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Sodium-glucose cotransporter-2 (SGLT2) inhibitors provide cardiovascular and kidney benefits to patients with heart failure (HF) and/or chronic kidney disease (CKD), regardless of diabetes status and left ventricular ejection fraction (LVEF). Despite robust data demonstrating the efficacy of SGLT-2 inhibitors in both ambulatory and hospital settings, real-world evidence suggests slow and varied adoption of SGLT2 inhibitors among patients hospitalized for HF. Barriers to implementation of SGLT2i may include clinicians' concerns regarding potential adverse events such as diabetic ketoacidosis (DKA), volume depletion, and symptomatic hypoglycemia; or concerns regarding physiologically expected reductions in eGFR. Guidelines lack specific, practical safety data and definitive recommendations regarding in-hospital initiation and continuation of SGLT2i in patients hospitalized with HF. In this review, we discuss the safety of in-hospital SGLT2 inhibitor initiation based on recent trials and highlight the clinical implications of their early use in patients hospitalized for HF. [ABSTRACT FROM AUTHOR]

Objectives Mobile health (mHealth) regimens can improve health through the continuous monitoring of biometric parameters paired with appropriate interventions. However, adherence to monitoring tends to decay over time. Our randomized controlled trial sought to determine: (1) if a mobile app with gamification and financial incentives significantly increases adherence to mHealth monitoring in a population of heart failure patients; and (2) if activity data correlate with disease-specific symptoms. Materials and Methods We recruited individuals with heart failure into a prospective 180-day monitoring study with 3 arms. All 3 arms included monitoring with a connected weight scale and an activity tracker. The second arm included an additional mobile app with gamification, and the third arm included the mobile app and a financial incentive awarded based on adherence to mobile monitoring. Results We recruited 111 heart failure patients into the study. We found that the arm including the financial incentive led to significantly higher adherence to activity tracker (95% vs 72.2%, P  = .01) and weight (87.5% vs 69.4%, P  = .002) monitoring compared to the arm that included the monitoring devices alone. Furthermore, we found a significant correlation between daily steps and daily symptom severity. Discussion and Conclusion Our findings indicate that mobile apps with added engagement features can be useful tools for improving adherence over time and may thus increase the impact of mHealth-driven interventions. Additionally, activity tracker data can provide passive monitoring of disease burden that may be used to predict future events. [ABSTRACT FROM AUTHOR]

Background: Almost half of the US adult population has obesity, which predisposes to atherosclerosis and can lead to poor prognosis in coronary artery disease (CAD). We aim to identify CAD and obesity‐related mortality trends among adults in the United States stratified by age, sex, race and geographical location. Methods: The CDC‐WONDER database was used to extract death certificate data for adults aged ≥ 25 years. Crude mortality rates (CMR) and age‐adjusted mortality rates (AAMRs) per 100,000 persons were calculated, and temporal trends were described by calculating annual percent change (APC) and the average APC (AAPC) in the rates using Joinpoint regression analysis. Results: From 1999 to 2022, a total of 273,761 CAD and obesity‐related deaths were recorded in the United States. The AAMR increased consistently from 1999 to 2018 (APC: 4.3, 95% confidence interval (CI): 3.4–4.9) and surged thereafter till 2022 (APC: 11.4; 95% CI: 7.7–19.1). During the COVID‐19 pandemic (2020–2022), AAMR almost doubled that of the rest of the study period. Additionally, the AAMR for males was nearly twice that of females. Non‐Hispanic (NH) Blacks or African Americans displayed the highest AAMR, followed by NH Whites, Hispanic or Latino, and other NH populations. AAMRs showed minimal variation by census regions. Rural areas exhibited a higher AAMR (AAMR: 5.9, 95% CI: 5.8–5.9) than urban areas (AAMR: 4.4, 95% CI: 4.4–4.5). Conclusions: We observed increasing trends in CAD and obesity‐related deaths throughout the study period reaching a peak during the COVID‐19 pandemic. [ABSTRACT FROM AUTHOR]

