase in longest diameter). On multivariable Cox proportional hazards analysis, melanoma-associated lesions (HR 0.48, 95% CI 0.34–0.67; p < 0.001) and larger lesion size (HR 0.94, 95% CI 0.93–0.96; p < 0.001) showed lower odds of lesion regression, while a higher biologically effective dose was associated with higher odds (HR 1.001, 95% CI 1.0001–1.00023; p < 0.001). A total of 237 patients (47.3%) had overall progression (local failure or intracranial progressive disease), with a median time to progression of 10.03 months after the index SRS. Factors found to be associated with increased hazards of progression included male sex (HR 1.48, 95% CI 1.108–1.99; p = 0.008), while administration of immunotherapy (before or after SRS) was found to be associated with lower hazards of overall progression (HR 0.62, 95% CI 0.460–0.85; p = 0.003). A total of 121 patients (23.95%) died during the follow-up period, with a median survival of 19.4 months from the time of initial SRS. A higher recursive partitioning analysis score (HR 21.3485, 95% CI 1.53202–3.6285; p < 0.001) was found to be associated with higher hazards of mortality, while single-fraction treatment compared with hypofractionated treatment (HR 0.082, 95% CI 0.011–0.61; p = 0.015), administration of immunotherapy (HR 0.385, 95% CI 0.233–0.64; p < 0.001), and presence of single compared with > 3 lesions (HR 0.427, 95% CI 0.187–0.98; p = 0.044) were found to be associated with lower risk of mortality. CONCLUSIONS The comparability of results between this study and those of previously published clinical trials affirms the value of multicenter databases with real-world data collected without predetermined research purpose.

INTRODUCTION: Cirrhosis is associated with cardiac dysfunction and distinct electrocardiogram (ECG) abnormalities. This study aimed to develop a proof-of-concept deep learning-based artificial intelligence (AI) model that could detect cirrhosis-related signals on ECG and generate an AI-Cirrhosis-ECG (ACE) score that would correlate with disease severity. METHODS: A review of Mayo Clinic's electronic health records identified 5,212 patients with advanced cirrhosis ≥18 years who underwent liver transplantation at the 3 Mayo Clinic transplant centers between 1988 and 2019. The patients were matched by age and sex in a 1:4 ratio to controls without liver disease and then divided into training, validation, and test sets using a 70%-10%-20% split. The primary outcome was the performance of the model in distinguishing patients with cirrhosis from controls using their ECGs. In addition, the association between the ACE score and the severity of patients' liver disease was assessed. RESULTS: The model's area under the curve in the test set was 0.908 with 84.9% sensitivity and 83.2% specificity, and this performance remained consistent after additional matching for medical comorbidities. Significant elevations in the ACE scores were seen with increasing model for end-stage liver disease-sodium score. Longitudinal trends in the ACE scores before and after liver transplantation mirrored the progression and resolution of liver disease. DISCUSSION: The ACE score, a deep learning model, can accurately discriminate ECGs from patients with and without cirrhosis. This novel relationship between AI-enabled ECG analysis and cirrhosis holds promise as the basis for future low-cost tools and applications in the care of patients with liver disease.

Abstract Background Cardiovascular disease (CVD) has been identified as a major threat to maternal health in the US and UK with cardiomyopathy being one of the most common acquired CVD in the pregnant and postpartum period. Diagnosing cardiomyopathy in pregnancy is challenging due to an overlap of cardiovascular symptoms with normal pregnancy symptoms. Purpose The purpose of this study was to evaluate the effectiveness of an ECG based deep learning model in identifying cardiomyopathy among pregnant and postpartum women. Methods We utilized an ECG based deep learning model to detect cardiomyopathy in a cohort of pregnant or postpartum women seen at multiple hospital sites. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters. Results 1,807 women were included. 7%, 10% and 13% had LVEF ≤35%, &lt;45% and &lt;50% respectively. The ECG based deep learning model identified cardiomyopathy with an AUC of 0.92 for left ventricular ejection fraction (LVEF) ≤35%, 0.89 for LVEF &lt;45% and 0.87 for LVEF &lt;50%. For LVEF ≤35%, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to white (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 and 0.72 respectively. Conclusions A deep learning model effectively identifies cardiomyopathy in pregnant or postpartum women, outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): This study was made possible using resources supported by the Mayo Clinic Women's Health Research Center and the Mayo Clinic Building Interdisciplinary Research Careers in Women's Health (BIRCWH) Program funded by the National Institutes of Health (NIH), grant number K12 HD065987. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Background: Left atrial (LA) myopathy is common in patients with heart failure and preserved ejection fraction and leads to the development of atrial fibrillation (AF). We investigated whether the likelihood of LA remodeling, LA dysfunction, altered hemodynamics, and risk for incident AF could be identified from a single 12-lead ECG using a novel artificial intelligence (AI)-enabled ECG analysis. Methods: Patients with heart failure and preserved ejection fraction (n=613) underwent AI-enabled ECG analysis, echocardiography, and cardiac catheterization. Individuals were grouped by AI-enabled ECG probability of contemporaneous AF, taken as an indicator of underlying LA myopathy. Results: Structural heart disease was more severe in patients with higher AI-probability of AF, with more left ventricular hypertrophy, larger LA volumes, and lower LA reservoir and booster strain. Cardiac filling pressures and pulmonary artery pressures were higher in patients with higher AI-probability, while cardiac output reserve was more impaired during exercise. Among patients with sinus rhythm and no prior AF, each 10% increase in AI-probability was associated with a 31% greater risk of developing new-onset AF (hazard ratio, 1.31 [95% CI, 1.20–1.42]; P <0.001). In the population as a whole, each 10% increase in AI-probability was associated with a 12% greater risk of death (hazard ratio, 1.12 [95% CI, 1.08–1.17]; P <0.001) during long-term follow-up, which was no longer significant after adjustments for baseline characteristics. Conclusions: A novel AI-enabled score derived from a single 12-lead ECG identifies the presence of underlying LA myopathy in patients with heart failure and preserved ejection fraction as evidenced by structural, functional, and hemodynamic abnormalities, as well as long-term risk for incident AF. Further research is required to determine the role of the AI-enabled ECG in the evaluation and care of patients with heart failure and preserved ejection fraction.