Search

Serum sodium is an established prognostic marker in heart failure (HF) patients and is associated with an increased risk of morbidity and mortality. We sought to study the prognostic value of serum sodium in left ventricular assist device (LVAD) patients and whether hyponatremia reflects worsening HF or an alternative mechanism. We identified HF patients that underwent LVAD implantation between 2008 and 2019. Hyponatremia was defined as Na ≤134 mEq/L at 3 months after implantation. We assessed for differences in hyponatremia before and after LVAD implantation. We also evaluated the association of hyponatremia with all-cause mortality and recurrent HF hospitalizations. There were 342 eligible LVAD patients with a sodium value at 3 months. Among them, there was a significant improvement in serum sodium after LVAD implantation compared to preoperatively (137.2 vs. 134.7 mEq/L, P0.0001). Patients with and without hyponatremia had no significant differences in echocardiographic and hemodynamic measurements. In a multivariate analysis, hyponatremia was associated with a markedly increased risk of all-cause mortality (HR 3.69, 95% CI, 1.93-7.05, P0.001) when accounting for age, gender, co-morbidities, use of loop diuretics, and B-type natriuretic peptide levels. Hyponatremia was also significantly associated with recurrent HF hospitalizations (HR 2.11, 95% CI, 1.02-4.37, P = 0.04). Hyponatremia in LVAD patients is associated with significantly higher risk of all-cause mortality and recurrent HF hospitalizations. Hyponatremia may be a marker of ongoing neurohormonal activation that is more sensitive than other lab values, echocardiography parameters, and hemodynamic measurements.

ObjectiveTo explore trends in blood pressure (BP) control before and during the COVID-19 pandemic.Patients and methodsHealth systems participating in the National Patient-Centered Clinical Research Network (PCORnet) Blood Pressure Control Laboratory Surveillance System responded to data queries, producing 9 BP control metrics. Averages of the BP control metrics (weighted by numbers of observations in each health system) were calculated and compared between two 1-year measurement periods (January 1, 2019, through December 31, 2019, and January 1, 2020, through December 31, 2020).ResultsAmong 1,770,547 hypertensive persons in 2019, BP control to

Heart failure with preserved ejection fraction (HFpEF) represents a prototypical cardiovascular condition in which machine learning may improve targeted therapies and mechanistic understanding of pathogenesis. Machine learning, which involves algorithms that learn from data, has the potential to guide precision medicine approaches for complex clinical syndromes such as HFpEF. It is therefore important to understand the potential utility and common pitfalls of machine learning so that it can be applied and interpreted appropriately. Although machine learning holds considerable promise for HFpEF, it is subject to several potential pitfalls, which are important factors to consider when interpreting machine learning studies.

Excess deaths during the coronavirus disease 2019 (COVID-19) pandemic have been largely attributed to cardiovascular disease (CVD); however, patterns in CVD hospitalizations after the first surge of the pandemic have not well-documented. Our brief report, examining trends in health care avoidance documents that CVD hospitalizations decreased in Chicago before significant burden of COVID-19 cases or deaths and normalized during the first COVID-19 surge. These data may help to inform health care systems responses in the coming months while mobilizing vaccinations to the population at large.

Left ventricular ejection fraction (EF) is a key measure in the diagnosis and treatment of heart failure (HF) and many patients experience changes in EF overtime. Large-scale analysis of longitudinal changes in EF using electronic health records (EHRs) is limited. In a multi-site retrospective study using EHR data from three academic medical centers, we investigated longitudinal changes in EF measurements in patients diagnosed with HF. We observed significant variations in baseline characteristics and longitudinal EF change behavior of the HF cohorts from a previous study that is based on HF registry data. Data gathered from this longitudinal study were used to develop multiple machine learning models to predict changes in ejection fraction measurements in HF patients. Across all three sites, we observed higher performance in predicting EF increase over a 1-year duration, with similarly higher performance predicting an EF increase of 30% from baseline compared to lower percentage increases. In predicting EF decrease we found moderate to high performance with low confidence for various models. Among various machine learning models, XGBoost was the best performing model for predicting EF changes. Across the three sites, the XGBoost model had an F1-score of 87.2, 89.9, and 88.6 and AUC of 0.83, 0.87, and 0.90 in predicting a 30% increase in EF, and had an F1-score of 95.0, 90.6, 90.1 and AUC of 0.54, 0.56, 0.68 in predicting a 30% decrease in EF. Among features that contribute to predicting EF changes, baseline ejection fraction measurement, age, gender, and heart diseases were found to be statistically significant.

