688 Background: Guidelines recommend patients with pancreatic ductal adenocarcinoma (PDAC) undergo genetic testing for germline pathogenic variants (PV). The aim of this study was to evaluate compliance with recently updated guidelines (May 2019) and assess subsequent uptake and outcomes of genetic testing in PDAC patients. In addition, social and clinical factors associated with genetic testing were assessed. Methods: A retrospective chart review of patients diagnosed with PDAC between May 2018 and August 2020 was performed. Discussion and subsequent uptake of genetic testing was reviewed and compared between a 12-month period before (pre-guideline) and a 12-month period after (post-guideline) guidelines were updated, accounting for a three-month transition period. Univariate and multivariate logistic regression analysis was used to assess factors predictive of undergoing genetic testing. Results: In total, 534 patients with PDAC were identified; 321 (60.1%) in the pre-guideline period and 213 (39.9%) in the post-guideline period. The mean age at diagnosis was 68 years and 47% were female. Genetic testing was discussed in 34% (109/321) of pre-guideline and in 39% (84/213) of post-guideline patients ( P = .23). Of those, 82% (89/109) of patients in pre-guideline and 75% (63/84) in post-guideline groups underwent subsequent genetic testing ( P = .71). In 26 (17.1%) of 152 tested patients, a PV was identified, of which 17 (11.2%; 17/152) had a PDAC-associated PV. Age, cancer stage at diagnosis, length of survival, and having a first-degree relative with pancreatic cancer were significant predictors of genetic testing on multivariate analysis. Conclusions: Adherence to recently updated guidelines is poor and germline genetic testing in PDAC patients remains insufficient. Efforts to increase awareness of benefits of testing, which include personalized therapies for patients and cascade testing in family members for subsequent enrollment in pancreatic cancer surveillance programs, could improve future uptake.[Table: see text]
Background and 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 (7,590 unique ECG-biopsy pairs, belonging to 1,427 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% 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.
Background Screening for left ventricular (LV) systolic dysfunction (defined as ejection fraction ≤35%) based on data from a standard 12-lead electrocardiogram (ECG) has become well established when standard digital ECGs are available–8 independent leads sampled at least 250 hertz for 10 seconds. As the algorithm has been incorporated into various clinical scenarios and ancillary research projects, a limitation of the binary classification at 35% has become apparent. Purpose The objective of this study was to develop and validate a deep learning-based algorithm that would classify LVEF into three categories based on only the digital ECG input. Methods After IRB approval, native digital resting ECGs acquired between 1/1/2010 and 12/31/2021 on patients seen in Mayo Clinic in Jacksonville were extracted from the institutional electronic ECG database management system (MUSE, GE Healthcare). These ECGs were matched with transthoracic echocardiograms obtained up to four days prior or 30 days after the ECGs acquisition. A convolutional neural network consisting of 8 layers of convolutions, batch normalization and pooling was trained using Keras and Tensorflow with hyper-parameter optimization for L1 and L2 regularization, learning rate adjustments, and class weights to predict three classes of LVEF: ≤35%, 36–51%, and ≥52% based on clinical relevance. The primary measure of overall performance was the detection of LVEF ≤35%; however, the triad of model predictions was also considered in translating the model output to human interpretable findings. Results A total of 30,153 patients (60,169 ECG pairings; mean age 63 years; 48% male) were randomly split at the patient level into training (24,172 patients), validation (2,973 patients) and testing (3,008 patients). The trained model provided robust discrimination in the withheld testing data – AUROC of 0.941 (95% CI: 0.931 to 0.950). Using the optimal model threshold based on Youden's index from the validation data (0.186), sensitivity and specificity were estimated to be 87.9% (95% CI: 83.8% to 91.2%) and 86.3% (95% CI: 85.4% to 87.2%) in the testing data. In instances where discordant predictions were observed, the posterior distribution of model probabilities provide additional insights into the possible underlying value of LVEF (Figure 1). Conclusions The utilization of a multi-category deep learning classification model for the detection of reduced ejection fraction adds new dimensions to the use of AI technologies on digital ECGs. This work shows high discrimination can still be obtained when using three classes of LVEF. Funding Acknowledgement Type of funding sources: None.
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