Sex Differences in Pulmonary Hypertension and Associated Right Ventricular Dysfunction

Background

Prior studies have established the impact of sex differences on pulmonary arterial hypertension (PAH). However, it remains unclear whether these sex differences extend to other hemodynamic subtypes of pulmonary hypertension (PH).

Methods

We examined sex differences in PH and hemodynamic PH subtypes in a hospital-based cohort of individuals who underwent right heart catheterization between 2005-2016. We utilized multivariable linear regression to assess the association of sex with hemodynamic indices of RV function [PA pulsatility index (PAPi), RV stroke work index (RVSWI), and right atrial: pulmonary capillary wedge pressure ratio (RA:PCWP)]. We then used Cox regression models to examine the association between sex and clinical outcomes among those with PH.

Results

Among 5208 individuals with PH (mean age 64 years, 39% women), there was no significant sex difference in prevalence of PH overall. However, when stratified by PH subtype, 31% of women vs 22% of men had pre-capillary (P<0.001), 39% vs 51% had post-capillary (P=0.03), and 30% vs 27% had mixed PH (P=0.08). Female sex was associated with better RV function by hemodynamic indices, including higher PAPi and RVSWI, and lower RA:PCWP ratio (P<0.001 for all). Over 7.3 years of follow-up, female sex was associated with a lower risk of heart failure hospitalization (HR 0.83, CI 95% CI 0.74- 0.91, p value <0.001).

Conclusions

Across a broad hospital-based sample, more women had pre-capillary and more men had post-capillary PH. Compared with men, women with PH had better hemodynamic indices of RV function and a lower risk of HF hospitalization.

CLINICAL PERSPECTIVE

What Is New? Although sex differences have been explored in pulmonary arterial hypertension, sex differences across pulmonary hypertension (PH) in broader samples inclusive of all hemodynamic subtypes remain less well definedWe delineate sex differences in hemodynamic subtypes of PH and associated right ventricular function in a large, heterogenous, hospital-based sample of individuals who underwent right heart catheterizationSex has a significant impact on prevalence of PH across hemodynamic subtypes as well as associated RV function What Are the Clinical Implications? Understanding sex differences across different PH hemodynamic subtypes is paramount to refining risk stratification between men and womenFurther elucidating sex differences in associated RV function and clinical outcomes may aid in developing sex-specific therapies or management strategies to improve clinical outcomes.

Coronary Microvascular Function Following Severe Preeclampsia

BACKGROUND

Preeclampsia is a pregnancy-specific hypertensive disorder associated with an imbalance in circulating proangiogenic and antiangiogenic proteins. Preclinical evidence implicates microvascular dysfunction as a potential mediator of preeclampsia-associated cardiovascular risk.

METHODS

Women with singleton pregnancies complicated by severe antepartum-onset preeclampsia and a comparator group with normotensive deliveries underwent cardiac positron emission tomography within 4 weeks of delivery. A control group of premenopausal, nonpostpartum women was also included. Myocardial flow reserve, myocardial blood flow, and coronary vascular resistance were compared across groups. sFlt-1 (soluble fms-like tyrosine kinase receptor-1) and PlGF (placental growth factor) were measured at imaging.

RESULTS

The primary cohort included 19 women with severe preeclampsia (imaged at a mean of 15.3 days postpartum), 5 with normotensive pregnancy (mean, 14.4 days postpartum), and 13 nonpostpartum female controls. Preeclampsia was associated with lower myocardial flow reserve (β, -0.67 [95% CI, -1.21 to -0.13]; P=0.016), lower stress myocardial blood flow (β, -0.68 [95% CI, -1.07 to -0.29] mL/min per g; P=0.001), and higher stress coronary vascular resistance (β, +12.4 [95% CI, 6.0 to 18.7] mm Hg/mL per min/g; P=0.001) versus nonpostpartum controls. Myocardial flow reserve and coronary vascular resistance after normotensive pregnancy were intermediate between preeclamptic and nonpostpartum groups. Following preeclampsia, myocardial flow reserve was positively associated with time following delivery (P=0.008). The sFlt-1/PlGF ratio strongly correlated with rest myocardial blood flow (r=0.71; P<0.001), independent of hemodynamics.

CONCLUSIONS

In this exploratory cross-sectional study, we observed reduced coronary microvascular function in the early postpartum period following preeclampsia, suggesting that systemic microvascular dysfunction in preeclampsia involves coronary microcirculation. Further research is needed to establish interventions to mitigate the risk of preeclampsia-associated cardiovascular disease.

Coronary Microvascular Function Following Severe Preeclampsia

Background

Preeclampsia is a pregnancy-specific hypertensive disorder associated with an imbalance in circulating pro- and anti-angiogenic proteins. Preclinical evidence implicates microvascular dysfunction as a potential mediator of preeclampsia-associated cardiovascular risk.

Methods

Women with singleton pregnancies complicated by severe antepartum-onset preeclampsia and a comparator group with normotensive deliveries underwent cardiac positron emission tomography (PET) within 4 weeks of delivery. A control group of pre-menopausal, non-postpartum women was also included. Myocardial flow reserve (MFR), myocardial blood flow (MBF), and coronary vascular resistance (CVR) were compared across groups. Soluble fms-like tyrosine kinase receptor-1 (sFlt-1) and placental growth factor (PlGF) were measured at imaging.

Results

The primary cohort included 19 women with severe preeclampsia (imaged at a mean 16.0 days postpartum), 5 with normotensive pregnancy (mean 14.4 days postpartum), and 13 non-postpartum female controls. Preeclampsia was associated with lower MFR (β=-0.67 [95% CI -1.21 to -0.13]; P=0.016), lower stress MBF (β=-0.68 [95% CI, -1.07 to -0.29] mL/min/g; P=0.001), and higher stress CVR (β=+12.4 [95% CI 6.0 to 18.7] mmHg/mL/min/g; P=0.001) vs. non-postpartum controls. MFR and CVR after normotensive pregnancy were intermediate between preeclamptic and non-postpartum groups. Following preeclampsia, MFR was positively associated with time following delivery (P=0.008). The sFlt-1/PlGF ratio strongly correlated with rest MBF (r=0.71; P<0.001), independent of hemodynamics.

Conclusions

In this exploratory study, we observed reduced coronary microvascular function in the early postpartum period following severe preeclampsia, suggesting that systemic microvascular dysfunction in preeclampsia involves the coronary microcirculation. Further research is needed to establish interventions to mitigate risk of preeclampsia-associated cardiovascular disease.

Infertility and Subclinical Antecedents of Heart Failure With Preserved Ejection Fraction in the Framingham Heart Study

BACKGROUND

Infertility has been shown to be associated with a greater risk of incident heart failure with preserved ejection fraction. We studied the association of infertility with subclinical markers of heart failure with preserved ejection fraction, including echocardiographic signs of cardiac remodeling and cardiac biomarkers.

METHODS AND RESULTS

A history of infertility was ascertained in 2002 women enrolled in the Framingham Heart Study. We examined the association of infertility with echocardiographic measures and cardiac biomarkers with multivariable-adjusted linear regression models. Among 2002 women (mean age 40.84 ± 9.71 years), 285 (14%) reported a history of infertility. Infertility was associated with a greater E/e' ratio (β = 0.120, standard error 0.057, P = .04), even after adjustment for common confounders. Infertility was not associated with other echocardiographic measures or cardiac biomarkers.

CONCLUSIONS

Infertility was associated with a greater E/e' ratio, a marker of diastolic dysfunction that may signal earlier subclinical cardiac remodeling in women with infertility.

Natural Language Processing for Adjudication of Heart Failure in a Multicenter Clinical Trial: A Secondary Analysis of a Randomized Clinical Trial

Importance

The gold standard for outcome adjudication in clinical trials is medical record review by a physician clinical events committee (CEC), which requires substantial time and expertise. Automated adjudication of medical records by natural language processing (NLP) may offer a more resource-efficient alternative but this approach has not been validated in a multicenter setting.

Objective

To externally validate the Community Care Cohort Project (C3PO) NLP model for heart failure (HF) hospitalization adjudication, which was previously developed and tested within one health care system, compared to gold-standard CEC adjudication in a multicenter clinical trial.

Design, Setting, and Participants

This was a retrospective analysis of the Influenza Vaccine to Effectively Stop Cardio Thoracic Events and Decompensated Heart Failure (INVESTED) trial, which compared 2 influenza vaccines in 5260 participants with cardiovascular disease at 157 sites in the US and Canada between September 2016 and January 2019. Analysis was performed from November 2022 to October 2023.

Exposures

Individual sites submitted medical records for each hospitalization. The central INVESTED CEC and the C3PO NLP model independently adjudicated whether the cause of hospitalization was HF using the prepared hospitalization dossier. The C3PO NLP model was fine-tuned (C3PO + INVESTED) and a de novo NLP model was trained using half the INVESTED hospitalizations.

Main Outcomes and Measures

Concordance between the C3PO NLP model HF adjudication and the gold-standard INVESTED CEC adjudication was measured by raw agreement, κ, sensitivity, and specificity. The fine-tuned and de novo INVESTED NLP models were evaluated in an internal validation cohort not used for training.

Results

Among 4060 hospitalizations in 1973 patients (mean [SD] age, 66.4 [13.2] years; 514 [27.4%] female and 1432 [72.6%] male]), 1074 hospitalizations (26%) were adjudicated as HF by the CEC. There was good agreement between the C3PO NLP and CEC HF adjudications (raw agreement, 87% [95% CI, 86-88]; κ, 0.69 [95% CI, 0.66-0.72]). C3PO NLP model sensitivity was 94% (95% CI, 92-95) and specificity was 84% (95% CI, 83-85). The fine-tuned C3PO and de novo NLP models demonstrated agreement of 93% (95% CI, 92-94) and κ of 0.82 (95% CI, 0.77-0.86) and 0.83 (95% CI, 0.79-0.87), respectively, vs the CEC. CEC reviewer interrater reproducibility was 94% (95% CI, 93-95; κ, 0.85 [95% CI, 0.80-0.89]).

