Predictive Accuracy of Heart Failure-Specific Risk Equations in an Electronic Health Record-Based Cohort

Emergency Department Utilization and Outcomes Among Adults With Cirrhosis From 2008 to 2022 in the United States

BACKGROUND & AIMS

Globally, emergency departments (ED) are experiencing rising costs and crowding. Despite its importance, ED utilization and outcomes among patients with cirrhosis are understudied.

METHODS

We analyzed Optum's de-identified Clinformatics Data Mart Database, between 2008 and 2022, including adults with at least 180 days of enrollment. Liver transplant recipients were censored at the year of transplant. ED visits (stratified by liver vs non-liver related) were identified using validated billing code definitions. Linear regression was used to assess ED visits per year, and logistic regression was used to assess 90-day mortality rates and discharge dispositions, with models adjusted for patient- and visit-level characteristics.

RESULTS

Among 38,419,650 patients, 198,439 were with cirrhosis (median age, 66 [interquartile range, 57-72 years]; 54% male; 62% White). In age-adjusted analysis, ED visits per person-year were 1.72 (95% confidence interval [CI], 1.71-1.74) with cirrhosis vs 0.46 (95% CI, 0.46-0.46) without cirrhosis, 1.66 (95% CI, 1.66-1.66) for congestive heart failure (CHF), and 1.22 (95% CI, 1.22-1.22) for chronic obstructive pulmonary disease (COPD). Age-adjusted 90-day mortality rates were 12.2% (95% CI, 12.1%-12.4%) with cirrhosis vs 4.8% [95% CI, 4.8%-4.8%) without cirrhosis, 6.9% (95% CI, 6.9%-6.9%) for CHF, and 6.3% (95% CI, 6.3%-6.4%) for COPD. Non-liver (vs liver-related) ED visits were more likely to lead to discharge home among patients with compensated (52.8%; 95% CI, 52.2%-53.5% vs 39.2%; 95% CI, 38.5%-39.8%) and decompensated (42.2%; 95% CI, 41.5%-42.8% vs 29.5%; 95% CI, 29.0%-30.1%) cirrhosis. In exploratory analysis, among patients who remained alive and were not readmitted for 30 days after ED discharge, those without any outpatient follow-up had higher 90-day mortality (22.0%; 95% CI, 21.0%-23.0%) than those with both primary care and gastroenterology/hepatology follow-up within 30-days (7.9%; 95% CI, 7.3%-8.5%).

CONCLUSIONS

Patients with cirrhosis have higher ED utilization and almost 2-fold higher post-ED visit mortality than CHF and COPD. These findings provide impetus for ED-based interventions to improve cirrhosis-related outcomes.

Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system

BACKGROUND

Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems.

OBJECTIVE

To assess the performance and ease of implementation of Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a machine-learning model that uses structured data that is readily available in the EHR, and compare it with two commonly used risk scores: the Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score.

DESIGN

Retrospective, cohort study.

PARTICIPANTS

Data from 6764 adults with HF were abstracted from EHRs at a large integrated health system from 1/1/10 to 12/31/19.

MAIN MEASURES

One-year survival from time of first cardiology or primary care visit was estimated using MARKER-HF, SHFM, and MAGGIC. Discrimination was measured by the area under the receiver operating curve (AUC). Calibration was assessed graphically.

KEY RESULTS

Compared to MARKER-HF, both SHFM and MAGGIC required a considerably larger amount of data engineering and imputation to generate risk score estimates. MARKER-HF, SHFM, and MAGGIC exhibited similar discriminations with AUCs of 0.70 (0.69-0.73), 0.71 (0.69-0.72), and 0.71 (95% CI 0.70-0.73), respectively. All three scores showed good calibration across the full risk spectrum.

CONCLUSIONS

These findings suggest that MARKER-HF, which uses readily available clinical and lab measurements in the EHR and required less imputation and data engineering than SHFM and MAGGIC, is an easier tool to identify high-risk patients in ambulatory clinics who could benefit from referral to a HF specialist.

