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Abstract Background Artificial Intelligence (AI) algorithms enabled the detection of patients with paroxysmal atrial fibrillation (PAF) from a single normal sinus rhythm (NSR) ECG. Coronary microvascular dysfunction (CMD) is a precursor for coronary artery disease, which is a known risk factor for AF. Purpose The aim of this study is to examine the probability of PAF, according to AI-enabled algorithm estimation, in patients with CMD. Methods 1858 patients without persistent atrial fibrillation with signs and/or symptoms of ischemia and with non-obstructive CAD (<40% stenosis) who underwent invasive coronary microvascular functional assessment and the ECG closest to the functional assessment were included in this analysis. Patients with coronary flow velocity reserve (CFR) <2 in response to adenosine were labelled as endothelial-independent CMD; % increase in coronary blood flow (%ΔCBF) <50% in response to acetylcholine were labelled as endothelial-dependent CMD. Patients were categorized into 4 groups. G1: Normal (NL) CFR/NL %ΔCBF; G2: Abnormal (ABN) %ΔCBF only; G3: ABN CFR only; G4: ABL CFR & %ΔCBF. The probability of having PAF (%probAF) was calculated by a previously-trained and validated AI algorithm. AF Flag = %probAF >9%; which is a pre-set cut-off found to have the highest accuracy of identifying patients with PAF (Area Under the Curve = 0.87). Results Mean age for patients was 51.2±12.4 and 66.3% were females. 835 (45%) were in G1, 39 (2%) in G2, 911 (49%) in G3, and 73 (4%) in G4. Compared to G1 and G2, G3 and G4 were older, had more diabetes and higher smoking rates (p<0.05 for all). Furthermore, G4 had a significantly higher %probAF compared to other groups (Fig. 1). G4 were also more likely to be flagged by the algorithm as having PAF, even after adjusting for cardiovascular risk factors, with an odds ratio of 1.9 [CI 95% 1.1–3.3; p=0.03]) (Fig. 2). Conclusion Patients with combined CMD have a significantly higher probability of having PAF based on an AI-enabled algorithm. Further research is warranted to know if patients with CMD would benefit from formal AF screening at the time of diagnosis. Funding Acknowledgement Type of funding source: None

Background: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI–enabled electrocardiography (AI-ECG) as a predictor of future AF and assess its performance compared with the CHARGE-AF score (Cohorts for Aging and Research in Genomic Epidemiology–AF) in a population-based sample. Methods: We calculated the probability of AF using AI-ECG, among participants in the population-based Mayo Clinic Study of Aging who had no history of AF at the time of the baseline study visit. Cox proportional hazards models were fit to assess the independent prognostic value and interaction between AI-ECG AF model output and CHARGE-AF score. C statistics were calculated for AI-ECG AF model output, CHARGE-AF score, and combined AI-ECG and CHARGE-AF score. Results: A total of 1936 participants with median age 75.8 (interquartile range, 70.4–81.8) years and median CHARGE-AF score 14.0 (IQR, 13.2–14.7) were included in the analysis. Participants with AI-ECG AF model output of >0.5 at the baseline visit had cumulative incidence of AF 21.5% at 2 years and 52.2% at 10 years. When included in the same model, both AI-ECG AF model output (hazard ratio, 1.76 per SD after logit transformation [95% CI, 1.51–2.04]) and CHARGE-AF score (hazard ratio, 1.90 per SD [95% CI, 1.58–2.28]) independently predicted future AF without significant interaction ( P =0.54). C statistics were 0.69 (95% CI, 0.66–0.72) for AI-ECG AF model output, 0.69 (95% CI, 0.66–0.71) for CHARGE-AF, and 0.72 (95% CI, 0.69–0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the present study, both the AI-ECG AF model output and CHARGE-AF score independently predicted incident AF. The AI-ECG may offer a means to assess risk with a single test and without requiring manual or automated clinical data abstraction.

