Citations

Précis: In our study of the factors predictive of latanoprost response, we found that normotensive subjects with higher intraocular pressure, lower uveoscleral outflow, and higher body mass index had a greater response to 1-week treatment. Purpose: To evaluate relationships between variable intraocular pressure (IOP) responses to latanoprost and participant characteristics, baseline values and changes in aqueous humor dynamics (AHD) parameters. Methods: We assessed 226 eyes from 113 healthy participants. AHD parameters measured at baseline and after 1-week treatment with latanoprost included: IOP by pneumatonometry, episcleral venous pressure (EVP) by venomanometry, aqueous humor flow rate by fluorophotometry, outflow facility by 2-minute pneumatonography, and uveoscleral outflow calculated using the modified Goldmann equation. IOP responses were categorized into 4 groups: nonresponders (<10% reduction), all-responders (≥10% reduction), medium-responders (10%–20% reduction), and high-responders (≥20% reduction). Generalized estimating equation models were used to analyze treatment effects and compare groups. Results: Baseline IOP was higher in high-responders than medium-responders and nonresponders ( P =0.007 and P <0.001, respectively). Body mass index (BMI) was significantly higher in high-responders and all-responders compared with nonresponders ( P =0.02 and P =0.03, respectively). Responders had lower baseline uveoscleral outflow ( P =0.03). There were no significant differences in other baseline characteristics including EVP, outflow facility, and aqueous flow rate between the 4 groups. IOP decreased while outflow facility and uveoscleral outflow increased after latanoprost treatment in all responder groups but did not change in nonresponders. EVP and aqueous flow rate did not change significantly in any group. The change in uveoscleral outflow was greater in high-responders than in nonresponders ( P =0.004). Conclusions: In healthy subjects, higher baseline IOP, lower uveoscleral outflow and higher BMI are predictors of a greater IOP reduction by latanoprost. A greater increase in uveoscleral outflow is responsible for this larger response.

Purpose: To identify reasons for variable intraocular pressure (IOP) responses to latanoprost and timolol in healthy volunteers and to generate the control group as part of Eye Dynamics and Engineering Network. Methods: In this multicenter, randomized, crossover study (NCT01677507), both eyes of 106 healthy subjects (212 eyes) were treated with latanoprost or timolol for 7 days, with a 6-week washout between treatments. Ocular biometrics, tonometry, and aqueous humor dynamics (AHD) were assessed at baseline and day 8 of each treatment. Subjects were divided into responders and nonresponders using cutoffs of >15% or >10% IOP reduction. Treatment effects and correlations were analyzed with paired t-tests. Results: More subjects responded to latanoprost (54%) than timolol (27%) at >15% cutoff ( p < 0.01). Responders had higher mean baseline IOP than nonresponders for both drugs at both cutoffs ( p < 0.01). Among timolol nonresponders ( n = 56), 39% responded to latanoprost in both eyes, 20% in one eye, and 41% in neither. Among latanoprost nonresponders ( n = 31), 13% responded to timolol in both eyes, 13% in one eye, and 74% in neither. Latanoprost increased uveoscleral outflow, while timolol reduced aqueous flow and outflow facility. Low baseline uveoscleral outflow was associated with latanoprost response. Conclusions: Higher baseline IOP predicted better responses to both drugs. Higher baseline uveoscleral outflow predicted nonresponse to latanoprost. No AHD differences were linked to the timolol response. Timolol nonresponders were often responsive to latanoprost, but not vice versa.

Background We tested whether biologic age, as estimated by deficits, functional impairments, or Age‐Gap or their combination, provide improved estimation of cause‐specific death as compared with chronological age. Methods Cardiovascular and noncardiovascular deficits, functional impairments, and Age‐Gap were prospectively collected in 535 patients aged ≥55 years undergoing percutaneous coronary interventions between August 1, 2014, and March 31, 2018. Age‐Gap was calculated as the difference between chronological age and age estimated by artificial intelligence ECG using a convolutional neural network. The full biological age model included deficits, functional impairments, and Age‐Gap >2 SD. A multivariable reduced model with the least number of variables was also created to provide a comparable C index to the full model. Results The average chronological age was 72.1±9.5 years, and there were 68% of men. During a median follow‐up of 2.61 years, 124 (23%) patients died. There was a modest correlation between Age‐Gap and biological age ( r =0.28 [95% CI, 0.20–0.35]; P <0.001). When modeled with chronologic age as a covariate, Age‐Gap predicted all‐cause (hazard ratio [HR], 1.07 [95% CI, 1.04–1.10]; P <0.001) and cardiovascular (HR, 1.07 [95% CI, 1.04–1.11]; P <0.001) mortality. As compared with chronological age, the full biological age model noted significant improvement in the prediction of long‐term overall (95% CI, 0.65–0.78), cardiovascular (95% CI, 0.69–0.77), and noncardiovascular (95% CI, 0.55–0.86) mortality. In the reduced models, most prognostic information for noncardiovascular mortality (C index: 0.79) was obtained by subjective difficulty in performing tasks, whereas the deficit‐based estimation predicted cardiovascular mortality (C index: 0.72). Conclusions Estimated biological age from deficits and functional impairments was superior to chronological age in predicting long‐term cause‐specific mortality following percutaneous coronary interventions.

