Rose, Carl H.

Background: Increasing maternal body mass index (BMI) represents a risk factor for Gestational Diabetes Mellitus (GDM) and adverse obstetrical and perinatal outcomes. Objective: To stratify clinical outcomes for pregnancies affected by GDM according to maternal BMI. Methods: Retrospective cohort study including individuals ≥18 years of age who were diagnosed with GDM from 2018 to 2022. Universal GDM screening was employed with a 50 g oral glucose challenge test ± a 100 g oral glucose tolerance test. Maternal demographics, preexisting medical conditions, and selected obstetric and neonatal morbidities were evaluated. Results: A total of 2193 pregnancies in 2110 women affected by GDM were identified. This included 506 (23.0%) with normal baseline maternal BMI, 596 (27.2%) with overweight, and 1091 (49.7%) with obese BMI. Adverse maternal outcomes were more frequent in the obese compared to overweight or normal BMI categories (cesarean delivery: normal 26.9% vs. overweight 28.5% vs. obese 40.9%; p < 0.001; hypertensive disorders of pregnancy: normal 8.7% vs. overweight 12.1% vs. obese 16.8%; p < 0.001). Postpartum glucose intolerance was higher in women with obesity (normal 7.3% vs. overweight 5.9% vs. obese 14.9%; p < 0.001). Infants born to mothers with obesity had higher birthweights (normal 3.3 kg vs. overweight 3.4 kg vs. obese 3.5 kg; p < 0.001), were more likely to have neonatal hypoglycemia (normal 29.4% vs. overweight 24.3% vs. obese 41.9%; p < 0.001) and require intensive care unit admission (normal 8.1% vs. overweight 5.9% vs. obese 11.9%; p < 0.001). Conclusions: Patients with GDM and baseline BMI in the obese range experienced the highest rate of adverse outcomes, while those with overweight BMI had similar outcomes to individuals who had normal BMI at baseline.

INTRODUCTION: The SPEC-AI Nigeria trial (NCT05438576) was designed to use artificial intelligence (AI) to screen for pregnancy-related cardiomyopathy in the peripartum period. Using data from this study, we evaluated the utility of AI-predicted delta age (adjusted AI-predicted age − chronological age) as a surrogate for biological age. METHODS: We included 1,187 pregnant and postpartum women enrolled between August 2022 and September 2023 with follow-up through May 2024. Standard 12-lead electrocardiograms (ECGs) were recorded at study entry to generate AI age predictions. Artificial intelligence-predicted age was adjusted using estimated reference ranges obtained from a community-dwelling cohort of 25,144 individuals with AI-ECG age estimated and documented chronological age. Logistic and Cox-proportional hazards regression were used to examine associations with comorbid cardiovascular conditions and maternal mortality, respectively. RESULTS: Adjusted AI-predicted age was significantly higher among women with any cardiovascular condition, peripartum cardiomyopathy, or who died within 18 months (7, 14, and 23 years older, respectively) compared to those without these conditions who had values similar to the normal reference ranges (adjusted AI-predicted age difference less than 1 year). Artificial intelligence-predicted delta age greater than the 75th percentile was associated with an odds ratio (OR) of 2.06 for any cardiovascular condition, OR of 4.98 for left ventricular systolic dysfunction, and a hazard ratio of 32.81 for all-cause mortality; all values of P<.001. CONCLUSIONS/IMPLICATIONS: Artificial intelligence-ECG-derived biological age appears to be a potentially useful measure of cardiovascular health status and risk among pregnant and postpartum women. However, its role in monitoring maternal health requires further exploration.

First-trimester US is imperative in evaluation of early pregnancy, since understanding of first-trimester anatomy and genetic testing allows early detection of structural and chromosomal abnormalities.