Conclusions and Relevance

The C3PO NLP model developed within 1 health care system identified HF events with good agreement relative to the gold-standard CEC in an external multicenter clinical trial. Fine-tuning the model improved agreement and approximated human reproducibility. Further study is needed to determine whether NLP will improve the efficiency of future multicenter clinical trials by identifying clinical events at scale.

Deep learned representations of the resting 12-lead electrocardiogram to predict at peak exercise

AIMS

To leverage deep learning on the resting 12-lead electrocardiogram (ECG) to estimate peak oxygen consumption (V˙O2peak) without cardiopulmonary exercise testing (CPET).

METHODS AND RESULTS

V ˙ O 2 peak estimation models were developed in 1891 individuals undergoing CPET at Massachusetts General Hospital (age 45 ± 19 years, 38% female) and validated in a separate test set (MGH Test, n = 448) and external sample (BWH Test, n = 1076). Three penalized linear models were compared: (i) age, sex, and body mass index ('Basic'), (ii) Basic plus standard ECG measurements ('Basic + ECG Parameters'), and (iii) basic plus 320 deep learning-derived ECG variables instead of ECG measurements ('Deep ECG-V˙O2'). Associations between estimated V˙O2peak and incident disease were assessed using proportional hazards models within 84 718 primary care patients without CPET. Inference ECGs preceded CPET by 7 days (median, interquartile range 27-0 days). Among models, Deep ECG-V˙O2 was most accurate in MGH Test [r = 0.845, 95% confidence interval (CI) 0.817-0.870; mean absolute error (MAE) 5.84, 95% CI 5.39-6.29] and BWH Test (r = 0.552, 95% CI 0.509-0.592, MAE 6.49, 95% CI 6.21-6.67). Deep ECG-V˙O2 also outperformed the Wasserman, Jones, and FRIEND reference equations (P < 0.01 for comparisons of correlation). Performance was higher in BWH Test when individuals with heart failure (HF) were excluded (r = 0.628, 95% CI 0.567-0.682; MAE 5.97, 95% CI 5.57-6.37). Deep ECG-V˙O2 estimated V˙O2peak <14 mL/kg/min was associated with increased risks of incident atrial fibrillation [hazard ratio 1.36 (95% CI 1.21-1.54)], myocardial infarction [1.21 (1.02-1.45)], HF [1.67 (1.49-1.88)], and death [1.84 (1.68-2.03)].

CONCLUSION

Deep learning-enabled analysis of the resting 12-lead ECG can estimate exercise capacity (V˙O2peak) at scale to enable efficient cardiovascular risk stratification.

Systemic Arterial Oxygen Levels Differentiate Pre- and Post-capillary Predominant Hemodynamic Abnormalities During Exercise in Undifferentiated Dyspnea on Exertion

BACKGROUND

Whether systemic oxygen levels (SaO2) during exercise can provide a window into invasively derived exercise hemodynamic profiles in patients with undifferentiated dyspnea on exertion is unknown.

METHODS

We performed cardiopulmonary exercise testing with invasive hemodynamic monitoring and arterial blood gas sampling in individuals referred for dyspnea on exertion. Receiver operator analysis was performed to distinguish heart failure with preserved ejection fraction from pulmonary arterial hypertension.

RESULTS

Among 253 patients (mean ± SD, age 63 ± 14 years, 55% female, arterial O2 [PaO2] 87 ± 14 mmHg, SaO2 96% ± 4%, resting pulmonary capillary wedge pressure [PCWP] 18 ± 4mmHg, and pulmonary vascular resistance [PVR] 2.7 ± 1.2 Wood units), there was no exercise PCWP threshold, measured up to 49 mmHg, above which hypoxemia was consistently observed. Exercise PaO2 was not correlated with exercise PCWP (rho = 0.04; P = 0.51) but did relate to exercise PVR (rho = -0.46; P < 0.001). Exercise PaO2 and SaO2 levels distinguished left-heart-predominant dysfunction from pulmonary-vascular-predominant dysfunction with an area under the curve of 0.89 and 0.89, respectively.

CONCLUSION

Systemic O2 levels during exercise distinguish relative pre- and post-capillary pulmonary hemodynamic abnormalities in patients with undifferentiated dyspnea. Hypoxemia during upright exercise should not be attributed to isolated elevation in left heart filling pressures and should prompt consideration of pulmonary vascular dysfunction.

Novel Prediction Equations for Absolute Risk Assessment of Total Cardiovascular Disease Incorporating Cardiovascular-Kidney-Metabolic Health: A Scientific Statement From the American Heart Association

Cardiovascular-kidney-metabolic (CKM) syndrome is a novel construct recently defined by the American Heart Association in response to the high prevalence of metabolic and kidney disease. Epidemiological data demonstrate higher absolute risk of both atherosclerotic cardiovascular disease (CVD) and heart failure as an individual progresses from CKM stage 0 to stage 3, but optimal strategies for risk assessment need to be refined. Absolute risk assessment with the goal to match type and intensity of interventions with predicted risk and expected treatment benefit remains the cornerstone of primary prevention. Given the growing number of therapies in our armamentarium that simultaneously address all 3 CKM axes, novel risk prediction equations are needed that incorporate predictors and outcomes relevant to the CKM context. This should also include social determinants of health, which are key upstream drivers of CVD, to more equitably estimate and address risk. This scientific statement summarizes the background, rationale, and clinical implications for the newly developed sex-specific, race-free risk equations: PREVENT (AHA Predicting Risk of CVD Events). The PREVENT equations enable 10- and 30-year risk estimates for total CVD (composite of atherosclerotic CVD and heart failure), include estimated glomerular filtration rate as a predictor, and adjust for competing risk of non-CVD death among adults 30 to 79 years of age. Additional models accommodate enhanced predictive utility with the addition of CKM factors when clinically indicated for measurement (urine albumin-to-creatinine ratio and hemoglobin A1c) or social determinants of health (social deprivation index) when available. Approaches to implement risk-based prevention using PREVENT across various settings are discussed.

How Would You Manage This Patient With Heart Failure With Preserved Ejection Fraction? : Grand Rounds Discussion From Beth Israel Deaconess Medical Center

The proportion of patients with new-onset heart failure who have preserved rather than reduced left ventricular ejection fraction (HFpEF and HFrEF) has been increasing over recent decades. In fact, HFpEF now outweighs HFrEF as the predominant heart failure subtype and likely remains underdiagnosed in the community. This is due in part to an aging population and a rise in other risk factors for HFpEF, including obesity and associated cardiometabolic disease. Whereas the diagnosis of HFrEF is relatively straightforward, the diagnosis of HFpEF is often more challenging because there can be other causes for symptoms, including dyspnea and fatigue, and cardinal physical examination findings of elevated jugular venous pressure or pulmonary congestion may not be evident at rest. In 2022, the American College of Cardiology, the American Heart Association, and the Heart Failure Society of America published a comprehensive guideline on heart failure that included recommendations for the management of HFpEF. The use of diuretics for the management of congestion remained the only class 1 (strong) recommendation. New recommendations included broader use of sodium-glucose cotransporter-2 inhibitors (SGLT2i, class 2a), and angiotensin receptor-neprilysin inhibitors (class 2b). In 2023, the American College of Cardiology published an expert consensus decision pathway for the management of HFpEF that suggests treatment strategies based on sex assigned at birth, ejection fraction, clinical evidence of congestion, and candidacy for SGLT2i therapy. Here, 2 experts, a cardiologist and a geriatrician, discuss their approach to the diagnosis and management of HFpEF and how they would apply guidelines to an individual patient.

Circulating extracellular vesicles in human cardiorenal syndrome promote renal injury in a kidney-on-chip system

BACKGROUNDCardiorenal syndrome (CRS) - renal injury during heart failure (HF) - is linked to high morbidity. Whether circulating extracellular vesicles (EVs) and their RNA cargo directly impact its pathogenesis remains unclear.METHODSWe investigated the role of circulating EVs from patients with CRS on renal epithelial/endothelial cells using a microfluidic kidney-on-chip (KOC) model. The small RNA cargo of circulating EVs was regressed against serum creatinine to prioritize subsets of functionally relevant EV-miRNAs and their mRNA targets investigated using in silico pathway analysis, human genetics, and interrogation of expression in the KOC model and in renal tissue. The functional effects of EV-RNAs on kidney epithelial cells were experimentally validated.RESULTSRenal epithelial and endothelial cells in the KOC model exhibited uptake of EVs from patients with HF. HF-CRS EVs led to higher expression of renal injury markers (IL18, LCN2, HAVCR1) relative to non-CRS EVs. A total of 15 EV-miRNAs were associated with creatinine, targeting 1,143 gene targets specifying pathways relevant to renal injury, including TGF-β and AMPK signaling. We observed directionally consistent changes in the expression of TGF-β pathway members (BMP6, FST, TIMP3) in the KOC model exposed to CRS EVs, which were validated in epithelial cells treated with corresponding inhibitors and mimics of miRNAs. A similar trend was observed in renal tissue with kidney injury. Mendelian randomization suggested a role for FST in renal function.CONCLUSIONPlasma EVs in patients with CRS elicit adverse transcriptional and phenotypic responses in a KOC model by regulating biologically relevant pathways, suggesting a role for EVs in CRS.TRIAL REGISTRATIONClinicalTrials.gov NCT03345446.FUNDINGAmerican Heart Association (AHA) (SFRN16SFRN31280008); National Heart, Lung, and Blood Institute (1R35HL150807-01); National Center for Advancing Translational Sciences (UH3 TR002878); and AHA (23CDA1045944).

