Naser, Jwan A.

Importance Timely identification of aortic stenosis (AS) is essential for appropriate clinical management, yet screening remains limited by dependence on comprehensive echocardiography and trained imaging personnel. Objective To develop and validate a deep learning algorithm for detection of moderate or greater AS and prospectively evaluate its performance using artificial intelligence (AI)–guided focused cardiac ultrasound (FoCUS) acquired by novice operators. Design, Setting, and Participants This diagnostic study included retrospective algorithm development and validation and prospective evaluation of AI-guided FoCUS across Mayo Clinic sites in the Midwest, Arizona, and Florida. The model was developed using 6753 patients and evaluated in internal validation (n = 852), internal test (n = 844), and validation (n = 1912) cohorts. Performance was assessed on FoCUS acquired by experienced sonographers (n = 602) and prospectively by novice operators (n = 1302). The retrospective model development and validation cohorts comprised studies performed from January 2005 through September 2022. Prospective study was conducted in 2 enrollment periods from June to August 2024 and from June to September 2025. Participants from both periods were combined to comprise the final prospective cohort. Exposure AI-guided FoCUS acquisition and automated deep learning–based assessment for detection of moderate or greater AS. Main Outcomes and Measures Detection of moderate or greater AS. Performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and positive predictive value. Results The model demonstrated excellent discrimination in the internal test cohort (AUROC, 0.99; 95% CI, 0.98-1.00) and geographically distinct validation cohorts in Arizona (AUROC, 0.99; 95% CI, 0.97-1.00) and Florida (AUROC, 0.99; 95% CI, 0.96-1.00). Among FoCUS examinations acquired by experienced sonographers, sensitivity was 95% (95% CI, 82-99) and specificity was 97% (95% CI, 95-98). In the prospective novice-operator cohort, 1258 of 1302 examinations (96.6%) were suitable for automated analysis. Sensitivity was 93% (95% CI, 82-99) and specificity was 96% (95% CI, 95-97). Expert review of AI-positive and uninterpretable examinations increased the positive predictive value from 49.4% to 91.1%, with sensitivity of 85.4%. Conclusions and Relevance A deep learning algorithm accurately detected moderate or greater AS across validation cohorts in this study. In prospective evaluation, novice operators were able to acquire AI-guided FoCUS examinations that enabled accurate detection of moderate or greater AS. These findings support a scalable strategy that may expand access to AS detection in settings with limited echocardiography resources.

Abstract Background and Aims Exercise stress echocardiography is recommended as an alternative to invasive testing for diagnosing heart failure with preserved ejection fraction (HFpEF), but an evidence-based operational framework guiding its application is lacking. Methods Patients with chronic unexplained dyspnoea underwent invasive haemodynamic exercise testing with simultaneous echocardiography to test the hypotheses that (i) the current diagnostic algorithms (H2FPEF, HFA-PEFF, and HFpEF-ABA scores) could be enhanced when combined with exercise echocardiography; and (ii) incorporating resting left atrial (LA) compliance (LA reservoir strain divided by E/e′) could further improve diagnostic triage, using separate cut points optimizing sensitivity and specificity. Findings were then validated in an international multicentre cohort. Results Of 482 patients, HFpEF was present in 386 and non-cardiac dyspnoea in 96. Sensitivity to detect HFpEF was only 55%–60% and accuracy 61%–67% using individual diagnostic scores with currently recommended exercise echocardiography. Addition of abnormal resting LA compliance to exercise echocardiography increased sensitivity to 84%–85% but increased the false-positive rate to 31%–43%. Applying separate cut points that optimize specificity and sensitivity (either exercise E/e′ ≥ 13.8 or resting LA compliance ≤1.6% to rule-in HFpEF; both exercise E/e′ < 7.2 and resting LA compliance >4.4% to rule-out HFpEF, remaining patients indeterminate who require invasive testing) improved sensitivity to 95%–99% among definitively classified patients and reduced the number of patients that require invasive exercise testing from ∼60% to ∼30%. Findings were replicated in a multicentre, international validation cohort of patients undergoing rest–exercise echocardiography and invasive exercise testing. Conclusions Currently used non-invasive approaches to HFpEF diagnosis have inadequate sensitivity, indicating that exercise echocardiography cannot supplant invasive testing. Addition of LA strain imaging with optimized rule-in or rule-out values improves diagnostic triage by expanding non-invasive rule-in/rule-out classifications while identifying patients who still require invasive testing in the evaluation of HFpEF.

