Tobah, Yvonne Butler

Abstract Patients with hypertensive disorders of pregnancy (HDPs) are recommended to attend an early postpartum follow-up visit for blood pressure monitoring. Follow-up frequency for patients with HDPs can vary widely, with some patients requiring multiple office visits. We sought to determine factors associated with multiple postpartum blood pressure visits among patients with HDPs. We retrospectively identified patients with HDPs who delivered at our maternity center in 2019 and compared factors between patients who attended a single blood pressure follow-up appointment without requiring further clinical evaluation for hypertension and patients who required multiple postpartum clinical evaluations for persistent hypertension. Univariate logistic regression models were used to identify factors associated with increased odds of having multiple clinically indicated postpartum visits for blood pressure monitoring. We identified 328 patients with HDPs, of whom 260 (79.3%) attended an initial postpartum blood pressure follow-up appointment and were included in further analyses. Of the 260 patients, 70 (26.9%) had multiple blood pressure visits. Factors associated with multiple blood pressure visits included delivery between 34 and less than 37 weeks of gestation (odds ratio [OR], 3.62; 95% CI, 1.07–12.30), systolic blood pressure before discharge of 140 mm Hg or higher (OR, 5.02; 95% CI, 2.57–9.82), and discharge with blood pressure medication (OR, 3.42; 95% CI, 1.82–6.41). Patients with HDPs who deliver preterm, have persistent hypertension before discharge, or are discharged with antihypertensive medication require continued close postpartum observation and continuity of care.

Background: Cardiomyopathy occurring during pregnancy or in the postpartum period is a leading case of maternal death in the US. The most commonly described form of pregnancy related cardiomyopathy (PRCM) associated with left ventricular systolic dysfunction is peripartum cardiomyopathy, defined as left ventricular ejection fraction (LVEF) <45% on echocardiography. PRCM symptoms often overlap with those of normal pregnancy and the lack of validated screening tools limits effective recognition, contributing to diagnostic delays. Recently, several artificial intelligence enabled electrocardiogram (AI-ECG) models have been developed and evaluated for PRCM detection. Aim&Hypothesis: We sought to investigate the pooled diagnostic performance of AI-ECG models for detecting PRCM. We hypothesized that AI-ECG models would be effective for detecting PRCM. Methods: A systematic search of PubMed, Scopus, and Cochrane was performed by combining search terms “artificial intelligence”, “machine learning”, “deep learning”, “algorithm”, “ECG”, “EKG”, “electrocardiogram”, “peripartum cardiomyopathy”, “pregnancy”, “pregnancy related cardiomyopathy” , to identify studies investigating the use of AI-ECG models for PRCM detection. Studies included those utilizing standard 12-lead ECGs as well as single lead ECGs from various devices. For inclusion in the meta-analysis, studies had to provide complete confusion matrix data for the AI-ECG model. Inverse variance random effects model meta-analysis was performed. Pooled performance estimates with corresponding 95% CIs are presented in forest plots and a summary receiver operating characteristics (sROC) curve. Results: Our search identified a total of 201 studies (PubMed: 64, Embase: 117, Cochrane: 20) from which 6 studies involving 11 AI-ECG models and 3,006 patients were included in the meta-analysis (Fig. 1) . Following random effect bivariate meta-analysis, the AI-ECG models yielded a pooled sensitivity of 0.721 (95% CI 0.676–0.762); specificity of 0.931 (95% CI 0.932–0.938); diagnostic odds ratio (DOR) of 44.658 (95% CI 23.843–83.644); and AUC of 0.896 (95% CI 0.87–0.91), for PRCM detection (Fig. 2A-C, 3) . Conclusion: AI-ECG models appear to be effective tools for detecting PRCM and may represent a viable solution to reduce diagnostic delays consequently improving maternal outcomes. Additional studies are needed to evaluate its impact on clinical outcomes in pragmatic settings.

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.

