Sang, Philip

Abstract Aims Gender-affirming hormone therapy (GAHT) is used by some transgender individuals (TG), who comprise 1.4% of US population. However, the effects of GAHT on electrocardiogram (ECG) remain unknown. The objective is to assess the effects of GAHT on ECG changes in TG. Methods and results Twelve-lead ECGs of TG on GAHT at the Mayo Clinic were inspected using a validated artificial intelligence (AI) algorithm. The algorithm assigns a patient’s ECG male pattern probability on a scale of 0 (female) to 1 (male). In the primary analysis, done separately for transgender women (TGW) and transgender men (TGM), 12-lead ECGs were used to estimate the male pattern probability before and after GAHT. In a subanalysis, only patients with both pre- and post-GAHT EGCs were included. Further, the autopopulated PR, QRS, and QTc intervals were compared before and after GAHT. Among TGW (n = 86), the probability (mean ± SD) of an ECG male pattern was 0.84 ± 0.25 in the pre-GAHT group, and it was lowered to 0.59 ± 0.36 in the post-GAHT group (n = 173, P < 7.8 × 10−10). Conversely, among TGM, male pattern probability was 0.16 ± 0.28 (n = 47) in the pre-GAHT group, and it was higher at 0.41 ± 0.38 in the post-GAHT group (n = 53, P < 2.4×10−4). The trend persisted in the subanalysis. Furthermore, both the PR (P = 5.68 × 10−4) and QTc intervals (P = 6.65×10−6) prolonged among TGW. Among TGM, the QTc interval shortened (P = 4.8 × 10−2). Conclusion Among TG, GAHT is associated with ECG changes trending towards gender congruence, as determined by the AI algorithm and ECG intervals. Prospective studies are warranted to understand GAHT effects on cardiac structure and function.

Evaluating the Electrocardiographic Characteristics of Transgender Patients Undergoing Gender-affirming Hormone Therapy with Artificial Intelligence Introduction: An artificial intelligence (AI) electrocardiogram (ECG) algorithm has been demonstrated to identify a patient’s birth designated sex with 90.4% accuracy with an AUC = 0.97. However, the algorithm has not been evaluated in transgender and gender diverse individuals. We aimed to determine how the algorithm output varies among transgender individuals seen at the Mayo Clinic Transgender and Intersex Specialty Care Clinic (TISCC) who are undergoing gender-affirming hormonal treatment (GAHT). Methods: We applied the algorithm (which assessed the probability of “male” between 0 and 1, with < 0.5 considered as “female” and > 0.5 as “male”) to ECGs obtained from patients in the TISCC before and after the initiation of GAHT. GAHT frequently involves the use of testosterone in transgender men, and estrogen and anti-androgen agents in transgender women. Patient characteristics, including the date of the initiation of GAHT, were collected through chart review. Results: Among transgender women, the AI model probability of “male” decreased from 0.84± 0.25 (12/86 classified as males) to 0.59±0.36 (68/173 classified as male) with GAHT (p < 7.8e-10). Among transgender men, the AI model probability of “male” increased from 0.16 ± 0.28 (7/47 classified as male) to 0.41 ±0.38 (22/53 classified as male) with GAHT (p < 2.4e-4). Conclusion: After GAHT initiation, the Mayo Clinic AI ECG algorithm assigns a higher probability of being male among transgender men and a lower probability of being male among transgender women. These findings warrant further investigation into the electrocardiographic differences across genders and the effects of GAHT on the human heart.

Introduction: Stress echocardiography (SE) is a common functional imaging modality used for the detection of symptomatic coronary artery disease (CAD). Currently, the interpretation of SE relies on sex-based comparators for reporting parameters, including exercise time and functional aerobic capacity. These parameters are based on the cisgender male and female populations but have not been studied among transgender (TG) patients. Thus, we sought to characterize the SE parameters among a group of TG women. Methods: We designed a case-control study of TG women who underwent a SE at the Mayo Clinic. Each TG patient was age-matched with 2 cis-males and 2 cis-females. Demographics, medical history, and SE data were extracted. Conditional logistic regression analysis was performed to compare the TG cohort with each cisgender cohort separately. Results: Among the 43 TG females (age 57.7±9 years, 94.9% white), the prevalence of CAD was less than in cis-males (p=0.013) but similar to cis-females. For SE parameters, TG females had higher resting heart rates than cis-males (median [Q1, Q3]: 80 [71,93] vs 70 [64, 77]; p=0.002). Double product (24816 [21156, 27864] vs 26864 [22444, 30660]; p=0.016), exercise time (7.3 minutes [6,9] vs 8.5 [7.2, 9.5]; p=0.041), and metabolic equivalents (8.2 [7, 10] vs 9.5 [8.2, 10.5]; p=0.018), were all lower in TG females than cis-males. Exercise ejection fraction was higher in the transgender cohort than in cis-females (70 [70, 75] vs 70 [65, 70]; p=0.007). There was no difference in functional aerobic capacity or in the rest or stress wall motion score indices among the groups. Finally, there was no difference in the prevalence of positive electrocardiogram or SE studies. Conclusion: In conclusion, we report for the first time SE profiles for TG females which display an overall distinct phenotype compared to cis-males and cis-females. Larger prospective studies are warranted to further define these parameters for the TG population.