Eicosanoid and eicosanoid-related inflammatory mediators and exercise intolerance in heart failure with preserved ejection fraction

Systemic inflammation has been implicated in the pathobiology of heart failure with preserved ejection fraction (HFpEF). Here, we examine the association of upstream mediators of inflammation as ascertained by fatty-acid derived eicosanoid and eicosanoid-related metabolites with HFpEF status and exercise manifestations of HFpEF. Among 510 participants with chronic dyspnea and preserved LVEF who underwent invasive cardiopulmonary exercise testing, we find that 70 of 890 eicosanoid and related metabolites are associated with HFpEF status, including 17 named and 53 putative eicosanoids (FDR q-value < 0.1). Prostaglandin (15R-PGF2α, 11ß-dhk-PGF2α) and linoleic acid derivatives (12,13 EpOME) are associated with greater odds of HFpEF, while epoxides (8(9)-EpETE), docosanoids (13,14-DiHDPA), and oxylipins (12-OPDA) are associated with lower odds of HFpEF. Among 70 metabolites, 18 are associated with future development of heart failure in the community. Pro- and anti-inflammatory eicosanoid and related metabolites may contribute to the pathogenesis of HFpEF and serve as potential targets for intervention.

Deep Learning-Enabled Assessment of Left Heart Structure and Function Predicts Cardiovascular Outcomes

BACKGROUND

Deep learning interpretation of echocardiographic images may facilitate automated assessment of cardiac structure and function.

OBJECTIVES

We developed a deep learning model to interpret echocardiograms and examined the association of deep learning-derived echocardiographic measures with incident outcomes.

METHODS

We trained and validated a 3-dimensional convolutional neural network model for echocardiographic view classification and quantification of left atrial dimension, left ventricular wall thickness, chamber diameter, and ejection fraction. The training sample comprised 64,028 echocardiograms (n = 27,135) from a retrospective multi-institutional ambulatory cardiology electronic health record sample. Validation was performed in a separate longitudinal primary care sample and an external health care system data set. Cox models evaluated the association of model-derived left heart measures with incident outcomes.

RESULTS

Deep learning discriminated echocardiographic views (area under the receiver operating curve >0.97 for parasternal long axis, apical 4-chamber, and apical 2-chamber views vs human expert annotation) and quantified standard left heart measures (R2 range = 0.53 to 0.91 vs study report values). Model performance was similar in 2 external validation samples. Model-derived left heart measures predicted incident heart failure, atrial fibrillation, myocardial infarction, and death. A 1-SD lower model-left ventricular ejection fraction was associated with 43% greater risk of heart failure (HR: 1.43; 95% CI: 1.23-1.66) and 17% greater risk of death (HR: 1.17; 95% CI: 1.06-1.30). Similar results were observed for other model-derived left heart measures.

CONCLUSIONS

Deep learning echocardiographic interpretation accurately quantified standard measures of left heart structure and function, which in turn were associated with future clinical outcomes. Deep learning may enable automated echocardiogram interpretation and disease prediction at scale.

A Synopsis of the Evidence for the Science and Clinical Management of Cardiovascular-Kidney-Metabolic (CKM) Syndrome: A Scientific Statement From the American Heart Association

A growing appreciation of the pathophysiological interrelatedness of metabolic risk factors such as obesity and diabetes, chronic kidney disease, and cardiovascular disease has led to the conceptualization of cardiovascular-kidney-metabolic syndrome. The confluence of metabolic risk factors and chronic kidney disease within cardiovascular-kidney-metabolic syndrome is strongly linked to risk for adverse cardiovascular and kidney outcomes. In addition, there are unique management considerations for individuals with established cardiovascular disease and coexisting metabolic risk factors, chronic kidney disease, or both. An extensive body of literature supports our scientific understanding of, and approach to, prevention and management for individuals with cardiovascular-kidney-metabolic syndrome. However, there are critical gaps in knowledge related to cardiovascular-kidney-metabolic syndrome in terms of mechanisms of disease development, heterogeneity within clinical phenotypes, interplay between social determinants of health and biological risk factors, and accurate assessments of disease incidence in the context of competing risks. There are also key limitations in the data supporting the clinical care for cardiovascular-kidney-metabolic syndrome, particularly in terms of early-life prevention, screening for risk factors, interdisciplinary care models, optimal strategies for supporting lifestyle modification and weight loss, targeting of emerging cardioprotective and kidney-protective therapies, management of patients with both cardiovascular disease and chronic kidney disease, and the impact of systematically assessing and addressing social determinants of health. This scientific statement uses a crosswalk of major guidelines, in addition to a review of the scientific literature, to summarize the evidence and fundamental gaps related to the science, screening, prevention, and management of cardiovascular-kidney-metabolic syndrome.

Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association

Cardiovascular-kidney-metabolic health reflects the interplay among metabolic risk factors, chronic kidney disease, and the cardiovascular system and has profound impacts on morbidity and mortality. There are multisystem consequences of poor cardiovascular-kidney-metabolic health, with the most significant clinical impact being the high associated incidence of cardiovascular disease events and cardiovascular mortality. There is a high prevalence of poor cardiovascular-kidney-metabolic health in the population, with a disproportionate burden seen among those with adverse social determinants of health. However, there is also a growing number of therapeutic options that favorably affect metabolic risk factors, kidney function, or both that also have cardioprotective effects. To improve cardiovascular-kidney-metabolic health and related outcomes in the population, there is a critical need for (1) more clarity on the definition of cardiovascular-kidney-metabolic syndrome; (2) an approach to cardiovascular-kidney-metabolic staging that promotes prevention across the life course; (3) prediction algorithms that include the exposures and outcomes most relevant to cardiovascular-kidney-metabolic health; and (4) strategies for the prevention and management of cardiovascular disease in relation to cardiovascular-kidney-metabolic health that reflect harmonization across major subspecialty guidelines and emerging scientific evidence. It is also critical to incorporate considerations of social determinants of health into care models for cardiovascular-kidney-metabolic syndrome and to reduce care fragmentation by facilitating approaches for patient-centered interdisciplinary care. This presidential advisory provides guidance on the definition, staging, prediction paradigms, and holistic approaches to care for patients with cardiovascular-kidney-metabolic syndrome and details a multicomponent vision for effectively and equitably enhancing cardiovascular-kidney-metabolic health in the population.

Proteomics and Precise Exercise Phenotypes in Heart Failure With Preserved Ejection Fraction: A Pilot Study

BACKGROUND

While exercise impairments are central to symptoms and diagnosis of heart failure with preserved ejection fraction (HFpEF), prior studies of HFpEF biomarkers have mostly focused on resting phenotypes. We combined precise exercise phenotypes with cardiovascular proteomics to identify protein signatures of HFpEF exercise responses and new potential therapeutic targets.

METHODS AND RESULTS

We analyzed 277 proteins (Olink) in 151 individuals (N=103 HFpEF, 48 controls; 62±11 years; 56% women) with cardiopulmonary exercise testing with invasive monitoring. Using ridge regression adjusted for age/sex, we defined proteomic signatures of 5 physiological variables involved in HFpEF: peak oxygen uptake, peak cardiac output, pulmonary capillary wedge pressure/cardiac output slope, peak pulmonary vascular resistance, and peak peripheral O2 extraction. Multiprotein signatures of each of the exercise phenotypes captured a significant proportion of variance in respective exercise phenotypes. Interrogating the importance (ridge coefficient magnitude) of specific proteins in each signature highlighted proteins with putative links to HFpEF pathophysiology (eg, inflammatory, profibrotic proteins), and novel proteins linked to distinct physiologies (eg, proteins involved in multiorgan [kidney, liver, muscle, adipose] health) were implicated in impaired O2 extraction. In a separate sample (N=522, 261 HF events), proteomic signatures of peak oxygen uptake and pulmonary capillary wedge pressure/cardiac output slope were associated with incident HFpEF (odds ratios, 0.67 [95% CI, 0.50-0.90] and 1.43 [95% CI, 1.11-1.85], respectively) with adjustment for clinical factors and B-type natriuretic peptides.

CONCLUSIONS

The cardiovascular proteome is associated with precision exercise phenotypes in HFpEF, suggesting novel mechanistic targets and potential methods for risk stratification to prevent HFpEF early in its pathogenesis.

Circulating Cardiovascular Biomarkers in Cancer Therapeutics-Related Cardiotoxicity: Review of Critical Challenges, Solutions, and Future Directions

Cardiotoxicity is a growing concern in the oncology population. Transthoracic echocardiography and multigated acquisition scans have been used for surveillance but are relatively insensitive and resource intensive. Innovative imaging techniques are constrained by cost and availability. More sensitive, cost-effective cardiotoxicity surveillance strategies are needed. Circulating cardiovascular biomarkers could provide a sensitive, low-cost solution. Biomarkers such as troponins, natriuretic peptides (NPs), novel upstream signals of oxidative stress, inflammation, and fibrosis as well as panomic technologies have shown substantial promise, and guidelines recommend baseline measurement of troponins and NPs in all patients receiving potential cardiotoxins. Nonetheless, supporting evidence has been hampered by several limitations. Previous reviews have provided valuable perspectives on biomarkers in cancer populations, but important analytic aspects remain to be examined in depth. This review provides comprehensive assessment of critical challenges and solutions in this field, with focus on analytical issues relating to biomarker measurement and interpretation. Examination of evidence pertaining to common and serious forms of cardiotoxicity reveals that improved study designs incorporating larger, more diverse populations, registry-based approaches, and refinement of current definitions are key. Further efforts to harmonize biomarker methodologies including centralized biobanking and analyses, novel decision limits, and head-to-head comparisons are needed. Multimarker algorithms incorporating machine learning may allow rapid, personalized risk assessment. These improvements will not only augment the predictive value of circulating biomarkers in cardiotoxicity but may elucidate both direct and indirect relationships between cardiovascular disease and cancer, allowing biomarkers a greater role in the development and success of novel anticancer therapies.