Existing risk prediction models for hospitalized heart failure patients are limited. We identified patients hospitalized with a diagnosis of heart failure between 7 May 2013 and 26 April 2022 from a large academic, quaternary care medical centre (training cohort). Demographics, medical comorbidities, vitals, and labs were collected and were used to construct random forest machine learning models to predict in‐hospital mortality. Models were compared with logistic regression, and to commonly used heart failure risk scores. The models were subsequently validated in patients hospitalized with a diagnosis of heart failure from a second academic, community medical centre (validation cohort). The entire cohort comprised 21 802 patients, of which 14 539 were in the training cohort and 7263 were in the validation cohort. The median age (25th–75th percentile) was 70 (58–82) for the entire cohort, 43.2% were female, and 6.7% experienced inpatient mortality. In the overall cohort, 7621 (35.0%) patients had heart failure with reduced ejection fraction (EF ≤ 40%), 1271 (5.8%) had heart failure with mildly reduced EF (EF 41–49%), and 12 910 (59.2%) had heart failure with preserved EF (EF ≥ 50%). Random forest models in the validation cohort demonstrated a c‐statistic (95% confidence interval) of 0.96 (0.95–0.97), sensitivity (SN) of 87.3%, and specificity (SP) of 90.6% for the prediction of in‐hospital mortality. Models for those with HFrEF demonstrated a c‐statistic of 0.96 (0.94–0.98), SN 88.2%, and SP 91.0%, and those for patients with HFpEF showed a c‐statistic of 0.95 (0.93–0.97), SN 87.4%, and SP 89.5% for predicting in‐hospital mortality. The random forest model significantly outperformed logistic regression (c‐statistic 0.87, SN 75.9%, and SP 86.9%), and current existing risk scores including the Acute Decompensated Heart Failure National Registry risk score (c‐statistic of 0.70, SN 69%, and SP 62%), and the Get With the Guidelines‐Heart Failure risk score (c‐statistic 0.69, SN 67%, and SP 63%); P < 0.001 for comparison. Machine learning models built from commonly recorded patient information can accurately predict in‐hospital mortality among patients hospitalized with a diagnosis of heart failure. [ABSTRACT FROM AUTHOR]

Aims: Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF patients identified using International Classification of Diseases (ICD) codes. However, most HF patients are not hospitalized, and manual review of big electronic health record (EHR) data is not practical. The US Department of Veterans Affairs (VA) has the largest integrated healthcare system in the nation, and an estimated 1.5 million patients have ICD codes for HF (HF ICD‐code universe) in their VA EHR. The objective of our study was to develop artificial intelligence (AI) models to phenotype HF in these patients. Methods and results: The model development cohort (n = 20 000: training, 16 000; validation 2000; testing, 2000) included 10 000 patients with HF and 10 000 without HF who were matched by age, sex, race, inpatient/outpatient status, hospital, and encounter date (within 60 days). HF status was ascertained by manual chart reviews in VA's External Peer Review Program for HF (EPRP‐HF) and non‐HF status was ascertained by the absence of ICD codes for HF in VA EHR. Two clinicians annotated 1000 random snippets with HF‐related keywords and labelled 436 as HF, which was then used to train and test a natural language processing (NLP) model to classify HF (positive predictive value or PPV, 0.81; sensitivity, 0.77). A machine learning (ML) model using linear support vector machine architecture was trained and tested to classify HF using EPRP‐HF as cases (PPV, 0.86; sensitivity, 0.86). From the 'HF ICD‐code universe', we randomly selected 200 patients (gold standard cohort) and two clinicians manually adjudicated HF (gold standard HF) in 145 of those patients by chart reviews. We calculated NLP, ML, and NLP + ML scores and used weighted F scores to derive their optimal threshold values for HF classification, which resulted in PPVs of 0.83, 0.77, and 0.85 and sensitivities of 0.86, 0.88, and 0.83, respectively. HF patients classified by the NLP + ML model were characteristically and prognostically similar to those with gold standard HF. All three models performed better than ICD code approaches: one principal hospital discharge diagnosis code for HF (PPV, 0.97; sensitivity, 0.21) or two primary outpatient encounter diagnosis codes for HF (PPV, 0.88; sensitivity, 0.54). Conclusions: These findings suggest that NLP and ML models are efficient AI tools to phenotype HF in big EHR data to create contemporary HF registries for clinical studies of effectiveness, quality improvement, and hypothesis generation. [ABSTRACT FROM AUTHOR]

