Heart Failure Registry Research Articles

Quality of life in patients with heart failure and implantable cardioverter defibrillator. Results from 42 heart failure clinics in norway

Abstract Background Patients with heart failure (HF) have an increased risk of sudden cardiac death, and treatment with an implantable cardioverter defibrillator (ICD) has become standard treatment for selected patient groups. HF is associated with impaired QoL, but there is no consensus regarding the implication of having an ICD. Knowledge regarding QoL in patients with HF and ICD attending follow-up in outpatient HF-clinics is sparse. Purpose The aim of this study was to determine QoL in patients with HF and ICD. Further, to compare QoL in patients with HF with and without ICD, using data from the first and follow-up visits in outpatient HF clinics. Methods Longitudinal data from the National Norwegian Heart Failure Registry (NNHFR), including 9 274 patients, of which 1 206 with an ICD, were studied. QoL was assessed using the Minnesota Living with Heart Failure Questionnaire (MLHFQ). Data was collected at the first and follow-up visits in outpatient HF clinics approximately six months after optimal treatment was achieved. Results Patients with an ICD were younger, more often men, and had higher frequency of coronary artery disease (all p<0.001). At the first visit, there was no significant difference in QoL between patients with and without ICD, with both groups reporting moderate QoL. Both groups had a significant improvement in QoL from the first visit to the follow-up visit (p<0.001). However, patients with ICD reported significantly less improvement in QoL (p<0.001) at follow-up. This disparity was evident regarding both the physical (p<0.001) and the emotional (p<0.001) dimension. Conclusion QoL in patients with HF and ICD improves after follow-up in outpatient HF clinics, although not to the same extent as in patients without ICD. Thus, tailored interventions in outpatient HF clinics may be necessary to improve outcomes in this subgroup of patients with both HF and ICD.

American registry of ambulatory and acute decompensated heart failure (AMERICCAASS registry): Rationale and design.

Heart failure (HF) is a highly prevalent and progressive condition associated with significant morbidity and mortality rates. Acute decompensated HF precipitates millions of hospitalizations each year. Despite therapeutic advances, the overall prognosis of HF is poor. The varying clinical courses and outcomes of patients with this disease may be due to region-specific gaps and since most HF studies are conducted in developed countries, the participation of Latin American and Caribbean countries is low. Considering this, the American Registry of Ambulatory and Acute Decompensated Heart Failure (AMERICCAASS) aims to characterize the population with ambulatory and acute decompensated HF in the American continent and to determine rehospitalization and survival outcomes during the 12months of follow-up. AMERICCAASS Registry is an observational, prospective, and hospital-based registry recruiting patients with ambulatory or acute decompensated HF. The registry plans to include between two and four institutions per country from at least 20 countries in the Americas, and at least 60 patients recruited from each participant institution regardless of their ambulatory or acutely decompensated condition. Ambulatory patients with confirmed HF diagnosis or inpatients presenting with acute decompensated HF will be included. Follow-up will be performed at 12months in ambulatory patients or 1, 6, and 12months after hospital discharge in acutely decompensated HF patients. This ongoing study began on 1 April 2022, with recruitment scheduled to end on 30 November 2023, and follow-up on 31 January 2025. Ethics approval was obtained from the Biomedical Research Ethics Committee of Fundación Valle del Lili. Data collected in the AMERICCAASS registry is being stored on the electronic platform REDCap (Research Electronic Data Capture), which allows different forms for patient groups to enable unbiased analyses. For quantitative variables comparison, we will use the Student's t-test or non-parametric tests accordingly. Categorical variables will be presented as proportions, and groups will be compared with Fisher's exact test. The significance level will be <0.05 for comparisons. Readmissions and post-discharge mortality will be calculated as proportions at 1, 6, and 12months, with a survival analysis by conditional probability and the Kaplan-Meier method. AMERICCAASS Registry is intended to be the most important registry of the continent for obtaining important information about demographics, aetiology, co-morbidities, and treatment received, either ambulatory or hospitalized. This registry may contribute to the optimization of national and regional evidence and public policies for the diagnosis and treatment of HF disease.

Design and rationale of the Engage-HF study: the impact of a gamified engagement toolkit on participation and engagement in a heart failure registry.