Obesity Modifies Clinical Outcomes of Right Ventricular Dysfunction

BACKGROUND

Right ventricular (RV) dysfunction is associated with increased mortality across a spectrum of cardiovascular diseases. The role of obesity in RV dysfunction and adverse outcomes is unclear.

METHODS

We examined patients undergoing right heart catheterization between 2005 and 2016 in a hospital-based cohort. Linear regression was used to examine the association of obesity with hemodynamic indices of RV dysfunction (pulmonary artery pulsatility index, right atrial pressure:pulmonary capillary wedge pressure ratio, RV stroke work index). Cox models were used to examine the association of RV function measures with clinical outcomes.

RESULTS

Among 8285 patients (mean age, 63 years; 40% women), higher body mass index was associated with worse indices of RV dysfunction, including lower pulmonary artery pulsatility index (β, -0.23; SE, 0.01; P<0.001), higher right atrium:pulmonary capillary wedge pressure ratio (β, 0.25; SE, 0.01; P<0.001), and lower RV stroke work index (β, -0.05; SE, 0.01; P<0.001). Over median of 7.3 years of follow-up, we observed 3006 mortality and 2004 heart failure hospitalization events. RV dysfunction was associated with a greater risk of mortality (eg, pulmonary artery pulsatility index:hazard ratio, 1.11 per 1-SD increase [95% CI, 1.04-1.18]), with similar associations with risk of heart failure hospitalization. Body mass index modified the effect of RV dysfunction on all-cause mortality (Pinteraction≤0.005 for PAPi and RA:PCWP ratio), such that the effect of RV dysfunction was more pronounced at higher body mass index.

CONCLUSIONS

Patients with obesity had worse hemodynamic measured indices of RV function across a broad hospital-based sample. While RV dysfunction was associated with worse clinical outcomes including mortality and heart failure hospitalization, this association was especially pronounced among individuals with higher body mass index.

Obesity-Related Biomarkers Are Associated With Exercise Intolerance and HFpEF

BACKGROUND

Obesity and adiposity are associated with an increased risk of heart failure with preserved ejection fraction (HFpEF); yet, specific underlying mechanisms remain unclear. We sought to examine the association of obesity-related biomarkers including adipokines (leptin, resistin, adiponectin), inflammatory markers (CRP [C-reactive protein], IL-6 [interleukin-6]), and insulin resistance (HOMA-IR) with HFpEF status, exercise capacity, and cardiovascular outcomes.

METHODS

We studied 509 consecutive patients with left ventricular ejection fraction ≥50% and chronic dyspnea, who underwent clinically indicated cardiopulmonary exercise test with invasive hemodynamic monitoring between 2006 and 2017. We defined HFpEF based on the presence of elevated left ventricular filling pressures at rest or during exercise. Fasting blood samples collected at the time of the cardiopulmonary exercise test were used to assay obesity-related biomarkers. We examined the association of log-transformed biomarkers with HFpEF status and exercise traits using multivariable-adjusted logistic regression models.

RESULTS

We observed associations of obesity-related biomarkers with measures of impaired exercise capacity including peak VO2 (P≤0.002 for all biomarkers). The largest effect size was seen with leptin, where a 1-SD higher leptin was associated with a 2.35 mL/kg per min lower peak VO2 (β, -2.35±0.19; P<0.001). In addition, specific biomarkers were associated with distinct measures of exercise reserve including blood pressure (homeostatic model assessment of insulin resistance, leptin, adiponectin; P≤0.002 for all), and chronotropic response (CRP, IL-6, homeostatic model assessment of insulin resistance, leptin, and resistin; P<0.05 for all). Our findings suggest that among the obesity-related biomarkers studied, higher levels of leptin and CRP are independently associated with increased odds of HFpEF, with odds ratios of 1.36 (95% CI, 1.09-1.70) and 1.25 (95% CI, 1.03-1.52), respectively.

CONCLUSIONS

Specific obesity-related pathways including inflammation, adipokine signaling, and insulin resistance may underlie the association of obesity with HFpEF and exercise intolerance.

Recent successes in heart failure treatment

Remarkable recent advances have revolutionized the field of heart failure. Survival has improved among individuals with heart failure and a reduced ejection fraction and for the first time, new therapies have been shown to improve outcomes across the entire ejection fraction spectrum of heart failure. Great strides have been taken in the treatment of specific cardiomyopathies such as cardiac amyloidosis and hypertrophic cardiomyopathy, whereby conditions once considered incurable can now be effectively managed with novel genetic and molecular approaches. Yet there remain substantial residual unmet needs in heart failure. The translation of successful clinical trials to improved patient outcomes is limited by large gaps in implementation of care, widespread lack of disease awareness and poor understanding of the socioeconomic determinants of outcomes and how to address disparities. Ongoing clinical trials, advances in phenotype segmentation for precision medicine and the rise in technology solutions all offer hope for the future.

The association of eicosanoids and eicosanoid-related metabolites with pulmonary hypertension

BACKGROUND

Eicosanoids are bioactive lipids that regulate systemic inflammation and exert vasoactive effects. Specific eicosanoid metabolites have previously been associated with pulmonary hypertension (PH), yet their role remains incompletely understood.

METHODS

We studied 482 participants with chronic dyspnoea who underwent clinically indicated cardiopulmonary exercise testing (CPET) with invasive haemodynamic monitoring. We performed comprehensive profiling of 888 eicosanoids and eicosanoid-related metabolites using directed non-targeted mass spectrometry, and examined associations with PH (mean pulmonary arterial pressure (mPAP) >20 mmHg), PH subtypes and physiological correlates, including transpulmonary metabolite gradients.

RESULTS

Among 482 participants (mean±sd age 56±16 years, 62% women), 200 had rest PH. We found 48 eicosanoids and eicosanoid-related metabolites that were associated with PH. Specifically, prostaglandin (11β-dhk-PGF2α), linoleic acid (12,13-EpOME) and arachidonic acid derivatives (11,12-DiHETrE) were associated with higher odds of PH (false discovery rate q<0.05 for all). By contrast, epoxide (8(9)-EpETE), α-linolenic acid (13(S)-HOTrE(γ)) and lipokine derivatives (12,13-DiHOME) were associated with lower odds. Among PH-related eicosanoids, 14 showed differential transpulmonary metabolite gradients, with directionality suggesting that metabolites associated with lower odds of PH also displayed pulmonary artery uptake. In individuals with exercise PH, eicosanoid profiles were intermediate between no PH and rest PH, with six metabolites that differed between rest and exercise PH.

CONCLUSIONS

Our findings highlight the role of specific eicosanoids, including linoleic acid and epoxide derivatives, as potential regulators of inflammation in PH. Of note, physiological correlates, including transpulmonary metabolite gradients, may prioritise future studies focused on eicosanoid-related pathways as important contributors to PH pathogenesis.

Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis

INTRODUCTION

The classification and management of pulmonary hypertension (PH) is challenging due to clinical heterogeneity of patients. We sought to identify distinct multimorbid phenogroups of patients with PH that are at particularly high-risk for adverse events.

METHODS

A hospital-based cohort of patients referred for right heart catheterization between 2005-2016 with PH were included. Key exclusion criteria were shock, cardiac arrest, cardiac transplant, or valvular surgery. K-prototypes was used to cluster patients into phenogroups based on 12 clinical covariates.

RESULTS

Among 5208 patients with mean age 64±12 years, 39% women, we identified 5 distinct multimorbid PH phenogroups with similar hemodynamic measures yet differing clinical outcomes: (1) "young men with obesity", (2) "women with hypertension", (3) "men with overweight", (4) "men with cardiometabolic and cardiovascular disease", and (5) "men with structural heart disease and atrial fibrillation." Over a median follow-up of 6.3 years, we observed 2182 deaths and 2002 major cardiovascular events (MACE). In age- and sex-adjusted analyses, phenogroups 4 and 5 had higher risk of MACE (HR 1.68, 95% CI 1.41-2.00 and HR 1.52, 95% CI 1.24-1.87, respectively, compared to the lowest risk phenogroup 1). Phenogroup 4 had the highest risk of mortality (HR 1.26, 95% CI 1.04-1.52, relative to phenogroup 1).

CONCLUSIONS

Cluster-based analyses identify patients with PH and specific comorbid cardiometabolic and cardiovascular disease burden that are at highest risk for adverse clinical outcomes. Interestingly, cardiopulmonary hemodynamics were similar across phenogroups, highlighting the importance of multimorbidity on clinical trajectory. Further studies are needed to better understand comorbid heterogeneity among patients with PH.

Natural Language Processing for Adjudication of Heart Failure Hospitalizations in a Multi-Center Clinical Trial

Background

The gold standard for outcome adjudication in clinical trials is chart review by a physician clinical events committee (CEC), which requires substantial time and expertise. Automated adjudication by natural language processing (NLP) may offer a more resource-efficient alternative. We previously showed that the Community Care Cohort Project (C3PO) NLP model adjudicates heart failure (HF) hospitalizations accurately within one healthcare system.

