Publications

BACKGROUND: Whether the 2025 American Society of Echocardiography (ASE) diastolic dysfunction (DD) algorithm (DD25) improves mortality prognostication compared to the 2016 algorithm (DD16) in real-world practice is uncertain.

METHODS: We applied the DD25 algorithm to adult transthoracic echocardiography reports across a large academic multisite echocardiography laboratory from 2018 2024, linked to state mortality data, and determined reclassification of DD and mortality risk.

RESULTS: Of 12,948 included (age 62.8 ± 18.1, 51.4% women, 55.8% outpatient), 10,205 (78.8%) had diastology quantified by the 2016 and 2025 guidelines. Of these, 2,601 (25.5%) were reclassified by DD25 with increases in DD grade in 1,428 (54.9%) and decreases in 1,173 (45.1%). Among those reclassified, 2,391 (91.9%) had a single grade change in DD severity. A larger proportion of female patients (52% vs 48.7%) were classified as having DD by DD25 versus DD16. The rate of indeterminate DD was lower by DD25 (1,358 [10.4%]) vs DD16 (1,983 [15.3%]). The DD25 algorithm improved discrimination of mortality risk compared to DD16 (difference in areas under the curve = 0.02; 95% CI, 0.001-0.04; P = .03), although the magnitude of the associated risk across the follow-up period was similar after multivariable adjustment (P value for comparison of adjusted hazard ratios = .67).

CONCLUSIONS: In a large academic health system, one-quarter of patients had reclassification of diastolic function by the 2025 ASE diastology guidelines. As compared to the 2016 algorithm, the 2025 algorithm resulted in a larger proportion with DD, less indeterminate diastolic function, and improved discrimination of mortality.

BACKGROUND: Sepsis remains a leading cause of mortality, and optimizing treatment is challenging due to patient heterogeneity. Identification of cardiac phenotypes may inform precision medicine approaches and guide resuscitation. We performed a clustering analysis of patients with sepsis using echocardiographic data without using any a priori definitions of cardiac dysfunction or outcomes to establish the subgroups.

METHODS: This was a retrospective cohort study of patients admitted to the hospital with sepsis at a single academic center. Patients were identified using sepsis-related ICD codes, and those who had echocardiogram performed within 14 days of admission underwent chart review to ensure sepsis-3 criteria were met. Those with preexisting heart disease were excluded. Clustering by echocardiographic variables was performed using latent profile analysis. Clinical features such as patient characteristics, laboratory studies, sepsis source, and outcomes were compared across the clusters.

RESULTS: There were 2,071 patients included in the analysis. Our cluster analysis yielded five phenotypes: cluster 1, elevated mean E/e' 24.5 (SD 9.6); cluster 2, reduced ejection fraction, mean 33.1% (SD 10.6), and cardiac index 2.6 L/min/m2 (SD 0.9); cluster 3, right ventricular dilation with right ventricular basal diameter 4.5 cm (SD 0.9) and elevated tricuspid regurgitation gradient 60.0 mmHg (SD 13.5); cluster 4, hyperdynamic with mean left ventricular ejection fraction 75% (SD10.9) and mean cardiac index 6.6 L/min/m2 (SD 2.6); and lastly cluster 5, normal echocardiographic parameters. Group 3 had the highest mortality compared to the normal group (36.9% vs. 19.6%, p = 0.002), with an odds ratio of 2.3 (95%CI 1.4-3.9).

CONCLUSIONS: Using an unsupervised clustering analysis, we identified five phenotypes of cardiac function in sepsis based on commonly recorded echocardiographic data: diastolic dysfunction, left ventricular systolic dysfunction with low cardiac index, right ventricular dilation with elevated tricuspid regurgitation gradient, hyperdynamic cardiac function, and normal. The right ventricular dilation group had the highest mortality. Future research should explore mechanisms and potential treatment implications for these groups.

BACKGROUND: Low left ventricular ejection fraction (LEF) can progress undiagnosed. Artificial intelligence-based electrocardiogram (ECG-AI) screening may provide a scalable means to detect LEF.

OBJECTIVES: The purpose of this study was to validate a complete ECG-AI software as a medical device for LEF detection.

METHODS: Four geographically diverse sites in the United States identified patients with both ECGs and transthoracic echocardiograms performed within 30 days of each other in clinical practice. Data were electronically extracted to specific guidelines and transmitted to the coordinating center for analysis.