Heart failure (HF) registries provide valuable insights into patient management and quality of care. However, healthcare professionals face challenges due to the administrative burden of participation in registries. This study aims to evaluate the impact of education through an engagement toolkit on HF nurse practitioners' participation rate and data completeness in a national registry: the Netherlands Heart Registration-Heart Failure (NHR-HF) registry. Engage-HF is an observational study (intervention at the HF nurse level) with a pretest-posttest design within the participating hospitals. Between December 2022 and April 2024, 28 HF nurse practitioners from 12 hospitals will participate in a 24-week educational programme using the Engage-HF engagement toolkit. The main interaction platform in this toolkit is a gamified smartphone-based educational application called BrightBirds. The complete toolkit includes this educational application with weekly challenges, interactive posters, pop-ups, and alert messages, and a follow-up call at Week 4. The primary endpoints are the NHR-HF participation rates and data completeness at 1 and 6 months after using the toolkit. Additionally, we will analyse the experience of participants with the toolkit concerning their HF registry and knowledge of ESC 2021 HF guidelines. The Engage-HF study is the first to explore the impact of education through a gamified engagement toolkit to boost participation rates in a HF registry (NHR-HF) and test participant knowledge of the ESC 2021 HF guidelines. This innovative approach addresses challenges in the rollout of healthcare registries and the implementation of guidelines by providing a contemporary support base and a time-efficient method for education.

Systemic Immune-Inflammation Index as A Predictor of Mortality and Rehospitalization in Heart Failure Patients

ABSTRACT Systemic Immune-Inflammation Index as A Predictor of Mortality and Rehospitalization in Heart Failure Patients Background Heart failure (HF) is a disease closely associated with inflammation and the Systemic Immune inflammation Index (SII) is a novel inflammatory marker. SII is a combined inflammatory indicator that incorporates three significant immune cells namely neutrophils, lymphocytes and platelets. SII is considered an excellent indicator of local immune response and systemic inflammation. SII is an inexpensive and easily accessible laboratory test in peripheral health facilities. Research Objective This study aims to determine whether SII can be used as a predictor of clinical outcomes, which are mortality and rehospitalization in patients with chronic heart failure. Research Methods This study is a retrospective cohort study using data of chronic heart failure patients who underwent hospitalization from the HF (Heart Failure) registry of Dr. Sardjito Hospital for the period January 2022 - February 2023. SII was calculated as platelet count × absolute neutrophil count / absolute lymphocyte count. Results There were 188 patients who met the inclusion criteria as research subjects from 282 chronic heart failure patients who underwent hospitalization. Subjects had a mean age of 58.80 ± 11.48 years. A total of 67,6% (n=127) of the subjects were male. SII value for mortality based on receiver operating characteristic (ROC) cut-off was 1459,59 (area under curve (AUC) 0.600, sensitivity (Sn) 55,6%, specificity (Sp) 68,8%) and the ROC cut-off for rehospitalization was 0,474. Kaplan-Meier analysis of SII with mortality, subjects with high SII values (&gt;1459.59) had a 50,5% survival rate with a mean survival of 8,5 months and low SII (&lt;1459.59) had a 74% survival rate with a mean survival of 9,9 months. Multivariate regression analysis found SII as an independent predictor of mortality odds ratio (OR 3,08, 95% confidence interval (CI) 1,59 – 5,95, p=&lt;0.001) but not significant for rehospitalization. Conclusion Systemic Immune-inflammation Index (SII) is an independent predictor of mortality, but not a predictor of rehospitalization in chronic heart failure patients undergoing hospitalization. Keywords: Systemic Immune-inflammation Index (SII), heart failure (HF), mortality and rehospitalization

OPTImal PHARMacological therapy for patients with heart failure: Rationale and design of the OPTIPHARM-HF registry.