Methods

This study externally validated the C3PO NLP model against CEC adjudication in the INVESTED trial. INVESTED compared influenza vaccination formulations in 5260 patients with cardiovascular disease at 157 North American sites. A central CEC adjudicated the cause of hospitalizations from medical records. We applied the C3PO NLP model to medical records from 4060 INVESTED hospitalizations and evaluated agreement between the NLP and final consensus CEC HF adjudications. We then fine-tuned the C3PO NLP model (C3PO+INVESTED) and trained a de novo model using half the INVESTED hospitalizations, and evaluated these models in the other half. NLP performance was benchmarked to CEC reviewer inter-rater reproducibility.

Results

1074 hospitalizations (26%) were adjudicated as HF by the CEC. There was high agreement between the C3PO NLP and CEC HF adjudications (agreement 87%, kappa statistic 0.69). C3PO NLP model sensitivity was 94% and specificity was 84%. The fine-tuned C3PO and de novo NLP models demonstrated agreement of 93% and kappa of 0.82 and 0.83, respectively. CEC reviewer inter-rater reproducibility was 94% (kappa 0.85).

Conclusion

Our NLP model developed within a single healthcare system accurately identified HF events relative to the gold-standard CEC in an external multi-center clinical trial. Fine-tuning the model improved agreement and approximated human reproducibility. NLP may improve the efficiency of future multi-center clinical trials by accurately identifying clinical events at scale.

Protein Biomarkers of Early Menopause and Incident Cardiovascular Disease

Background Premature and early menopause are independently associated with greater risk of cardiovascular disease (CVD). However, mechanisms linking age of menopause with CVD remain poorly characterized. Methods and Results We measured 71 circulating CVD protein biomarkers in 1565 postmenopausal women enrolled in the FHS (Framingham Heart Study). We examined the association of early menopause with biomarkers and tested whether early menopause modified the association of biomarkers with incident cardiovascular outcomes (heart failure, major CVD, and all-cause death) using multivariable-adjusted linear regression and Cox models, respectively. Among 1565 postmenopausal women included (mean age 62 years), 395 (25%) had a history of early menopause. Of 71 biomarkers examined, we identified 7 biomarkers that were significantly associated with early menopause, of which 5 were higher in women with early menopause including adrenomedullin and resistin, and 2 were higher in women without early menopause including insulin growth factor-1 and CNTN1 (contactin-1) (Benjamini-Hochberg adjusted P<0.1 for all). Early menopause also modified the association of specific biomarkers with incident cardiovascular outcomes including adrenomedullin (Pint<0.05). Conclusions Early menopause is associated with circulating levels of CVD protein biomarkers and appears to modify the association between select biomarkers with incident cardiovascular outcomes. Identified biomarkers reflect several distinct biological pathways, including inflammation, adiposity, and neurohormonal regulation. Further investigation of these pathways may provide mechanistic insights into the pathogenesis, prevention, and treatment of early menopause-associated CVD.

Global Comparison of Readmission Rates for Patients With Heart Failure

BACKGROUND

Heart failure (HF) readmission rates are low in some jurisdictions. However, international comparisons are lacking and could serve as a foundation for identifying regional patient management strategies that could be shared to improve outcomes.

OBJECTIVES

This study sought to summarize 30-day and 1-year all-cause readmission and mortality rates of hospitalized HF patients across countries and to explore potential differences in rates globally.

METHODS

We performed a systematic review and meta-analysis using MEDLINE, Embase, and CENTRAL for observational reports on hospitalized adult HF patients at risk for readmission or mortality published between January 2010 and March 2021. We conducted a meta-analysis of proportions using a random-effects model, and sources of heterogeneity were evaluated with meta-regression.

RESULTS

In total, 24 papers reporting on 30-day and 23 papers on 1-year readmission were included. Of the 1.5 million individuals at risk, 13.2% (95% CI: 10.5%-16.1%) were readmitted within 30 days and 35.7% (95% CI: 27.1%-44.9%) within 1 year. A total of 33 papers reported on 30-day and 45 papers on 1-year mortality. Of the 1.5 million individuals hospitalized for HF, 7.6% (95% CI: 6.1%-9.3%) died within 30 days and 23.3% (95% CI: 20.8%-25.9%) died within 1 year. Substantial variation in risk across countries was unexplained by countries' gross domestic product, proportion of gross domestic product spent on health care, and Gini coefficient.

CONCLUSIONS

Globally, hospitalized HF patients exhibit high rates of readmission and mortality, and the variability in readmission rates was not explained by health care expenditure, risk of mortality, or comorbidities.

Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms

BACKGROUND

Artificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythmia. However, the factors forming the basis of risk predictions from AI models are usually not well understood. We hypothesized that there might be a genetic basis for an AI algorithm for predicting the 5-year risk of new-onset AF using 12-lead ECGs (ECG-AI)-based risk estimates.

METHODS

We applied a validated ECG-AI model for predicting incident AF to ECGs from 39 986 UK Biobank participants without AF. We then performed a genome-wide association study (GWAS) of the predicted AF risk and compared it with an AF GWAS and a GWAS of risk estimates from a clinical variable model.

RESULTS

In the ECG-AI GWAS, we identified 3 signals (P<5×10-8) at established AF susceptibility loci marked by the sarcomeric gene TTN and sodium channel genes SCN5A and SCN10A. We also identified 2 novel loci near the genes VGLL2 and EXT1. In contrast, the clinical variable model prediction GWAS indicated a different genetic profile. In genetic correlation analysis, the prediction from the ECG-AI model was estimated to have a higher correlation with AF than that from the clinical variable model.

CONCLUSIONS

Predicted AF risk from an ECG-AI model is influenced by genetic variation implicating sarcomeric, ion channel and body height pathways. ECG-AI models may identify individuals at risk for disease via specific biological pathways.

Fibrotic Marker Galectin-3 Identifies Males at Risk of Developing Cancer and Heart Failure

Background

Cancer and heart failure (HF) are the leading causes of death in the Western world. Shared mechanisms such as fibrosis may underlie either disease entity, furthermore it is unknown whether this relationship is sex-specific.

Objectives

We sought to investigate how fibrosis-related biomarker galectin-3 (gal-3) aids in identifying individuals at risk for new-onset cancer and HF, and how this differs between sexes.

Methods

Gal-3 was measured at baseline and at 4-year follow-up in 5,786 patients of the PREVEND (Prevention of Renal and Vascular Endstage Disease) study. The total follow-up period was 11.5 years. An increase of ≥50% in gal-3 levels between measurements was considered relevant. We performed sex-stratified log-rank tests and Cox regression analyses overall and by sex to evaluate the association of gal-3 over time with both new-onset cancer and new-onset HF.

Results

Of the 5,786 healthy participants (50% males), 399 (59% males) developed new-onset cancer, and 192 (65% males) developed new-onset HF. In males, an increase in gal-3 was significantly associated with new-onset cancer (both combined and certain cancer-specific subtypes), after adjusting for age, body mass index, hypertension, smoking status, estimated glomerular filtration rate, diabetes mellitus, triglycerides, coronary artery disease, and C-reactive protein (HR: 1.89; 95% CI: 1.32-2.71; P < 0.001). Similar analyses demonstrated an association with new-onset HF in males (HR: 1.77; 95% CI: 1.07-2.95; P = 0.028). In females, changes in gal-3 over time were neither associated with new-onset cancer nor new-onset HF.

Conclusions

Gal-3, a marker of fibrosis, is associated with new-onset cancer and new-onset HF in males, but not in females.

Multiple Prior Live Births Are Associated With Cardiac Remodeling and Heart Failure Risk in Women

OBJECTIVE

Greater parity has been associated with cardiovascular disease risk. We sought to find whether the effects on cardiac remodeling and heart failure risk are clear.

METHODS

We examined the association of number of live births with echocardiographic measures of cardiac structure and function in participants of the Framingham Heart Study (FHS) using multivariable linear regression. We next examined the association of parity with incident heart failure with preserved (HFpEF) or reduced (HFrEF) ejection fraction using a Fine-Gray subdistribution hazards model in a pooled analysis of n = 12,635 participants in the FHS, the Cardiovascular Health Study, the Multi-Ethnic Study of Atherosclerosis, and Prevention of Renal and Vascular Endstage Disease. Secondary analyses included major cardiovascular disease, myocardia infarction and stroke.

RESULTS

Among n = 3931 FHS participants (mean age 48 ± 13 years), higher numbers of live births were associated with worse left ventricular fractional shortening (multivariable β -1.11 (0.31); P = 0.0005 in ≥ 5 live births vs nulliparous women) and worse cardiac mechanics, including global circumferential strain and longitudinal and radial dyssynchrony (P < 0.01 for all comparing ≥ 5 live births vs nulliparity). When examining HF subtypes, women with ≥ 5 live births were at higher risk of developing future HFrEF compared with nulliparous women (HR 1.93, 95% CI 1.19-3.12; P = 0.008); by contrast, a lower risk of HFpEF was observed (HR 0.58, 95% CI 0.37-0.91; P = 0.02).

CONCLUSIONS

Greater numbers of live births are associated with worse cardiac structure and function. There was no association with overall HF, but a higher number of live births was associated with greater risk for incident HFrEF.

Outcomes With Peripheral Venoarterial Extracorporeal Membrane Oxygenation for Suspected Acute Myocarditis: 10-Year Experience From the Extracorporeal Life Support Organization Registry

BACKGROUND

Acute myocarditis can result in severe hemodynamic compromise requiring venoarterial extracorporeal membrane oxygenation (VA-ECMO). Outcomes and factors associated with mortality among myocarditis patients are not well described in the modern ECMO era.