RESULTS: Records of 16,000 subjects were extracted, resulting in an evaluable set of 13,960 subjects (mean age 66 years; 52% male). The device demonstrated excellent discrimination (AUROC: 0.92 [95% CI: 0.91-0.93]) and was 84.5% (95% CI: 82.2%-86.6%) sensitive and 83.6% (95% CI: 82.9%-84.2%) specific for LEF. The overall prevalence of LEF in the study data set was 7.9%, with LEF among 1.6% of the ECG-AI negative and 30.5% of ECG-AI positive subjects, contributing to positive and negative predictive values of 30.5% (95% CI: 28.8%-32.1%) and 98.4% (95% CI: 98.2%-98.7%), respectively.

CONCLUSIONS: External validation studies such as this one provide a rigorous framework to validate an algorithm's performance. This study demonstrated the algorithm's strong diagnostic accuracy over a geographically diverse, independent set of patients. In this generally unselected population, the algorithm produced a test negative result in 78% of the cases, suggesting potential utility as a rule-out strategy to defer echocardiography when other clinical findings are absent.

• It is uncertain if the 2017 ASCO recommendations on postanthracycline surveillance echocardiography impacted practice. • Release of the ASCO guidelines was not associated with changes in echocardiogram frequency with under half of patients receiving an echocardiogram within 18 months following chemotherapy initiation.

BACKGROUND: Polyethelene glycol (PEG) is a key component of several ultrasound enhancing agents (UEA) but has been recognized as contributing to anaphylactoid reactions, resulting in new contraindications to use in those with known or suspected PEG allergy. Despite these recommendations, no clinical tools currently exist to screen for those with suspected PEG-allergy in echocardiography laboratories.

METHODS: We developed a screening survey to identify patients with potential PEG allergy and prospectively implemented it in a pilot study involving 8 patients with confirmed PEG allergy by skin prick testing and 50 prospectively enrolled patients undergoing clinically-indicated echocardiography without known PEG allergy, June - July 2025.

RESULTS: All patients completed the survey. A positive response to at least 2 of the first 4 questions on the screening survey had a sensitivity of 100% (95% CI 67.6-100%), specificity of 100% (95% CI 92.9-100%), positive predictive value of 100% (95% CI 67.6-100%), and a negative predictive value of 100% (95% CI 92.9-100%) to identify individuals with known PEG allergy.

CONCLUSIONS: In this pilot multicenter study, a brief screening survey identified all patients with proven allergy to PEG, suggesting possible utility to its use to identify those with potential PEG allergy who would benefit from a non-PEGylated UEA, though further clinical validation is needed.

BACKGROUND: Frailty is a proxy for biologic aging that confers risk independently of chronologic age. Most frailty indices correlate strongly with chronologic age, making independent features of biologic aging challenging to identify.

METHODS: We aimed to create a novel Age Less-Dependent Frailty (AGELESS) Score less-associated with chronologic age than the Fried frailty phenotype. Among Cardiovascular Health Study participants with available echocardiographic data, we identified demographic, clinical, serologic, and echocardiographic variables more correlated with a continuous version of the Fried frailty phenotype than age, then used LASSO regression for variable selection. In a 25% leave-out sample, we internally validated the score's association with age-adjusted all-cause and cardiovascular mortality and compared model characteristics with the Fried frailty phenotype.

RESULTS: In 4,029 individuals (mean age 72 ± 5.0 years, 59.6% female), serum cystatin C, depression, diabetes, educational attainment, forced expiratory volume in 1 s, and income were more associated with frailty than age and selected for inclusion in the AGELESS Score. Adjusted for age, individuals in the highest vs. lowest quartiles of the AGELESS Score had a higher risk of all-cause (HR: 1.44, 95% CI: 1.17-1.79, p < 0.001) and CV death (HR: 1.64, 95% CI: 1.43-1.87, p = 0.002). The AGELESS Score was less correlated with age (AGELESS r = 0.23, 95% CI: 0.16-0.30; Fried r = 0.28, 95% CI: 0.21-0.34; p-value for comparison of correlations < 0.001) and more closely associated with all-cause and CV mortality within each age quartile than the Fried frailty phenotype.

