Blood volume phenotypes and patient sex in resistant hypertension.

The relationship of blood volume (BV) to systemic blood pressure (BP) is not well defined in resistant hypertension (RH). The goal of this study was to examine the extent to which systemic BP stratified by patient sex would impact BV phenotypes. A retrospective analysis of clinical and quantitative BV data was undertaken in a cohort of ambulatory patients with a history of controlled and uncontrolled RH. We analyzed 253 unique BVs with 54% of patients above goal BP of <150 mmHg. BV phenotypes were highly variable but no correlation of systolic BP to absolute BV or percentage deviation from normal volume was identified in either sex. Males demonstrated overall larger absolute BVs with higher prevalence of large plasma volume (PV) expansion; females were overall more hypovolemic by total BV but with a higher frequency of normal PV than males. Females trended towards more RBC mass deficit (true anemia) (49% vs. 38%. P  = 0.084) while more males demonstrated RBC mass excess (erythrocythemia) (21% vs. 11%, P  = 0.029). Importantly, a significant portion (52%) of patients with true anemia identified by BVA would go undetected by hemoglobin measurement alone. BV phenotypes are highly diverse in patients with RH. However, absolute BV or variability in BV phenotypes even when stratified by patient sex did not demonstrate an association with systemic BP. BV phenotyping provides a key to optimizing clinical management by identifying RBC mass profiles particularly distinguishing true anemia, dilutional anemia, and erythrocythemia and the contribution of PV expansion. Findings support the clinical utility of BV phenotyping in RH.

A Comparison of Amyloid Deposition in Endoscopic and Open Carpal Tunnel Release.

PURPOSE: Previous investigations assessing the incidence of amyloidosis detected with biopsy during carpal tunnel release (CTR) have focused on open CTR (OCTR). Prior authors have suggested that biopsy may be more technically challenging during endoscopic carpal tunnel release (ECTR). Our purpose was to compare differences in the incidence of amyloid deposition detected during ECTR versus OCTR. METHODS: We reviewed all primary ECTR and OCTR during which a biopsy for amyloid was obtained between February 2022 and June 2023. All procedures were performed by five upper-extremity surgeons from a single institution. Congo red staining was used to determine the presence of amyloid deposition in either the transverse carpal ligament (TCL) or tenosynovium. All positive cases underwent subtype analysis and protein identification through liquid chromatography-tandem mass spectrometry. Baseline demographics were recorded for each case, and the incidence of positive biopsy was compared between ECTR and OCTR cases. RESULTS: A total of 282 cases were included for analysis (143 ECTR and 139 OCTR). The mean age was 67 years, and 45% of cases were women. Baseline demographics were similar except for a significantly higher incidence of diabetes in OCTR cases (13% vs 33%). Overall, 13% of CTR cases had a positive biopsy. There was a statistically significant difference in the incidence of amyloid deposition detected during biopsy in ECTR cases (3.5%) compared with OCTR cases (23%). CONCLUSIONS: Biopsy performed during ECTR may result in a lower incidence of amyloid detection. Future basic science investigation may be necessary to determine histologic differences between tenosynovium proximal and distal to the leading edge of the TCL. When surgeons plan a biopsy during surgical release of the carpal tunnel, an open approach may be advantageous. TYPE OF STUDY/LEVEL OF EVIDENCE: Prognostic II.

A Unique Case of Cardiac Hypereosinophilia With Recurrent Valve Thrombosis After Mechanical Mitral Valve Replacement in the Setting of a Supratherapeutic International Normalized Ratio.

Hypereosinophilia (HES) is a rare, but highly fatal, disease that results in excess eosinophils causing multiorgan damage, mainly manifesting as extensive inflammation contributing to fibrosis. Notably, cardiac involvement occurs in almost half the cases and can often lead to thrombus development. This is a unique case of HES contributing to recurrent cardiac thrombus formation on a mechanical mitral valve in the setting of a patient who had a supratherapeutic international normalized ratio (INR) while on coumadin. The rarity of this case is also displayed in the patient's negativity for one of the fusion genes that are highly suggestive of cardiac HES, the demographics of her female gender, and her first objective sign being T-wave inversions versus the usual heart failure signs and symptoms. This case raises awareness of the disorder but also the importance of keeping its potential exacerbations on the differential, even in the setting of atypical presentations. With this, it also begs the question of whether coumadin is the proper anticoagulant of choice in these patients and whether other parameters should be considered.