ImportanceThe advanced ambulatory heart failure (HF) population comprises patients who have progressed beyond the pillars of recommended stage C HF therapies but can still find meaningful life-years ahead. Although these patients are commonly encountered in practice, national databases selectively capture the small groups accepted for heart transplant listing or left ventricular assist devices. The epidemiology, trajectories, and therapies for other ambulatory patients with advanced HF are poorly understood.ObservationsIn December 2022, the National Heart, Lung and Blood Institute convened a team of experts to identify knowledge gaps and research priorities for the ambulatory population with limiting daily symptoms and transition toward refractory end-stage D HF, designated as stage C2D. This article summarizes the findings from that 3-day workshop. Workshop participants surveyed the initial challenges and knowledge gaps for (1) recognition of ambulatory C2D HF, (2) estimation of the magnitude of the affected population and identifiable subpopulations, and (3) physiologic phenotypes, such as low cardiac output, right HF, cardiorenal syndromes, congestive hepatopathy and frailty, which offer distinct targets for existing and emerging therapies. Social drivers of HF and patient preferences for quality/length of survival were highlighted as essential modifiers for personalization of therapies.Conclusions and RelevanceTen key points summarized workshop findings, with target cohorts for study proposed as a crucial next step. This workshop summary is intended as a call for action to address knowledge gaps and develop new strategies to improve outcomes in the large ambulatory population with C2D HF.

Introduction: Identifying patients at risk for ischemic events after percutaneous coronary intervention (PCI) relies on traditional analysis of limited clinical and imaging variables. Machine learning (ML) has shown promise in effectively predicting cardiovascular risk in population studies. While existing ML models mainly predict mortality and incorporate clinical variables, there is a lack of tools that have utilized genetic data and that predict ischemic events. Aims: This study aims to develop and validate a ML model incorporating genotyping and clinical data to enhance prediction of ischemic outcomes for PCI patients utilizing large prospectively derived diverse datasets. Methods: Patients from the TAILOR-PCI trial (n=5302) were utilized for model development. 50% of the sample was utilized for Boruta feature selection and 50% for training and testing using cross validation. Features included demographics, medical history, medications, PCI characteristics, and genetic data (specifically, CYP2C19 *2, *3, *17 alleles). The primary endpoint was a composite of cardiovascular death, myocardial infarction, stroke, stent thrombosis, and severe recurrent ischemia at 12 months. Multiple ML classification algorithms, including Support Vector Machine (SVM) polynomial, Random Forest, Light gradient boost, XG Boost, among others, were benchmarked for their prediction performance on rare events. The top performing classifiers were externally validated on an independent dataset from the PRECISION PCI study (n=3,745). Results: Mean participant age of the training set was 64.2 ± 11.0 years, with 75.4% being male. During follow-up of 12 months, among 4,572 patients in the entire cohort 343 (7.5%) met the primary outcome. The SVM polynomial model demonstrated the highest area under the curve (AUC) of 0.67 for predicting the primary outcome with test dataset. The sensitivity, specificity, precision, and recall were 0.87, 0.28, 0.07, and 0.87 respectively (Figure). Peripheral arterial disease, body mass index, and age were among the top variables by feature importance. Conclusion: ML models incorporating both clinical and genetic data are feasible and highly promising in predicting major adverse cardiac events that may help guide use of anti-platelet drug therapy. The AUC values are reasonable given imbalances and misclassifications in datasets, and further model optimization with prospective utilization of the model will be paramount.

Background: Cardiac allograft vasculopathy (CAV) is one of the leading causes of graft failure and death after heart transplantation (HTx). Conventionally, CAV is characterized by concentric intimal thickening, however the long-term progression patterns of plaque morphology are not yet well understood. Research Question: This study aimed to assess the plaque morphology, including histology and eccentricity, in a temporal manner post-HTx using virtual histology intravascular ultrasonography (VH-IVUS). Methods: We included 50 patients who underwent serial VH-IVUS assessment at three assessment points (A1:0-1 year, A2:3-5 years, A3:7-10 years) after HTx. Differences in VH-IVUS findings between timepoints A1 to A2 (early period) and A2 to A3 (late period) were investigated. Plaque volume index was defined as (plaque volume/vessel volume) × 100%. Eccentricity index (EI) was defined as (maximum intimal thickness – minimum intimal thickness)/maximum intimal thickness and was evaluated in 39 patients with significant plaque (maximal intimal thickness >0.50 mm at the A3) at each timepoint. The serial changes of EI and relationship between EI and other VH-IVUS findings were investigated. Results: Of 50 patients, mean age was 51±13 years, 36 (72%) were males. In both early and late periods, the plaque volume (PV) index showed a significant increase, however, the rate of progression was significantly slower in late period. (5.8% [0.9, 13.2] vs 2.4% [-0.2, 7.4], p=0.015). The EI showed a trend of decrease in the early period, compared to increases in the late period (-0.040 [-0.103, 0.004] vs 0.003 [-0.030, 0.052], p=0.036). Patients with EI change in late period higher than median value had significantly lower proportion of fibrous plaque (70.8% [67.2, 75.1] vs 62.7 [55.5, 66.9], p=0.002) and higher dense calcium (1.1% [0.9, 1.9], 4.5% [1.8, 6.9], p=0.022) at A3. Conclusion: In heart transplant patients, coronary artery plaque progression in the late period showed more eccentric progression with lower fibrous plaque composition, a pattern commonly observed in typical and conventional coronary atherosclerosis. These findings may provide a novel mechanism and therapeutic insight into the temporal patterns in CAV progression during long term follow up.

