Background: Heart failure (HF) remains a significant public health challenge globally. This study aims to systematically analyze the global HF disease burden from 1990 to 2021 across temporal, spatial, and demographic dimensions to provide evidence for targeted prevention and control strategies. Methods: Using data from the Global Burden of Disease (GBD) 2021 study, we analyzed the global HF burden through prevalent cases, years lived with disability (YLDs), and age-standardized rates per 100,000 population. Temporal trends were evaluated using estimated annual percentage change (EAPC) and joinpoint regression analysis. The relationship between the Socio-demographic Index (SDI) and disease burden was explored through Pearson correlation analysis, while attribution analysis identified the main causes of HF. When appropriate, analyses were stratified by 5 SDI regions, 21 GBD regions, 204 countries and territories, 20 age groups, and both sexes. Results: Global HF prevalence and YLDs burden showed substantial increases from 1990 to 2021, with age-standardized prevalence increasing from 641.14 to 676.68 per 100,000 population. Notably, high-SDI regions exhibited a declining burden since 2019, indicating a potential global turning point. High-income North America bears the heaviest burden while South Asia shows the fastest growth rate. The correlation between disease burden and SDI level was negligible. The disease burden in males consistently exceeded that in females, with prevalence and YLDs rates rising sharply after age 60. The main causes and their attributable proportions were: ischemic heart disease (34.53%), hypertensive heart disease (22.53%), other cardiomyopathies (7.61%), chronic obstructive pulmonary disease (6.51%), and congenital heart anomalies (5.69%), with their distribution patterns differing across age groups and regions. Conclusion: Global burden of HF increased significantly over recent decades, with a potential turning point in 2019 and marked regional disparities. It is essential to prioritize regions with heavy burdens or rapid growth rates, strengthen the management of major causes, and monitor HF burden trends in the post-COVID era.

Background: Red blood cell distribution width (RDW) and N‐terminal pro brain natriuretic peptide (NT‐proBNP) may predict the prognosis of heart failure (HF). However, the impact of combined RDW and NT‐proBNP levels as a prognostic marker of HF remains unclear and the significance of this combination at various time‐points has not been sufficiently studied.Hypothesis: RDW can predict prognosis in HF at various time‐points and combination with NT‐proBNP improves the prognostic value.Methods: Patients admitted to HF care unit of Fuwai Hospital CAMS&PUMC (Beijing, China) with a diagnosis of HF from November 2008 to November 2018 were analyzed retrospectively.Results: In total, 3231 patients with available RDW data at admission were evaluated (median age 58 years, 71.9% males, 39.7% coronary heart disease, 68.6% New York Heart Association [NYHA] III or IV). Median RDW and NT‐proBNP at admission were 13.4% (interquartile range [IQR]: 12.7%–14.5%), and 1723.00 pg/ml (IQR: 754.00–4006.25 pg/ml), respectively. During 2.9‐year median follow‐up, all‐cause death occurred in 1075 (33.27%) patients. Kaplan–Meier survival curve and Cox proportional‐hazard models, showed patients in the top quarter RDW had a 32.0% increased mortality compared to the bottom quarter (hazard ratio: 4.39, 95% confidence interval: 3.59–5.38; p <.001). The top quarter RDW retained independent prognostic value across HF with reduced ejection fraction [HFrEF], HF with mid‐range ejection fraction [HFmrEF], and HF with preserved ejection fraction [HFpEF] subgroups. Patients were subsequently divided into four groups by median RDW and NT‐proBNP. Comparison of Kaplan–Meier survival curves for various groups showed good risk stratification (p < .001).Conclusions: RDW is an independent predictor of mortality among patients with HF in the short‐, medium‐, and long‐term. Combination of RDW and NT‐proBNP improves the prognostic value. This is true across all clinical subtypes of heart failure (HFrEF, HFmrEF, HFpEF), and among most subgroups of patients with various comorbidities (infection, diabetes, hypertension).

