Carter, Rickey E.

The Omicron variant of SARS-CoV-2 is associated with milder symptoms and lower hospitalization and mortality rates than Delta variants, although the impact of Omicron on immunocompromised patients, especially solid organ transplant (SOT) recipients, is still unclear. This study compares the hospitalization rate and outcomes between immunocompromised, immunocompetent, and SOT patients during the Delta and Omicron periods. We included adult patients who tested positive for SARS-CoV-2 on PCR or nasopharyngeal antigen test between 26 June 2021 to 8 September 2022, at our institution. A total of 12,401 COVID-19 patients were included, of which 11,055 were immunocompetent, and 1346 were immunocompromised (375 SOT recipients). Throughout the Delta and Omicron outbreaks, immunocompromised patients exhibited higher comorbidities and 30-day hospitalizations, but rates of mechanical ventilation and ICU-level care were like immunocompetent patients. During the Omicron wave, immunocompromised patients had higher unadjusted relative risk estimates (RR = 2.37, 95% CI 1.96–2.87, p < 0.05) than Delta (RR = 1.58, 95% CI 1.24–2.01, p < 0.05), with higher adjusted relative risk for hospitalization in Omicron (RR = 1.50, 95% CI 1.10–2.03, p = 0.01). Analyses show increased hospitalization risk in immunocompromised during the Omicron wave compared to the Delta wave, with no significant difference in hospitalization outcomes. The relative risk of hospitalization for SOT patients was higher in both waves.

Background Patients with atrial fibrillation (AF) often complain of dyspnea, raising the question that symptoms could be related to unrecognized heart failure (HF) with preserved ejection fraction (HFpEF). Methods We used the HFpEF‐age, body mass index and history of AF algorithm to estimate the probability of undiagnosed HFpEF in CABANA (Catheter Ablation vs. Antiarrhythmic Drug Therapy), and its interaction with catheter ablation on quality of life (QOL). Probable HFpEF was defined as patient‐reported dyspnea with HFpEF‐age, body mass index and history of AF probability≥75%. Absence of dyspnea or HFpEF‐age, body mass index and history of AF probability<75% was considered to reflect patients without HFpEF. The effect of randomization to catheter ablation on QOL (Mayo Atrial Fibrillation‐Specific Symptom Inventory questionnaire and EuroQol‐5 Dimension‐3 Level score) was assessed using mixed models. Results Of participants without known HF, 70% (n=1225) had probable HFpEF and the remaining 30% (n=522) did not. Those with probable HFpEF had worse New York Heart Association class, EuroQol‐5 Dimension‐3 Level score, Mayo Atrial Fibrillation‐Specific Symptom Inventory severity, and Mayo Atrial Fibrillation‐Specific Symptom Inventory frequency scores ( P <0.0001 for all), with higher risk of HF hospitalization (hazard ratio [HR], 2.19 [95% CI, 1.11–4.31], P =0.01). Ablation resulted in greater improvement in EuroQol‐5 Dimension‐3 Level and Mayo Atrial Fibrillation‐Specific Symptom Inventory severity/frequency scores in probable HFpEF (interactions P =0.005, P =0.05, and P =0.04 respectively). In probable HFpEF, catheter ablation was associated with lower risk of cardiovascular hospitalization (HR, 0.78 [95% CI, 0.66–0.92], P =0.003, interaction P =0.03) but not HF hospitalization (HR, 0.94 [95% CI, 0.55–1.64], P =0.84). Conclusions Nearly three quarters of CABANA participants had potentially undiagnosed HFpEF, with worse QOL and risk of HF hospitalization. Catheter ablation in probable HFpEF resulted in greater improvement in QOL, but residual QOL impairment and HF risk remained elevated despite ablation. These data reinforce the importance of diligent consideration of HFpEF among patients with symptomatic AF to ensure optimal use of foundational treatments for HF.

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.