A Rare Case of I127V Heterozygous Transthyretin Amyloidosis With Atypical Transthoracic Echocardiogram Findings Presenting As Upper Extremity Sensorimotor Polyneuropathy.

Hereditary transthyretin amyloidosis (hATTR) is a class of disorders with various systemic clinical manifestations, most often cardiac and neurologic in origin. The I127V mutation is a known but uncommon type of hATTR that typically affects males in their sixth or seventh decade of life. We present a case of this rare genetic variant with an atypical presentation of upper, followed by lower extremity sensorimotor polyneuropathy, with an uncharacteristic transthoracic echocardiogram (TTE) pattern but strongly positive pyrophosphate (PYP) scan, confirming the amyloidosis (AL) diagnosis.

Generalizability and quality control of deep learning-based 2D echocardiography segmentation models in a large clinical dataset.

Use of machine learning (ML) for automated annotation of heart structures from echocardiographic videos is an active research area, but understanding of comparative, generalizable performance among models is lacking. This study aimed to (1) assess the generalizability of five state-of-the-art ML-based echocardiography segmentation models within a large Geisinger clinical dataset, and (2) test the hypothesis that a quality control (QC) method based on segmentation uncertainty can further improve segmentation results. Five models were applied to 47,431 echocardiography studies that were independent from any training samples. Chamber volume and mass from model segmentations were compared to clinically-reported values. The median absolute errors (MAE) in left ventricular (LV) volumes and ejection fraction exhibited by all five models were comparable to reported inter-observer errors (IOE). MAE for left atrial volume and LV mass were similarly favorable to respective IOE for models trained for those tasks. A single model consistently exhibited the lowest MAE in all five clinically-reported measures. We leveraged the tenfold cross-validation training scheme of this best-performing model to quantify segmentation uncertainty. We observed that removing segmentations with high uncertainty from 14 to 71% studies reduced volume/mass MAE by 6-10%. The addition of convexity filters improved specificity, efficiently removing < 10% studies with large MAE (16-40%). In conclusion, five previously published echocardiography segmentation models generalized to a large, independent clinical dataset-segmenting one or multiple cardiac structures with overall accuracy comparable to manual analyses-with variable performance. Convexity-reinforced uncertainty QC efficiently improved segmentation performance and may further facilitate the translation of such models.

Genomic Screening for Pathogenic Transthyretin Variants Finds Evidence of Underdiagnosed Amyloid Cardiomyopathy From Health Records.

BACKGROUND: New treatments for transthyretin amyloidosis improve survival, but diagnosis remains challenging. Pathogenic or likely pathogenic (P/LP) variants in the transthyretin (TTR) gene are one cause of transthyretin amyloidosis, and genomic screening has been proposed to identify at-risk individuals. However, data on disease features and penetrance are lacking to inform the utility of such population-based genomic screening for TTR. OBJECTIVES: This study characterized the prevalence of P/LP variants in TTR identified through exome sequencing and the burden of associated disease from electronic health records for individuals with these variants from a large (N = 134,753), primarily European-ancestry cohort. METHODS: We compared frequencies of common disease features and cardiac imaging findings between individuals with and without P/LP TTR variants. RESULTS: We identified 157 of 134,753 (0.12%) individuals with P/LP TTR variants (43% male, median age 52 [Q1-Q3: 37-61] years). Seven P/LP variants accounted for all observations, the majority being V122I (p.V142I; 113, 0.08%). Approximately 60% (n = 91) of individuals with P/LP TTR variants (all V122I) had African ancestry. Diagnoses of amyloidosis were limited (2 of 157 patients), although related heart disease diagnoses, including cardiomyopathy and heart failure, were significantly increased in individuals with P/LP TTR variants who were aged >60 years. Fourteen percent (7 of 49) of individuals aged ≥60 or older with a P/LP TTR variant had heart disease and ventricular septal thickness >1.2 cm, only one of whom was diagnosed with amyloidosis. CONCLUSIONS: Individuals with P/LP TTR variants identified by genomic screening have increased odds of heart disease after age 60 years, although amyloidosis is likely underdiagnosed without knowledge of the genetic variant.