Aims: Red blood cell distribution width-to-albumin ratio (RAR), an innovate biomarker of inflammation, can independently predict adverse cardiovascular outcomes. However, the association between RAR and prognosis in patients with non-ischaemic heart failure (NIHF) remains unclear. Methods and results: A total of 2077 NIHF patients admitted to the Heart Failure Care Unit, Fuwai Hospital, were consecutively enrolled from December 2006 to October 2017 in this retrospective study. The primary endpoint was a composite outcome of all-cause mortality and heart transplantation. The correlation between RAR and the composite outcome was assessed by the Kaplan–Meier survival analysis and the Cox regression analysis. Incremental predictive values and the clinical performance of RAR for all-cause mortality or heart transplantation were also assessed based on a 12-variable traditional risk model. The median follow-up time in this study was 1433 (1341, 1525) days. As the gender no longer satisfied the Cox proportional risk assumption after 1150 days, we set 1095 days as the follow-up time for analysis. A total of 500 patients reached the composite outcome. Multivariable Cox regression showed that per log2 increase of RAR was significantly associated with a 132.9% [hazard ratio 2.329, 95% confidence interval (CI) 1.677–3.237, P < 0.001] increased risk of all-cause mortality or heart transplantation. Better model discrimination [concordance index: 0.766 (95% CI 0.754–0.778) vs. 0.758 (95% CI 0.746–0.770), P < 0.001], calibration (Akaike information criterion: 1487.3 vs. 1495.74; Bayesian information criterion: 1566.25 vs. 1569.43; Brier score: 1569.43 vs. 1569.43; likelihood ratio test P < 0.001), and reclassification (integrated discrimination improvement: 1.35%, 95% CI 0.63–2.07%, P < 0.001; net reclassification improvement: 13.73%, 95% CI 2.05–27.18%, P = 0.034) were improved after adding RAR to the traditional model (P < 0.001 for all). A higher overall net benefit was also obtained in the threshold risk probability of 20–55%. Conclusions: High level of RAR was an independent risk factor of poor outcome in NIHF.

Aims: Inflammatory biomarkers, including CRP, the neutrophil-to-lymphocyte ratio (NLR), and the neutrophil-to-eosinophil ratio (NER), may predict outcomes in cancer. However, their value in immune checkpoint inhibitor (ICI) therapy-associated cardiotoxicity remains elusive. We aimed to characterize the relationship of inflammatory markers with severity of ICI-related cardiotoxicities (iRCs) and prognosis among patients with iRCs. Methods: Patients who were diagnosed with iRCs between January 2019 and December 2021 were retrospectively enrolled and were dichotomized based on iRC severity into low-grade (grade 1–2) vs. high-grade (grade 3–4) groups. Results: Forty-seven patients were included. The median time-to-event from first ICI infusion to onset of iRCs was 35 days (IQR: 19.0–65.5 days). When compared with respective baseline values, cardiac biomarkers and inflammatory markers were significantly elevated at onset of iRCs. Compared with low-grade iRCs, NER at iRC onset was significantly increased among patients with high-grade iRCs (Group × Time, P < 0.01). When grouped by the median NER (184.33) at iRC onset, NER ≥ 184.33 was associated with high-grade iRCs (OR: 10.77, P < 0.05) and had a 36.3% increased mortality compared to the lower NER group (HR: 2.67, P < 0.05). Conclusions: In patients who develop iRCs, NER is significantly elevated at iRC onset, and higher NER correlates with greater iRC severity and higher mortality. Larger datasets are needed to validate these findings.

Background: Machine‐learning‐based prediction models (MLBPMs) have shown satisfactory performance in predicting clinical outcomes in patients with heart failure with reduced and preserved ejection fraction. However, their usefulness has yet to be fully elucidated in patients with heart failure with mildly reduced ejection fraction. This pilot study aims to evaluate the prediction performance of MLBPMs in a heart failure with mildly reduced ejection fraction cohort with long‐term follow‐up data. Methods and Results: A total of 424 patients with heart failure with mildly reduced ejection fraction were enrolled in our study. The primary outcome was all‐cause mortality. Two feature selection strategies were introduced for MLBPM development. The “All‐in” (67 features) strategy was based on feature correlation, multicollinearity, and clinical significance. The other strategy was the CoxBoost algorithm with 10‐fold cross‐validation (17 features), which was based on the selection result of the “All‐in” strategy. Six MLBPMs with 5‐fold cross‐validation based on the “All‐in” and the CoxBoost algorithm with 10‐fold cross‐validation strategy were developed by the eXtreme Gradient Boosting, random forest, and support vector machine algorithms. The logistic regression model with 14 benchmark predictors was used as a reference model. During a median follow‐up of 1008 (750, 1937) days, 121 patients met the primary outcome. Overall, MLBPMs outperformed the logistic model. The “All‐in” eXtreme Gradient Boosting model had the best performance, with an accuracy of 85.4% and a precision of 70.3%. The area under the receiver‐operating characteristic curve was 0.916 (95% CI, 0.887–0.945). The Brier score was 0.12. Conclusions: The MLBPMs could significantly improve outcome prediction in patients with heart failure with mildly reduced ejection fraction, which would further optimize the management of these patients.