Citations

Background. Mortality risk assessment before kidney transplantation (KT) is imperfect. An emerging risk factor for death in nontransplant populations is physiological age as determined by the application of artificial intelligence to the electrocardiogram (ECG). The aim of this study was to examine the relationship between ECG age and KT waitlist mortality. Methods. We applied a previously developed convolutional neural network to the ECGs of KT candidates evaluated 2014 to 2019 to determine ECG age. We used a Cox proportional hazard model to examine whether ECG age was associated with waitlist mortality. Results. Of the 2183 patients evaluated, 59.1% were male, 81.4% were white, and 11.4% died during follow-up. Mean ECG age was 59.0 ± 12.0 y and mean chronological age at ECG was 53.3 ± 13.6 y. After adjusting for chronological age, comorbidities, and other characteristics associated with mortality, each increase in ECG age of >10 y than the average ECG age for patients of a similar chronological age was associated with an increase in mortality risk (hazard ratio 3.59 per 10-y increase; 95% confidence interval, 2.06-5.72; P < 0.0001). Conclusions. ECG age is a risk factor for KT waitlist mortality. Determining ECG age through artificial intelligence may help guide risk-benefit assessment when evaluating candidates for KT.

Abstract Aims An artificial intelligence-enabled electrocardiogram (AI-ECG) is a promising tool to detect patients with aortic stenosis (AS) before developing symptoms. However, functional, structural, or haemodynamic components reflected in AI-ECG responsible for its detection are unknown. Methods and results The AI-ECG model that was developed at Mayo Clinic using a convolutional neural network to identify patients with moderate–severe AS was applied. In patients used as the testing group, the correlation between the AI-ECG probability of AS and echocardiographic parameters was investigated. This study included 102 926 patients (63.0 ± 16.3 years, 52% male), and 28 464 (27.7%) were identified as AS positive by AI-ECG. Older age, atrial fibrillation, hypertension, diabetes, coronary artery disease, and heart failure were more common in the positive AI-ECG group than in the negative group (P &lt; 0.001). The AI-ECG was correlated with aortic valve area (ρ = −0.48, R2 = 0.20), peak velocity (ρ = 0.22, R2 = 0.08), and mean pressure gradient (ρ = 0.35, R2 = 0.08). The AI-ECG also correlated with left ventricular (LV) mass index (ρ = 0.36, R2 = 0.13), E/e′ (ρ = 0.36, R2 = 0.12), and left atrium volume index (ρ = 0.42, R2 = 0.12). Neither LV ejection fraction nor stroke volume index had a significant correlation with the AI-ECG. Age correlated with the AI-ECG (ρ = 0.46, R2 = 0.22) and its correlation with echocardiography parameters was similar to that of the AI-ECG. Conclusion A combination of AS severity, diastolic dysfunction, and LV hypertrophy is reflected in the AI-ECG to detect AS. There seems to be a gradation of the cardiac anatomical/functional features in the model and its identification process of AS is multifactorial.

397784 Background: Ripretinib, a switch-control tyrosine kinase inhibitor (TKI), is indicated for patients (pts) with gastrointestinal stromal tumor (GIST) who received prior treatment with ≥3 TKIs, including imatinib. Sunitinib is approved for advanced GIST after imatinib failure. Circulating tumor DNA (ctDNA) analysis may provide insight into the efficacy of these agents in second-line advanced GIST. Here, we present exploratory baseline ctDNA results from INTRIGUE. Methods: INTRIGUE is an open-label, phase 3 study that enrolled adult pts with advanced GIST who progressed on or had intolerance to imatinib (NCT03673501). Randomization was 1:1 to ripretinib 150 mg once daily (QD) or sunitinib 50 mg QD (4 wks on/2 wks off). Baseline peripheral whole blood was analyzed by Guardant360, a 74-gene ctDNA next-generation sequencing (NGS)-based assay. Only KIT mutations are reported here. Results: Of 453 pts in the overall intent-to-treat (ITT) population, 362 (80%) samples were analyzed. ctDNA was detected in 280/362 (77%), with KIT mutations detected in 213/280 (76%). Common resistance mutations were in the KIT activation loop (AL; exons 17/18; 89/213, 42%) and ATP-binding pocket (ATP-BP; exons 13/14; 81/213, 38%). Efficacy in pts with detectable ctDNA in the KIT exon 11 and overall ITT populations was consistent with the primary analysis based on tumor data used for randomization. Pts with KIT exon 11 + 17/18 (−9/13/14) mutations had superior progression-free survival (PFS), objective response rate (ORR), and overall survival (OS) with ripretinib vs sunitinib, whereas pts with exon 11 + 13/14 (−9/17/18) mutations had better PFS, ORR, and OS with sunitinib vs ripretinib (Table). Subgroup safety profiles were consistent with the primary analysis. Conclusions: While KIT ATP-BP mutations predicted clinical benefit from sunitinib vs ripretinib, pts harboring resistance mutations in the KIT AL derived meaningful clinical benefit from ripretinib but not sunitinib. This study demonstrates the value of ctDNA NGS-based sequencing of the complex landscape of KIT mutations to predict the clinical benefit of ripretinib or sunitinib as second-line therapy in pts with advanced GIST. Clinical trial information: NCT03673501 . [Table: see text]