Incident heart failure in chronic kidney disease: proteomics informs biology and risk stratification

BACKGROUND AND AIMS

Incident heart failure (HF) among individuals with chronic kidney disease (CKD) incurs hospitalizations that burden patients and health care systems. There are few preventative therapies, and the Pooled Cohort equations to Prevent Heart Failure (PCP-HF) perform poorly in the setting of CKD. New drug targets and better risk stratification are urgently needed.

METHODS

In this analysis of incident HF, SomaScan V4.0 (4638 proteins) was analysed in 2906 participants of the Chronic Renal Insufficiency Cohort (CRIC) with validation in the Atherosclerosis Risk in Communities (ARIC) study. The primary outcome was 14-year incident HF (390 events); secondary outcomes included 4-year HF (183 events), HF with reduced ejection fraction (137 events), and HF with preserved ejection fraction (165 events). Mendelian randomization and Gene Ontology were applied to examine causality and pathways. The performance of novel multi-protein risk models was compared to the PCP-HF risk score.

RESULTS

Over 200 proteins were associated with incident HF after adjustment for estimated glomerular filtration rate at P < 1 × 10-5. After adjustment for covariates including N-terminal pro-B-type natriuretic peptide, 17 proteins remained associated at P < 1 × 10-5. Mendelian randomization associations were found for six proteins, of which four are druggable targets: FCG2B, IGFBP3, CAH6, and ASGR1. For the primary outcome, the C-statistic (95% confidence interval [CI]) for the 48-protein model in CRIC was 0.790 (0.735, 0.844) vs. 0.703 (0.644, 0.762) for the PCP-HF model (P = .001). C-statistic (95% CI) for the protein model in ARIC was 0.747 (0.707, 0.787).

CONCLUSIONS

Large-scale proteomics reveal novel circulating protein biomarkers and potential mediators of HF in CKD. Proteomic risk models improve upon the PCP-HF risk score in this population.

Heart Failure Management through Telehealth: Expanding Care and Connecting Hearts

Heart failure (HF) is a leading cause of morbidity worldwide, imposing a significant burden on deaths, hospitalizations, and health costs. Anticipating patients' deterioration is a cornerstone of HF treatment: preventing congestion and end organ damage while titrating HF therapies is the aim of the majority of clinical trials. Anyway, real-life medicine struggles with resource optimization, often reducing the chances of providing a patient-tailored follow-up. Telehealth holds the potential to drive substantial qualitative improvement in clinical practice through the development of patient-centered care, facilitating resource optimization, leading to decreased outpatient visits, hospitalizations, and lengths of hospital stays. Different technologies are rising to offer the best possible care to many subsets of patients, facing any stage of HF, and challenging extreme scenarios such as heart transplantation and ventricular assist devices. This article aims to thoroughly examine the potential advantages and obstacles presented by both existing and emerging telehealth technologies, including artificial intelligence.

Predicting incident heart failure from population-based nationwide electronic health records: protocol for a model development and validation study

INTRODUCTION

Heart failure (HF) is increasingly common and associated with excess morbidity, mortality, and healthcare costs. Treatment of HF can alter the disease trajectory and reduce clinical events in HF. However, many cases of HF remain undetected until presentation with more advanced symptoms, often requiring hospitalisation. Predicting incident HF is challenging and statistical models are limited by performance and scalability in routine clinical practice. An HF prediction model implementable in nationwide electronic health records (EHRs) could enable targeted diagnostics to enable earlier identification of HF.

METHODS AND ANALYSIS

We will investigate a range of development techniques (including logistic regression and supervised machine learning methods) on routinely collected primary care EHRs to predict risk of new-onset HF over 1, 5 and 10 years prediction horizons. The Clinical Practice Research Datalink (CPRD)-GOLD dataset will be used for derivation (training and testing) and the CPRD-AURUM dataset for external validation. Both comprise large cohorts of patients, representative of the population of England in terms of age, sex and ethnicity. Primary care records are linked at patient level to secondary care and mortality data. The performance of the prediction model will be assessed by discrimination, calibration and clinical utility. We will only use variables routinely accessible in primary care.

ETHICS AND DISSEMINATION

Permissions for CPRD-GOLD and CPRD-AURUM datasets were obtained from CPRD (ref no: 21_000324). The CPRD ethical approval committee approved the study. The results will be submitted as a research paper for publication to a peer-reviewed journal and presented at peer-reviewed conferences.