Abstract Aims Women with congenital heart disease (CHD) are at risk of pregnancy-related adverse outcomes (PRAO). The purpose of this study was to assess temporal changes in cardiac structure and function (cardiac remodelling) during pregnancy, and the association with PRAO in women with CHD. Methods and results Retrospective study of pregnant women with CHD and serial echocardiograms (2003–2021). Cardiac structure and function were assessed at pre-specified time points: prepregnancy, early pregnancy, late pregnancy, and postnatal period. PRAO was defined as the composite of maternal cardiovascular, obstetric, and neonatal complications. The study comprised 81 women with CHD (age, 29 ± 5 years). Compared to the baseline echocardiogram, there was a relative increase in right ventricular systolic pressure (RVSP) (relative change 13 ± 5%, P &lt; 0.001, in early pregnancy; and 18 ± 5%, P &lt; 0.001, in late pregnancy). There was a relative decrease in right ventricle free wall strain (RVFWS) (relative change −11 ± 3%, P &lt; 0.001, in late pregnancy; and −11 ± 4%, P = 0.003, in postnatal period), and a relative decrease in RVFWS/RVSP (relative change, −10 ± 5%, P = 0.02 in early pregnancy, −26 ± 7%, P &lt; 0.001, in late pregnancy, and −14 ± 5%, P &lt; 0.001, in postnatal period). Baseline right ventricular to pulmonary arterial (RV–PA) coupling, and temporal change in RV–PA coupling were associated with PRAO, after adjustment for maternal age and severity of cardiovascular disease. Conclusion Women with CHD had a temporal decrease in RV systolic function and RV–PA coupling, and these changes were associated with PRAO. Further studies are required to delineate the aetiology of deterioration in RV–PA coupling during pregnancy, and the long-term implications of right heart dysfunction observed in the postnatal period.

e21554 Background: Pregnancy associated melanoma (PAM) occurs in 1% of female patients with melanoma and affects 1 in 2200 pregnancies. Antepartum evaluation of melanoma must incorporate both maternal and fetal factors, and currently there is lack of standardization. Our study aimed to review the incidence of antepartum melanoma at our institution and highlight patterns in management and treatment during this high-risk scenario. Methods: After IRB approval, the Mayo Clinic electronic medical record was searched with key words “melanoma” and “pregnancy”. 25 patients with melanoma during pregnancy were identified from 1987-2023. 6 patients were excluded as skin changes noted during pregnancy were not formally diagnosed as melanoma until after delivery. Abstracted data included demographic information, disease characteristics, staging (per AJCC guidelines at time of diagnosis), and clinical outcomes. Survival data were calculated. Results: 19 patients were included. Diagnosis occurred at an average maternal age of 30 years (range 24-38 years) and a mean gestational age of 20 weeks (range 6-35 weeks). All patients had a term delivery. 4 patients underwent labor induction at 37 weeks for staging/treatment. 60% of patients had a delay in imaging or treatment attributed to pregnancy, including 1 patient who was upstaged with metastatic disease based on postpartum imaging. 82% of stage I-III patients underwent wide local excision during pregnancy; however, 35% of sentinel lymph node sampling was delayed until postpartum. 3 stage II/III patients received adjuvant therapy (interferon or sargramostim) after delivery. Adjuvant immunotherapy was discussed with 2 patients in the postpartum period but ultimately not given due to the time lapse between surgery and delivery. 1 patient with stage IV disease diagnosed at a gestational age of 20 weeks was evaluated with MRI PET scan then treated with vemurafenib monotherapy during pregnancy. Workup and outcomes were reviewed by stage (Table 1). 7 of 19 patients had subsequent pregnancies, and 1 had recurrence of melanoma during a second pregnancy (stage II followed by stage IV). Due to a low number of censoring events, median progression free survival and overall survival were not reached. Conclusions: Melanoma diagnosed during pregnancy can be safely and effectively staged with sentinel lymph node surgery, MRI imaging (PET-MRI vs standard), and CT imaging with shielding without a need for early induction of labor nor a delay in cancer care. Furthermore, we report a patient with stage IV disease treated with vemurafenib during and immunotherapy after pregnancy, indicating that treating stage IV PAM with systemic therapy is a consideration. [Table: see text]