METHODS

We queried the Extracorporeal Life Support Organization registry from 2011 to 2020 for adults with suspected acute myocarditis undergoing peripheral VA-ECMO support. The primary outcome was in-hospital mortality and was compared to all-comers receiving VA-ECMO in the registry over the same period. Secondary outcomes were rates of bridging to advanced therapies and ECMO complications. We used multivariable logistic regression to examine factors associated with in-hospital mortality.

RESULTS

Among 850 patients with suspected acute myocarditis receiving peripheral VA-ECMO, the mean age was 41 years, 52% were men, 39% Asian race, and 14.8% underwent extracorporeal cardiopulmonary resuscitation. During the study period, in-hospital mortality steadily declined and was 58.3% for all all-comers receiving VA-ECMO compared with 34.9% for patients with myocarditis (P<0.001). After multivariable modeling, risk factors for mortality were earlier year of support, older age, higher weight, Asian race, need for extracorporeal cardiopulmonary resuscitation, sepsis, and lower mean arterial pressure and pH prior to ECMO initiation. ECMO complications including bleeding, limb ischemia, infections and ischemic stroke were more common among nonsurvivors and significantly declined during the study period.

CONCLUSIONS

Compared with all-comers supported with VA-ECMO, in-hospital mortality for patients with acute myocarditis is significantly lower, with nearly two-thirds of patients surviving to discharge. Major modifiable risk factors for mortality were ongoing cardiopulmonary resuscitation requiring ECMO and markers of illness severity prior to ECMO.

Effect of 2022 ACC/AHA/HFSA Criteria on Stages of Heart Failure in a Pooled Community Cohort

BACKGROUND

The 2022 American College of Cardiology (ACC)/American Heart Association (AHA)/Heart Failure Society of America (HFSA) clinical practice guideline proposed an updated definition for heart failure (HF) stages.

OBJECTIVES

This study aimed to compare prevalence and prognosis of HF stages according to classification/definition originally described in 2013 and 2022 ACC/AHA/HFSA definitions.

METHODS

Study participants from 3 longitudinal cohorts (the MESA [Multi-Ethnic Study of Atherosclerosis], CHS [Cardiovascular Health Study], and the FHS [Framingham Heart Study]), were categorized into 4 HF stages according to the 2013 and 2022 criteria. Cox proportional hazards regression was used to assess predictors of progression to symptomatic HF and adverse clinical outcomes associated with each HF stage.

RESULTS

Among 11,618 study participants, according to the 2022 staging, 1,943 (16.7%) were healthy, 4,348 (37.4%) were in stage A (at risk), 5,019 (43.2%) were in stage B (pre-HF), and 308 (2.7%) were in stage C/D (symptomatic HF). Compared to the classification/definition originally described in 2013, the 2022 ACC/AHA/HFSA approach resulted in a higher proportion of individuals with stage B HF (increase from 15.9% to 43.2%); this shift disproportionately involved women as well as Hispanic and Black individuals. Despite the 2022 criteria designating a greater proportion of individuals as stage B, the relative risk of progression to symptomatic HF remained similar (HR: 10.61; 95% CI: 9.00-12.51; P < 0.001).

CONCLUSIONS

New standards for HF staging resulted in a substantial shift of community-based individuals from stage A to stage B. Those with stage B HF in the new system were at high risk for progression to symptomatic HF.

Heart Failure With Preserved Ejection Fraction: JACC Scientific Statement

The incidence and prevalence of heart failure with preserved ejection fraction (HFpEF) continue to rise in tandem with the increasing age and burdens of obesity, sedentariness, and cardiometabolic disorders. Despite recent advances in the understanding of its pathophysiological effects on the heart, lungs, and extracardiac tissues, and introduction of new, easily implemented approaches to diagnosis, HFpEF remains under-recognized in everyday practice. This under-recognition presents an even greater concern given the recent identification of highly effective pharmacologic-based and lifestyle-based treatments that can improve clinical status and reduce morbidity and mortality. HFpEF is a heterogenous syndrome and recent studies have suggested an important role for careful, pathophysiological-based phenotyping to improve patient characterization and to better individualize treatment. In this JACC Scientific Statement, we provide an in-depth and updated examination of the epidemiology, pathophysiology, diagnosis, and treatment of HFpEF.

Assessment of valvular function in over 47,000 people using deep learning-based flow measurements

Valvular heart disease is associated with a high global burden of disease. Even mild aortic stenosis confers increased morbidity and mortality, prompting interest in understanding normal variation in valvular function at scale. We developed a deep learning model to study velocity-encoded magnetic resonance imaging in 47,223 UK Biobank participants. We calculated eight traits, including peak velocity, mean gradient, aortic valve area, forward stroke volume, mitral and aortic regurgitant volume, greatest average velocity, and ascending aortic diameter. We then computed sex-stratified reference ranges for these phenotypes in up to 31,909 healthy individuals. In healthy individuals, we found an annual decrement of 0.03cm ² in the aortic valve area. Participants with mitral valve prolapse had a 1 standard deviation [SD] higher mitral regurgitant volume (P=9.6 × 10 ^-12 ), and those with aortic stenosis had a 4.5 SD-higher mean gradient (P=1.5 × 10 ^-431 ), validating the derived phenotypes' associations with clinical disease. Greater levels of ApoB, triglycerides, and Lp(a) assayed nearly 10 years prior to imaging were associated with higher gradients across the aortic valve. Metabolomic profiles revealed that increased glycoprotein acetyls were also associated with an increased aortic valve mean gradient (0.92 SD, P=2.1 x 10 ^-22 ). Finally, velocity-derived phenotypes were risk markers for aortic and mitral valve surgery even at thresholds below what is considered relevant disease currently. Using machine learning to quantify the rich phenotypic data of the UK Biobank, we report the largest assessment of valvular function and cardiovascular disease in the general population.

Cost-effectiveness of Sodium-Glucose Cotransporter-2 Inhibitors for the Treatment of Heart Failure With Preserved Ejection Fraction

Importance

Adding a sodium-glucose cotransporter-2 inhibitor (SGLT2-I) to standard-of-care treatment in patients with heart failure with preserved ejection fraction (HFpEF) reduces the risk of a composite outcome of worsening heart failure or cardiovascular mortality, but the cost-effectiveness in US patients with HFpEF is uncertain.

Objective

To evaluate the lifetime cost-effectiveness of standard therapy plus an SGLT2-I compared with standard therapy in individuals with HFpEF.

Design, Setting, and Participants

In this economic evaluation conducted from September 8, 2021, to December 12, 2022, a state-transition Markov model simulated monthly health outcomes and direct medical costs. Input parameters including hospitalization rates, mortality rates, costs, and utilities were extracted from HFpEF trials, published literature, and publicly available data sets. The base-case annual cost of SGLT2-I was $4506. A simulated cohort with similar characteristics as participants of the Empagliflozin in Heart Failure With a Preserved Ejection Fraction (EMPEROR-Preserved) and Dapagliflozin in Heart Failure With Mildly Reduced or Preserved Ejection Fraction (DELIVER) trials was used.

Exposures

Standard of care plus SGLT2-I vs standard of care.

Main Outcomes and Measures

The model simulated hospitalizations, urgent care visits, and cardiovascular and noncardiovascular death. Future medical costs and benefits were discounted by 3% per year. Main outcomes were quality-adjusted life-years (QALYs), direct medical costs (2022 US dollars), and incremental cost-effectiveness ratio (ICER) of SGLT2-I therapy from a US health care sector perspective. The ICER of SGLT2-I therapy was evaluated according to the American College of Cardiology/American Heart Association value framework (high value: <$50 000; intermediate value: $50 000 to <$150 000; and low value: ≥$150 000).

Results

The simulated cohort had a mean (SD) age of 71.7 (9.5) years and 6828 of 12 251 participants (55.7%) were male. Standard of care plus SGLT2-I increased quality-adjusted survival by 0.19 QALYs at an increased cost of $26 300 compared with standard of care. The resulting ICER was $141 200 per QALY gained, with 59.1% of 1000 probabilistic iterations indicating intermediate value and 40.9% indicating low value. The ICER was most sensitive to SGLT2-I costs and effect of SGLT2-I therapy on cardiovascular death (eg, increasing to $373 400 per QALY gained if SGLT2-I therapy was assumed to have no effect on mortality).

Conclusions and Relevance

Results of this economic evaluation suggest that at 2022 drug prices, adding an SGLT2-I to standard of care was of intermediate or low economic value compared with standard of care in US adults with HFpEF. Efforts to expand access to SGLT2-I for individuals with HFpEF should be coupled with efforts to lower the cost of SGLT2-I therapy.

Association of Initial and Longitudinal Changes in C-reactive Protein With the Risk of Cardiovascular Disease, Cancer, and Mortality

OBJECTIVE

To evaluate the value of serial C-reactive protein (CRP) measurements in predicting the risk of cardiovascular disease (CVD), cancer, and mortality.

METHODS

The analysis was performed using data from two prospective, population-based observational cohorts: the Prevention of Renal and Vascular End-Stage Disease (PREVEND) study and the Framingham Heart Study (FHS). A total of 9253 participants had CRP measurements available at two examinations (PREVEND: 1997-1998 and 2001-2002; FHS Offspring cohort: 1995-1998 and 1998-2001). All CRP measurements were natural log-transformed before analyses. Cardiovascular disease included fatal and nonfatal cardiovascular, cerebrovascular and peripheral vascular events, and heart failure. Cancer included all malignancies except nonmelanoma skin cancers.

