Roger, Véronique L.

Introduction: Heart Failure (HF) is a syndrome of high comorbidity and mortality. The impact of comorbidities on life expectancy in HF is well studied, but guideline-recommended disease-centric risk scores such as the Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) score may not fully account for the effect of comorbidities. The Charlson Comorbidity Index (Charlson) is a validated measure that quantifies the prognostic impact of comorbidities. Objective: To examine the incremental value of Charlson over MAGGIC in predicting death in HF. Methods: In a population-based HF cohort, we computed MAGGIC and Charlson scores using electronically linked medical records. Patients were divided based on ejection fraction (EF) into reduced [EF <50%, HFrEF] or preserved [EF ≥50%, HFpEF]. Cox regression was performed to assess the association between the risk scores and mortality. The incremental value of Charlson over MAGGIC was tested by calculating the area under the ROC [receiver operating characteristic] curves (AUCs) with 95% confidence interval (CI). Results: We studied 1,388 patients with HF validated using the Framingham criteria (mean age, 75±13 years, 48% female, mean EF, 49±17%). The median Charlson was 7.0 (interquartile range 5.0-9.0) and was higher in HFpEF than HFrEF (7.0 [5.0-9.0] vs. 6.0 [4.0-8.3] respectively. Over a median follow-up of 13.9 years (13.3-14.2), mortality was 17.4% (95% CI, 15.4 -19.3%) and 36.8% (95% CI, 34.2-39.3%) at 1, and 3 years respectively. In regression analysis, increasing MAGGIC and Charlson were independently associated with death (HR 1.09, 95% CI 1.08-1.11 and HR,1.22, 95% CI 1.20-1.24, both p <0.001) irrespective of EF. Discrimination ability was improved by adding Charlson over MAGGIC (Table), reaching statistical significance at 3 years. Conclusion: In this HF cohort, the Charlson index provided an added value over MAGGIC in predicting the risk of death at 3 years. These findings emphasize the importance of accounting for comorbidities in mid-term risk stratification in HF.

Background: Heart failure (HF) is associated with metabolic alterations, including ketogenesis. However, determinants of ketogenesis and risk of mortality in HF is not defined. Total ketone bodies (KB) include β-hydroxybutyrate, acetoacetate, and acetone and can be measured in plasma by nuclear magnetic resonance (NMR). The aim of this study is to determine the relationship between KB and clinical characteristics in a community HF cohort and to assess the association between KB and all-cause mortality. Methods: A population-based cohort of 1,389 HF patients was prospectively enrolled between 2003 and 2012. Plasma KB was measured by LP4 NMR LipoProfile ® assay/test on the Vantera® NMR analyzer platform. A conditional inference tree method (ctree R Package) was used to determine optimal KB group cut points. Associations between clinical characteristics and KB were measured with Wilcoxon rank sum test and Pearson’s Chi-squared test. Kaplan-Meier method estimated survival. Cox regression analyses were used to estimate associations between KB concentrations and mortality. Results: Among the 1,382 HF patients with KB measurements, the median age was 78 years (IQR 68-84) and 52% were men. Median KB was 180 μM (IQR 134-308). Patients were divided into two groups with lower KB (≤471.5 μM) and higher KB (>471.5 μM). Patients with higher KB (N=210) had lower BMI, higher BNP, and were more likely to be in the New York Heart Association class III-IV; however, these patients were less likely to have hyperlipidemia, coronary disease, or diabetes mellitus (P < 0.05). Age, sex, creatinine, ejection fraction, or Meta-Analysis Global Group in Chronic HF (MAGGIC) score did not differ by KB group. Higher KB was associated with worse survival (figure). After adjustment for the MAGGIC score, higher KB was associated with increased risk of mortality (HR 1.3; 95% CI, 1.08-1.48). Conclusions: In this community HF cohort, higher KB was associated with increased mortality, independent of the MAGGIC score.