Background: Myocardial injury results in release of cardiac troponin (cTn) into the bloodstream, readily detected by high-sensitivity cTn (hs-cTn) assays. A noninvasive, rapid, home-based test to exclude acute myocardial injury at the time of the test and for the next 7 hours would help manage patients with suspected myocardial infarction. Since myocyte injury is associated with ECG changes, we hypothesized an artificial intelligence ECG (AI-ECG) could identify absence of injury. Objective: To train and test an AI-ECG convolutional neural network (CNN) to identify patients using a single ECG who are suspected of myocardial infarction who are low risk, with hs-cTn levels below the 99 th percentile at test time and for the subsequent 7 hours. Methods: A CNN tuned to identify the absence of a hs-cTnT (5 th Gen cTnT Roche Diagnostics) >15ng/L for men and >10ng/L for women was developed. All ECGs were recorded within one hour of the hs-cTnT assay. We used 73,012 ECGs and hs-cTnT pairs from 47,542 unique patients to train the network, 9031 ECGs from 5,811 patients for internal validation to optimize hyperparameters, and 11,904 ECGs with 21,191 hs-cTnT measurements up to 7 hours after the ECG, from 11,904 different patients as a holdout test set. Results: The mean age was 63.9±17.5 years, and 30,348 of the 59,446 patients (51%) were male. 5,852 patients (49.1%) had no elevation of hs-cTnT and 6,052 (50.9%) had an hs-cTnT above the 99 th percentile at baseline or within 7 hours of the test. Of the 11,904 patients in the test set, using a sensitive threshold, the 12 lead AI ECG identified 1037 patients (8.7%) likely to have a low risk for subsequent hs-cTnT increases > 99 th % (AUC 0.86), and the single lead ECG identified 685 patients. Of the 1037 low risk pts, 59 had an hs-cTnT > 99 th percentile within 7 hours. The mean maximum hs-cTnT among the 59 low-risk patients was 53 ng/L ± 92 vs 184 ng/L ± 1474 in the others. None of these low risk patients died within 14 days of the test. Conclusion: Use of the AI ECG to identify patients without cardiac injury at the time of the test and for 7 subsequent hours is feasible. This may permit rapid, point of care, home-based, bloodless, painless testing to exclude cardiac injury, facilitating care and management of patients with suspected ischemia.

Background: High-sensitivity cardiac troponin (hs-cTn) assays quantify cTn in patients at very low concentrations. Myocyte injury due to ischemia or other pathologies cause blood levels to increase, which is prognostic. A noninvasive, rapid, broadly available, home-based test to detect hs-cTn increases would facilitate risk-stratification. Since myocyte injury is associated with ECG changes, we hypothesized an artificial intelligence ECG (AI-ECG) could non-invasively predict current or impending hs-cTnT elevations. Objective: To develop an AI-ECG convolutional neural network (CNN) to detect an abnormal hs-cTnT (5 th Gen cTnT, Roche Diagnostics) concentration using a 12-lead ECG, and a single lead ECG (lead I), which would enable smartphone, home-based detection. Methods: We developed single lead and 12-lead ECG CNNs to detect a) hs-cTnT concentrations that were at or above the 6ng/L limit that can be reported b) above the 99 th percentile upper limits of >15ng/L for men and >10ng/L for women. All ECGs were recorded within one hour of the hs-cTnT measurements. We used 73,012 ECG and hs-cTnT pairs from 47,542 unique patients to train the network, 9031 ECGs from 5,811 patients for internal validation to optimize hyperparameters, and 18,276 ECG and hs-cTnT pairs from 11,904 different patients as a holdout test set to determine the area under the receiver-operator curve (AUC). Results: The mean age was 63.9±17.5 years, and 30,348 of the 59,446 patients (51%) were male. Of the 91,288 hs-cTnT pairs 73,271 (80.2%) were above 6ng/L and 50,799 (55.6%) are above the 99 th percentile. In the test set, the AUC for the detection of a hs-cTnT level higher than 6ng/L was 0.88 using the 12 lead ECG and 0.834 with the single lead. For the detection of hs-cTnT level above of 99 th percentile, the 12 lead ECG AUC was 0.853 and the single lead was 0.806. Conclusion: The AI-ECG permits detection of levels of hs-cTnT consistent with myocardial injury. This may allow a home-based, non-invasive test that would be massively scalable and could further enhance rapid-risk stratification and patient triage with potentially significant cost reductions and enable novel triage strategies at sites without hs-cTn assays.

