Arruda-Olson, Adelaide A

Introduction: Predictive algorithms using multiple clinical variables can identify patients at higher risk for abdominal aortic aneurysms (AAA), however they are cumbersome and cannot used directly by patients. A simple, cheap, noninvasive, and readily available (virtual) screening tool for identification of AAA could aid in screening algorithms. Aims: We sought to determine if a machine learning algorithm could predict the presence of an AAA by using a image of the face Methods: Diagnostic imaging studies were extracted from the electronic health record (EHR) from 5/7/2018 through 1/1/2023 and analyzed by a natural language processing algorithm previously validated to identify abdominal aortic aneurysms. Patient EHR profile pictures were extracted and matched to imaging studies. Various deep neural network (DNN) architectures were explored, all trained as classifiers on the face images along with clinical variables to predict clinical outcomes. All models used were CNNs with an EfficientNet architecture. Model performance was evaluated by standard metrics such as the area under the curve (AUC), specificities (Sp), and sensitivities (Sn). All performance metrics reported results from the test subset of the primary dataset and did not contain any observations considered during training. Results: A total of 5522 patients with facial images and AAA diagnostic studies were analyzed, among whom 1314 (23.8%) had a AAA. The optimal model had an AUC of 0.72 (Sn 0.44, Sp 0.83) for predicting AAA and remained similar in women (AUC 0.7), men (AUC 0.65), age >65 (AUC 0.63), and age <=65 (AUC 0.72) (Table). Conclusions: Using only an image of a patients face, the presence of an AAA could be identified with moderate performance in men, women, and in those less than 65. The model’s performance in demographics not typically screened for AAA may allow for targeted screening programs in these groups.

Background: An electrocardiogram (ECG)-based artificial intelligence (AI) algorithm has shown good performance in detecting hypertrophic cardiomyopathy (HCM AI-score). However, its clinical application may be challenging due to the low disease prevalence and potentially high false-positive rate when screening unenriched cohorts. Aims: To identify clinical characteristics associated with false-positive HCM AI-score results to improve screening tool application. Methods: From a cohort of 20,677 patients with a digital 12-lead ECG in routine clinical practice in 2021, we performed a detailed chart review of the 200 patients with highest HCM AI-score to determine their HCM status and collect clinical data. Stepwise forward logistic regression was used to create a clinical variable-based “candidacy for HCM screening” score (CHS score), differentiating true- and likely true-positive from likely false-positive HCM AI-score results. We then validated the CHS score in 200 patients with the highest HCM AI-scores from an independent cohort of 15,147 patients with an ECG in 2022. Results: Among the 200 patients in 2021 (median age 71 years, 48% female), 96 (48%) were likely false-positive HCM AI-score results, 71 (36%) definite or likely true-positive results, and 33 (16%) indeterminate. The CHS score included a history of surgical septal myectomy, coronary artery bypass graft surgery, cardiac valve replacement, and pacemaker, and had an AUC of 0.88 (95% CI 0.83-0.92) for the differentiation of true- versus false-positive AI results. In the 2022 cohort, the CHS score had an AUC of 0.82 (95% CI 0.76 - 0.88). After limiting the cohort to HCM candidates identified by the CHS score, the false-positive results rate was reduced to 27/114 (24%). Conclusions: By applying the CHS score to patients with positive HCM AI-screening results, the false-positive rate decreased from 49% to 24%. Utilizing a clinical model used in tandem with an AI model improved diagnostic yield.

Introduction: Hypertrophic cardiomyopathy (HCM) is a disease characterized by genotypic and phenotypic heterogeneity. Correct classification of genetic variation is critical for clinical interpretation. Herein, variants of uncertain significance (VUS) pose a dilemma in clinical practice, limiting physicians’ decision making and causing anguish to patients and family members. Furthermore, with increasing knowledge and changing guidelines, continuous reassessment is crucial to maintain usefulness in practice. This study sought to assess contemporary variant classification in a large cohort of patients with HCM. Methods: Retrospective analysis of all patients with HCM seen at Mayo Clinic and all patients with a variant in one of 8 genes with strong disease association for HCM were included. Genetic test reports were obtained from electronic health records along with their original variant classification assigned by either a commercial or research laboratory. Reassessment of variant pathogenicity was performed using the 2015 ACMG guideline criteria. Population frequency was obtained using Gnomad. Co-segregation presence or absence was determined by family history and co-segregation studies. Combined Annotation Dependent Depletion (CADD) was used as computational evidence of genetic deleterious effects for single nucleotide variants. Results: Between 2003 to 2019, a total of 1,782 patients underwent laboratory or commercial HCM genetic testing. Of these, 787 (44%) had positive genetic test with a variant at least 1 of the 8 genes. The original pathogenic classification was obtained for 754 patients. Of those, 430 (57%) variants where within the gene MYBPC3, 244 (32%) within MYH7 and 80 (11%) within either ACTC1, MYL2, MYL3, TINNI3, TNNT2 or TPM1. Overall, 222 patients (29%) originally had one or more variants classified as a VUS. Upon reassessment, 120 (54%) were reclassified as either pathogenic (33%), likely pathogenic (58%), benign (5%) or likely benign (3%). Conclusion: Periodic reassessment for HCM genetic variants, especially VUS, is critical for adequate patient care and management. Cascade screening could decrease patients’ uncertainty regarding their prognosis and management.