Maheshwari, Arvind

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.

Background: While primary hip arthroplasty is the most common operative procedure in orthopedic surgery, a periprosthetic joint infection is its most severe complication. Early detection and prediction are crucial. In this study, we aimed to determine the value of postoperative C-reactive protein (CRP) and develop a formula to predict this rare, but devastating complication. Methods: We retrospectively evaluated 708 patients with primary hip arthroplasty. CRP, white blood cell count (WBC), and several patient characteristics were assessed for 20 days following the operative procedure. Results: Eight patients suffered an early acute periprosthetic infection. The maximum CRP predicted an infection with a sensitivity and specificity of 75% and 56.9%, respectively, while a binary logistic regression reached values of 75% and 80%. A multinominal logistic regression, however, was able to predict an early infection with a sensitivity and specificity of 87.5% and 78.9%. With a one-phase decay, 71.6% of the postoperative CRP-variance could be predicted. Conclusion: To predict early acute periprosthetic joint infection after primary hip arthroplasty, a multinominal logistic regression is the most promising approach. Including five parameters, an early infection can be predicted on day 5 after the operative procedure with 87.5% sensitivity, while it can be excluded with 78.9% specificity.

Abstract Background: Accurate interpretation of laboratory values with sex-specific reference ranges presents a challenge in transgender individuals on gender affirming hormone therapy (GAHT). Creatinine (Cr), the most common marker used for kidney function, varies significantly with body mass and composition. Both Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-Epi) equations account for sex in estimating glomerular filtration rate. GAHT can effect Cr values in 2 potential ways: 1) by causing changes in muscle mass and body fat redistribution as early as 3 months after GAHT initiation and 2) by direct effects of sex hormones on kidney function. Previous studies have shown Cr values approaching affirmed gender identity as early as 6 months when on GAHT without mention of sex steroid levels. In this study we sought to describe the changes in serum Cr after initiation of GAHT in an effort to better understand expected changes and interpretation of lab data in TG individuals. Methods: A retrospective chart analysis on all adult TG patients initiated on GAHT at our institution from January 2011 to 2020 was completed. We reviewed demographics, baseline health information, body mass index, and lab values including Cr, sex hormone levels, A1C, and fasting blood glucose. Lab values were obtained prior to GAHT, at the start of GAHT, at 3, 6, and 12 months after GAHT. Matched pair testing was conducted with sex steroid levels and Cr values in transgender men (TM) on testosterone and transgender women (TW) on estradiol in order to compare the median pre GAHT Cr to median Cr levels at 3, 6, and 12 months. Results: 84 TW with a median age of 30 and 24 TM with a median age of 23 were included for analysis. TW and TM had a low rate of existing kidney disease (4.9%, 0%), diabetes mellitus (4.8%, 0%), and hypertension (10.8%, 4.5%) respectively. TW on GAHT achieved a goal estradiol level (≥100 pg/ml) at a rate of 37.3%, 51.7%, and 71.1% and suppressed testosterone to a goal level (&lt;60ng/ml) at a rate of 44.4%, 54.7%, and 76.5% at 3, 6, and 12 months respectively. There was no significant change in Cr values at 3 months, but significantly decreased on average by -0.07 (p&lt;0.001) at 6 months, and by -0.09 (p&lt;0.001) at 12 months. TM on GAHT achieved a goal testosterone level (≥240 ng/dl) at a rate of 64.3%, 80.0%, and 72.3% at 3, 6, and 12 months respectively. Cr values increased significantly on average by 0.14 (p=0.036) at 3 months, by 0.21 (p=0.004) at 6 months, and by 0.15 (p=0.003) at 12 months. Conclusions: In TW on GAHT, clinicians can consider using affirmed gender Cr reference ranges as early as 6 months. Similarly in TM on GAHT, affirmed gender Cr reference ranges can be used as early at 3 months. It remains to be seen whether changes in Cr levels reflect changes in sex steroid levels or sex steroid direct effects. Additionally, research is needed to determine if change in Cr levels reflect true changes in GFR.

AbstractNonsuppressed levels of testosterone are seen in up to a quarter of transgender women on gender-affirming feminizing hormonal treatment. Multiple factors contribute to this situation, including patient, medication, laboratory, and organ-specific concerns. We propose a stepwise approach to determine the etiology of nonsuppressed levels of testosterone in transgender women. This may lead to an appropriate feminizing hormonal therapy regimen and diagnosis of manageable medical conditions.