Patients with heart failure (HF) remain often undertreated for multiple reasons, including treatment inertia, contraindications, and intolerance. The OPTIimal PHARMacological therapy for patients with Heart Failure (OPTIPHARM-HF) registry is designed to evaluate the prevalence of evidence-based medical treatment prescription and titration, as well as the causes of its underuse, in a broad real-world population of consecutive patients with HF across the whole ejection fraction spectrum and among different clinical phenotypes. The OPTIPHARM-HF registry (NCT06192524) is a prospective, multicenter, observational, national study of adult patients with symptomatic HF, as defined by current international guidelines, regardless of ejection fraction. Both outpatients and inpatients with chronic and acute decompensated HF will be recruited. The study will enroll up to 2500 patients with chronic HF at approximately 35 Italian HF centres. Patients will be followed for a maximum duration of 24 months. The primary objective of the OPTIPHARM-HF registry is to assess prescription and adherence to evidence-based guideline-directed medical therapy (GDMT) in patients with HF. The primary outcome is to describe the prevalence of GDMT use according to target guideline recommendation. Secondary objectives include implementation of comorbidity treatment, evaluation of sequence of treatment introduction and up-titration, description of GDMT implementation in the specific HF population, main causes of GDMT underuse, and assessment of cumulative rate of cardiovascular events. The OPTIPHARM-HF registry will provide important implications for improving patient care and adoption of recommended medical therapy into clinical practice among HF patients.

Eligibility of sodium-glucose cotransporter-2 inhibitors in heart failure with preserved ejection fraction: Insights from the Colombian heart failure registry (RECOLFACA)

BackgroundThe value of Sodium-glucose cotransporter-2 inhibitors (SGLT-2 inhibitor) therapy in individuals with heart failure with preserved EF (HFpEF) was unknown until the EMPEROR-Preserved trial. We aimed to assess the proportion of patients with HFpEF that are eligible for empagliflozin therapy within the Colombian Heart Failure Registry (RECOLFACA). MethodsRECOLFACA enrolled adult patients with a HF diagnosis during 2017–2019 from 60 medical centers in Colombia. Criteria of the EMPEROR-Preserved Trial were used to recruit participants. The main outcome was individual eligibility with N-terminal pro-B-type natriuretic peptide (NT-proBNP) criteria, while the secondary outcome was eligibility without NT-proBNP data. ResultsRECOLFACA had 799 patients with HFpEF (mean age70.7 ± 13.5; 50.7 % males). According to the major selection criteria of the EMPEROR Preserved Trial, 73.7 % patients would be eligible for empagliflozin therapy initiation when considering the NT-proBNP threshold. The NT-proBNP threshold represented the main determinant of ineligibility in patients with this biomarker measure (13.6 %; n = 16). In patients without NT-proBNP data, the main reasons for exclusion were the diagnosis of symptomatic hypotension or a systolic blood pressure below 100 mmHg (7.5 %), having an eGFR < 20 ml/min/1.73 m2 (4.3 %), and haemoglobin < 9 g/dl (3.1 %). Excluding NT-proBNP criteria increased empagliflozin eligibility to 80.6 %. ConclusionMost patients with HFpEF from RECOLFACA are potential candidates for empagliflozin therapy initiation according to the EMPEROR-Preserved trial criteria. These findings favor the utilization of SGLT-2 inhibitor medications in daily medical practice, which may further decrease morbidity and mortality in HF patients, regardless of their EF classification.

Artificial intelligence approaches for phenotyping heart failure in U.S. Veterans Health Administration electronic health record.

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. The model development cohort (n=20000: training, 16000; validation 2000; testing, 2000) included 10000 patients with HF and 10000 without HF who were matched by age, sex, race, inpatient/outpatient status, hospital, and encounter date (within 60days). 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). 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.

Phenotype clustering of hospitalized high-risk patients with COVID-19 - a machine learning approach within the multicentre, multinational PCHF-COVICAV registry.

The high-risk population of patients with cardiovascular (CV) disease or risk factors (RF) suffering from COVID-19 is heterogeneous. Several predictors for impaired prognosis have been identified. However, with machine learning (ML) approaches, certain phenotypes may be confined to classify the affected population and to predict outcome. This study aimed to phenotype patients using unsupervised ML technique within the International Postgraduate Course Heart Failure Registry for patients hospitalized with COVID-19 and Cardiovascular disease and/or RF (PCHF-COVICAV). Patients from the eight centres with follow-up data available from the PCHF-COVICAV registry were included in this ML analysis (K-medoids algorithm). Out of 617 patients included into the prospective part of the registry, 458 [median age: 76 (IQR:65-84) years, 55% male] were analyzed and 46 baseline variables, including demographics, clinical status, comorbidities and biochemical characteristics were incorporated into the ML. Three clusters were extracted by this ML method. Cluster 1 (n = 181) represents mainly women with the least number of overall comorbidities and cardiovascular RF. Cluster 2 (n = 227) is characterized mainly by men with non-CV conditions and less severe symptoms of infection. Cluster 3 (n=50) mainly represents men with the highest prevalence of cardiac comorbidities and RF, more extensive inflammation and organ dysfunction with the highest 6-month all-cause mortality risk. The ML process has identified three important clinical clusters from hospitalized COVID-19 CV and/or RF patients. The cluster of males with severe CV disease, particularly HF, and multiple RF presenting with increased inflammation had a particularly poor outcome.