Abstract Aims Significant secondary tricuspid regurgitation (STR) is associated with worse morbidity and mortality. To-date, evidence on isolated tricuspid valve surgery in STR remains controversial with uncertainty regarding management strategies. We aimed to investigate sex differences in the association of isolated tricuspid valve surgical repair/replacement (TVR) with outcomes of mortality and heart failure (HF) hospitalizations in STR. Methods and results Patients with severe STR without significant aortic valve or organic mitral valve disease, congenital heart disease, pacemakers, previous valve intervention, malignancy, or end-stage kidney/liver disease were identified. Isolated TVR was studied as a time-dependent covariate in association with mortality and HF hospitalizations. Among 2,857 patients (median age 76 years; 62% women), 143 underwent isolated TVR (58% repair; 42% replacement; 64% with low TRI-SCORE risk category) at median 56 days. Over median 1.6 (IQR 0.2-5.2) years, 1,771 patients died and 1,259 had HF hospitalization. When adjusting to age, comorbidities, left/right ventricular function, pulmonary hypertension, hemoglobin level, and dyspnea, isolated TVR was associated with lower mortality in females (HR 0.54 95% CI 0.35-0.83) but not males (HR 1.29 95% CI 0.90-1.83), p-interaction<0.01; this remained the case when restricting to groups with normal left/right ventricular function. Isolated TVR was associated with HF hospitalization without interaction with sex (HR 0.68 95% CI 0.46-0.998), p-interaction=0.51. Conclusion Isolated TVR for STR was associated with decreased HF hospitalizations in both females and males but improved survival only in females. Future studies are needed to understand differences in the natural history of TR between sexes and stratify outcomes after TR intervention by sex.

Abstract Aims Heart failure is prevalent; however, there is no cost-effective screening option. Against formal echocardiography, we assessed the diagnostic performance of focused cardiac ultrasound (FoCUS), performed by novice users guided and interpreted by artificial intelligence (AI) for the assessment of left ventricular ejection fraction (LVEF). Methods and results We prospectively enrolled 496 adults referred for diagnostic echocardiography. Novice operators (without clinical or imaging experience) underwent a 4-h imaging workshop, then performed FoCUS using a point-of-care device (Philips Lumify) utilizing real-time AI-image guidance (UltraSight). Images were assessed by AI-image interpretation software (Mayo Clinic), and two expert blinded echocardiologists. Median age was 67, 39% female, and body mass index range 17–56 kg/m2. Forty-one subjects (8.3%) exhibited moderate or greater LV dilatation and 28 (5.6%) had LVEF <40%. The median scan time was 4 min (IQR 3–5). Adequate views were achieved in 95.0% and 97.4% of subjects for AI interpretation and expert analysis, respectively. A two-step diagnostic screening process for low EF in which only abnormal AI reads or uninterpretable scans were reviewed by experts (15.1%) had a sensitivity 96.2%, specificity 95.4%, PPV 54.3%, and NPV 99.8%. A subsequent prospective validation study of 344 subjects demonstrated similar results (sensitivity 100%, specificity 99.4%). Conclusion In this early feasibility investigation, AI-guidance technology embedded on a handheld device, enabled novice users without clinical or imaging experience to acquire sufficient quality images to accurately assess LVEF with minimal training. The technology evaluated in this study may hold promise for AI-guidance and interpretation to facilitate low-cost screening for cardiac dysfunction.