CONCLUSIONS: We derived and internally validated a novel frailty score that is less associated with chronologic age than existing indices and predicts mortality within age strata better than the existing reference standard for phenotypic frailty. This score could help identify high-risk patients with frailty across the age spectrum and may provide insights into mechanisms of biologic aging.

BACKGROUND: Left ventricular hypertrophy (LVH) categories are based on left ventricular mass index (LVMi). This study aimed to generate and externally validate sex-stratified LVMi cutoffs according to incremental mortality risk.

METHODS: LVH information was determined on 155 668 men (aged 61.3±17.3 years) and 147 880 women (61.8±18.3 years) from the National Echocardiography Database of Australia. Sex-specific mild to severe thresholds of the increasing 5-year mortality rate based on LVMi increments were generated. These new thresholds were then validated in a US Medicare-linked echocardiographic database.

RESULTS: In the National Echocardiography Database, 36198 men (23.3%) and 38 898 women (26.3%) had LVH, with an actual 5-year mortality rate of 38.3% and 31.2%, respectively. The statistical threshold at which LVMi was associated with an increased mortality rate was lower than traditional criteria in both men (≥88 g/m2 versus ≥115 g/m2) and women (≥82 g/m2 versus ≥95 g/m2). In men, compared with the lowest-risk LVMi stratum, the fully adjusted risk of 5-year death was 14% (95% CI, 3%-25%) and 68% higher (95% CI, 49%-90%) when LVMi levels were mildly (88 to <116 g/m2) to severely (≥140 g/m2) increased, respectively. In women, the equivalent LVMi thresholds of 82 to <112 g/m2 and ≥140 g/m2 were associated with a 13% (95% CI, 3%-24%), and 81% higher (95% CI, 58%-208%) risk. The association of these LVMi thresholds and mortality risk was confirmed in the US validation cohort selected for the absence of 27 separate comorbidities (n=12 355; mean age, 65.9±13.1 years; 49.7% female).

CONCLUSIONS: A high proportion of men and women have LVMi levels associated with an elevated mortality risk, despite absence of LVH. Such individuals may benefit from more proactive recognition and clinical management.

BACKGROUND: It is unclear whether geographic distance to a cardiovascular imaging center (CVIC) is associated with receipt of cardiovascular imaging (CVI).

OBJECTIVES: This study sought to assess temporal trends in distance to a CVIC and examine the relationship of distance to a CVIC and receipt of CVI overall and by modality.

METHODS: Among 64,260,530 older U.S. Medicare fee-for-service and Medicare Advantage beneficiaries from 2018 to 2021, the study measured individual distances to the nearest CVIC. Poisson regression was used to evaluate the likelihood of receipt of CVI as a function of distance, overall and by modality.

RESULTS: Of those beneficiaries included (age: 73.0 ± 8 years; 54.6% female; 80.1% White), 17.5% underwent CVI. The number of CVICs increased (0.02% per year), but median distances to CVICs remained stable (3.3-3.4 miles). Compared with beneficiaries living 10 to 16 miles from a CVIC, distance >16 miles from a CVIC was associated with lower likelihood of receipt (rate ratio: 0.957 [95% CI: 0.956-0.959]; P < 0.001). The lowest likelihood of receipt was within 10 miles of services (rate ratio: 0.923 [95% CI: 0.921-0.924]; P < 0.001). Distances to cardiac computed tomography (CCT), cardiac magnetic resonance (CMR), and positron emission tomography (PET) services were longer than distances to echocardiography and single-photon emission computed tomography (SPECT) services: (median distance: CCT: 8.1 miles [Q1-Q3: 3.7-21.3 miles]; CMR: 17.4 miles [Q1-Q3: 7.3-43.3 miles]; and PET: 88.9 miles [Q1-Q3: 26.2-194.6 miles] vs echocardiography: 3.4 miles [Q1-Q3: 0.4-7.0 miles]; and SPECT: 3.8 miles [Q1-Q3: 1.3-7.9 miles]).

CONCLUSIONS: From 2018 to 2021, the number of CVICs increased, although distances to CVICs remained stable. The lowest likelihood receipt of imaging overall was among those patients living within 10 miles of a CVIC, a finding suggesting that proximity is insufficient for access. CCT, CMR, and PET services were concentrated in large metropolitan academic centers.