Reassessing heart failure therapy in Thailand: Patient insights and treatment outcomes from the Thai heart failure registry

BackgroundThis research analyzed the demographics, management, and outcomes of patients with heart failure (HF) in Thailand. MethodsThe Thai Heart Failure Registry prospectively enrolled patients diagnosed with HF from 36 hospitals in Thailand. Follow-up data were recorded at 6, 12, 18, and 24 months. This study primarily focused on two outcomes: mortality and HF-related hospitalizations. ResultsThe study included 2639 patients aged at least 18. Their mean age was 59.2 ± 14.5 years, and most were male (68.4%). Patients were classified as having HF with reduced ejection fraction (HFrEF, 80.7%), HF with preserved ejection fraction (HFpEF, 9.0%), or HF with mildly reduced ejection fraction (HFmrEF, 10.3%). Guideline-directed medical therapy utilization varied. Beta-blockers had the highest usage (93.2%), followed by mineralocorticoid receptor antagonists (65.7%), angiotensin-converting enzyme inhibitors (39.3%), angiotensin receptor blockers (28.2%), angiotensin receptor-neprilysin inhibitors (16.1%), and sodium-glucose cotransporter-2 inhibitors (8.0%). The study monitored a composite of mortality and HF incidents, revealing incidence rates of 11.74, 12.50, and 8.93 per 100 person-years for the overall, HFrEF, and HFmrEF/HFpEF populations, respectively. ConclusionsDespite high guideline-directed medical therapy adherence, the Thai Heart Failure Registry data revealed high mortality and recurrent HF rates. These findings underscore limitations in current HF treatment efficacy. The results indicate the need for further investigation and improvements of HF management to enhance patient outcomes.

PREDICT HF: Risk stratification in advanced heart failure using novel hemodynamic parameters.

Invasive hemodynamics are fundamental in assessing patients with advanced heart failure (HF). Several novel hemodynamic parameters have been studied; however, the relative prognostic potential remains ill-defined. Advanced hemodynamic parameters provide additional prognostication beyond the standard hemodynamic assessment. Patients from the PRognostic Evaluation During Invasive CaTheterization for Heart Failure (PREDICT-HF) registry who underwent right heart catheterization (RHC) were included in the analysis. The primary endpoint was survival to orthotopic heart transplant (OHT) or durable left ventricular assist device (LVAD), or death within 6 months of RHC. Of 846 patients included, 176 (21%) met the primary endpoint. In a multivariate model that included traditional hemodynamic variables, pulmonary capillary wedge pressure (PCWP) (OR: 1.10, 1.04-1.15, p < .001), and cardiac index (CI) (OR: 0.86, 0.81-0.92, p < .001) were shown to be predictive of adverse outcomes. In a separate multivariate model that incorporated advanced hemodynamic parameters, cardiac power output (CPO) (OR: 0.76, 0.71-0.83, p < .001), aortic pulsatility index (API) (OR: 0.94, 0.91-0.96, p < .001), and pulmonary artery pulsatility index (OR: 1.02, 1.00-1.03, p .027) were all significantly associated with the primary outcome. Positively concordant API and CPO afforded the best freedom from the endpoint (94.7%), whilst negatively concordant API and CPO had the worst freedom from the endpoint (61.5%, p < .001). Those with discordant API and CPO had similar freedom from the endpoint. The advanced hemodynamic parameters API and CPO are independently associated with death or the need for OHT or LVAD within 6 months. Further prospective studies are needed to validate these parameters and elucidate their role in patients with advanced HF.

Machine Learning-Driven Mortality Prediction in Heart Failure Patients with Atrial Fibrillation: Evidence from the Jordanian Heart Failure Registry

Machine Learning-Driven Mortality Prediction in Heart Failure Patients with Atrial Fibrillation: Evidence from the Jordanian Heart Failure Registry

Eligibility for omecamtiv mecarbil in a real-world heart failure population: Data from the Swedish Heart Failure Registry.