TRIAL REGISTRATION DETAILS

The study was registered on Clinical Trials.gov (NCT05756127). A systematic review for the project was registered on PROSPERO (registration number: CRD42022380892).

Preventing new-onset heart failure: Intervening at stage A

Heart failure (HF) prevention is an urgent public health need with national and global implications. Stage A HF patients do not show HF symptoms or structural heart disease but are at risk of HF development. There are no unique recommendations on detecting Stage A patients. Patients in Stage A are heterogeneous; many patients have different combinations of risk factors and, therefore, have markedly different absolute risks for HF. Comprehensive strategies to prevent HF at Stage A include intensive blood pressure lowering, adequate glycemic and lipid management, and heart-healthy behaviors (adopting Life's Essential 8). First and foremost, it is imperative to improve public awareness of HF risk factors and implement healthy lifestyle choices very early. In addition, recognize the HF risk-enhancing factors, which are nontraditional cardiovascular (CV) risk factors that identify individuals at high risk for HF (genetic susceptibility for HF, atrial fibrillation, chronic kidney disease, chronic liver disease, chronic inflammatory disease, sleep-disordered breathing, adverse pregnancy outcomes, radiation therapy, a history of cardiotoxic chemotherapy exposure, and COVID-19). Early use of biomarkers, imaging markers, and echocardiography (noninvasive measures of subclinical systolic and diastolic dysfunction) may enhance risk prediction among individuals without established CV disease and prevent chemotherapy-induced cardiomyopathy. Efforts are needed to address social determinants of HF risk for primordial HF prevention.Central illustrationPolicies developed by organizations such as the American Heart Association, American College of Cardiology, and the American Diabetes Association to reduce CV disease events must go beyond secondary prevention and encompass primordial and primary prevention.

Prediction models for heart failure in the community: A systematic review and meta-analysis

AIMS

Multivariable prediction models can be used to estimate risk of incident heart failure (HF) in the general population. A systematic review and meta-analysis was performed to determine the performance of models.

METHODS AND RESULTS

From inception to 3 November 2022 MEDLINE and EMBASE databases were searched for studies of multivariable models derived, validated and/or augmented for HF prediction in community-based cohorts. Discrimination measures for models with c-statistic data from ≥3 cohorts were pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using PROBAST. We included 36 studies with 59 prediction models. In meta-analysis, the Atherosclerosis Risk in Communities (ARIC) risk score (summary c-statistic 0.802, 95% confidence interval [CI] 0.707-0.883), GRaph-based Attention Model (GRAM; 0.791, 95% CI 0.677-0.885), Pooled Cohort equations to Prevent Heart Failure (PCP-HF) white men model (0.820, 95% CI 0.792-0.843), PCP-HF white women model (0.852, 95% CI 0.804-0.895), and REverse Time AttentIoN model (RETAIN; 0.839, 95% CI 0.748-0.916) had a statistically significant 95% PI and excellent discrimination performance. The ARIC risk score and PCP-HF models had significant summary discrimination among cohorts with a uniform prediction window. 77% of model results were at high risk of bias, certainty of evidence was low, and no model had a clinical impact study.

CONCLUSIONS

Prediction models for estimating risk of incident HF in the community demonstrate excellent discrimination performance. Their usefulness remains uncertain due to high risk of bias, low certainty of evidence, and absence of clinical effectiveness research.

Electronic Health Record-Driven Approaches in Primary Care to Strengthen Hypertension Management Among Racial and Ethnic Minoritized Groups in the United States: Systematic Review

BACKGROUND

Managing hypertension in racial and ethnic minoritized groups (eg, African American/Black patients) in primary care is highly relevant. However, evidence on whether or how electronic health record (EHR)-driven approaches in primary care can help improve hypertension management for patients of racial and ethnic minoritized groups in the United States remains scarce.

OBJECTIVE

This review aims to examine the role of the EHR in supporting interventions in primary care to strengthen the hypertension management of racial and ethnic minoritized groups in the United States.