Introduction: Over six million U.S. adults have heart failure (HF). Evaluation of kidney function is critical to the care of patients with HF but may not be fully captured by traditional measures, such as estimated glomerular filtration rate (eGFR) or the Meta-Analysis Global Group in Chronic HF (MAGGIC) risk score. Identifying patients at highest risk of progressive chronic renal insufficiency (PCRI) may improve risk stratification in HF, beyond these traditional measures. High-throughput proteomics has allowed for the development of PCRI risk scores based on novel kidney function biomarkers. However, the prognostic value of a proteomic-based PCRI risk score in HF has not been explored. Hypothesis: Proteomic-based PCRI risk scores will improve risk stratification in HF. Methods: Clinical data and plasma were collected from 1,389 patients in a HF community cohort from Southeastern Minnesota (2003-2012). Results from the aptamer-based technology SomaScan® were used to derive PCRI risk scores using the SomaSignal™ Kidney Prognosis test, a protein-based algorithm developed to predict risk of PCRI within four years. Cox proportional hazard models were used to estimate the association between quintiles of PCRI and mortality, after adjustment for the MAGGIC score. Results: PCRI risk scores were available for 1,349 patients who were on average 75±13 years of age, 48% female, and had a median eGFR of 57 mL/min/1.73m 2 . There was a positive association with mortality across PCRI risk quintiles, after adjustment for the MAGGIC score. (Figure). Overall, the highest quintile was associated over a two-fold higher risk of mortality compared with the lowest quintile (HR 2.3, 95% CI 1.8,2.8). The higher risk of mortality remained in analyses stratified by patients with (HR 1.5, 95% CI 1.1,2.1) and without (HR 2.2, 95% CI 1.4,3.5) an eGFR <60 mL/min/1.73 m 2 . Conclusions: In this community HF cohort, proteomic-based PCRI risk scores improve risk stratification, independent of traditional measures of kidney function.

Background Larger within‐patient variability of lipid levels has been associated with increased risk of cardiovascular disease (CVD); however, measures of lipid variability require ≥3 measurements and are not currently used clinically. We investigated the feasibility of calculating lipid variability within a large electronic health record–based population cohort and assessed associations with incident CVD. Methods and Results We identified all individuals ≥40 years of age who resided in Olmsted County, MN, on January 1, 2006 (index date), without prior CVD, defined as myocardial infarction, coronary artery bypass graft surgery, percutaneous coronary intervention, or CVD death. Patients with ≥3 measurements of total cholesterol, low‐density lipoprotein cholesterol, high‐density lipoprotein cholesterol, or triglycerides during the 5 years before the index date were retained. Lipid variability was calculated using variability independent of the mean. Patients were followed through December 31, 2020 for incident CVD. We identified 19 652 individuals (mean age 61 years; 55% female), who were CVD‐free and had variability independent of the mean calculated for at least 1 lipid type. After adjustment, those with highest total cholesterol variability had a 20% increased risk of CVD (Q5 versus Q1 hazard ratio, 1.20 [95% CI, 1.06–1.37]). Results were similar for low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol. Conclusions In a large electronic health record–based population cohort, high variability in total cholesterol, high‐density lipoprotein cholesterol, and low‐density lipoprotein cholesterol was associated with an increased risk of CVD, independent of traditional risk factors, suggesting it may be a possible risk marker and target for intervention. Lipid variability can be calculated in the electronic health record environment, but more research is needed to determine its clinical utility.