RESULTS

The mean age of the study population at baseline was 52.4±12.1 years and 51.2% (n=4733) were women. Advanced age, female sex, smoking, body mass index, and total cholesterol were associated with greater increases in CRP levels over time (Pall<.001 in the multivariable model). Baseline CRP, as well as increase in CRP over time (ΔCRP), were associated with incident CVD (hazard ratio [HR]: 1.29 per 1-SD increase; 95% confidence interval [CI]: 1.29 to 1.47, and HR per 1-SD increase: 1.19; 95% CI: 1.09 to 1.29 respectively). Similar findings were observed for incident cancer (baseline CRP, HR: 1.17; 95% CI: 1.09 to 1.26; ΔCRP, HR: 1.08; 95% CI: 1.01 to 1.15) and mortality (baseline CRP, HR: 1.29; 95% CI: 1.21 to 1.37; ΔCRP, HR: 1.10; 95% CI: 1.05 to 1.16).

CONCLUSION

Initial as well as subsequent increases in CRP levels predict future CVD, cancer, and mortality in the general population.

Clinical and genetic associations of deep learning-derived cardiac magnetic resonance-based left ventricular mass

Left ventricular mass is a risk marker for cardiovascular events, and may indicate an underlying cardiomyopathy. Cardiac magnetic resonance is the gold-standard for left ventricular mass estimation, but is challenging to obtain at scale. Here, we use deep learning to enable genome-wide association study of cardiac magnetic resonance-derived left ventricular mass indexed to body surface area within 43,230 UK Biobank participants. We identify 12 genome-wide associations (1 known at TTN and 11 novel for left ventricular mass), implicating genes previously associated with cardiac contractility and cardiomyopathy. Cardiac magnetic resonance-derived indexed left ventricular mass is associated with incident dilated and hypertrophic cardiomyopathies, and implantable cardioverter-defibrillator implant. An indexed left ventricular mass polygenic risk score ≥90th percentile is also associated with incident implantable cardioverter-defibrillator implant in separate UK Biobank (hazard ratio 1.22, 95% CI 1.05-1.44) and Mass General Brigham (hazard ratio 1.75, 95% CI 1.12-2.74) samples. Here, we perform a genome-wide association study of cardiac magnetic resonance-derived indexed left ventricular mass to identify 11 novel variants and demonstrate that cardiac magnetic resonance-derived and genetically predicted indexed left ventricular mass are associated with incident cardiomyopathy.

Family History of Modifiable Risk Factors and Association With Future Cardiovascular Disease

Background A parental history of cardiovascular disease (CVD) confers greater risk of future CVD among offspring. Whether the presence of parental modifiable risk factors contribute to or modify CVD risk in offspring is unclear. Methods and Results We studied 6278 parent-child trios in the multigenerational longitudinal Framingham Heart Study. We assessed parental history of CVD and modifiable risk factors (smoking, hypertension, diabetes, obesity, and hyperlipidemia). Multivariable Cox models were used to evaluate the association of parental history and future CVD among offspring. Among 6278 individuals (mean age 45±11 years), 44% had at least 1 parent with history of CVD. Over a median follow-up of 15 years, 353 major CVD events occurred among offspring. Parental history of CVD conferred 1.7-fold increased hazard of future CVD (hazard ratio [HR], 1.71 [95% CI, 1.33-2.21]). Parental obesity and smoking status were associated with higher hazard of future CVD (obesity: HR, 1.32 [95% CI, 1.06-1.64]; smoking: HR, 1.34 [95% CI, 1.07-1.68], attenuated after adjusting for offspring smoking status). By contrast, parental history of hypertension, diabetes, and hypercholesterolemia were not associated with future CVD in offspring (P>0.05 for all). Furthermore, parental risk factors did not modify the association of parental CVD history on future offspring CVD risk. Conclusions Parental history of obesity and smoking were associated with a higher hazard of future CVD in offspring. By contrast, other parental modifiable risk factors did not alter offspring CVD risk. In addition to parental CVD, the presence of parental obesity should prompt a focus on disease prevention.

Artificial Intelligence Enabled Classification of Hypertrophic Heart Diseases Using Electrocardiograms

Background

Differentiating among cardiac diseases associated with left ventricular hypertrophy (LVH) informs diagnosis and clinical care.

Objective

To evaluate if artificial intelligence-enabled analysis of the 12-lead electrocardiogram (ECG) facilitates automated detection and classification of LVH.

Methods

We used a pretrained convolutional neural network to derive numerical representations of 12-lead ECG waveforms from patients in a multi-institutional healthcare system who had cardiac diseases associated with LVH (n = 50,709), including cardiac amyloidosis (n = 304), hypertrophic cardiomyopathy (n = 1056), hypertension (n = 20,802), aortic stenosis (n = 446), and other causes (n = 4766). We then regressed LVH etiologies relative to no LVH on age, sex, and the numerical 12-lead representations using logistic regression ("LVH-Net"). To assess deep learning model performance on single-lead data analogous to mobile ECGs, we also developed 2 single-lead deep learning models by training models on lead I ("LVH-Net Lead I") or lead II ("LVH-Net Lead II") from the 12-lead ECG. We compared the performance of the LVH-Net models to alternative models fit on (1) age, sex, and standard ECG measures, and (2) clinical ECG-based rules for diagnosing LVH.

Results

The areas under the receiver operator characteristic curve of LVH-Net by specific LVH etiology were cardiac amyloidosis 0.95 [95% CI, 0.93-0.97], hypertrophic cardiomyopathy 0.92 [95% CI, 0.90-0.94], aortic stenosis LVH 0.90 [95% CI, 0.88-0.92], hypertensive LVH 0.76 [95% CI, 0.76-0.77], and other LVH 0.69 [95% CI 0.68-0.71]. The single-lead models also discriminated LVH etiologies well.

Conclusion

An artificial intelligence-enabled ECG model is favorable for detection and classification of LVH and outperforms clinical ECG-based rules.

Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Machine Learning to Understand Genetic and Clinical Factors Associated With the Pulse Waveform Dicrotic Notch

BACKGROUND

Absence of a dicrotic notch on finger photoplethysmography is an easily ascertainable and inexpensive trait that has been associated with age and prevalent cardiovascular disease. However, the trait exists along a continuum, and little is known about its genetic underpinnings or prognostic value for incident cardiovascular disease.

METHODS

In 169 787 participants in the UK Biobank, we identified absent dicrotic notch on photoplethysmography and created a novel continuous trait reflecting notch smoothness using machine learning. Next, we determined the heritability, genetic basis, polygenic risk, and clinical relations for the binary absent notch trait and the newly derived continuous notch smoothness trait.

RESULTS

Heritability of the continuous notch smoothness trait was 7.5%, compared with 5.6% for the binary absent notch trait. A genome-wide association study of notch smoothness identified 15 significant loci, implicating genes including NT5C2 (P=1.2×10-26), IGFBP3 (P=4.8×10-18), and PHACTR1 (P=1.4×10-13), compared with 6 loci for the binary absent notch trait. Notch smoothness stratified risk of incident myocardial infarction or coronary artery disease, stroke, heart failure, and aortic stenosis. A polygenic risk score for notch smoothness was associated with incident cardiovascular disease and all-cause death in UK Biobank participants without available photoplethysmography data.

CONCLUSIONS

We found that a machine learning derived continuous trait reflecting dicrotic notch smoothness on photoplethysmography was heritable and associated with genes involved in vascular stiffness. Greater notch smoothness was associated with greater risk of incident cardiovascular disease. Raw digital phenotyping may identify individuals at risk for disease via specific genetic pathways.

Obesity Modifies Clinical Outcomes of Right Ventricular Dysfunction

Introduction

Right ventricular (RV) dysfunction is associated with increased mortality across a spectrum of cardiovascular diseases. The role of obesity in RV dysfunction and adverse outcomes is unclear.

Methods

We examined patients undergoing right heart catheterization between 2005-2016 in a hospital-based cohort. Linear regression was used to examine the association of obesity with hemodynamic indices of RV dysfunction [pulmonary artery pulsatility index (PAPi), right atrial pressure: pulmonary capillary wedge pressure ratio (RAP:PCWP), RV stroke work index (RVSWI)]. Cox models were used to examine the association of RV function measures with clinical outcomes.

Results

Among 8285 patients (mean age 63 years, 40% women), higher BMI was associated with worse indices of RV dysfunction, including lower PAPi (β -0.26, SE 0.01, p <0.001), higher RA:PCWP ratio (β 0.25, SE 0.01, p-value <0.001), and lower RVSWI (β -0.05, SE 0.01, p-value <0.001). Over 7.3 years of follow-up, we observed 3006 mortality and 2004 heart failure (HF) hospitalization events. RV dysfunction was associated with greater risk of mortality (eg PAPi: HR 1.11 per 1-SD increase, 95% CI 1.04-1.18), with similar associations with risk of HF hospitalization. BMI modified the effect of RV dysfunction on outcomes (P-interaction <=0.005 for both), such that the effect of RV dysfunction was more pronounced at higher BMI.

Conclusions

Patients with obesity had worse hemodynamic measured indices of RV function across a broad hospital-based sample. While RV dysfunction was associated with worse clinical outcomes including mortality and HF hospitalization, this association was especially pronounced among individuals with higher BMI.

The Association of Protein Biomarkers With Incident Heart Failure With Preserved and Reduced Ejection Fraction

BACKGROUND

Heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF) are distinct clinical entities, yet there is scant evidence for associations of proteomic signatures with future development of HFpEF versus HFrEF.

METHODS

We evaluated the association of 71 protein biomarkers with incident HFpEF versus HFrEF (left ventricular ejection fraction ≥ versus <50%) among Framingham Heart Study participants using multivariable Cox models.