METHODS

A search strategy based on the PICO (Population, Intervention, Comparison, and Outcome) guidelines was utilized to query and identify peer-reviewed articles on the Web of Science and PubMed databases. The search strategy was based on terms related to racial and ethnic minoritized groups, hypertension, primary care, and EHR-driven interventions. Articles were excluded if the focus was not hypertension management in racial and ethnic minoritized groups or if there was no mention of health record data utilization.

RESULTS

A total of 29 articles were included in this review. Regarding populations, Black/African American patients represented the largest population (26/29, 90%) followed by Hispanic/Latino (18/29, 62%), Asian American (7/29, 24%), and American Indian/Alaskan Native (2/29, 7%) patients. No study included patients who identified as Native Hawaiian/Pacific Islander. The EHR was used to identify patients (25/29, 86%), drive the intervention (21/29, 72%), and monitor results and outcomes (7/29, 59%). Most often, EHR-driven approaches were used for health coaching interventions, disease management programs, clinical decision support (CDS) systems, and best practice alerts (BPAs). Regarding outcomes, out of 8 EHR-driven health coaching interventions, only 3 (38%) reported significant results. In contrast, all the included studies related to CDS and BPA applications reported some significant results with respect to improving hypertension management.

CONCLUSIONS

This review identified several use cases for the integration of the EHR in supporting primary care interventions to strengthen hypertension management in racial and ethnic minoritized patients in the United States. Some clinical-based interventions implementing CDS and BPA applications showed promising results. However, more research is needed on community-based interventions, particularly those focusing on patients who are Asian American, American Indian/Alaskan Native, and Native Hawaiian/Pacific Islander. The developed taxonomy comprising "identifying patients," "driving intervention," and "monitoring results" to classify EHR-driven approaches can be a helpful tool to facilitate this.

Virtual healthcare solutions in heart failure: a literature review

The widespread adoption of mobile technologies offers an opportunity for a new approach to post-discharge care for patients with heart failure (HF). By enabling non-invasive remote monitoring and two-way, real-time communication between the clinic and home-based patients, as well as a host of other capabilities, mobile technologies have a potential to significantly improve remote patient care. This literature review summarizes clinical evidence related to virtual healthcare (VHC), defined as a care team + connected devices + a digital solution in post-release care of patients with HF. Searches were conducted on Embase (06/12/2020). A total of 171 studies were included for data extraction and evidence synthesis: 96 studies related to VHC efficacy, and 75 studies related to AI in HF. In addition, 15 publications were included from the search on studies scaling up VHC solutions in HF within the real-world setting. The most successful VHC interventions, as measured by the number of reported significant results, were those targeting reduction in rehospitalization rates. In terms of relative success rate, the two most effective interventions targeted patient self-care and all-cause hospital visits in their primary endpoint. Among the three categories of VHC identified in this review (telemonitoring, remote patient management, and patient self-empowerment) the integrated approach in remote patient management solutions performs the best in decreasing HF patients' re-admission rates and overall hospital visits. Given the increased amount of data generated by VHC technologies, artificial intelligence (AI) is being investigated as a tool to aid decision making in the context of primary diagnostics, identifying disease phenotypes, and predicting treatment outcomes. Currently, most AI algorithms are developed using data gathered in clinic and only a few studies deploy AI in the context of VHC. Most successes have been reported in predicting HF outcomes. Since the field of VHC in HF is relatively new and still in flux, this is not a typical systematic review capturing all published studies within this domain. Although the standard methodology for this type of reviews was followed, the nature of this review is qualitative. The main objective was to summarize the most promising results and identify potential research directions.

Quantifying days at home in patients with cirrhosis: A national cohort study

BACKGROUND AND AIMS

Days at home (DAH) is a patient-centric metric developed by the Medicare Payment Advisory Commission, capturing annual health care use, including and beyond hospitalizations and mortality. We quantified DAH and assessed factors associated with DAH differences among patients with cirrhosis.