Background: Clinical factors including previous history of AF, heart failure, hypertension, valvular heart disease, increased age and male gender increase the risk of AF in CLL patients (Shanafelt, Leukemia and Lymphoma 2017). Treatment with Bruton tyrosine kinase inhibitors (BTKi) such as ibrutinib has also been associated with an increased risk of AF in CLL. We evaluated the role of artificial intelligence electrocardiography (AI-ECG) in predicting ibrutinib-induced AF (and, for reference, AF unrelated to ibrutinib) in patients with CLL. Methods: We identified two cohorts of CLL patients using the Mayo Clinic CLL Database. Cohort 1 included patients evaluated within 12 months of CLL diagnosis who did not ever receive ibrutinib. Cohort 2 included patients who were treated with ibrutinib. The electrocardiographic signature of AF in sinus rhythm was detected by an AI-ECG algorithm previously developed using a convolutional neural network (Attia, Lancet 2019). The baseline AI-ECG AF score (positive defined as >0.10 on a scale of 0-1 which offers best balance between sensitivity and specificity per Attia et al.) was computed based on ECGs obtained within 10 years prior to CLL diagnosis (Cohort 1) or 10 years prior to initiation of ibrutinib therapy (Cohort 2). Patients with AF at baseline, missing data, or with ECGs previously used to train the AI algorithm were excluded. Reverse Kaplan Meier diagrams were plotted for both cohorts grouped by AI-ECG positivity. Cox proportional hazards were fitted to assess the predictive ability of AI-ECG in both cohorts. Results: After screening 2,739 patients and applying exclusion criteria (126 patients had baseline AF) a total of 1,149 patients with median 4 (interquartile range [IQR] 2-9) baseline ECGs were included in the analysis (Figure 1A). Cohort 1 included 951 patients with a median follow up of 3.0 (IQR 0.6-7.0) years and positive baseline AI-ECG in 546 (57%) patients. Cohort 2 included 198 patients with a median follow up of 1.6 (IQR 0.7-3.2) years and positive baseline AI-ECG in 91 (46%) patients. In Cohort 1, the median age was 67 years (IQR 58-72), 681 (72%) of patients were men, 68% had low/intermediate risk CLL-International Prognostic Index (IPI), and 32% had high/very high-risk CLL-IPI. In Cohort 2, the median age was 69 years (IQR 62-75), 139 (70%) of patients were men, 13% had low/intermediate risk CLL-IPI, and 87% had high/very high-risk CLL-IPI. AF occurred during follow up in 164 patients (17%) in Cohort 1 and 46 patients (23%) in Cohort 2. In both Cohorts 1 and 2, a positive baseline AI-ECG significantly increased the incidence of AF during follow up (log rank <0.001) (Figure 1B and C). Hazard ratios (for positive vs. negative AI-ECG) were 33.9 (95% confidence interval [CI] 15.0-76.6) for Cohort 1 and 14.8 (95% CI 5.3-41.3) for Cohort 2. Conclusion: The addition of AI to a standard 12-lead ECG obtained during normal sinus rhythm - an inexpensive and ubiquitous test - predicts the occurrence of future AF in patients with CLL. This holds true irrespective of BTKi -based therapy and has important implications for the management of CLL patients. Disclosures Ding: Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; DTRM: Research Funding; Astra Zeneca: Research Funding; Abbvie: Research Funding; Octapharma: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; alexion: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees. Kenderian:BMS: Research Funding; Gilead: Research Funding; Novartis: Patents & Royalties, Research Funding; Mettaforge: Patents & Royalties; Juno: Research Funding; MorphoSys: Research Funding; Lentigen: Research Funding; Sunesis: Research Funding; Tolero: Research Funding; Kite: Research Funding; Humanigen: Consultancy, Patents & Royalties, Research Funding; Torque: Consultancy. Wang:Novartis: Research Funding; Innocare: Research Funding; Incyte: Research Funding. Kay:Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; Abbvie: Research Funding; MEI Pharma: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding. Kapoor:Cellectar: Consultancy; Janssen: Research Funding; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Celgene: Honoraria; GlaxoSmithKline: Research Funding. Parikh:MorphoSys: Research Funding; Janssen: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; Merck: Research Funding; AbbVie: Honoraria, Research Funding; Ascentage Pharma: Research Funding; Genentech: Honoraria; Verastem Oncology: Honoraria; GlaxoSmithKline: Honoraria; TG Therapeutics: Research Funding; Pharmacyclics: Honoraria, Research Funding.