We assessed eligibility for omecamtiv mecarbil (OM) in a real-world cohort with heart failure with reduced ejection fraction (HFrEF) according to the selection criteria of the GALACTIC-HF trial (trial scenario) and selected trial´s criteria more likely to impact real-world use (pragmatic scenario). We included 31,015 patients with HFrEF lasting ≥3 months and registered in the Swedish HF registry between 2000-2021. Trial eligibility was calculated by applying all the GALACTIC-HF selection criteria. The pragmatic scenario considered only the New York Heart Association class, history of worsening HF, N-terminal pro-B-type natriuretic peptides (NT-proBNP), blood pressure and renal failure criteria defined as in the trial. Eligibility for OM in chronic HFrEF was 21% and 36% in the trial and pragmatic scenarios, respectively. Eligibility was higher in those with EF<30% (trial: 27%, pragmatic: 44%), in-patients (trial:30%, pragmatic:57%), severe HF (trial: 35%, pragmatic: 60%), NYHA class III-IV (trial: 26%, pragmatic: 45%), and NT-proBNP≥5,000pg/mL (trial: 30%, pragmatic: 51%). The criteria that most limited eligibility were history of a recent worsening HF event (60% eligible in chronic HFrEF), elevated NT-proBNP (82% eligible), and deviating blood pressure (82% eligible). Overall, eligible patients were characterized by more severe HF and higher CV event-rates in both scenarios, and higher comorbidity burden in the pragmatic scenario. Approximately 21% of real-world chronic HFrEF patients would be eligible for OM according to the GALACTIC-HF selection criteria, and 36% according to the criteria more likely to affect OM use in clinical practice. Criteria in both scenarios identified a patient-group with severe HF and high CV event-rates.

Machine Learning-Based Mortality Prediction in Chronic Kidney Disease among Heart Failure Patients: Insights and Outcomes from the Jordanian Heart Failure Registry

Background and Objectives: Heart failure (HF) is a prevalent and debilitating condition that imposes a significant burden on healthcare systems and adversely affects the quality of life of patients worldwide. Comorbidities such as chronic kidney disease (CKD), arterial hypertension, and diabetes mellitus (DM) are common among HF patients, as they share similar risk factors. This study aimed to identify the prognostic significance of multiple factors and their correlation with disease prognosis and outcomes in a Jordanian cohort. Materials and Methods: Data from the Jordanian Heart Failure Registry (JoHFR) were analyzed, encompassing medical records from acute and chronic HF patients attending public and private cardiology clinics and hospitals across Jordan. An online form was utilized for data collection, focusing on three kidney function tests, estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN), and creatinine levels, with the eGFR calculated using the Cockcroft–Gault formula. We also built six machine learning models to predict mortality in our cohort. Results: From the JoHFR, 2151 HF patients were included, with 644, 1799, and 1927 records analyzed for eGFR, BUN, and creatinine levels, respectively. Age negatively impacted all measures (p ≤ 0.001), while smokers surprisingly showed better results than non-smokers (p ≤ 0.001). Males had more normal eGFR levels compared to females (p = 0.002). Comorbidities such as hypertension, diabetes, arrhythmias, and implanted devices were inversely related to eGFR (all with p-values &lt;0.05). Higher BUN levels were associated with chronic HF, dyslipidemia, and ASCVD (p ≤ 0.001). Higher creatinine levels were linked to hypertension, diabetes, dyslipidemia, arrhythmias, and previous HF history (all with p-values &lt;0.05). Low eGFR levels were associated with increased mechanical ventilation needs (p = 0.049) and mortality (p ≤ 0.001), while BUN levels did not significantly affect these outcomes. Machine learning analysis employing the Random Forest Classifier revealed that length of hospital stay and creatinine &gt;115 were the most significant predictors of mortality. The classifier achieved an accuracy of 90.02% with an AUC of 80.51%, indicating its efficacy in predictive modeling. Conclusions: This study reveals the intricate relationship among kidney function tests, comorbidities, and clinical outcomes in HF patients in Jordan, highlighting the importance of kidney function as a predictive tool. Integrating machine learning models into clinical practice may enhance the predictive accuracy of patient outcomes, thereby supporting a more personalized approach to managing HF and related kidney dysfunction. Further research is necessary to validate these findings and to develop innovative treatment strategies for the CKD population within the HF cohort.