Aims Prognostic enrichment strategies can make trials more efficient, although potentially at the cost of diminishing external validity. Whether using a risk score to identify a population at increased mortality risk could improve trial efficiency is uncertain. We aimed to assess whether Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a previously validated risk score, could improve clinical trial efficiency. Methods and results Mortality rates and association of MARKER-HF with all-cause death by 1 year were evaluated in four community-based heart failure (HF) and five HF clinical trial cohorts. Sample size required to assess effects of an investigational therapy on mortality was calculated assuming varying underlying MARKER-HF risk and proposed treatment effect profiles. Patients from community-based HF cohorts (n = 11 297) had higher observed mortality and MARKER-HF scores than did clinical trial patients (n = 13 165) with HF with either reduced ejection fraction (HFrEF) or preserved ejection fraction (HFpEF). MARKER-HF score was strongly associated with risk of 1-year mortality both in the community (hazard ratio [HR] 1.48, 95% confidence interval [CI] 1.44-1.52) and clinical trial cohorts with HFrEF (HR 1.41, 95% CI 1.30-1.54), and HFpEF (HR 1.74, 95% CI 1.53-1.98), per 0.1 increase in MARKER-HF. Using MARKER-HF to identify patients for a hypothetical clinical trial assessing mortality reduction with an intervention, enabled a reduction in sample size required to show benefit. Conclusion Using a reliable predictor of mortality such as MARKER-HF to enrich clinical trial populations provides a potential strategy to improve efficiency by requiring a smaller sample size to demonstrate a clinical benefit.

BACKGROUND: Neighborhood-level characteristics, such as poverty, have been associated with risk factors for heart failure (HF), including hypertension and diabetes mellitus. However, the independent association between neighborhood poverty and incident HF remains understudied. OBJECTIVE: To evaluate the association between neighborhood poverty and incident HF using a “real-world” clinical cohort. DESIGN: Retrospective cohort study of electronic health records from a large healthcare network. Individuals’ residential addresses were geocoded at the census-tract level and categorized by poverty tertiles based on American Community Survey data (2007–2011). PARTICIPANTS: Patients from Northwestern Medicine who were 30–80 years, free of cardiovascular disease at index visit (January 1, 2005–December 1, 2013), and followed for at least 5 years. MAIN MEASURES: The association of neighborhood-level poverty tertile (low, intermediate, and high) and incident HF was analyzed using generalized linear mixed effect models adjusting for demographics (age, sex, race/ethnicity) and HF risk factors (body mass index, diabetes mellitus, hypertension, smoking status). KEY RESULTS: Of 28,858 patients included, 75% were non-Hispanic (NH) White, 43% were men, 15% lived in a high-poverty neighborhood, and 522 (1.8%) were diagnosed with incident HF. High-poverty neighborhoods were associated with a 1.80 (1.35, 2.39) times higher risk of incident HF compared with low-poverty neighborhoods after adjustment for demographics and HF risk factors. CONCLUSIONS: In a large healthcare network, incident HF was associated with neighborhood poverty independent of demographic and clinical risk factors. Neighborhood-level interventions may be needed to complement individual-level strategies to prevent and curb the growing burden of HF. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11606-021-06785-7.

Sodium-glucose cotransporter-2 inhibitors (SGLT2is) and glucagon-like peptide-1 receptor agonists (GLP1-RAs) reduce cardiovascular events and mortality in patients with type 2 diabetes mellitus (T2DM). We sought to describe trends in prescribing for SGLT2is and GLP1-RAs in diverse care settings, including (1) the outpatient clinics of a midwestern integrated health system and (2) small- and medium-sized community-based primary care practices and health centers in 3 midwestern states. We included adults with T2DM and ≥1 outpatient clinic visit. The outcomes of interest were annual active prescription rates for SGLT2is and GLP1-RAs (separately). In the integrated health system, 22,672 patients met the case definition of T2DM. From 2013 to 2019, the overall prescription rate for SGLT2is increased from 1% to 15% (absolute difference [AD] 14%, 95% confidence interval [CI] 13% to 15%, p0.01). The GLP1-RA prescription rate was stable at 10% (AD 0%, 95% CI -1% to 1%, p = 0.9). In community-based primary care practices, 43,340 patients met the case definition of T2DM. From 2013 to 2017, the SGLT2i prescription rate increased from 3% to 7% (AD 4%, 95% CI 3% to 6%, p0.01), whereas the GLP1-RA prescription rate was stable at 2% to 3% (AD 1%, 95% CI -1 to 1%, p = 0.40). In a fully adjusted regression model, non-Hispanic Black patients had lower odds of SGLT2i or GLP1-RA prescription (odds ratio 0.56, 95% CI 0.34 to 0.89, p = 0.016). In conclusion, the increase in prescription rates was greater for SGLT2is than for GLP1-RAs in patients with T2DM in a large integrated medical center and community primary care practices. Overall, prescription rates for eligible patients were low, and racial disparities were observed.