Background: Patients with normal resting left ventricular ejection fraction (LVEF) without either coronary artery disease (CAD) or a hypertensive response can paradoxically develop reduced LVEF during exercise stress echocardiography (ESE). The clinical phenotype and outcomes of these patients is unknown. Hypothesis: Patients who paradoxically develop reduced LVEF during exercise may represent a sub-phenotype of heart failure with preserved LVEF (HFpEF). Aims: To describe the baseline characteristics and clinical outcomes of this unique patient population. Methods: Among all ESE performed between January 2003 and December 2022, patients without a hypertensive response to exercise and without CAD by angiogram within 90 days of ESE, who had a resting LVEF ≥50% with a ≥5% LVEF decrease during ESE were identified. All-cause mortality, HF hospitalization, and atrial fibrillation (AF) outcomes were assessed. Kaplan-Meier and Cox regression methods were used to analyze time-to-event outcomes. Results: Among 213,643 stress echocardiograms performed, 134 patients met eligibility criteria (Table 1). The mean age of the population was 66±10 years, 76% were women, and 16% had AF at baseline. Mean LVEF was 58±4% at rest and 43±4% at peak stress. Stress electrocardiogram met criteria for ischemia in 14%. The 10-year all-cause mortality risk was 12.9% (95% CI 5.5-20.3) with 10 (37%) of 27 deaths due to cancer (Figure 1, Panel A). The 10-year estimated risk of HF hospitalization was 17.6% (95% CI 9.0-26.2) (Figure 1, Panel B). Among 112 patients without AF at baseline, the 10-year risk of developing AF was 23.4% (95% CI 13.4-33.4). Conclusions: Patients with exercise-induced reduced LVEF in the absence of obstructive CAD have a high incidence of HF hospitalizations and AF. Given the preponderance of women, risk for AF, HF hospitalizations, and cancer-related deaths, the possibility that this condition is an early sub-phenotype of HFpEF should be investigated.

Background: Electrocardiography-based artificial intelligence (AI-ECG) validated models that detect cardiac disease are increasingly being applied in clinical practice. The utility of such tools to detect insidious cardiac disease in patients with normal baseline left ventricular ejection fraction (LVEF) who paradoxically develop reduced LVEF during exercise stress echocardiography (ESE), and do not have coronary artery disease (CAD) or hypertension, is unknown. Hypothesis: AI-ECG is useful to diagnose insidious cardiac disease and predict clinical outcomes in patients with exercise-induced cardiomyopathy. Aims: To assess the utility of AI ECG in patients with exercise-induced cardiomyopathy. Methods: Among all ESE performed between January 2003 and December 2022, patients without a hypertensive response to exercise and without CAD (confirmed by coronary angiography within 90 days after ESE), with resting LVEF ≥50% and a paradoxical ≥5% LVEF decrease during ESE were identified. A previously validated AI-ECG algorithm that predicts atrial fibrillation (AF), reduced LVEF, and cardiac amyloidosis (CA) was applied to the baseline ECG closest to the time of ESE. The predicted probability of AF, reduced LVEF, and CA if above the published thresholds, was determined. Results: There were 134 patients with exercise-induced cardiomyopathy who were identified. The mean age of this cohort was 66±10 years, 76% were women and 16% had AF at baseline. Mean LVEF was 58±4% at rest and 43±4% at peak stress. The median follow-up period was 6.8 years (IQR 3.0-12.2). Among patients without a baseline history of AF (n=112), AI-ECG identified 29% with a significant probability of AF, which was associated with a subsequent AF diagnosis at univariable analysis (HR 2.505, 95%CI 1.016-6.177, p=0.046). There were 10 patients with an AI-ECG prediction of reduced LVEF among whom 3 were subsequently hospitalized with HF. AI-ECG was positive for CA in 8 and 3 amongst these had subsequent HF hospitalizations (Table). Conclusions: Baseline AI-ECG may help predict subsequent AF and diagnose preclinical amyloid cardiomyopathy in patients who by resting echocardiography do not seem to have cardiac disease but with exercise develop reduced LVEF.