RESULTS

Among 7038 participants (mean age 49 years; 54% women), 5 biomarkers were associated with increased risk of incident HFpEF (false discovery rate q<0.05): NT-proBNP (N-terminal pro-B-type natriuretic peptide; hazard ratio [HR], 2.13; 95% CI, 1.52-2.99; P<0.001), growth differentiation factor-15 (HR, 1.67; 95% CI, 1.32-2.12; P<0.001), adrenomedullin (HR, 1.58; 95% CI, 1.23-2.04; P<0.001), uncarboxylated matrix Gla protein (HR, 1.55; 95% CI 1.23-1.95; P<0.001), and C-reactive protein (HR, 1.46; 95% CI, 1.17-1.83; P=0.001). Fourteen biomarkers were associated with incident HFrEF (multivariable P<0.001, q<0.05 for all). Of these, 3 biomarkers were associated with both HF subtypes (NT-proBNP, growth differentiation factor-15, and C-reactive protein). When compared directly, myeloperoxidase, resistin, and paraoxanase-1 were more strongly associated with HFrEF than HFpEF.

CONCLUSIONS

We identified 5 protein biomarkers of new-onset HFpEF representing pathways of inflammation, cardiac stress, and vascular stiffness, which partly overlapped with HFrEF. We found 14 biomarkers associated with new-onset HFrEF, with some distinct associations including myeloperoxidase, resistin, and paraoxanase-1. Taken together, these findings provide insights into similarities and differences in the development of HF subtypes.

REGISTRATION

URL: https://clinicaltrials.gov/ct2/show/NCT00005121; Unique identifier: NCT0005121.

Systemic Inflammation is Associated with Cardiometabolic Risk Factors and Clinical Outcomes

Purpose

Assessing an individual's systemic inflammatory state is vital to understand inflammation's role in cardiometabolic diseases and identify those at the greatest risk of disease. We generated global inflammation scores and investigated their associations with cardiometabolic risk factors and adverse outcomes.

Patients and Methods

Aggregate Inflammation Scores (AIS) and Principal Component Analysis (PCA) scores were generated for 7287 Framingham Heart Study participants using up to 26 inflammation-related proteins, with higher scores reflecting a pro-inflammatory milieu. Multivariable regression and proportional hazards analyses were conducted to investigate the associations of inflammation with cardiometabolic risk factors and outcomes. The primary outcomes for cross-sectional analyses included age, cigarette smoking, fasting lipid and glucose levels, blood pressure, body mass index (BMI), and hypertension, diabetes, and obesity. For prospective analyses, new-onset hypertension, diabetes, obesity, cardiovascular disease and all-cause mortality were investigated.

Results

Higher inflammation scores were associated with smoking and older age, higher BMI, systolic blood pressure, lipids, and glucose levels, and with greater odds of hypertension and diabetes after adjusting for age, sex, cohort, and BMI (all p < 0.001). Higher baseline scores were associated with greater odds of new-onset hypertension after adjusting for traditional risk factors (OR [95% CI] per one standard deviation [1-SD] increase, AIS: 1.33 [1.21-1.47], PCA score: 1.26 [1.12-1.42], p < 0.001). The AIS also was associated with new-onset diabetes (1.32 [1.14-1.52], p < 0.001). Proportional hazards analyses revealed greater risk of new-onset cardiovascular disease events and all-cause mortality (HR [95% CI] per 1-SD, AIS: 1.25 [1.14-1.37] and 1.32 [1.23-1.42], PCA score: 1.22 [1.13-1.33] and 1.40 [1.31-1.49], p < 0.001).

Conclusion

Global inflammation scores encompassing an array of pro- and anti-inflammatory proteins and pathways may enhance risk assessment for cardiometabolic diseases. The AIS and PCA scores provide further opportunities to investigate the mechanisms of inflammation-related risk of disease.

One Clinician Is All You Need-Cardiac Magnetic Resonance Imaging Measurement Extraction: Deep Learning Algorithm Development

BACKGROUND

Cardiac magnetic resonance imaging (CMR) is a powerful diagnostic modality that provides detailed quantitative assessment of cardiac anatomy and function. Automated extraction of CMR measurements from clinical reports that are typically stored as unstructured text in electronic health record systems would facilitate their use in research. Existing machine learning approaches either rely on large quantities of expert annotation or require the development of engineered rules that are time-consuming and are specific to the setting in which they were developed.

OBJECTIVE

We hypothesize that the use of pretrained transformer-based language models may enable label-efficient numerical extraction from clinical text without the need for heuristics or large quantities of expert annotations. Here, we fine-tuned pretrained transformer-based language models on a small quantity of CMR annotations to extract 21 CMR measurements. We assessed the effect of clinical pretraining to reduce labeling needs and explored alternative representations of numerical inputs to improve performance.

METHODS

Our study sample comprised 99,252 patients that received longitudinal cardiology care in a multi-institutional health care system. There were 12,720 available CMR reports from 9280 patients. We adapted PRAnCER (Platform Enabling Rapid Annotation for Clinical Entity Recognition), an annotation tool for clinical text, to collect annotations from a study clinician on 370 reports. We experimented with 5 different representations of numerical quantities and several model weight initializations. We evaluated extraction performance using macroaveraged F1-scores across the measurements of interest. We applied the best-performing model to extract measurements from the remaining CMR reports in the study sample and evaluated established associations between selected extracted measures with clinical outcomes to demonstrate validity.

RESULTS

All combinations of weight initializations and numerical representations obtained excellent performance on the gold-standard test set, suggesting that transformer models fine-tuned on a small set of annotations can effectively extract numerical quantities. Our results further indicate that custom numerical representations did not appear to have a significant impact on extraction performance. The best-performing model achieved a macroaveraged F1-score of 0.957 across the evaluated CMR measurements (range 0.92 for the lowest-performing measure of left atrial anterior-posterior dimension to 1.0 for the highest-performing measures of left ventricular end systolic volume index and left ventricular end systolic diameter). Application of the best-performing model to the study cohort yielded 136,407 measurements from all available reports in the study sample. We observed expected associations between extracted left ventricular mass index, left ventricular ejection fraction, and right ventricular ejection fraction with clinical outcomes like atrial fibrillation, heart failure, and mortality.

CONCLUSIONS

This study demonstrated that a domain-agnostic pretrained transformer model is able to effectively extract quantitative clinical measurements from diagnostic reports with a relatively small number of gold-standard annotations. The proposed workflow may serve as a roadmap for other quantitative entity extraction.

Cardiogenic shock related cardiovascular disease mortality trends in US population: Heart failure vs. acute myocardial infarction as contributing causes

BACKGROUND

Observational and trial data have revealed significant improvement in cardiogenic shock (CS) mortality due to acute myocardial infarction (AMI) after introducing early coronary revascularization. Less is known about CS mortality due to heart failure (HF), which is increasingly recognized as a distinct entity from AMI-CS.

METHODS AND RESULTS

In this nationwide observational study, the CDC WONDER database was used to identify national trends in age-adjusted mortality rates (AAMR) due to CS (HF vs. AMI related) per 100,000 people aged 35-84. AAMR from AMI-CS decreased significantly from 1999 to 2009 (AAPC: -6.9% [95%CI -7.7, -6.1]) then stabilized from 2009 to 2020. By contrast, HF-CS associated AAMR rose steadily from 2009 to 2020 (AAPC: 13.3% [95%CI 11.4,15.2]). The mortality rate was almost twice as high in males compared to females in both AMI-CS and HF-CS throughout the study period. HF-CS mortality in the non-Hispanic Black population is increasing more quickly than that of the non-Hispanic White population (AAMR in 2020: 4.40 vs. 1.97 in 100,000). The AMI-CS mortality rate has been consistently higher in rural than urban areas (30% higher in 1999 and 28% higher in 2020).

CONCLUSIONS

These trends highlight the fact that HF-CS and AMI-CS represent distinct clinical entities. While mortality associated with AMI-CS has primarily declined over the last two decades, the mortality related to HF-CS has increased significantly, particularly over the last decade, and is increasing rapidly among individuals younger than 65. Accordingly, a dramatic change in the demographics of CS patients in modern intensive care units is expected.

Spatially Distinct Genetic Determinants of Aortic Dimensions Influence Risks of Aneurysm and Stenosis

BACKGROUND

The left ventricular outflow tract (LVOT) and ascending aorta are spatially complex, with distinct pathologies and embryologic origins. Prior work examined the genetics of thoracic aortic diameter in a single plane.

OBJECTIVES

We sought to elucidate the genetic basis for the diameter of the LVOT, aortic root, and ascending aorta.

METHODS

Using deep learning, we analyzed 2.3 million cardiac magnetic resonance images from 43,317 UK Biobank participants. We computed the diameters of the LVOT, the aortic root, and at 6 locations of ascending aorta. For each diameter, we conducted a genome-wide association study and generated a polygenic score. Finally, we investigated associations between these scores and disease incidence.

RESULTS

A total of 79 loci were significantly associated with at least 1 diameter. Of these, 35 were novel, and most were associated with 1 or 2 diameters. A polygenic score of aortic diameter approximately 13 mm from the sinotubular junction most strongly predicted thoracic aortic aneurysm (n = 427,016; mean HR: 1.42 per SD; 95% CI: 1.34-1.50; P = 6.67 × 10^-21). A polygenic score predicting a smaller aortic root was predictive of aortic stenosis (n = 426,502; mean HR: 1.08 per SD; 95% CI: 1.03-1.12; P = 5 × 10^-6).

CONCLUSIONS

We detected distinct genetic loci underpinning the diameters of the LVOT, aortic root, and at several segments of ascending aorta. We spatially defined a region of aorta whose genetics may be most relevant to predicting thoracic aortic aneurysm. We further described a genetic signature that may predispose to aortic stenosis. Understanding genetic contributions to proximal aortic diameter may enable identification of individuals at risk for aortic disease and facilitate prioritization of therapeutic targets.