Development of interpretable machine learning models to predict in-hospital prognosis of acute heart failure patients.

In recent years, there has been remarkable development in machine learning (ML) models, showing a trend towards high prediction performance. ML models with high prediction performance often become structurally complex and are frequently perceived as black boxes, hindering intuitive interpretation of the prediction results. We aimed to develop ML models with high prediction performance, interpretability, and superior risk stratification to predict in-hospital mortality and worsening heart failure (WHF) in patients with acute heart failure (AHF). Based on the Kyoto Congestive Heart Failure registry, which enrolled 4056 patients with AHF, we developed prediction models for in-hospital mortality and WHF using information obtained on the first day of admission (demographics, physical examination, blood test results, etc.). After excluding 16 patients who died on the first or second day of admission, the original dataset (n=4040) was split 4:1 into training (n=3232) and test datasets (n=808). Based on the training dataset, we developed three types of prediction models: (i) the classification and regression trees (CART) model; (ii) the random forest (RF) model; and (iii) the extreme gradient boosting (XGBoost) model. The performance of each model was evaluated using the test dataset, based on metrics including sensitivity, specificity, area under the receiver operating characteristic curve (AUC), Brier score, and calibration slope. For the complex structure of the XGBoost model, we performed SHapley Additive exPlanations (SHAP) analysis, classifying patients into interpretable clusters. In the original dataset, the proportion of females was 44.8% (1809/4040), and the average age was 77.9±12.0. The in-hospital mortality rate was 6.3% (255/4040) and the WHF rate was 22.3% (900/4040) in the total study population. In the in-hospital mortality prediction, the AUC for the XGBoost model was 0.816 [95% confidence interval (CI): 0.815-0.818], surpassing the AUC values for the CART model (0.683, 95% CI: 0.680-0.685) and the RF model (0.755, 95% CI: 0.753-0.757). Similarly, in the WHF prediction, the AUC for the XGBoost model was 0.766 (95% CI: 0.765-0.768), outperforming the AUC values for the CART model (0.688, 95% CI: 0.686-0.689) and the RF model (0.713, 95% CI: 0.711-0.714). In the XGBoost model, interpretable clusters were formed, and the rates of in-hospital mortality and WHF were similar among each cluster in both the training and test datasets. The XGBoost models with SHAP analysis provide high prediction performance, interpretability, and reproducible risk stratification for in-hospital mortality and WHF for patients with AHF.

Total, ageand sex-specific mortality after discharge of patients with heart failure: the first large-scale cohort real-world study on Russian population

Aim. To assess total, ageand sex-specific survival rate after discharge of patients with heart failure (HF) using real-world electronic health data.Material and methods. This retrospective analysis of data from the St. Petersburg Chronic Heart Failure Registry was performed. Hospitalizations of patients aged 18 years with a diagnosis code I50.x (International Classification of Diseases, 10th revision) were included in the period from January 1, 2019 to December 31, 2023. Cases with acute myocardial infarction, cerebrovascular accident, coronavirus disease 2019, hospitalization duration of &gt;30 days, and death during the current hospitalization were not included.Results. The study included 73450 patients aged 18 to 99 years (mean age, 73±12 years; women, 59,1%). During a median follow-up of 388 days, 16212 (22,1%) patients died. The cumulative death probability within one and five years after discharge was 16,3% and 48,9%. At each time period, the rate was higher in men when stratified by age and as age increased.The mortality rate was 15,3 (95% confidence interval 15,1 to 15,6) per 100 patientyears. For all subgroups, the highest value was recorded in the first month after discharge (50,1 per 100 patient-years), reaching a maximum in the subgroup of elderly and senile patients (60,7 per 100 patient-years).Conclusion. Within 1 year and 5 years after hospital discharge, 16,3% and 48,9% of patients with HF die, respectively. The highest death risk is typical in the first month after discharge, especially for elderly and senile patients.

Contemporary guideline-directed medical therapy in de novo, chronic, and worsening heart failure patients: First data from the TITRATE-HF study.