Abstract Aims Chronic kidney disease (CKD) is common in patients with heart failure (HF) with preserved ejection fraction (HFpEF), and its presence is associated with more severe echocardiographic abnormalities and poorer outcomes through unclear mechanisms. CKD-associated endothelial dysfunction, inflammation, and activation of profibrotic pathways could worsen pulmonary vascular disease (PVD) in HFpEF, but such relationships have not been explored. Methods Consecutively evaluated patients with HFpEF undergoing invasive haemodynamic cardiopulmonary exercise testing were stratified by baseline kidney function to characterize potential differences in pulmonary vascular loading, haemodynamics, cardiopulmonary reserve, and outcomes based upon the presence and severity of kidney dysfunction. Results Of 925 patients with HFpEF, 319 (34.5%) had estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m2. Patients with more severe kidney dysfunction were older and more likely to have diabetes, atrial fibrillation, and hypertension. At rest, reduced kidney function was associated with lower haemoglobin, higher biventricular filling pressures, and lower cardiac output, but the severity of kidney dysfunction was most conspicuously associated with worsening PVD, evidenced by increasing pulmonary arterial (PA) pressures due to marked increases in pulmonary vascular resistance (PVR) as kidney dysfunction progressed (median [IQR] 1.3 [0.86, 1.88] to 2.8 [2.0, 3.8] WU from eGFR ≥ 90 to eGFR < 30, P < .001), along with progressively lower PA compliance (PAC, 4.7 [3.6, 5.8] to 2.2 [1.8, 3.4] ml/mmHg from eGFR ≥ 90 to eGFR < 30, P < .001). With exercise, differences in the severity of PVD became even more striking, with more severe elevation in PA pressures and PVR, and lower PA compliance, leading to lower transmural left-sided distending pressures, with no difference in exertional PA wedge pressure. Patients with worse kidney dysfunction displayed the most dramatic cardiac output limitations with exercise, which along with more severe anaemia and markedly reduced O2 delivery, caused marked impairment in aerobic capacity. Worsening kidney function was associated with a striking gradient of increased risk of a composite of all-cause death and HF hospitalization [ranging from HR 2.38 (95% CI 1.01–5.59) for eGFR 60–90 to HR 16.77 (95% CI 6.65–42.28) for eGFR < 30 (vs. reference eGFR ≥ 90)]. Conclusions Kidney dysfunction in patients with HFpEF is characterized by more severe PVD at rest and with exercise, leading to reduced cardiac output reserve, impaired exercise capacity, and increased risk for adverse events. Further study is warranted to better understand causal relationships between CKD and PVD, and determine whether novel therapies to improve kidney function might target these impairments to improve clinical status.

Background Tricuspid regurgitation (TR) is a common valvular disorder that can affect patients’ quality of life and survival. The impact of TR etiology on overall survival and the associated risk factors in each subgroup are not well studied. Methods Multisite, retrospective study evaluating survival based on TR etiology using Kaplan–Meier and Cox regression models. Stepwise approach was used to define TR etiology (≥moderate): primary (PTR; presence of a primary valvular pathology), lead associated (LTR; lead interfering with closure), and secondary TR (STR) in the setting of left‐sided valvular disease, left ventricular disease, other causes of pulmonary hypertension, right ventricular disease, and atrial secondary TR. Results A total of 12 899 patients were included (3% PTR, 90% STR, 7% LTR). The mean age was 68±14, 73±13, and 71±13 years, respectively (44%–55% female patients). Adjusting for age, sex, and TR severity, patients with PTR had better overall survival (PTR: reference; STR hazard ratio [HR], 1.4 [1.2–1.6], LTR HR, 1.5 [1.1–1.7]). On multivariable Cox regression, the Tricuspid Regurgitation Impact on Outcomes score was associated with mortality across all groups ( P <0.05 for all). In addition, other risk factors associated with worse outcomes included pulmonary hypertension and ≥moderate right ventricular enlargement in PTR; left‐sided valvular disease, pulmonary hypertension, and ≥moderate right ventricular dysfunction in STR; and left ventricular disease and ≥moderate right ventricular dysfunction in LTR. Conclusions Patients with PTR have better survival than STR and LTR. Tricuspid Regurgitation Impact on Outcomes score was associated with mortality across etiologies, whereas other risk factors are etiology specific. Improved risk profiling may enhance patient selection, treatment decisions, and outcomes.