APPROACH AND RESULTS

Using a national claims database (Optum) between 2014 and 2018, we calculated DAH (365 minus mortality, inpatient, observation, postacute, and emergency department days). Among 20,776,597 patients, 63,477 had cirrhosis (median age, 66, 52% males, and 63% non-Hispanic White). Age-adjusted mean DAH for cirrhosis was 335.1 days (95% CI: 335.0 to 335.2) vs 360.1 (95% CI: 360.1 to 360.1) without cirrhosis. In mixed-effects linear regression, adjusted for demographic and clinical characteristics, patients with decompensated cirrhosis spent 15.2 days (95% CI: 14.4 to 15.8) in postacute, emergency, and observation settings and 13.8 days (95% CI: 13.5 to 14.0) hospitalized. Hepatic encephalopathy (-29.2 d, 95% CI: -30.4 to -28.0), ascites (-34.6 d, 95% CI: -35.3 to -33.9), and combined ascites and hepatic encephalopathy (-63.8 d, 95% CI: -65.0 to -62.6) were associated with decreased DAH. Variceal bleeding was not associated with a change in DAH (-0.2 d, 95% CI: -1.6 to +1.1). Among hospitalized patients, during the 365 days after index hospitalization, patients with cirrhosis had fewer age-adjusted DAH (272.8 d, 95% CI: 271.5 to 274.1) than congestive heart failure (288.0 d, 95% CI: 287.7 to 288.3) and chronic obstructive pulmonary disease (296.6 d, 95% CI: 296.3 to 297.0).

CONCLUSIONS

In this national study, we found that patients with cirrhosis spend as many, if not more, cumulative days receiving postacute, emergency, and observational care, as hospitalized care. Ultimately, up to 2 months of DAH are lost annually with the onset of liver decompensation. DAH may be a useful metric for patients and health systems alike.

Validation of the WATCH-DM and TRS-HFDM Risk Scores to Predict the Risk of Incident Hospitalization for Heart Failure Among Adults With Type 2 Diabetes: A Multicohort Analysis

Background The WATCH-DM (weight [body mass index], age, hypertension, creatinine, high-density lipoprotein cholesterol, diabetes control [fasting plasma glucose], ECG QRS duration, myocardial infarction, and coronary artery bypass grafting) and TRS-HF_DM (Thrombolysis in Myocardial Infarction [TIMI] risk score for heart failure in diabetes) risk scores were developed to predict risk of heart failure (HF) among individuals with type 2 diabetes. WATCH-DM was developed to predict incident HF, whereas TRS-HF_DM predicts HF hospitalization among patients with and without a prior HF history. We evaluated the model performance of both scores to predict incident HF events among patients with type 2 diabetes and no history of HF hospitalization across different cohorts and clinical settings with varying baseline risk. Methods and Results Incident HF risk was estimated by the integer-based WATCH-DM and TRS-HF_DM scores in participants with type 2 diabetes free of baseline HF from 2 randomized clinical trials (TECOS [Trial Evaluating Cardiovascular Outcomes With Sitagliptin], N=12 028; and Look AHEAD [Look Action for Health in Diabetes] trial, N=4867). The integer-based WATCH-DM score was also validated in electronic health record data from a single large health care system (N=7475). Model discrimination was assessed by the Harrell concordance index and calibration by the Greenwood-Nam-D'Agostino statistic. HF incidence rate was 7.5, 3.9, and 4.1 per 1000 person-years in the TECOS, Look AHEAD trial, and electronic health record cohorts, respectively. Integer-based WATCH-DM and TRS-HF_DM scores had similar discrimination and calibration for predicting 5-year HF risk in the Look AHEAD trial cohort (concordance indexes=0.70; Greenwood-Nam-D'Agostino P>0.30 for both). Both scores had lower discrimination and underpredicted HF risk in the TECOS cohort (concordance indexes=0.65 and 0.66, respectively; Greenwood-Nam-D'Agostino P<0.001 for both). In the electronic health record cohort, the integer-based WATCH-DM score demonstrated a concordance index of 0.73 with adequate calibration (Greenwood-Nam-D'Agostino P=0.96). TRS-HF_DM score could not be validated in the electronic health record because of unavailability of data on urine albumin/creatinine ratio in most patients in the contemporary clinical practice. Conclusions The WATCH-DM and TRS-HF_DM risk scores can discriminate risk of HF among intermediate-risk populations with type 2 diabetes.