Background: Pulmonary hypertension (PH) is a life-threatening disease affecting up to 1% of the global population. Diagnosis can be challenging and is often delayed due to the need for advanced imaging and invasive procedures. The use of artificial intelligence applied to ECGs (ECG-AI) has been shown to detect subtle patterns in voltage-time data and may be a valuable tool for the early detection of PH. Hypothesis and Purpose: To evaluate the performance of a previously trained, ECG-AI algorithm to detect PH (ECG-AI PH) using real-world data (RWD) collected in a multicenter, validation study. A joint primary hypothesis required sensitivity (Sn), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) to exceed the null values of 76%, 70%, 10% and 90%. Study Design and Methods: This retrospective validation study was conducted at 5 geographically diverse U.S. health systems. Adult subjects were eligible for inclusion if they had a 12-lead ECG paired with an echocardiogram (Echo) in which tricuspid regurgitation velocity (TRV) was recorded, following presentation with dyspnea. Patients were classified according to echocardiographic criteria as either PH (PH+, TRV >3.4 m/s) or controls (PH-, TRV ≤2.8 m/s) to simulate the real-world use of ECG-AI, where a positive result could lead to a follow-up Echo. The study database was locked before processing the digital ECGs with ECG-AI PH. Performance was also estimated in a subset of subjects that later had a right heart catheterization using mPAP ≥ 20mmHg as the definition of PH. Results: A total of 14281 subjects (53% female, 63% aged 50+ years) met the inclusion criteria, including 3019 PH+ cases and 11262 PH- controls (Figure). Sn and Sp were 84.0% (95% CI: 82.6%, 85.3%) and 71.7% (95% CI: 70.9%, 72.6%), respectively. The positive and negative predictive values were 18.9% (95% CI: 18.4%, 19.4%) and 98.3% (95% CI: 98.1%, 98.4%), respectively, at 7.3% prevalence. Each endpoint met pre-defined performance criteria. In the subset of 1683 subjects with a follow up RHC, performance remained robust (Sn 85% (1155/1358); Sp 46% (151/325)). Conclusion: While ECG-AI PH was first developed as an investigational tool to detect PH, continued development as software as a medical device for clinical use demonstrated that the algorithm retained strong performance to detect PH in diverse, non-overlapping clinical settings and patient populations.

Background: Critical congenital heart disease (CCHD) screening algorithms based on pulse oximetry have up to 27% false positive rate at high altitudes (>2500 m) despite altitude-specific cutoff changes. We examined the added value of an adult-based AI model applied to phonocardiography (AI-PCG) in the digital stethoscope EKO Core 500 in these settings. Research Question: What is the diagnostic performance of AI-PCG model compared to pulse oximetry in detecting neonatal CCHD across different altitudes in Latin America? Methods: This observational, prospective, case-control study included newborns born at different altitudes from 0 to 4380 meters in Peru, Mexico, Colombia and Bolivia. All underwent preductal and postductal oximetry at least 18 hours after birth, electrocardiography and PCG with the digital stethoscope. All CCHD cases were confirmed by echocardiography. Non-CCHD cases were defined by echocardiography or clinically if they were alive for 1 month and had 1) negative pulse oximetry, 2) no hospitalization due to cardiac or pulmonary causes and 3) no cyanosis or pneumonia. Those with abnormal pulse oximetry, genetic syndromes or murmur detection underwent echocardiography. Diagnostic performance metrics [area under the receiving operating characteristic curve (AUROC), sensitivity, specificity and false positive rate] were evaluated per modality. Results: A total of 1152 newborns were enrolled, 725 at <2500 m [13 (1.8%) with CCHD] and 427 at >2500 m [6 (1.4%) with CCHD]. Out of 19 CCHD cases, 17 had positive pulse oximetry and 9 had a positive AI-PCG model. One newborn with severe coarctation of the aorta and atrial septal defect had negative pulse oximetry and AI-PCG model. Pulse oximetry showed superior AUROC (0.932 vs 0.664), sensitivity (89.5% vs 47.4%), specificity (97% vs 85.5%) and lower false positive rate (3% vs 14.5%) compared to the AI-PCG model across altitudes. Diagnostic accuracy varied with altitude (Figure): at <2500 m, pulse oximetry showed higher AUROC (0.999 vs 0.612) and lower false positive rate (0.1% vs 16%) than AI-PCG model, but at >2500 m, pulse oximetry had similar AUROC (0.794 vs 0.774) and lower false positive rate (7.8% vs 11.9%) than the AI-PCG model. Conclusion: Pulse oximetry has superior diagnostic performance than an adult-based AI-PCG model when screening for neonatal CCHD at <2500 m, but similar performance is found >2500 m.