Abstract Aims Current non-invasive screening methods for cardiac allograft rejection have shown limited discrimination and are yet to be broadly integrated into heart transplant care. Given electrocardiogram (ECG) changes have been reported with severe cardiac allograft rejection, this study aimed to develop a deep-learning model, a form of artificial intelligence, to detect allograft rejection using the 12-lead ECG (AI-ECG). Methods and results Heart transplant recipients were identified across three Mayo Clinic sites between 1998 and 2021. Twelve-lead digital ECG data and endomyocardial biopsy results were extracted from medical records. Allograft rejection was defined as moderate or severe acute cellular rejection (ACR) based on International Society for Heart and Lung Transplantation guidelines. The extracted data (7590 unique ECG-biopsy pairs, belonging to 1427 patients) was partitioned into training (80%), validation (10%), and test sets (10%) such that each patient was included in only one partition. Model performance metrics were based on the test set (n = 140 patients; 758 ECG-biopsy pairs). The AI-ECG detected ACR with an area under the receiver operating curve (AUC) of 0.84 [95% confidence interval (CI): 0.78–0.90] and 95% (19/20; 95% CI: 75–100%) sensitivity. A prospective proof-of-concept screening study (n = 56; 97 ECG-biopsy pairs) showed the AI-ECG detected ACR with AUC = 0.78 (95% CI: 0.61–0.96) and 100% (2/2; 95% CI: 16–100%) sensitivity. Conclusion An AI-ECG model is effective for detection of moderate-to-severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.

Abstract Aims Our goal was to evaluate a previously validated artificial intelligence–augmented electrocardiography (AI-ECG) screening tool for left ventricular systolic dysfunction (LVSD) in patients undergoing high-sensitivity-cardiac troponin T (hs-cTnT). Methods and results Retrospective application of AI-ECG for LVSD in emergency department (ED) patients undergoing hs-cTnT. AI-ECG scores (0–1) for probability of LVSD (left ventricular ejection fraction ≤ 35%) were obtained. An AI-ECG score ≥0.256 indicates a positive screen. The primary endpoint was a composite of post-discharge major adverse cardiovascular events (MACEs) at two years follow-up. Among 1977 patients, 248 (13%) had a positive AI-ECG. When compared with patients with a negative AI-ECG, those with a positive AI-ECG had a higher risk for MACE [48 vs. 21%, P &lt; 0.0001, adjusted hazard ratio (HR) 1.39, 95% confidence interval (CI) 1.11–1.75]. This was largely because of a higher rate of deaths (32 vs. 14%, P &lt; 0.0001; adjusted HR 1.26, 95% 0.95–1.66) and heart failure hospitalizations (26 vs. 6.1%, P &lt; 0.001; adjusted HR 1.75, 95% CI 1.25–2.45). Together, hs-cTnT and AI-ECG resulted in the following MACE rates and adjusted HRs: hs-cTnT &lt; 99th percentile and negative AI-ECG: 116/1176 (11%; reference), hs-cTnT &lt; 99th percentile and positive AI-ECG: 28/107 (26%; adjusted HR 1.54, 95% CI 1.01–2.36), hs-cTnT &gt; 99th percentile and negative AI-ECG: 233/553 (42%; adjusted HR 2.12, 95% CI 1.66, 2.70), and hs-cTnT &gt; 99th percentile and positive AI-ECG: 91/141 (65%; adjusted HR 2.83, 95% CI 2.06, 3.87). Conclusion Among ED patients evaluated with hs-cTnT, a positive AI-ECG for LVSD identifies patients at high risk for MACE. The conjoint use of hs-cTnT and AI-ECG facilitates risk stratification.

Abstract Purpose: The purpose of this pilot study was to examine the clinical efficacy and safety of serial triamcinolone injections for the treatment of desmoid tumors. Patients and Methods: Nine patients were enrolled into this prospective study and underwent three serial ultrasound-guided triamcinolone injections (120 mg) at 6-week intervals. MRI was compared at baseline and every 6 months, out to 24 months. Safety and tolerability were assessed by clinical evaluation and questionnaires, including the 12-item short form survey (SF-12), visual analog scale (VAS), and desmoid patient-reported outcome (PRO) tool. Results: At 24 months, 8 (88.9%) patients demonstrated a reduction in the volume of their tumor while 1 (11.1%) enlarged. Median tumor volume change was −26.9% (−81.1% to 34.6%; P = 0.055) All 9 tumors remained stable based on World Health Organization criteria, whereas 2 (22.2%) demonstrated partial response based on RECIST. There was a significant decrease in the tumor:muscle postcontrast mean signal intensity ratio at 6 months (P = 0.008) and 24 months (P = 0.004). There was a similar decrease in the tumor:muscle T2 mean signal intensity ratio at 24 months (P = 0.02). We found no difference in the SF-12 and VAS scores, but there were significant improvements in the desmoid PRO. Conclusions: Treatment of desmoid tumors with serial triamcinolone injections appears safe and well tolerated by patients, with a 22% partial response based on RECIST. Further research is needed to confirm our results and determine factors predictive of response.

Abstract This study aimed to investigate the influence of stroke lesions in predefined highly interconnected (rich‐club) brain regions on functional outcome post‐stroke, determine their spatial specificity and explore the effects of biological sex on their relevance. We analyzed MRI data recorded at index stroke and ~3‐months modified Rankin Scale (mRS) data from patients with acute ischemic stroke enrolled in the multisite MRI‐GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas‐defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS > 2) was modeled in a Bayesian logistic regression framework. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non‐rich club regions. In spatial specificity analyses, we randomized the split into “rich club” and “non‐rich club” regions and compared the effect of the actual rich club regions to the distribution of effects from 1000 combinations of six random regions. In sex‐specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female‐specific rich club effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club‐combination had a higher relevance than 98.4% of random constellations. Rich club regions were substantially more important in explaining long‐term outcome in women than in men. All in all, lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women.