New strategies and therapies for the prevention of heart failure in high-risk patients

Despite declines in total cardiovascular mortality rates in the United States, heart failure (HF) mortality rates as well as hospitalizations and readmissions have increased in the past decade. Increases have been relatively higher among young and middle-aged adults (<65 years). Therefore, identification of individuals HF at-risk (Stage A) or with pre-HF (Stage B) before the onset of overt clinical signs and symptoms (Stage C) is urgently needed. Multivariate risk models (e.g., Pooled Cohort Equations to Prevent Heart Failure [PCP-HF]) have been externally validated in diverse populations and endorsed by the 2022 HF Guidelines to apply a risk-based framework for the prevention of HF. However, traditional risk factors included in the PCP-HF model only account for half of an individual's lifetime risk of HF; novel risk factors (e.g., adverse pregnancy outcomes, impaired lung health, COVID-19) are emerging as important risk-enhancing factors that need to be accounted for in personalized approaches to prevention. In addition to determining the role of novel risk-enhancing factors, integration of social determinants of health (SDoH) in identifying and addressing HF risk is needed to transform the current clinical paradigm for the prevention of HF. Comprehensive strategies to prevent the progression of HF must incorporate pharmacotherapies (e.g., sodium glucose co-transporter-2 inhibitors that have also been termed the "statins" of HF prevention), intensive blood pressure lowering, and heart-healthy behaviors. Future directions include investigation of novel prediction models leveraging machine learning, integration of risk-enhancing factors and SDoH, and equitable approaches to interventions for risk-based prevention of HF.

Validation of Heart Failure-Specific Risk Equations in 1.3 Million Israeli Adults and Usefulness of Combining Ambulatory and Hospitalization Data from a Large Integrated Health Care Organization

Heart failure (HF) prevalence is increasing worldwide and is associated with significant morbidity and mortality. Guidelines emphasize prevention in those at-risk, but HF-specific risk prediction equations developed in United States population-based cohorts lack external validation in large, real-world datasets outside of the United States. The purpose of this study was to assess the model performance of the pooled cohort equations to prevent HF (PCP-HF) within a contemporary electronic health record for 5- and 10-year risk. Using a retrospective cohort study design of Israeli residents between 2008 and 2018 with continuous membership until end of follow-up, HF, or death, we quantified 5- and 10-year estimated risks of HF using the PCP-HF equations, which integrate demographics (age, gender, and race) and risk factors (body mass index, systolic blood pressure, glucose, medication use for hypertension or diabetes, and smoking status). Of 1,394,411 patients included, 56% were women with mean age of 49.6 (SD 13.2) years. Incident HF occurred in 1.2% and 4.5% of participants over 5 and 10 years of follow-up. The PCP-HF model had excellent discrimination for 5- and 10-year predictions of incident HF (C Statistic 0.82 [0.82 to 0.82] and 0.84 [0.84 to 0.84]), respectively. In conclusion, HF-specific risk equations (PCP-HF) accurately predict the risk of incident HF in ambulatory and hospitalized patients using routinely available clinical data.

Ten-Year Risk-Prediction Equations for Incident Heart Failure Hospitalizations in Chronic Kidney Disease: Findings from the Chronic Renal Insufficiency Cohort Study and the Multi-Ethnic Study of Atherosclerosis

BACKGROUND

Heart failure (HF) is a leading contributor to cardiovascular morbidity and mortality in the population with chronic kidney disease (CKD). HF risk prediction tools that use readily available clinical parameters to risk-stratify individuals with CKD are needed.

METHODS

We included Black and White participants aged 30-79 years with CKD stages 2-4 who were enrolled in the Chronic Renal Insufficiency Cohort (CRIC) study and were without self-reported cardiovascular disease. We assessed model performance of the Pooled Cohort Equations to Prevent Heart Failure (PCP-HF) to predict incident hospitalizations due to HF and refit the PCP-HF in the population with CKD by using CRIC data-derived coefficients and survival from CRIC study participants in the CKD population (PCP-HFCKD). We investigated the improvement in HF prediction with inclusion of estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR) into the PCP-HFCKD equations by change in C-statistic, net reclassification improvement (NRI), and integrated discrimination improvement index (IDI). We validated the PCP-HFCKD with and without eGFR and UACR in Multi-Ethnic Study of Atherosclerosis (MESA) participants with CKD.