Abstract Aims We aimed to assess regression and development rates of ≥moderate secondary mitral regurgitation (MR) and tricuspid regurgitation (TR) in patients with heart failure and reduced ejection fraction (HFrEF) receiving contemporary guideline-directed medical therapy (GDMT). Methods Patients with a &lt;1 year diagnosis of HFrEF with mildly (40% ≤ EF &lt; 50%) or moderately (&lt;40%) reduced EF enrolled in GDMT titration programme 2019–2024 were identified. Regression rates of ≥moderate MR/TR to ≤mild and development rates of incident ≥moderate MR/TR were calculated. Results Among 844 patients [median age 64; 68% men; 26% mildly reduced EF; 24% New York Heart Association (NYHA) Class III/IV], 28% had ≥moderate MR and 14% had ≥moderate TR. Over median 7.1 months, 171 (75%) patients with ≥moderate MR and 87 (75%) of patients with ≥moderate TR experienced regression to no/mild MR and TR, respectively. Conversely, over median 13 months, incident ≥moderate MR and TR developed in 5% and 6%, respectively, of patients with no/mild baseline MR and TR. Number of GDMT medications at follow-up was less in patients who did not have MR/TR regression (vs who did) and in patients who developed MR/TR (vs who did not). Patients with NYHA Class I/II received a similar number of GDMT and had a similar rate of MR and TR regression and development vs patients with NYHA Class III/IV. However, patients with mildly reduced EF received less GDMT medications vs patients with moderately reduced EF and experienced less MR regression. Conclusion In patients with a recent diagnosis of HFrEF receiving contemporary GDMT, the majority with ≥moderate secondary MR and TR achieved regression of MR/TR, and the minority without significant baseline MR/TR developed ≥moderate regurgitation at follow-up.

Abstract Background Approximately one-third of patients with heart failure and preserved ejection fraction (EF) (HFpEF) have exercise-induced left atrial hypertension (EILAH), characterized by normal pulmonary capillary wedge pressure (PCWP) at rest, but with pathologic PCWP elevation during exercise. It has been proposed that EILAH may represent an early stage of HFpEF that progresses to resting LA hypertension (RELAH) over time, but this has never been shown. Purpose This observational study aimed to investigate the natural history of patients with EILAH, evaluating patients who underwent multiple invasive hemodynamic evaluations over time. Methods Consecutively-evaluated patients with HFpEF and EILAH (PCWP&lt;15 mmHg at rest and ≥25 mmHg during exercise) who underwent subsequent invasive hemodynamic testing were identified. Prevalence and characteristics of transition to RELAH (PCWP≥15 mmHg at rest) were evaluated. Results Of 659 patients with EILAH, 84 underwent repeat invasive hemodynamic evaluations over a median follow-up of 1.81 (IQR 0.48-3.40) years (55 female, mean age 66 years). Of this group, 33 (39%) patients changed to RELAH and 51 (61%) remained EILAH. Patients with change to RELAH experienced greater increase in NT-proBNP (+16.6%; 95% CI: 7.1% to 26.0%, P&lt;0.001), decreased left ventricular EF (-6.1%, 95% CI: -9.7 to -2.5, P=0.001), right ventricular (RV) global longitudinal strain (-4.5%, 95% CI: -8.8 to -0.3, P=0.037), RV free wall longitudinal strain (-7.3%, 95% CI: -13.1 to -1.6, P=0.013) and worsening tricuspid regurgitation (15% vs 2%, P=0.025) compared to those with no change (Figure 1). Upon invasive hemodynamic at rest, patients with change to RELAH experienced an increase in right atrial pressure (RAP) (+4.4 mmHg, 95% CI: 2.6 to 6.1, P&lt;0.001) and mean pulmonary artery (PA) pressure (+6.6 mmHg, 95% CI: 4.2 to 9.0, P&lt;0.001) (Figure 1). At the baseline, there were no significant differences in the exercise-induced increases in PCWP, mean PAP, and RAP between the two groups from rest to peak exercise (Figure 2). However, at the final evaluation, patients with change to RELAH displayed a greater increase in PCWP, mean PAP, and RAP during exercise as compared to those who remained EILAH (Figure 2). Of characteristics at the index evaluation, history of atrial fibrillation (OR 3.73; 95% CI 1.09-12.8, P=0.036), and lower resting PA compliance (OR 0.49; 95% CI 0.24-0.99, P=0.048) were independently associated with the transition to RELAH after adjusting the duration from baseline to final examination. Conclusions Approximately 40% of patients with HFpEF that first present with EILAH to RELAH, suggesting that EILAH can be considered an earlier form of HFpEF. History of atrial fibrillation and reduced PA compliance are associated with progression to RELAH, suggesting that interventions to address atrial myopathy and pulmonary vascular disease may be effective to attenuate or prevent this transition.