Introduction: Intergenerational patterns are an important part of understanding disease, but much of the family-based research can be costly or biased. Emergency contacts in electronic health records (EHR) can be used to link family members using a population that is more representative of a community than traditional family cohort studies. Hypothesis: Creating family trees using emergency contacts will result in robust data that can be used in intergenerational research. Methods: We revised a published algorithm: relationship inference from the electronic health record (RIFTEHR). Our version, pythonic RIFTEHR (P-RIFTEHR) was run on 8/5/21 in the Northwestern Medicine Electronic Data Warehouse (NMEDW) on approximately 3.7 million individuals and was validated using the existing link between children born at NM hospitals and their mothers as the gold-standard. As proof-of-concept, we modeled the association between parent and child obesity using logistic regression. Results: The P-RIFTEHR algorithm matched 1,427,622 individuals in 500,408 families. The median family size is 2, the largest family is approximately 300 persons, and 115 families span four generations or more. Of the EDW-linked mother-child pairs, all matching pairs in P-RIFTEHR were correctly identified as mother-child, resulting in 100% sensitivity. Children are two times more likely to be obese if a parent is obese (OR: 2.01; 95% CI: 1.94, 2.09). This association persists after adjustment (OR: 2.19; 95% CI: 2.10, 2.28) and in mixed models nesting children within parents (OR: 2.70; 95% CI: 2.56, 2.85). Conclusions: P-RIFTEHR works well in a large, diverse population in an integrated health system. Our obesity results are consistent with the literature, including a 2017 meta-analysis by Wang, et al, showing a strong parent-child obesity association (pooled OR: 2.22; 95% CI: 2.09, 2.36). While the information used in the EHR can be completely deidentified, privacy concerns will be addressed before these data are more widely available for research.

Heart failure management requires intensive care coordination. Guideline-directed medical therapies have been shown to save lives but are practically challenging to implement because of the fragmented care that heart failure patients experience. Electronic health record adoption has transformed the collection and storage of clinical data, but accessing these data often remains prohibitively difficult. Current legislation aims to increase the interoperability of software systems so that providers and patients can easily access the clinical information they desire. Novel heart failure devices and technologies leverage patient-generated data to manage heart failure patients, whereas new data standards make it possible for this information to guide clinical decision-making.

Over 6.5 million Americans are living with heart failure (HF) today and the prevalence continues to increase.1 Despite substantial advances in medical therapy, many patients progress to end-stage disease with a 5-year absolute mortality rate of approximately 50%.2 Moreover, HF is a significant burden on the United States health care system, accounting for ~800,000 hospitalizations in 2016 and projected to cost $69.7 billion annually by the year 2030.1,3 Identifying high risk HF patients is therefore an important, yet challenging pursuit for clinicians and health care systems alike. Prognosis of individual patients with HF is highly variable in contemporary cohorts, and the risk of serious clinical outcomes such as mortality and hospitalization for HF can differ more than 20-fold.4,5 Predicting adverse outcomes in patients with HF could theoretically help direct resources to patients at the highest levels of risk who might benefit the most from earlier and more intensive monitoring and treatment (e.g. targeted medications, cardiac devices, home monitoring systems, and social services), while avoiding unnecessary interventions and costs for patients at low risk.6–9 Ideally, this would translate into improved outcomes and cost-efficiency in providing care for patients with HF. Given the importance of risk prediction for process improvement in HF, we aim to review the available evidence on prognostic variables and the current state of risk prediction for patients with HF. Additionally, we will discuss limitations of traditional risk modeling and provide a glimpse into the future of risk prediction in HF.