RESULTS

Among 2328 CRIC Study participants, 340 incident HF hospitalizations occurred over a mean follow-up of 9.5 years. The PCP-HF equations did not perform well in most participants with CKD and had inadequate discrimination and insufficient calibration (C-statistic 0.64-0.71, Greenwood-Nam-D'Agostino (GND) chi-square statistic P value < 0.05), with modest improvement and good calibration after being refit (PCP-HFCKD: C-statistic 0.61-0.78), GND chi-square statistic P value > 0.05). Addition of UACR, but not eGFR, to the refit PCP-HFCKD improved model performance in all race-sex groups (C-statistic [0.73-0.81], GND chi-square statistic P value > 0.05, delta C-statistic ranging from 0.03-0.11 and NRI and IDI P values < 0.01). External validation of the PCP-HFCKD in MESA demonstrated good discrimination and calibration.

CONCLUSIONS

Routinely available clinical data that include UACR in patients with CKD can reliably identify individuals at risk of HF hospitalizations.

Moving the Paradigm Forward for Prediction and Risk-Based Primary Prevention of Heart Failure in Special Populations

PURPOSE OF REVIEW

Heart failure (HF) treatment paradigms increasingly recognize the importance of primary prevention. This review explores factors that enhance HF risk, summarizes evidence supporting the pharmacologic primary prevention of HF, and notes barriers to the implementation of primary prevention of HF with a focus on female and sexual and gender minority patients.

RECENT FINDINGS

HF has pathophysiologic sex-specific distinctions, suggesting that sex-specific preventive strategies may be beneficial. Pharmacologic agents that have shown benefit in reducing the risk of HF address the pathobiology underpinning these sex-specific risk factors. The implementation of pharmacologic therapies for primary prevention of HF needs to consider a risk-based model. Current pharmacotherapies hold mechanistic promise for the primary prevention of HF in females and gender and sexual minorities, although research is needed to understand the specific populations most likely to benefit. There are significant systemic barriers to the equitable provision of HF primary prevention.

Rationale and Design of a Pharmacist-led Intervention for the Risk-Based Prevention of Heart Failure: The FIT-HF Pilot Study

Background: Given rising morbidity, mortality, and costs due to heart failure (HF), new approaches for prevention are needed. A quantitative risk-based strategy, in line with established guidelines for atherosclerotic cardiovascular disease prevention, may efficiently select patients most likely to benefit from intensification of preventive care, but a risk-based strategy has not yet been applied to HF prevention. Methods and Results: The Feasibility of the Implementation of Tools for Heart Failure Risk Prediction (FIT-HF) pilot study will enroll 100 participants free of cardiovascular disease who receive primary care at a single integrated health system and have a 10-year predicted risk of HF of ≥5% based on the previously validated Pooled Cohort equations to Prevent Heart Failure. All participants will complete a health and lifestyle questionnaire and undergo cardiac biomarker (B-type natriuretic peptide [BNP] and high-sensitivity cardiac troponin I [hs-cTn]) and echocardiography screening at baseline and 1-year follow-up. Participants will be randomized 1:1 to either a pharmacist-led intervention or usual care for 1 year. Participants in the intervention arm will undergo consultation with a pharmacist operating under a collaborative practice agreement with a supervising cardiologist. The pharmacist will perform lifestyle counseling and recommend initiation or intensification of therapies to optimize risk factor (hypertension, diabetes, and cholesterol) management according to the most recent clinical practice guidelines. The primary outcome is change in BNP at 1-year, and secondary and exploratory outcomes include changes in hs-cTn, risk factor levels, and cardiac mechanics at follow-up. Feasibility will be examined by monitoring retention rates. Conclusions: The FIT-HF pilot study will offer insight into the feasibility of a strategy of quantitative risk-based enrollment into a pharmacist-led prevention program to reduce heart failure risk. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT04684264.

Distribution and Correlates of Incident Heart Failure Risk in South Asian Americans: The MASALA Study

BACKGROUND

South Asian Americans experience disproportionately high burden of cardiovascular diseases. Estimating predicted heart failure (HF) risk distribution may facilitate targeted prevention. We estimated the distribution of 10-year predicted risk of incident HF in South Asian Americans and evaluated the associations with social determinants of health and clinical risk factors.