Background: Oral anticoagulants (OACs) are routinely used for the management of atrial fibrillation (AF) while antiplatelet agents are used in coronary artery disease (CAD). However, data regarding the comparative clinical outcomes of OAC monotherapy versus dual antithrombotic therapy (anticoagulant plus antiplatelet agent) in patients with AF and stable CAD are limited. Methods: A comprehensive search of major databases including PubMed/MEDLINE, Cochrane Library, and Embase was performed from inception to September 1, 2024 to identify randomized control trials (RCTs) that compared OAC monotherapy with dual antithrombotic therapy in patients with AF and stable CAD. The risk ratios (RRs) were estimated with corresponding 95% confidence intervals (CIs) for all outcomes. Results: A total of three RCTs reported data for 3945 patients with AF and stable CAD. The mean age of patients was 73.8 (±11.85) years and the mean follow‐up was 22 months. OAC monotherapy was associated with a significantly reduced relative risk of major bleeding (RR: 0.55, 95% CI: 0.32–0.95) compared to dual therapy. The risk of all‐cause death (RR: 0.85, 95% CI: 0.49–1.48), cardiovascular death (RR: 0.84, 95% CI: 0.50–1.41), any stroke event (RR: 0.74, 95% CI: 0.46–1.18), and myocardial infarction (RR: 1.57, 95% CI: 0.79–3.12) remained comparable across the two groups. Conclusion: OAC monotherapy led to a significant relative risk reduction for major bleeding with similar rates of ischemic events and mortality compared to dual antithrombotic therapy in patients with AF and stable CAD. [ABSTRACT FROM AUTHOR]

Objective: Population-based national data on the trends in expenditures related to coexisting atherosclerotic cardiovascular diseases (ASCVD) and diabetes is scarce. We assessed the trends in direct health care expenditures for ASCVD among individuals with and without diabetes, which can help to better define the burden of the co-occurrence of diabetes and ASCVD. Methods: We used 12-year data (2008–2019) from the US national Medical Expenditure Panel Survey including 28,144 U.S individuals aged ≥ 18 years. Using a two-part model (adjusting for demographics, comorbidities and time), we estimated mean and adjusted incremental medical expenditures by diabetes status among individuals with ASCVD. The costs were direct total health care expenditures (out-of-pocket payments and payments by private insurance, Medicaid, Medicare, and other sources) from various sources (office-based visits, hospital outpatient, emergency room, inpatient hospital, pharmacy, home health care, and other medical expenditures). Results: The total direct expenditures for individuals with ASCVD increased continuously by 30% from $14,713 (95% confidence interval (CI): $13,808–$15,619) in 2008–2009 to $19,145 (95% CI: $17,988–$20,301) in 2008–2019. Individuals with diabetes had a 1.5-fold higher mean expenditure that those without diabetes. A key driver of the observed increase in direct costs was prescription drug costs, which increased by 37% among all individuals with ASCVD. The increase in prescription drug costs was more pronounced among individuals with ASCVD and diabetes, in whom a 45% increase in costs was observed, from $5184 (95% CI: $4721–$5646) in 2008–2009 to $7501 (95% CI: $6678–$8325) in 2018–2019. Individuals with ASCVD and diabetes had $5563 (95% CI: $4643–$6483) higher direct incremental expenditures compared with those without diabetes, after adjusting for demographics and comorbidities. Among US adults with ASCVD, the estimated adjusted total direct excess medical expenditures were $42 billion per year among those with diabetes vs. those without diabetes. Conclusions: In the setting of ASCVD, diabetes is associated with significantly increased health care costs, an increase that was driven by marked increase in medication costs. [ABSTRACT FROM AUTHOR]