Background Effective quality improvement (QI) strategies are needed for small practices. Objective The objective of this study was to compare practice facilitation implementing point-of-care (POC) QI strategies alone versus facilitation implementing point-of-care plus population management (POC+PM) strategies on preventive cardiovascular care. Design Two arm, practice-randomized, comparative effectiveness study. Participants Small and mid-sized primary care practices. Interventions Practices worked with facilitators on QI for 12 months to implement POC or POC+PM strategies. Measures Proportion of eligible patients in a practice meeting "ABCS" measures: (Aspirin) Aspirin/antiplatelet therapy for ischemic vascular disease, (Blood pressure) Controlling High Blood Pressure, (Cholesterol) Statin Therapy for the Prevention and Treatment of Cardiovascular Disease, and (Smoking) Tobacco Use: Screening and Cessation Intervention, and the Change Process Capability Questionnaire. Measurements were performed at baseline, 12, and 18 months. Results A total of 226 practices were randomized, 179 contributed follow-up data. The mean proportion of patients meeting each performance measure was greater at 12 months compared with baseline: Aspirin 0.04 (95% confidence interval: 0.02-0.06), Blood pressure 0.04 (0.02-0.06), Cholesterol 0.05 (0.03-0.07), Smoking 0.05 (0.02-0.07); P 0.05 for all. Change Process Capability Questionnaire improved slightly, mean change 0.30 (0.09-0.51) but did not significantly differ across arms. Conclusion Facilitator-led QI promoting population management approaches plus POC improvement strategies was not clearly superior to POC strategies alone.

Background The National Patient‐Centered Clinical Research Network Blood Pressure Control Laboratory Surveillance System was established to identify opportunities for blood pressure (BP) control improvement and to provide a mechanism for tracking improvement longitudinally. Methods and Results We conducted a serial cross‐sectional study with queries against standardized electronic health record data in the National Patient‐Centered Clinical Research Network (PCORnet) common data model returned by 25 participating US health systems. Queries produced BP control metrics for adults with well‐documented hypertension and a recent encounter at the health system for a series of 1‐year measurement periods for each quarter of available data from January 2017 to March 2020. Aggregate weighted results are presented overall and by race and ethnicity. The most recent measurement period includes data from 1 737 995 patients, and 11 956 509 patient‐years were included in the trend analysis. Overall, 15% were Black, 52% women, and 28% had diabetes. BP control ( Conclusions Major opportunities exist for improving BP control and reducing disparities, especially through consistent medication intensification when BP is uncontrolled. These data demonstrate substantial room for improvement and opportunities to close health equity gaps.

Background: Immune regulation and inflammation play a role in the pathogenesis and progression of acute and chronic heart failure (HF). Although the clinical course of acute, severe inflammatory cardiomyopathy is well described, the effects of chronic systemic inflammation on cardiovascular function over time are less clear. To investigate this question, we compared trajectories over time in left ventricular ejection fraction for patients with HF with different chronic inflammatory diseases (CIDs): HIV, systemic lupus erythematosus, systemic sclerosis, rheumatoid arthritis, inflammatory bowel disease, and/or psoriasis. Methods: Using a database of patients receiving care in a large metropolitan health care system since January 1, 2000, we analyzed serial, clinically indicated echocardiograms from patients with HF with CIDs and frequency-matched patients with HF without CIDs. We included patients with ≥3 serial echocardiograms (N=974; median 6.1 years between first and most recent echo). We assessed left ventricular ejection fraction trajectories over time using latent trajectory models, then investigated differences in left ventricular ejection fraction trajectories for specific CID subtypes compared with controls. Results: Overall, the majority of patients studied (N=687; 70.5%) had left ventricular ejection fraction trajectories consistent with HF with preserved or midrange EF, whereas 255 (26.2%) had HF with reduced EF and 32 (3.3%) had HF with recovered EF. Compared with non-CID controls with HF, patients with rheumatoid arthritis, inflammatory bowel disease, and systemic lupus erythematosus were significantly more likely than controls to have HF with preserved or midrange EF whereas patients with HIV were significantly more likely to have HF with reduced EF. Conclusions: Among patients with HF with CIDs, distinct left ventricular ejection fraction trajectory patterns associate with different specific individual CIDs. This highlights the heterogeneity of HF subtypes and changes over time across different CIDs.