Background/Goal: Parkinson’s disease (PD) disrupts dopaminergic transmission, leading to motor deficits and altered activity in the primary motor cortex (M1). While M1 modulation is critical for motor control, its response to dopaminergic degeneration and treatment remains unclear. This study aimed to characterize M1 neuronal activity and motor behavior in hemiparkinsonian rats using in vivo calcium imaging across naïve, lesioned, and levodopa-treated states. Methods: Thirteen Sprague Dawley rats were injected with GCaMP6f in the M1 and implanted with a GRIN lens and guide cannula targeting the medial forebrain bundle. Calcium imaging and motor behavior were assessed longitudinally using a single pellet reaching test (SPRT) before and after unilateral 6-hydroxydopamine (6-OHDA) lesioning and subsequent levodopa/carbidopa treatment. Dopaminergic lesion severity was quantified via tyrosine hydroxylase immunohistochemistry. Calcium event frequency and influx were analyzed with CNMF-E and statistical modeling. Results: Levodopa treatment improved fine motor performance as shown by a significant reduction in grasp errors (mean difference: −8.91, 95% CI: −16.66 to −1.16, p = 0.031) and increased reaching duration (mean difference: 4.13, 95% CI: 0.94 to 7.32, p = 0.019) compared to the lesioned state. M1 calcium activity showed modulation dependent on lesion severity: low-lesion rats exhibited reduced event frequency (mean difference: 0.04 Hz, 95% CI: 0.001 to 0.08, p = 0.045) and increased influx post-lesion (mean difference: −0.20 z·s, 95% CI: −0.38 to −0.02, p = 0.038), while high-lesion rats showed increased influx only after levodopa treatment (mean difference: −0.34 z·s, 95% CI: −0.52 to −0.16, p = 0.003). Correlation analyses revealed that calcium influx, but not frequency, was negatively correlated with lesion severity during levodopa treatment (Spearman r = −0.857, p = 0.024). Conclusion: M1 neuronal activity appears to be differentially modulated by dopaminergic degeneration and levodopa treatment in a lesion-dependent manner. These preliminary findings suggest dynamic cortical responses in PD and support the utility of calcium imaging for monitoring circuit-level changes in disease and therapy. Further research with larger cohorts and complementary methodologies will be necessary to validate and extend these observations.

Objective: To evaluate the documentation of menopause-related International Classification of Diseases-10 (ICD-10) codes in the electronic health record (EHR) among midlife women with moderate or greater menopause symptoms receiving primary care. Methods: This cross-sectional study from the Hormones and Experiences of Aging (HERA) cohort included women aged 45-60 years receiving primary care at one of 4 Mayo Clinic sites who completed a one-time survey between March 1, 2021 and June 30, 2021. The survey captured demographic data, menopause symptoms using the Menopause Rating Scale (MRS), health care utilization, and treatment. Women with an MRS score ≥12 were included. The primary outcome was documentation of a menopause-related ICD-10 code in the EHR in the 12 months before survey completion. Results: Of 5,254 women with completed surveys, 2,414 (49%) had an MRS score ≥12 and were included. Among these, 1,519 (63%) reported seeking care for their menopause symptoms in the past 12 months, but only 345 (23%) had a menopause-related ICD-10 code. Women with an ICD-10 code had higher MRS scores (18 [IQR: 14-22] vs 17 [IQR: 14-20]; P = 0.002) and were more likely to use systemic hormone therapy (HT; 26% vs 9%; P < 0.001), and vaginal HT (20% vs 6%; P < 0.001). Conclusions: Menopause-related ICD-10 diagnosis codes were under-documented in women with significant menopause symptom burden. Those with a code were more likely to report menopause treatment. These findings highlight a critical gap between symptom burden and diagnosis coding in the EHR, underscoring the need to improve identification and management of menopause symptoms.