Increasing knowledge of the processes leading to heart failure (HF) has allowed significant developments in therapies for HF over the past few decades. Despite the evolution of HF treatment, it still places a large burden on patients and health care systems across the world. We used clinicaltrials.gov to gather information about clinical trials as of August 2023 studying pharmacotherapy for HF. We included interventional trials that were "active, not recruiting", "recruiting", or looking for participants but "not yet recruiting". In total, 119 studies met our criteria of ongoing clinical trials studying novel as well as currently approved HF pharmacotherapies. The major interventions were novel medications/already approved medications for other diseases 29 % (34 trials), sodium-glucose co-transporter inhibitors 21 % (25 trials), angiotensin receptor blocker-neprilysin inhibitors 10 % (12 trials), diuretics 14 % (17 trials) and mineralocorticoid receptor antagonists 5 % (6 trials). Ongoing research will aid in reducing the impact of HF and we summarize clinical trials leading the way to better HF treatment in this review. [ABSTRACT FROM AUTHOR]

Aims: According to the Kidney Disease: Improving Global Outcomes (KDIGO) guideline, the definition of chronic kidney disease (CKD) requires the presence of abnormal kidney structure or function for >3 months with implications for health. CKD in patients with heart failure (HF) has not been defined using this definition, and less is known about the true health implications of CKD in these patients. The objective of the current study was to identify patients with HF who met KDIGO criteria for CKD and examine their outcomes. Methods and results: Of the 1 419 729 Veterans with HF not receiving kidney replacement therapy, 828 744 had data on ≥2 ambulatory serum creatinine >90 days apart. CKD was defined as estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2 (n = 185 821) or urinary albumin‐to‐creatinine ratio (uACR) >30 mg/g (n = 32 730) present twice >3 months apart. Normal kidney function (NKF) was defined as eGFR ≥60 ml/min/1.73 m2, present for >3 months, without any uACR >30 mg/g (n = 365 963). Patients with eGFR <60 ml/min/1.73 m2 were categorized into four stages: 45–59 (n = 72 606), 30–44 (n = 74 812), 15–29 (n = 32 077), and <15 (n = 6326) ml/min/1.73 m2. Five‐year all‐cause mortality occurred in 40.4%, 57.8%, 65.6%, 73.3%, 69.7%, and 47.5% of patients with NKF, four eGFR stages, and uACR >30mg/g (albuminuria), respectively. Compared with NKF, hazard ratios (HR) (95% confidence intervals [CI]) for all‐cause mortality associated with the four eGFR stages and albuminuria were 1.63 (1.62–1.65), 2.00 (1.98–2.02), 2.49 (2.45–2.52), 2.28 (2.21–2.35), and 1.22 (1.20–1.24), respectively. Respective age‐adjusted HRs (95% CIs) were 1.13 (1.12–1.14), 1.36 (1.34–1.37), 1.87 (1.84–1.89), 2.24 (2.18–2.31) and 1.19 (1.17–1.21), and multivariable‐adjusted HRs (95% CIs) were 1.11 (1.10–1.12), 1.24 (1.22–1.25), 1.46 (1.43–1.48), 1.42 (1.38–1.47), and 1.13 (1.11–1.16). Similar patterns were observed for associations with hospitalizations. Conclusion: Data needed to define CKD using KDIGO criteria were available in six out of ten patients, and CKD could be defined in seven out of ten patients with data. HF patients with KDIGO‐defined CKD had higher risks for poor outcomes, most of which was not explained by abnormal kidney structure or function. Future studies need to examine whether CKD defined using a single eGFR is characteristically and prognostically different from CKD defined using KDIGO criteria. [ABSTRACT FROM AUTHOR]