INTRODUCTION: Cardiovascular disease is a leading cause of maternal mortality. Our aim was to evaluate the feasibility and quality of artificial intelligence (AI)-guided focused cardiac ultrasound (FoCUS) by novice scanners to screen for left ventricular (LV) systolic dysfunction in pregnant individuals. METHODS: Patients were prospectively enrolled during routine obstetric appointments. FoCUS images were acquired by individuals without scanning or clinical experience using AI-guided image acquisition software (UltraSight) on a Philips Lumify device. Standard two-dimensional imaging views were attempted: parasternal long axis (PSLA), parasternal short axis mid-level (PSSA Mid), apical four chamber (A4C), three chamber (A3C), and two chamber (A2C). Two experienced cardiologists reviewed all images and graded diagnostic quality: poor, adequate, good, excellent. RESULTS: A total of 101 pregnant participants were enrolled; median age was 32 (interquartile range [IQR], 29, 35) years. The median gestational age was 23 (IQR, 16, 31) weeks; 16% first trimester, 45% second, and 39% third. The most feasible views were the PSLA and PSSA Mid, 98% and 97% with adequate or greater image quality, respectively. A3C and A2C were least feasible, acquired in 34% and 40% with adequate or greater image quality in 28% and 35%, respectively. Average scan time was 6 minutes, 46 seconds ± 0.2. The LV ejection fraction could be determined for the entire cohort and was normal (50–75%) for all. CONCLUSIONS/IMPLICATIONS: Implementation of AI-guided FoCUS in pregnant individuals is feasible, even in the absence of an experienced sonographer. This approach could allow for low-cost screening and early diagnosis of LV systolic dysfunction during pregnancy.

BACKGROUND: Senescence, a mechanism of cellular aging, which is characterized by irreversible proliferation arrest and a proinflammatory secretory phenotype, has been documented in women with preeclampsia. As cellular senescence can persist and progress, we postulated that it is associated with accelerated aging phenotype and accumulation of comorbidities in women with a history of preeclampsia. METHODS: We included a cohort of women with a history of preeclampsia (n=40) age- and parity-matched to a group of referent women with normotensive pregnancies (n=40). Women with prior major cardiovascular events, neurological, or autoimmune conditions were excluded. We collected urine and blood samples to study markers of aging, data on multimorbidity at the time of enrollment, and prospectively followed them for events over the course of 6 years, on average. RESULTS: Women with a history of preeclampsia exhibited unfavorable aging profiles compared with referent women, including decreased urinary α-Klotho ( P =0.018); increased leptin ( P =0.016) and leptin/adiponectin ratio ( P =0.027), and increased extracellular vesicles positive for tissue factor ( P =0.025). Women with a history of preeclampsia likewise had a higher rate of comorbidities at the time of enrollment ( P =0.003) and had a 4× higher risk of developing major cardiovascular events compared with referent women ( P =0.003). CONCLUSIONS: Our data suggest that a history of preeclampsia is associated with accelerated aging as indicated by senescence marker differences and the accumulation of multimorbidity later in life. Targeting cellular senescence may offer novel, mechanism-based approaches for the diagnosis and treatment of adverse health outcomes in women with a history of preeclampsia.

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 BackgroundTraditional prenatal care includes up to 13 in person office visits, and the cost of this care is not well-described. Alternative models are being explored to better meet the needs of patients and providers. OB Nest is a telemedicine-enhanced program with a reduced frequency of in-person prenatal visits. The cost implications of connected care services added to prenatal care packages are unclear.MethodsUsing data from the OB Nest randomized, controlled trial we analyzed the provider and staff time associated with prenatal care in the traditional and OB Nest models. Fewer visits were required for OB Nest, but given the compensatory increase in connected care activity and supplies, the actual cost difference is not known.Nursing and provider staff time was prospectively recorded for all patients enrolled in the OB Nest clinical trial. Published 2015 national wages for healthcare workers were used to calculate the actual labor cost of providing either traditional or OB Nest prenatal care in 2015 US dollars. Overhead expenses and opportunity costs were not considered.ResultsTotal provider cost was decreased caring for the OB Nest participants, but nursing cost was increased. OB Nest care required an average of 160.8 (+/- 45.0) minutes provider time and 87.8 (+/- 25.1) minutes nursing time, compared to 215.0 (+/- 71.6) and 99.6 (+/- 29.7) minutes for traditional prenatal care (P<0.01). This translated into decreased provider cost and increased nursing cost (P<0.01). Supply costs increased, travel costs declined, and overhead costs declined in the OB Nest model. ConclusionsIn this trial, labor cost for OB Nest prenatal care was 34% higher than for traditional prenatal care. The increased cost is largely attributable to additional nursing connected care time, and in some practice settings may be offset by decreased overhead costs and increased provider billing opportunities. Future efforts will be focused on development of digital solutions for some routine nursing tasks to decrease the overall cost of the model. Trial Registrations: ClinicalTrials.gov Identifier: NCT02082275