Deep-learning-assisted analysis of echocardiographic videos improves predictions of all-cause mortality.

Machine learning promises to assist physicians with predictions of mortality and of other future clinical events by learning complex patterns from historical data, such as longitudinal electronic health records. Here we show that a convolutional neural network trained on raw pixel data in 812,278 echocardiographic videos from 34,362 individuals provides superior predictions of one-year all-cause mortality. The model's predictions outperformed the widely used pooled cohort equations, the Seattle Heart Failure score (measured in an independent dataset of 2,404 patients with heart failure who underwent 3,384 echocardiograms), and a machine learning model involving 58 human-derived variables from echocardiograms and 100 clinical variables derived from electronic health records. We also show that cardiologists assisted by the model substantially improved the sensitivity of their predictions of one-year all-cause mortality by 13% while maintaining prediction specificity. Large unstructured datasets may enable deep learning to improve a wide range of clinical prediction models.

A Machine Learning Approach to Management of Heart Failure Populations.

BACKGROUND: Heart failure is a prevalent, costly disease for which new value-based payment models demand optimized population management strategies. OBJECTIVES: This study sought to generate a strategy for managing populations of patients with heart failure by leveraging large clinical datasets and machine learning. METHODS: Geisinger electronic health record data were used to train machine learning models to predict 1-year all-cause mortality in 26,971 patients with heart failure who underwent 276,819 clinical episodes. There were 26 clinical variables (demographics, laboratory test results, medications), 90 diagnostic codes, 41 electrocardiogram measurements and patterns, 44 echocardiographic measurements, and 8 evidence-based "care gaps": flu vaccine, blood pressure of <130/80 mm Hg, A1c of <8%, cardiac resynchronization therapy, and active medications (active angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker/angiotensin receptor-neprilysin inhibitor, aldosterone receptor antagonist, hydralazine, and evidence-based beta-blocker) were collected. Care gaps represented actionable variables for which associations with all-cause mortality were modeled from retrospective data and then used to predict the benefit of prospective interventions in 13,238 currently living patients. RESULTS: Machine learning models achieved areas under the receiver-operating characteristic curve (AUCs) of 0.74 to 0.77 in a split-by-year training/test scheme, with the nonlinear XGBoost model (AUC: 0.77) outperforming linear logistic regression (AUC: 0.74). Out of 13,238 currently living patients, 2,844 were predicted to die within a year, and closing all care gaps was predicted to save 231 of these lives. Prioritizing patients for intervention by using the predicted reduction in 1-year mortality risk outperformed all other priority rankings (e.g., random selection or Seattle Heart Failure risk score). CONCLUSIONS: Machine learning can be used to priority-rank patients most likely to benefit from interventions to optimize evidence-based therapies. This approach may prove useful for optimizing heart failure population health management teams within value-based payment models.

Pharmacologic and Endovascular Reversal of Left Ventricular Remodeling.

Pathologic left ventricular (LV) remodeling as described by adverse changes in LV mass, volume, geometry, and composition in response to mechanical and systemic neurohormonal activation portends a poor prognosis in patients with underlying LV systolic dysfunction. Conversely, reversal of LV remodeling is associated with improved morbidity and mortality. Improvement in LV function and size may result from either change in loading conditions or reversal of remodeling (RR). When complete normalization of LV function and geometry occurs (ejection fraction >50% and indexed LV end-diastolic dimension <33 mm/m(2)), true reversal of LV alteration is likely to have occurred. Sustained improvement in function and dimensions after therapy withdrawal further supports RR. In the absence of complete RR one cannot readily differentiate incomplete RR from changes in loading conditions. In this review, we evaluate the role of renin-angiotensin-aldosterone system inhibition, beta-adrenergic receptor blockade, cardiac resynchronization therapy, and endovascular mitral repair on LVRR and improvement in LV geometry and function.