Aims: Patients are at high risk of death or readmission following hospitalization for heart failure (HF). We tested the effect of a transitional care model that included month‐long nurse‐led home visits and long‐term heart function clinic visits ‐ with services titrated to estimated risk of clinical events ‐ on 3‐year outcomes following hospitalization. Methods and results: In a pragmatic, stepped‐wedge cluster randomized trial, 10 hospitals were randomized to the intervention versus usual care. The primary outcome was a composite of all‐cause death, readmission, or emergency department (ED) visit. Secondary outcomes included components of the primary composite outcomes, HF readmissions and healthcare resource utilization. There were 2494 patients (50.4% female) with mean age of 77.7 years. The primary outcome was reached in 1040 (94.2%) patients in the intervention and 1314 (94.5%) in the usual care group at 3 years. The intervention did not reduce the risk of the primary composite outcome (hazard ratio [HR] 0.92, 95% confidence interval [CI] 0.81–1.05) nor the component outcomes overall, although numerically reduced the risk of ED visits in women but not men (HR 0.79, 95% CI 0.63–1.00 vs. HR 0.98, 95% CI 0.80–1.19; sex‐treatment interaction p = 0.23). The uptake of guideline‐directed medical therapy was no different with the intervention than with usual care, with the exception of sacubitril/valsartan, which increased with the intervention (3.3% vs 1.5%; relative risk 6.2, 95% CI 1.92–20.06). Conclusions: More than 9 of 10 patients hospitalized for HF experienced all‐cause death, readmission, or ED visit at 3 years. A transitional care model with services titrated to risk did not improve the composite of these endpoints, likely because there were no major differences in uptake of medical therapies between the groups. Clinical Trial Registration: ClinicalTrials.gov Identifier NCT02112227. [ABSTRACT FROM AUTHOR]

This article discusses the challenges and effectiveness of early post-discharge follow-up for patients hospitalized with heart failure (HF). While guidelines recommend early follow-up visits after discharge, the evidence supporting their effectiveness is limited. A study from France found that combined follow-up with a general practitioner and cardiologist was associated with a lower risk of death or rehospitalization, but a higher risk of HF readmission. The article emphasizes the importance of optimizing guideline-directed medical therapy (GDMT) during these visits and initiating GDMT prior to discharge to improve outcomes for HF patients. However, the article acknowledges the limitations of the study and the need for further research in this area. [Extracted from the article]

Objective: We aimed to characterize the association of hospital procedural volumes with outcomes among acute ischemic stroke (AIS) patients undergoing endovascular therapy (EVT). Methods: This was a retrospective, observational cohort study using data prospectively collected from January 1, 2016 to December 31, 2019 in the Get with the Guidelines–Stroke registry. Participants were derived from a cohort of 60,727 AIS patients treated with EVT within 24 hours at 626 hospitals. The primary cohort excluded patients with pretreatment National Institutes of Health Stroke Scale (NIHSS) < 6, onset‐to‐treatment time > 6 hours, and interhospital transfers. There were 2 secondary cohorts: (1) the EVT metrics cohort excluded patients with missing data on time from door to arterial puncture and (2) the intravenous thrombolysis (IVT) metrics cohort only included patients receiving IVT ≤4.5 hours after onset. Results: The primary cohort (mean ± standard deviation age = 70.7 ± 14.8 years; 51.2% female; median [interquartile range] baseline NIHSS = 18.0 [13–22]; IVT use, 70.2%) comprised 21,209 patients across 595 hospitals. The EVT metrics cohort and IVT metrics cohort comprised 47,262 and 16,889 patients across 408 and 601 hospitals, respectively. Higher procedural volumes were significantly associated with higher odds (expressed as adjusted odds ratio [95% confidence interval] for every 10‐case increase in volume) of discharge to home (1.03 [1.02–1.04]), functional independence at discharge (1.02 [1.01–1.04]), and lower rates of in‐hospital mortality (0.96 [0.95–0.98]). All secondary measures were also associated with procedural volumes. Interpretation: Among AIS patients primarily presenting to EVT‐capable hospitals (excluding those transferred from one facility to another and those suffering in‐hospital strokes), EVT at hospitals with higher procedural volumes was associated with faster treatment times, better discharge outcomes, and lower rates of in‐hospital mortality. ANN NEUROL 2024;95:146–155 [ABSTRACT FROM AUTHOR]

The article is a systematic review of randomized controlled trials comparing the effectiveness and safety of two diuretics, torsemide and furosemide, in heart failure patients. The review included 9 trials with a total of 3928 patients. The results showed no significant difference in mortality, hospitalization, cardiac mortality, and adverse events between the two diuretics. However, more research is needed to explore potential benefits of torsemide. [Extracted from the article]