Abstract Background: Accumulating evidence suggests a relationship between endometrial cancer and ovarian cancer. Independent genome-wide association studies (GWAS) for endometrial cancer and ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. We aimed to identify joint endometrial and ovarian cancer risk loci by performing a meta-analysis of GWAS summary statistics from these two cancers. Methods: Using LDScore regression, we explored the genetic correlation between endometrial cancer and ovarian cancer. To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e., inverse-variance meta-analysis, colocalization, and M-values) and performed analyses stratified by subtype. Candidate target genes were then prioritized using functional genomic data. Results: Genetic correlation analysis revealed significant genetic correlation between the two cancers (rG = 0.43, P = 2.66 × 10−5). We found seven loci associated with risk for both cancers (PBonferroni < 2.4 × 10−9). In addition, four novel subgenome-wide regions at 7p22.2, 7q22.1, 9p12, and 11q13.3 were identified (P < 5 × 10−7). Promoter-associated HiChIP chromatin loops from immortalized endometrium and ovarian cell lines and expression quantitative trait loci data highlighted candidate target genes for further investigation. Conclusions: Using cross-cancer GWAS meta-analysis, we have identified several joint endometrial and ovarian cancer risk loci and candidate target genes for future functional analysis. Impact: Our research highlights the shared genetic relationship between endometrial cancer and ovarian cancer. Further studies in larger sample sets are required to confirm our findings.

The field of cardiac electrophysiology has been on the cutting edge of advanced digital technologies for many years. More recently, medical device development through traditional clinical trials has been supplemented by direct to consumer products with advancement of wearables and health care apps. The rapid growth of innovation along with the mega-data generated has created challenges and opportunities. This review summarizes the regulatory landscape, applications to clinical practice, opportunities for virtual clinical trials, the use of artificial intelligence to streamline and interpret data, and integration into the electronic medical records and medical practice. Preparation of the new generation of physicians, guidance and promotion by professional societies, and advancement of research in the interpretation and application of big data and the impact of digital technologies on health outcomes will help to advance the adoption and the future of digital health care.

Background: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD). Methods: We retrospectively applied a validated artificial intelligence-enabled ECG algorithm for the identification of LVSD (defined as LV ejection fraction ≤35%) to a cohort of patients aged ≥18 years who were evaluated in the ED at a Mayo Clinic site with dyspnea. Patients were included if they had at least one standard 12-lead ECG acquired on the date of the ED visit and an echocardiogram performed within 30 days of presentation. Patients with prior LVSD were excluded. We assessed the model performance using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. Results: A total of 1606 patients were included. Median time from ECG to echocardiogram was 1 day (Q1: 1, Q3: 2). The artificial intelligence-enabled ECG algorithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86–0.91) and accuracy of 85.9%. Sensitivity, specificity, negative predictive value, and positive predictive value were 74%, 87%, 97%, and 40%, respectively. To identify an ejection fraction <50%, the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.85 (95% CI, 0.83–0.88), 86%, 63%, and 91%, respectively. NT-proBNP (N-terminal pro-B-type natriuretic peptide) alone at a cutoff of >800 identified LVSD with an area under the receiver operating characteristic curve of 0.80 (95% CI, 0.76–0.84). Conclusions: The ECG is an inexpensive, ubiquitous, painless test which can be quickly obtained in the ED. It effectively identifies LVSD in selected patients presenting to the ED with dyspnea when analyzed with artificial intelligence and outperforms NT-proBNP. Graphic Abstract: A graphic abstract is available for this article.