Abstract Background Heart failure (HF) is a complex clinical syndrome that affects over 60 million persons worldwide.1 The 5-year survival rate of HF is less than 50% irrespective of ejection fraction (EF) and the limitations of EF in sufficiently phenotyping HF are recognized.2,3 High throughput proteomics assays are an attractive option for precision phenotyping.4 Purpose To explore the proteomic profiles and biomarker pathways associated with death in an HF community cohort. Methods We measured proteomics in plasma specimens collected from an HF cohort (2003–2012) of patients predominantly of European Ancestry (92%). The relative concentrations of circulating plasma proteins were measured using an aptamer-based assay containing 7335 human protein targets. Proteins significantly associated with mortality were selected while adjusting for age, sex, and eGFR. These proteins were employed in clustering analysis and subjected to 10-fold cross-validation. We quantified and corrected the clustering bias via bootstrapping and subsequently validated the results via semi-supervised clustering. Multivariable Cox regression was implemented for the cluster variable while adjusting for the Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) risk score. Additionally, we performed enrichment and pathway analysis of the outcome-associated proteins considering significance thresholds of −log10(P-value) &gt;2, FDR corrected P&lt;0.05 (regulator analysis) and Z-scores of &gt;2 and &lt;−2 as thresholds for activation and inhibition of proteins/pathways. Results From a total of 1,388 patients (mean age 75±13; 52% male), 2 distinct clusters, (HR: 2.04; 95% CI [1.78–2.34]; p-value: &lt;0.0001 independent of MAGGIC) were identified, cluster 1; n=722, mean age 72±14; NYHA class III/IV: 41%/26%; EF: 47%±17 and cluster 2; n=666, mean age 79±11; NYHA class III/IV: 41%/31%, EF: 48%±16 based on the 447 proteins [354 positively and 93 negatively] significantly associated with mortality (1,158 events). Gene-set enrichment analysis of all 447 proteins revealed cell adhesion, extracellular matrix organization, enzyme-linked protein/tyrosine kinase signaling pathways, humoral immune response, tissue development, growth factor signaling, post-translational protein modification, and platelet degranulation as the dominant biological themes. Finally, we found 18 upstream regulators predicted to be driving the expressions of the proteins in the dataset including a predicted outcome-associated protein-Transforming Growth Factor Beta 1 – TGFB1 (p=7.46E-09), Interleukin 1B, IL1B (p=9.40E-08), and oncostatin M, OSM (p=6.61E-05) as the top 3 regulators (predicted activation) based on FDR-corrected statistical significance of P&lt;0.05 (Figure 1) Conclusion We identified distinct clusters based on proteomic profiles associated with HF mortality independently of clinical prognostic makers. Further, we delineated protein regulators driving the major pathways associated with HF survival. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): Department of Intramural Research, NIH/NHLBI

Cardiovascular disease surveillance involves quantifying the evolving population‐level burden of cardiovascular outcomes and risk factors as a data‐driven initial step followed by the implementation of interventional strategies designed to alleviate this burden in the target population. Despite widespread acknowledgement of its potential value, a national surveillance system dedicated specifically to cardiovascular disease does not currently exist in the United States. Routinely collected health care data such as from electronic health records (EHRs) are a possible means of achieving national surveillance. Accordingly, this article elaborates on some key strengths and limitations of using EHR data for establishing a national cardiovascular disease surveillance system. Key strengths discussed include the: (1) ubiquity of EHRs and consequent ability to create a more “national” surveillance system, (2) existence of a common data infrastructure underlying the health care enterprise with respect to data domains and the nomenclature by which these data are expressed, (3) longitudinal length and detail that define EHR data when individuals repeatedly patronize a health care organization, and (4) breadth of outcomes capable of being surveilled with EHRs. Key limitations discussed include the: (1) incomplete ascertainment of health information related to health care–seeking behavior and the disconnect of health care data generated at separate health care organizations, (2) suspect data quality resulting from the default information‐gathering processes within the clinical enterprise, (3) questionable ability to surveil patients through EHRs in the absence of documented interactions, and (4) the challenge in interpreting temporal trends in health metrics, which can be obscured by changing clinical and administrative processes.

Introduction: The Framingham Heart Study Dementia Risk Score (FDRS) was developed in a general population of elderly individuals. It is unknown how the FDRS variables predict dementia in heart failure (HF) and atrial fibrillation (AF) populations. Thus, we aimed to evaluate the predictive ability of the FDRS variables in population-based cohorts of individuals with HF and AF and to determine whether the addition of other comorbidities and risk factors improves risk prediction for dementia. Methods: Residents aged ≥50 from 7 southeast MN counties with a first diagnosis of HF (ICD-9 428/ICD-10 I50) or AF (ICD-9 427.3/ICD-10 I48 with AF on electrocardiogram/Holter monitor) between 1/1/2013 and 12/31/2017 were identified. Patients with dementia (defined by ICD codes) prior to or within 6 months after their index event and patients who died within 6 months after index were excluded. For both cohorts, models were constructed to predict dementia ≥ 6 months post index including the variables in the FDRS (age, marital status, BMI, stroke/TIA, diabetes and cancer). Additional comorbidities and risk factors were added to the models. For all models c-statistics were calculated. Results: Among 3,291 patients with HF (mean age 76 years, 53% male), 626 cases of dementia occurred and among 4,107 patients with AF (mean age 74 years, 57% male), 736 cases of dementia occurred. Among patients with HF, the FDRS variables predicted dementia with a c-statistic of 0.68 (Table). Adding additonal comorbidities and risk factors impoved the c-statistic slightly to 0.69 (p=0.01). Among patients with AF, the FDRS variables also performed well (c-stastic: 0.73; Table) and adding other comorbidities and risk factors improved the performance (c-statistic: 0.75; p<0.01). Conclusions: The variables from the FDRS predict dementia well in both HF and AF populations. The addition of comorbidities and other CVD risk factors only modestly improved prediction, thus indicating that the FDRS variables are appropriate to predict dementia in patients with HF and AF.