Background: Guidelines recommend placement of inferior vena cava (IVC) filters in patients with acute thrombotic episodes and a concurrent contraindication to anticoagulation, however filter placement during or immediately postpartum presents significant technical challenges. The use and complications of IVC filters in the peripartum period is poorly documented in current literature. Objective(s): To evaluate the use of and outcomes related to IVC filters in an obstetric population at a large tertiary care center. Study Design: Retrospective review of all patients at a tertiary academic medical center from 2001-2016 who either conceived with an IVC filter in place or underwent IVC filter placement during the antepartum or postpartum interval. Manual abstraction of each chart was performed, with data stored in a RedCap database. Results: A total of 24 patients were included in the final analysis: 5 with IVC filter placement prior to pregnancy, 7 with antepartum placement, and 12 with postpartum placement. One patient had multiple pregnancies during our study timeframe and was therefore included twice. The cohort of postpartum placement were the youngest mean age (23.7 years), had a higher number of IVC filters placed for polytrauma (n=4), and had the shortest time to removal (135.9 days). The vast majority (91.3%) of IVC filters placed during pregnancy were retrievable filters in the suprarenal position, while most deployed in an infrarenal position (83.3%) when placed prior to conception. Complications encountered included multiple attempts to remove IVC filter, severe angulation, embedded struts, fracture, and one case of cardiac tamponade during filter placement requiring emergent sternotomy and evacuation. Of the filters requiring multiple attempts for removal, in 1 patient (4%) it was unable to be removed despite multiple attempts and the filter was ultimately left in situ. Complications were more common when placement was performed prior to pregnancy or antepartum (40% and 57.1% respectively) versus postpartum (16.7%). From an obstetrical perspective, 1 patient (4%) required early preterm delivery due to preeclampsia; 1 maternal death occurred in a postpartum patient who experienced a massive pulmonary embolism and subsequently died despite emergent thrombectomy and IVC filter placement, and 1 neonatal death occurred due to congenital heart disease following periviable preterm delivery. Conclusion(s): In our study across 16 years at a large academic medical center we identified 23 patients who had an IVC filter placed prior to pregnancy or placed during the antepartum or postpartum periods. Data from our experience suggested a higher incidence of complications with preconceptual and antepartum placement, although the majority of these did not impact clinical outcomes. While not frequently needed, IVC filters remain an option for clinicians treating pregnant and postpartum patients but should be considered a last resource as they can lead to various complications.

Abstract Background Cardiomyopathy is a leading cause of pregnancy-related mortality and represents the number one cause of death in the late postpartum period. Purpose The goal of this study was to prospectively evaluate the effectiveness of an artificial intelligence-based electrocardiogram (AI-ECG) and a point-of-care artificial intelligence-enhanced digital stethoscope for cardiomyopathy screening among pregnant and postpartum women. Methods We conducted a single-arm prospective study of pregnant and postpartum women (up to 12 months following delivery) enrolled at three sites between October 2021 and October 2022. Study participants completed a standard 12-lead ECG, ECG + phonocardiogram recordings with a digital stethoscope, a demographic questionnaire, and a transthoracic echocardiogram (TTE) within a 24-hour period. Diagnostic performance of the AI-ECG and digital stethoscope for cardiomyopathy detection, defined as left ventricular ejection fraction (LVEF) &lt; 45%, was evaluated using the area under the curve (AUC) and standard measures of diagnostic performance. Results We included 100 consecutive pregnant and postpartum women who provided informed consent and had at least one ECG recorded, completed the questionnaire, and had an echocardiogram performed. All participants self-identified as female, and 41% were recruited from a federally qualified health center. The median age of participants was 31 years (Q1: 27, Q3: 34). Thirty-eight percent identified as non-Hispanic White, 32% as non-Hispanic Black, 21% as Hispanic or Latino, 6% as Asian, and 3% as Other (1 Native Hawaiian and 2 multiracial). Five percent (5/100) had LVEF &lt;45%. The 12-lead AI-ECG model demonstrated close to perfect discrimination (accuracy 99%; AUC 1.00, sensitivity 100%) for detection of cardiomyopathy with 1 false positive screen (LVEF 49%), Figure 1A. The digital stethoscope (angled position) had an AUC of 0.99 (95% CI: 0.97, 1.00) for detection of LVEF &lt;45%. Sensitivity, specificity, positive predictive value, and negative predictive value for the digital stethoscope were 60%, 98%, 60% and 98%, respectively for detection of LVEF &lt;45% (Figure 1B & 1C). Conclusions In this small prospective study, we found that 5% of pregnant and postpartum women had cardiomyopathy, a potentially life-threatening condition, and that a rapid, inexpensive, ubiquitous, massively scalable point-of-care test - the ECG - showed promising results, identifying it with an AUC &gt; 0.98 with the application of artificial intelligence.Figure 1