Despite clear guideline recommendations for initiating four drug classes in all patients with heart failure (HF) with reduced ejection fraction (HFrEF) and the availability of rapid titration schemes, information on real-world implementation lags behind. Closely following the 2021 ESC HF guidelines and 2023 focused update, the TITRATE-HF study started to prospectively investigate the use, sequencing, and titration of guideline-directed medical therapy (GDMT) in HF patients, including the identification of implementation barriers. TITRATE-HF is an ongoing long-term HF registry conducted in the Netherlands. Overall, 4288 patients from 48 hospitals were included. Among these patients, 1732 presented with de novo, 2240 with chronic, and 316 with worsening HF. The median age was 71 years (interquartile range [IQR] 63-78), 29% were female, and median ejection fraction was 35% (IQR 25-40). In total, 44% of chronic and worsening HFrEF patients were prescribed quadruple therapy. However, only 1% of HFrEF patients achieved target dose for all drug classes. In addition, quadruple therapy was more often prescribed to patients treated in a dedicated HF outpatient clinic as compared to a general cardiology outpatient clinic. In each GDMT drug class, 19% to 36% of non-use in HFrEF patients was related to side-effects, intolerances, or contraindications. In the de novo HF cohort, 49% of patients already used one or more GDMT drug classes for other indications than HF. This first analysis of the TITRATE-HF study reports relatively high use of GDMT in a contemporary HF cohort, while still showing room for improvement regarding quadruple therapy. Importantly, the use and dose of GDMT were suboptimal, with the reasons often remaining unclear. This underscores the urgency for further optimization of GDMT and implementation strategies within HF management.

Influence of prior myocardial infarction on outcome in patients with ischaemic HFrEF: insights from the EVIdence based TreAtment in Heart Failure (EVITA-HF) registry.

There is scarce information about the influence of prior myocardial infarction (pMI) on outcomes in patients (pts) with ischaemic HFrEF. We analysed data from the EVIdence based TreAtment in Heart Failure (EVITA-HF) registry. EVITA-HF comprises web-based case report data on demography, diagnostic measures, adverse events and 1-year follow-up of patients hospitalized for chronic heart failure ≥ 3months (CHF) and an ejection fraction ≤ 40%. In the present study, we focused on the outcomes of pts with and without pMI in ischaemic HFrEF. Between February 2009 and November 2015, a total of 2075 consecutive pts with ischaemic HFrEF were included from 16 centres in Germany. A total of 81.2% were male, and the mean age was 71years. A total of 61.5% of the pts with ischaemic HFrEF had a history of pMI. These pts were treated less often with PCI (20.0 vs. 31.0%, p < 0.001) or CABG (3.8 vs. 7.7%, p < 0.001). They more often received an ICD (40.9 vs. 28.7%, p < 0.001), but less often a CRT-D (11.3 vs. 19.4%, p < 0.001). After multivariate adjustment, pts with pMI had a greater all-cause mortality after 1year than those without pMI (hazard ratio 1.4; 95% CI, 1.10-1.79, p = 0.007). The combined endpoint of death, resuscitation or ICD shock after 1year was greater in patients with pMI (20.8 vs. 16.4%, p = 0.03). Mobility was more often reduced in pts with pMI (46.8% vs. 40.1%, p = 0.03), and overall health status was more frequently worse in patients with pMI than in those 12months ago (23.1 vs. 15.9%, p = 0.01). More than a quarter of the pts with ischaemic HFrEF were anxious or depressive. pMI in patients with CHF and ischaemic HFrEF was associated with increased mortality, increased event rates, and worsened health status. Hence, the subgroup of pts with ischaemic HFrEF and pMI is at higher risk and deserves special attention.

Sex differences in the prognostic role of achieving target doses of heart failure medications: Data from the Swedish Heart Failure Registry.

Guidelines recommend target doses (TD) of heart failure (HF) with reduced ejection fraction (HFrEF) medications regardless of sex. Differences in pharmacokinetics and pharmacodynamics may explain heterogeneity in treatment response, adverse reactions, and tolerability issues across sexes. The aim of this study was to explore sex-based differences in the association between TD achievement and mortality/morbidity in HFrEF. Patients with HFrEF and HF duration ≥6 months registered in the Swedish HF Registry between May 2000 and December 2020 (follow-up until December 2021) were analysed. Treatments of interest were renin-angiotensin system inhibitors (RASI) or angiotensin receptor-neprilysin inhibitors (ARNI), and beta-blockers. Multivariable Cox regression models were performed to explore the risk of cardiovascular mortality or hospitalization for HF across dose categories in females versus males. A total of 17 912 patients were analysed (median age 77.0 years, interquartile range [IQR] 70.0-83.0), 29% were female. Over a median follow-up of 1.33 years (IQR 0.29-3.22), for RASI/ARNI there was no significant difference in outcome for females achieving 50-99% versus 100% of TD (hazard ratio 0.92, 95% confidence interval 0.83-1.03), whereas males showed a gradual lowering in risk together with the achievement of higher % of TD (p-interaction = 0.030). For beta-blockers the achievement of TD was associated with the lowest risk of outcome regardless of sex. Our findings suggest that females and males might differently benefit from the same dose of RASI/ARNI, and do represent a general call for randomized controlled trials to consider sex-specific up-titration schemes when testing HFrEF treatments in need of up-titration.