Background Electronic medical records are increasingly used to identify disease cohorts; however, computable phenotypes using electronic medical record data are often unable to distinguish between prevalent and incident cases. Methods and Results We identified all Olmsted County, Minnesota residents aged ≥18 with a first‐ever International Classification of Diseases, Ninth Revision (ICD‐9) diagnostic code for atrial fibrillation or atrial flutter from 2000 to 2014 (N=6177), and a random sample with an International Classification of Diseases, Tenth Revision (ICD‐10) code from 2016 to 2018 (N=200). Trained nurse abstractors reviewed all medical records to validate the events and ascertain the date of onset (incidence date). Various algorithms based on number and types of codes (inpatient/outpatient), medications, and procedures were evaluated. Positive predictive value (PPV) and sensitivity of the algorithms were calculated. The lowest PPV was observed for 1 code (64.4%), and the highest PPV was observed for 2 codes (any type) >7 days apart but within 1 year (71.6%). Requiring either 1 inpatient or 2 outpatient codes separated by >7 days but within 1 year had the best balance between PPV (69.9%) and sensitivity (95.5%). PPVs were slightly higher using ICD‐10 codes. Requiring an anticoagulant or antiarrhythmic prescription or electrical cardioversion in addition to diagnostic code(s) modestly improved the PPVs at the expense of large reductions in sensitivity. Conclusions We developed simple, exportable, computable phenotypes for atrial fibrillation using structured electronic medical record data. However, use of diagnostic codes to identify incident atrial fibrillation is prone to some misclassification. Further study is warranted to determine whether more complex phenotypes, including unstructured data sources or using machine learning techniques, may improve the accuracy of identifying incident atrial fibrillation.

Background: Some patients with heart failure (HF) will go on to develop advanced HF, characterized by severe HF symptoms despite attempts to optimize medical therapy. The goals of this study were to examine the risk of developing advanced HF in patients with newly diagnosed HF, identify risk factors for developing advanced HF, and evaluate the impact of advanced HF on outcomes. Methods: This was a population-based, retrospective cohort study of Olmsted County, Minnesota, residents with a new clinical diagnosis of HF between 2007 and 2017. Risk factors for the development of advanced HF (2018 European Society of Cardiology criteria) were examined using cause-specific Cox proportional hazard regression models. The associations of development of advanced HF with risks of hospitalization and mortality were examined using the Andersen-Gill and Cox models, respectively. Results: There were 4597 residents with incident HF from 2007 to 2017. The cumulative incidence of advanced HF was 11.5% (95% CI, 10.5%–12.5%) at 6 years after incident HF diagnosis overall and was 14.4% (95% CI, 12.3%–16.9%), 11.4% (95% CI, 8.9%–14.6%), and 11.7% (95% CI, 10.3%–13.2%) in patients with incident HF with reduced, mildly reduced, and preserved ejection fraction, respectively. Key demographics, comorbidities, and echocardiographic characteristics were independently associated with the development of advanced HF. Development of advanced HF was associated with increased risks of all-cause hospitalization (adjusted hazard ratio, 3.0 [95% CI, 2.7–3.4]; P <0.001), HF hospitalization (hazard ratio, 10.2 [95% CI, 8.7–12.1]), all-cause mortality (hazard ratio, 5.0 [95% CI, 4.5–5.6]; P <0.001), and cardiovascular mortality (hazard ratio, 7.8 [95% CI, 6.7–9.1]). Conclusions: In this population-based study, development of advanced HF was common and was associated with markedly increased morbidity and mortality.