Abstract Background Pregnancy related cardiomyopathy is a significant cause of maternal morbidity and mortality globally. A presumed overlap between normal pregnancy-associated symptoms and clinical symptoms of cardiomyopathy contributes to delays in diagnosis and increased risk of maternal mortality. Purpose We sought to evaluate the association between patient-reported cardiovascular symptoms and the presence of cardiomyopathy among pregnant and postpartum patients. We hypothesize that individual cardiovascular symptoms are unrelated to the presence of cardiomyopathy. We also evaluated the use of a novel adaptation of a validated health status questionnaire in relation to cardiomyopathy. Methods We enrolled 48 pregnant (&gt;13 weeks) and postpartum (up to 12 months) participants in a prospective study between October 2021 and February 2022. All study participants completed a baseline questionnaire, which included current cardiovascular symptoms, an assessment of health status using an adapted version of the Kansas City Cardiomyopathy Questionnaire (KCCQ-12), followed by a resting transthoracic echocardiogram on the same day. We defined cardiomyopathy as a left ventricular ejection fraction (LVEF) &lt;50% based on 2-D echocardiography. Fisher's exact and Wilcoxon rank-sum tests were employed to evaluate the association between reported cardiovascular symptoms, the adapted KCCQ-12 (KCC-A) score, and cardiomyopathy. Results At the time of enrollment, 67% were pregnant and 33% postpartum. Forty-eight percent identified as White, 31% as Black, 10% as Asian, and 10% as other race. The median age was 31 years (Q1: 28, Q3: 35) and 6% had an LVEF &lt;50%. We found no statistically significant association between four reported cardiovascular symptoms (shortness of breath, orthopnea, fast breathing, and episodes of “asthma” that did not improve with inhalers or other treatment) and cardiomyopathy or medial E/e' ratio. KCC-A scores were low in the study population overall (median 52; Q1:40, Q3: 61). We demonstrated a significantly lower KCC-A score among women with LVEF &lt;50% (median 24; Q1: 15, Q3: 44) compared to women with LVEF ≥50% (median 54; Q1: 44, Q3: 61) p=0.02. Conclusions We showed no significant association between individual cardiovascular symptoms and cardiomyopathy in an obstetric population. However, we demonstrate for the first time that an adapted KCCQ-12 questionnaire for health status assessment could potentially identify women with a high-likelihood of cardiomyopathy during the peripartum period who may benefit from additional evaluation including echocardiography. Larger studies are needed to validate this finding. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): This study was funded by a research grant from the Miami Heart Research Institute, Florida Heart Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health

Abstract Background Cardiovascular disease (CVD) has been identified as a major threat to maternal health in the US and UK with cardiomyopathy being one of the most common acquired CVD in the pregnant and postpartum period. Diagnosing cardiomyopathy in pregnancy is challenging due to an overlap of cardiovascular symptoms with normal pregnancy symptoms. Purpose The purpose of this study was to evaluate the effectiveness of an ECG based deep learning model in identifying cardiomyopathy among pregnant and postpartum women. Methods We utilized an ECG based deep learning model to detect cardiomyopathy in a cohort of pregnant or postpartum women seen at multiple hospital sites. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters. Results 1,807 women were included. 7%, 10% and 13% had LVEF ≤35%, &lt;45% and &lt;50% respectively. The ECG based deep learning model identified cardiomyopathy with an AUC of 0.92 for left ventricular ejection fraction (LVEF) ≤35%, 0.89 for LVEF &lt;45% and 0.87 for LVEF &lt;50%. For LVEF ≤35%, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to white (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 and 0.72 respectively. Conclusions A deep learning model effectively identifies cardiomyopathy in pregnant or postpartum women, outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): This study was made possible using resources supported by the Mayo Clinic Women's Health Research Center and the Mayo Clinic Building Interdisciplinary Research Careers in Women's Health (BIRCWH) Program funded by the National Institutes of Health (NIH), grant number K12 HD065987. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.