METHODS AND RESULTS

In the Mediators of Atherosclerosis in South Asians Living in America (MASALA) Study, we calculated 10-year predicted HF risk using the Pooled Cohort Equations to Prevent Heart Failure multivariable model. Distributions of low (<1%), intermediate (1%-5%), and high (≥5%) HF risk, identified overall and by demographic and clinical characteristics, were compared. We evaluated age- and sex-adjusted associations of demographic characteristics and coronary artery calcium with predicted HF risk category using ordinal logistic regression. In 1159 participants (48% women), with a mean age of 57 ± 9 years, 40% had a low, 37% had an intermediate, and 24% had a high HF risk. Significant differences in HF risk distribution existed across demographic (income, education, birthplace) and clinical (diabetes, hypertension, body mass index, coronary artery calcium) groups (P < .01). Significant associations with high predicted HF risk were observed for a family of income 75,000/year or more (adjusted odds ratio 0.5 [95% confidence interval (CI) 0.4-0.7]), college education (0.6 [95% CI 0.4-0.9]), birthplace in another South Asian country (1.9 [95% CI 1.2-3.2], vs. born in India), and prevalent coronary artery calcium (2.6 [95% CI 1.9-3.6]).

CONCLUSIONS

Almost two-thirds of South Asian Americans in the MASALA cohort are at intermediate or high predicted 10-year HF risk, with varying risk across demographic and clinical characteristics.

Epidemiology of Heart Failure: A Contemporary Perspective

Designated as an emerging epidemic in 1997, heart failure (HF) remains a major clinical and public health problem. This review focuses on the most recent studies identified by searching the Medline database for publications with the subject headings HF, epidemiology, prevalence, incidence, trends between 2010 and present. Publications relevant to epidemiology and population sciences were retained for discussion in this review after reviewing abstracts for relevance to these topics. Studies of the epidemiology of HF over the past decade have improved our understanding of the HF syndrome and of its complexity. Data suggest that the incidence of HF is mostly flat or declining but that the burden of mortality and hospitalization remains mostly unabated despite significant ongoing efforts to treat and manage HF. The evolution of the case mix of HF continues to be characterized by an increasing proportion of cases with preserved ejection fraction, for which established effective treatments are mostly lacking. Major disparities in the occurrence, presentation, and outcome of HF persist particularly among younger Black men and women. These disturbing trends reflect the complexity of the HF syndrome, the insufficient mechanistic understanding of its various manifestations and presentations and the challenges of its management as a chronic disease, often integrated within a context of aging and multimorbidity. Emerging risk factors including omics science offer the promise of discovering new mechanistic pathways that lead to HF. Holistic management approaches must recognize HF as a syndemic and foster the implementation of multidisciplinary approaches to address major contributors to the persisting burden of HF including multimorbidity, aging, and social determinants of health.

Risk-Based Approach for the Prediction and Prevention of Heart Failure

Targeted prevention of heart failure (HF) remains a critical need given the high prevalence of HF morbidity and mortality. Similar to risk-based prevention of atherosclerotic cardiovascular disease, optimal HF prevention strategies should include quantification of risk in the individual patient. In this review, we discuss incorporation of a quantitative risk-based approach into the existing HF staging landscape and the clinical opportunity that exists to translate available data on risk estimation to help guide personalized decision making. We first summarize the recent development of key HF risk prediction tools that can be applied broadly at a population level to estimate risk of incident HF. Next, we provide an in-depth description of the clinical utility of biomarkers to personalize risk estimation in select patients at the highest risk of developing HF. We also discuss integration of genomics-enhanced approaches (eg, Titin [TTN]) and other risk-enhancing features to reclassify risk with a precision medicine approach to HF prevention. Although sequential testing is very likely to identify low and high-risk individuals with excellent accuracy, whether or not interventions based on these risk models prevent HF in clinical practice requires prompt attention including randomized placebo-controlled trials of candidate therapies in risk-enriched populations. We conclude with a summary of unanswered questions and gaps in evidence that must be addressed to move the field of HF risk assessment forward.