BACKGROUND: The appropriate dose of aspirin to lower the risk of death, myocardial infarction, and stroke and to minimize major bleeding in patients with established atherosclerotic cardiovascular disease is a subject of controversy. METHODS: Using an open-label, pragmatic design, we randomly assigned patients with established atherosclerotic cardiovascular disease to a strategy of 81 mg or 325 mg of aspirin per day. The primary effectiveness outcome was a composite of death from any cause, hospitalization for myocardial infarction, or hospitalization for stroke, assessed in a time-to-event analysis. The primary safety outcome was hospitalization for major bleeding, also assessed in a time-to-event analysis. RESULTS: A total of 15,076 patients were followed for a median of 26.2 months (interquartile range [IQR], 19.0 to 34.9). Before randomization, 13,537 (96.0% of those with available information on previous aspirin use) were already taking aspirin, and 85.3% of these patients were previously taking 81 mg of daily aspirin. Death, hospitalization for myocardial infarction, or hospitalization for stroke occurred in 590 patients (estimated percentage, 7.28%) in the 81-mg group and 569 patients (estimated percentage, 7.51%) in the 325-mg group (hazard ratio, 1.02; 95% confidence interval [CI], 0.91 to 1.14). Hospitalization for major bleeding occurred in 53 patients (estimated percentage, 0.63%) in the 81-mg group and 44 patients (estimated percentage, 0.60%) in the 325-mg group (hazard ratio, 1.18; 95% CI, 0.79 to 1.77). Patients assigned to 325 mg had a higher incidence of dose switching than those assigned to 81 mg (41.6% vs. 7.1%) and fewer median days of exposure to the assigned dose (434 days [IQR, 139 to 737] vs. 650 days [IQR, 415 to 922]). CONCLUSIONS: In this pragmatic trial involving patients with established cardiovascular disease, there was substantial dose switching to 81 mg of daily aspirin and no significant differences in cardiovascular events or major bleeding between patients assigned to 81 mg and those assigned to 325 mg of aspirin daily. (Funded by the Patient-Centered Outcomes Research Institute; ADAPTABLE ClinicalTrials.gov number, NCT02697916.).

Sudden cardiac death (SCD) accounts for more than half of all deaths from cardiovascular disease and is the first manifestation of heart disease in 50% of these individuals. We aimed to describe the distribution of predicted SCD risk in the general US population using a recently developed risk score. We previously developed a population-based, 10-year risk score for SCD using data from the multiracial Atherosclerosis Risk in Communities cohort, validated in the Framingham Study. We now estimate 10-year predicted SCD risk among National Health and Nutrition Examination Survey (NHANES) participants (pooled from cycles in 2005–2012) and evaluate the clinical profile of participants in lower risk (0–80(th) percentile of risk) or high risk (81(st)-100(th) percentile of risk) strata. A total of 10,811 participants were included; the mean age of participants was 48 years, and 50% were female. The average predicted 10-year risk of SCD was 3.6% among high risk participants (81(st)-100(th) percentile), and 0.37% among low risk participants (0–80(th) percentile). High risk participants were older, had higher blood pressure, total cholesterol and body mass index, lower high density lipoprotein, and were more likely to be male, black, smokers and diabetic. Among US adults free of cardiovascular disease, the majority of SCD risk appears confined to 10–20% of the population. This risk score, comprised of readily available clinical variables, identifies a subset of individuals in the population who are at an appreciably higher risk of SCD. This enriched cohort represents candidates for additional nuanced and selective screening techniques to further quantify SCD risk.

Background Given the rising prevalence of dysglycemia and disparities in heart failure (HF) burden, we determined race- and sex-specific lifetime risk of HF across the spectrum of fasting plasma glucose (FPG). Methods Individual-level data from adults without baseline HF was pooled from 6 population-based cohorts. Modified Kaplan–Meier analysis, Cox models adjusted for the competing risk of death, and Irwin’s restricted mean were used to estimate the lifetime risk, adjusted hazard ratio (aHR), and years lived free from HF in middle-aged (40–59 years) and older (60–79 years) adults with FPG Results In 40,117 participants with 638,910 person-years of follow-up, 4846 cases of incident HF occurred. The lifetime risk of HF was significantly higher among middle-aged White adults and Black women with prediabetes (range: 6.1% [95% CI 4.8%, 7.4%] to 10.8% [95% CI 8.3%, 13.4%]) compared with normoglycemic adults (range: 3.5% [95% CI 3.0%, 4.1%] to 6.5% [95% CI 4.9%, 8.1%]). Middle-aged Black women with diabetes had the highest lifetime risk (32.4% [95% CI 26.0%, 38.7%]) and aHR (4.0 [95% CI 3.0, 5.4]) for HF across race-sex groups. Middle-aged adults with prediabetes and diabetes lived on average 0.9–1.6 and 4.1–6.0 fewer years free from HF, respectively. Findings were similar in older adults except older Black women with prediabetes did not have a higher lifetime risk of HF. Conclusions Prediabetes was associated with higher lifetime risk of HF in middle-aged White adults and Black women, with the association attenuating in older Black women. Black women with diabetes had the highest lifetime risk of HF compared with other race-sex groups.

Additional file 1. Additional methods