Abstract Background Right ventricular (RV) systolic function is an independent predictor of outcome, but a robust automated method of quantitation is lacking. Purpose To develop and validate a deep learning model for quantifying RV systolic function from 2D echo clips and assess its prognostic value in various loading states and left-heart disease. Methods A deep learning convolutional neural network (CNN) was developed to quantitate RV systolic function using all available apical 4 chamber (A4C) views from 13,039 patients (61,187 A4C video clips) in a US institution. Videos were converted to frames of 256x256 grayscale pixels, with graphics, including the ECG, removed. The model used the S3D architecture, a 3D-CNN with many of the spatial and temporal convolutions separated. RV free wall longitudinal strain (RVFWLS) was used to define RV function: normal (≤-25%); mild (-20% to –24%); moderate (-15% to –19%) and severe RV dysfunction (0% to –14%). The AI-estimate was the average of multiple 32-frame segments for each clip identified as an A4C, including both standard and RV-focused variations. The model was validated in four distinct clinical cohorts from independent data sources: 1) 1365 patients referred to an external diagnostic echocardiography lab in a different US state; 2) 569 patients recruited prospectively with known or suspected pulmonary hypertension (PH); 3) 416 patients with moderate or greater tricuspid valve regurgitation (TR); 4) 872 patients imaged at an Eastern European University with left-sided cardiac diseases (valvular disease, cardiomyopathies, etc.), athletes, or healthy volunteers. Results Model output was strongly correlated with the presence of RV dysfunction by manual measurement [AUC 0.907 (95% CI: 0.888-0.926)] in the internal validation set. Results were similar in an external validation US cohort [N=1365; AUC 0.919 (95% CI: 0.899-0.939)]. When the model was applied to 569 patients with known or suspected PH, presence and severity of the AI measures of RV dysfunction were strongly associated with functional class, 6 min walk distance, NT-proBNP levels and right heart failure (p&lt;0.001). The presence and severity of RV dysfunction (as categorized by the AI model) was strongly associated with all-cause mortality in patients with PH (Figure 1A) and in patients with significant TR (Figure 1B). The European cohort was a geographically and institutionally distinct cohort with a low prevalence of &gt;mild TR (10.8%) or PH (6.4%). In this cohort the AI model predicted a reduced RVEF (&lt; 45%) with an AUC of 0.864 (95% CI: 0.831-0.898) and associated with both all-cause mortality (Figure 2A) and a composite endpoint of heart failure hospitalization or death (Figure 2B). Conclusions A fully automated, AI-based assessment of RV systolic function, using 2D apical 4 chamber echo views alone, is highly feasible and can accurately categorize disease severity and predict outcome across a wide spectrum of cardiovascular conditions.Figure 1 Figure 2