Impact of Diabetes Mellitus on Heart Failure Patients: Insights from a Comprehensive Analysis and Machine Learning Model Using the Jordanian Heart Failure Registry.

Heart failure (HF) is a common final pathway of various insults to the heart, primarily from risk factors including diabetes mellitus (DM) type 2. This study analyzed the clinical characteristics of HF in a Jordanian population with a particular emphasis on the relationship between DM and HF. This prospective study used the Jordanian Heart Failure Registry (JoHFR) data. Patients with HF were characterized by DM status and HF type: HF with preserved ejection fraction (HFpEF) or HF with reduced ejection fraction (HFrEF). Demographics, clinical presentations, and treatment outcomes were collected. Statistical analyses and machine learning techniques were carried out for the prediction of mortality among HF patients: Recursive Feature Elimination with Cross-Validation (RFECV) and Synthetic Minority Over-sampling Technique with Edited Nearest Neighbors (SMOTEENN) were employed. A total of 2007 patients with HF were included. Notable differences between diabetic and non-diabetic patients are apparent. Diabetic patients were predominantly male, older, and obese (p < 0.001 for all). A higher incidence of HFpEF was observed in the diabetes cohort (p = 0.006). Also, diabetic patients had significantly higher levels of cholesterol (p = 0.008) and LDL (p = 0.003), reduced hemoglobin levels (p < 0.001), and more severe renal impairment (eGFR; p = 0.006). Machine learning models, particularly the Random Forest Classifier, highlighted its superiority in mortality prediction, with an accuracy of 90.02% and AUC of 80.51%. Predictors of mortality included creatinine levels >115 µmol/L, length of hospital stay, and need for mechanical ventilation. This study underscores notable differences in clinical characteristics and outcomes between diabetic and non-diabetic heart failure patients in Jordan. Diabetic patients had higher prevalence of HFpEF and poorer health indicators such as elevated cholesterol, LDL, and impaired kidney function. High creatinine levels, longer hospital stays, and the need for mechanical ventilation were key predictors of mortality.

Glucagon-like peptide-1 receptor agonists use and associations with outcomes in heart failure and type 2 diabetes: data from the Swedish Heart Failure and Swedish National Diabetes Registries.

To assess the use and associations with outcomes of glucagon-like peptide-1 receptor agonists (GLP-1 RA) in a real-world population with heart failure (HF) and type 2 diabetes mellitus (T2DM). The Swedish HF Registry was linked with the National Diabetes Registry and other national registries. Independent predictors of GLP-1 RA use were assessed by multivariable logistic regressions and associations with outcomes were assessed by Cox regressions in a 1:1 propensity score-matched cohort. Of 8188 patients enrolled in 2017-21, 9% received a GLP-1 RA. Independent predictors of GLP-1 RA use were age <75 years, worse glycaemic control, impaired renal function, obesity, and reduced ejection fraction (EF). GLP-1 RA use was not significantly associated with a composite of HF hospitalization (HHF) or cardiovascular (CV) death regardless of EF, but was associated with a lower risk of major adverse CV events (CV death, non-fatal stroke/transient ischaemic attack, or myocardial infarction), and CV and all-cause death. In patients with body mass index ≥30 kg/m2, GLP-1 RA use was also associated with a lower risk of HHF/CV death and HHF alone. In patients with HF and T2DM, GLP-1 RA use was independently associated with more severe T2DM, reduced EF, and obesity and was not associated with a higher risk of HHF/CV death but with longer survival and less major CV adverse events. An association with lower HHF/CV death and HHF was observed in obese patients. Our findings provide new insights into GLP-1 RA use and its safety in HF and T2DM.