Abstract Insulin resistance (IR) is a modifiable risk factor for dementia, yet its effects on brain metabolism and function remain unclear. In older adults, greater IR was associated with reduced cerebral glucose uptake (indicating impaired mitochondrial metabolism), atrophy, and weakened connectivity between brain regions critical for cognition. In neuron-specific insulin receptor knockout mice, brain IR produced deficits in hippocampal- and prefrontal-dependent tasks accompanied by reduced brain mitochondrial ATP and elevated reactive oxygen species. To evaluate reversibility of IR-induced brain deficits, forty older adults with IR were randomized to 40-weeks of metformin or placebo. Metformin improved insulin sensitivity, increased brain glucose uptake, strengthened cognitive network connectivity, and preserved whole-brain and regional volumes implicated in decision-making and learning. Metformin also improved processing speed and working memory. Collectively, these findings highlight IR as a driver of brain metabolism and support the concept that insulin sensitization can prevent neurobiological deficits in older people with IR.

Abstract Objectives Determine differences in quantitative stenosis severity measurements for ultra-high-resolution (UHR) photon-counting detector (PCD) coronary CT angiography (cCTA) relative to energy-integrating detector (EID) cCTA in a large patient cohort. Methods Adult participants seen between November 2022 and March 2023 underwent a clinical dual-source EID-CT cCTA and a research dual-source PCD-CT cCTA on the same day. Percent diameter stenosis (PDS) was measured, and stenosis severity was assigned based on the PDS of the most severe lesion per patient to determine a coronary artery disease reporting and data system (CAD-RADS) score. Agreement between EID-CT and PCD-CT for PDS and CAD-RADS was determined. Results A total of 112 participants were enrolled, yielding 82 subjects with at least 1 stenosis at PCD-CT (mean age, 68.0 ± 10.8 years; 69.5% [57/82] males). A total of 177 paired stenoses were quantified. The percent decrease in mean PDS from PCD-CT (24.3%) to EID-CT (29.4%) was 17.3% (median difference in PDS: −5.0%, 95% CI: −5.7% to −4.0%). The assigned CAD-RADS score changed in 31/82 subjects. In 11/31 subjects, the most severe stenosis was missed at EID-CT due to partial volume averaging of a small calcification, yet PCD clearly identified stenosis at those locations. In 20/31 subjects, the CAD-RADS score decreased at PCD-CT due to the decreased calcium blooming resulting from the improved spatial resolution. Conclusions UHR PCD cCTA decreases quantitative measures of stenosis severity when a stenosis is identified at EID, leading to decreases in CAD-RADS assignments. Advances in knowledge With its improved spatial resolution, PCD-CT identifies stenoses missed on EID-CT.

Abstract Neurofibrillary tangles are dynamic neuropathologic hallmarks of Alzheimer’s disease with a hypothesized lifespan morphologically-defined by three maturity levels: pretangles, mature tangles, and ghost tangles. To better understand the progression of tangle pathophysiology, we characterized tangle maturity level predilection of 15 tau antibodies recognizing a broad range of linear, phosphorylation, conformational, and truncation epitopes in the hippocampus of 24 postmortem brains. We developed the tangle maturity scoring system to semi-quantitatively evaluate each tangle maturity level. Based on proportions of tangle maturity levels, we classified antibodies as “early” (mostly pretangles and mature tangles), “middling” (mature tangles with pretangles and ghost tangles), and “advanced” (mostly ghost tangles and mature tangles) tangle maturity markers. To summarize tangle maturity predilection, we developed the tangle maturity scale to integrate individual tangle maturity scores. Correlations showed stronger relationships between tangle maturity scale and subsector thickness for more advanced tangle maturity markers in CA1 and subiculum, whereas Braak tangle stage remained consistently correlated throughout markers of the tangle lifespan. To aid in scoring hippocampi, we used machine learning to recognize tangle maturity levels, which performed comparably to a domain expert and showed similar relationships by Spearman correlation. Pattern recognition software was used to assess tangle and neuritic tau burden separately, which generally correlated with Braak stage and neuronal counts. However, tangle-derived tau burden more consistently correlated with hippocampal subsector thickness. In conclusion, we developed manual and automated scoring systems to evaluate tangle maturity levels, demonstrating early 4R, phosphorylated, and oligomeric tau accumulation preceding more advanced 3R and truncated tau. Our study provides supportive evidence of disease-relevant ordering of tau posttranslational modifications in the brain, which may have implications for theragnostic development. These findings underscore the promise of computerized quantitative analyses (i.e., pathomics) for high-throughput feature extraction from whole-slide images to enhance our understanding of microscopically observed morphologic changes.