Introduction: Although lower socioeconomic status (SES) has been associated with worse in‐hospital outcomes among patients with heart failure, the in‐hospital outcomes for patients undergoing durable Left Ventricular Assist Device (LVAD) implantation or Heart Transplantation (HT) based on SES have not been well characterized. Methods: We analyzed data from the National Inpatient Sample of hospitalizations between January 2016 and December 2020 of patients aged 18 and over who underwent a HT or newly implanted LVAD. Quartile classification of the median household income of the patient's residential zip code was used to estimate SES. Multivariable analyses with logistic and linear regression were used to evaluate the effects of SES on inpatient outcomes including inpatient mortality, length of stay, and key inpatient complications. Results: A total of 16,265 weighted hospitalizations for new LVAD implantation and 14,320 weighted hospitalizations for HT were identified. In multivariable analysis, among patients undergoing HT or LVAD implantation respectively, there were no significant differences between the lowest and highest SES quartiles among important in‐hospital outcomes including length of stay (adj B‐coeff.56, (−3.59)–(4.71), p =.79 and adj B‐coeff 2.40, (−.21)–(5.02), p =.07) and mortality (aOR 1.02,.61–1.70, p =.94 and aOR 1.08,.72–1.62, p =.73). There were also no differences based on SES quartile in important inpatient complications including stroke and cardiac arrest. Conclusion: In this analysis from the National Inpatient Sample, we demonstrate that SES, evaluated by median zip code income, was not associated with important in‐hospital metrics including mortality and length of stay among patients undergoing LVAD or HT. [ABSTRACT FROM AUTHOR]

Background: An unmet need exists to reliably predict the risk of intracranial hemorrhage (ICH) in patients with atrial fibrillation (AF) treated with oral anticoagulants (OACs). Hypothesis: An externally validated model improves ICH risk stratification. Methods: Independent factors associated with ICH were identified by Cox proportional hazard modeling, using pooled data from the GARFIELD‐AF (Global Anticoagulant Registry in the FIELD‐Atrial Fibrillation) and ORBIT‐AF (Outcomes Registry for Better Informed Treatment of Atrial Fibrillation) registries. A predictive model was developed and validated by bootstrap sampling and by independent data from the Danish National Patient Register. Results: In the combined training data set, 284 of 53 878 anticoagulated patients had ICH over a 2‐year period (0.31 per 100 person‐years; 95% confidence interval [CI]: 0.28–0.35). Independent predictors of ICH included: older age, prior stroke or transient ischemic attack, concomitant antiplatelet (AP) use, and moderate‐to‐severe chronic kidney disease (CKD). Vitamin K antagonists (VKAs) were associated with a significantly higher risk of ICH compared with non‐VKA oral anticoagulants (NOACs) (adjusted hazard ratio: 1.61; 95% CI: 1.25–2.08; p =.0002). The ability of the model to discriminate individuals in the training set with and without ICH was fair (optimism‐corrected C‐statistic: 0.68; 95% CI: 0.65–0.71) and outperformed three previously published methods. Calibration between predicted and observed ICH probabilities was good in both training and validation data sets. Conclusions: Age, prior ischemic events, concomitant AP therapy, and CKD were important risk factors for ICH in anticoagulated AF patients. Moreover, ICH was more frequent in patients receiving VKA compared to NOAC. The new validated model is a step toward mitigating this potentially lethal complication. [ABSTRACT FROM AUTHOR]

The article "Diabetes, Functional Status, and Mortality in Chronic Heart Failure" published in Diabetes Care explores the associations of diabetes with exercise tolerance, functional status, and mortality in individuals with chronic heart failure. The study included 2,786 individuals from five well-characterized studies of chronic heart failure, revealing that diabetes was associated with lower exercise capacity and increased risk of death. The findings suggest the importance of optimizing metabolic health in individuals with chronic heart failure, potentially through the use of specific medications. Further research is needed to fully understand the connection between diabetes and heart failure outcomes. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]