Abstract Trial Information Click here to access other published clinical trials. Lessons Learned The results from the liposarcoma cohort of SARC024 confirm previously published data and do not support the routine use of regorafenib in this patient population. Continued exploration of novel therapies, including combination approaches, is warranted for a patient population in whom limited treatment options exist. Background Regorafenib is a multitargeted kinase inhibitor with a kinase profile overlapping, but distinct from, pazopanib, an agent approved for recurrent and metastatic non-gastrointestinal stromal tumor (GIST), non-adipocytic soft tissue sarcoma. We conducted a randomized, phase II study of regorafenib versus placebo in refractory liposarcoma patients. Methods Patients with advanced or metastatic, treatment-refractory liposarcoma were randomized 1:1 to receive regorafenib 160 mg or placebo once daily (3 weeks on, 1 week off). Patients with well-differentiated liposarcoma only were excluded. Crossover for placebo was allowed upon progression. The primary endpoint was progression-free survival (PFS), according to RECIST version 1.1. Results Forty-eight subjects with liposarcoma (34 dedifferentiated, 12 myxoid/round cell, 2 pleomorphic) were enrolled. Median PFS was 1.87 (95% confidence interval [CI], 0.92–3.67) months for regorafenib versus 2.07 (95% CI, 1.64–3.44) months for placebo; stratified hazard ratio [HR], 0.85 (95% CI, 0.46, 1.58), p = .62. No responses were seen on regorafenib. One PR was observed on placebo. Median overall survival was 6.46 (95% CI, 4.16–23.48) months for regorafenib and 4.89 (95% CI, 3.02–9.77) months for placebo, stratified HR, 0.66 (95% CI, 0.31–1.40), p = .28). Treatment-related adverse events were similar to the known safety profile of regorafenib. Conclusion Regorafenib did not appear to improve PFS in treatment-refractory liposarcoma. No new significant safety signals were observed.

Although checkpoint inhibitors have been approved in multiple cancers, they are still under investigation in soft tissue sarcoma (STS). We conducted a retrospective review to report the safety, efficacy, and prognostic factors related to checkpoint inhibitors in STS. A sequential cohort of metastatic STS patients from four institutions treated with checkpoint inhibitors was assembled. Logistic and Cox regression models were applied to determine the effect of patient characteristics, prior treatment, and baseline factors on achieving the best overall response of complete response (CR), partial response (PR), or stable disease (SD) as determined by the treating physician. Eighty-eight patients with two median prior therapies received checkpoint inhibitors. Treatments included pembrolizumab in 47, nivolumab in 6, ipilimumab in 1, combination ipilimumab/nivolumab in 27, and other combination immunotherapies in 7 patients. Immunotherapy was discontinued in 54 patients—72.2% for progression, 16.7% for toxicity, and 11.1% for other reasons. Median progression-free survival (PFS) was 4.1 months and median overall survival was 19.1 months. One patient with undifferentiated pleomorphic sarcoma (UPS) achieved a CR, while 20 patients had a PR, including 7 UPS, 9 leiomyosarcoma (LMS), and 1 each with alveolar soft part sarcoma, fibroblastic sarcoma, sclerosing epithelioid fibrosarcoma, and myxofibrosarcoma. Forty-five percent (9 of 20) of LMS patients achieved a PR. Twenty-eight patients had SD. Our results confirm the activity and safety of anti-PD-1 therapy in metastatic STS. A notable response rate was observed in UPS and LMS subtypes. This study expands the knowledge base beyond what is currently available from clinical trials involving checkpoint inhibitors in metastatic STS.