Blood pressure variability associated with in-hospital and 30-day mortality in heart failure patients: a multicenter cohort study

BackgroundThis study aimed to explore the impact of hypoxic hepatitis (HH) on survival in heart failure (HF) patients and to develop an effective machine learning model to predict 30-day mortality risk in HF patients with HH.MethodsIn the Medical Information Mart for Intensive Care (MIMIC)-III and IV databases, clinical data and survival situations of HF patients admitted to the intensive care unit (ICU) were retrospectively collected. Propensity Score Matching (PSM) analysis was used to balance baseline differences between HF patients with and without HH. Kaplan Meier analysis and multivariate Cox analysis were used to determining the effect of HH on the survival of CF patients. For developing a model that can predict 30-day mortality in CF patients with HH, the feature recurrence elimination (RFE) method was applied to feature selection, and seven machine learning algorithms were employed to model construction. After training and hyper-parameter optimization (HPO) of the model through cross-validation in the training set, a performance comparison was performed through internal and external validation. To interpret the optimal model, Shapley Additive Explanations (SHAP) were used along with the Local Interpretable Model-agnostic Explanations (LIME) and the Partial Dependence Plot (PDP) techniques.ResultsThe incidence of HH was 6.5% in HF patients in the MIMIC cohort. HF patients with HH had a 30-day mortality rate of 33% and a 1-year mortality rate of 51%, and HH was an independent risk factor for increased short-term and long-term mortality risk in HF patients. After RFE, 21 key features (21/56) were selected to build the model. Internal validation and external validation suggested that Categorical Boosting (Catboost) had a higher discriminatory capability than the other models (internal validation: AUC, 0.832; 95% CI, 0.819–0.845; external validation: AUC, 0.757 95% CI, 0.739–0.776), and the simplified Catboost model (S-Catboost) also had good performance in both internal validation and external validation (internal validation: AUC, 0.801; 95% CI, 0.787–0.813; external validation: AUC, 0.729, 95% CI, 0.711–0.745).ConclusionHH was associated with increased mortality in HF patients. Machine learning methods had good performance in identifying the 30-day mortality risk of HF with HH. With interpretability techniques, the transparency of machine learning models has been enhanced to facilitate user understanding of the prediction results.

Influenza infection is associated with increased risk for mortality and hospitalization in heart failure patients. Although there are no published randomized controlled trials examining the effect of influenza vaccination on clinical outcomes in heart failure patients, the effect has been examined in observational cohort studies. Nevertheless, results are inconsistent due partly to limited power with small sample sizes and use of different definitions of outcomes. We therefore aimed to conduct a systematic review and meta-analysis of the effect of influenza vaccination on mortality and hospitalization in heart failure patients. The search of electronic databases identified 6 observational cohort studies with 22,486 patients examining the effect of influenza vaccination on mortality and hospitalization in heart failure patients. Pooled analysis of confounder-adjusted hazard ratio showed that influenza vaccination was associated with reduced risk of mortality during 1-year follow-up (risk ratio [95% CI] = 0.76 [0.63-0.92], Pfix < 0.01) and during long-term (up to 4years) follow-up (0.80 [0.71-0.90], Pfix < 0.001). Furthermore, influenza vaccination was associated with reduced risk of mortality during influenza season (risk ratio [95% CI] = 0.52 [0.39-0.69], Prandom < 0.001) and during non-influenza season (0.79 [0.69-0.90], Pfix < 0.001). Only a few studies reported the effect of influenza vaccination on hospitalization, which did not permit us to perform pooled analysis. In conclusion, our meta-analysis showed that influenza vaccination was associated with reduced risk of mortality in heart failure patients. Large-scale and adequately powered randomized controlled trials should be planned to confirm our observed potential survival benefit of influenza vaccination in these patients.

Background: The stress-induced hyperglycemic ratio (SHR) is an index that reflects the imbalance between acute stress-induced glucose fluctuations and baseline glucose metabolism levels. Currently, there are few studies on the SHR index and its prognostic significance in heart failure (HF) patients undergoing invasive mechanical ventilation. This study aimed to investigate the relationship of SHR with the risk of death in HF patients requiring invasive ventilation. Methods: Conduct a retrospective cohort study based on the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Include adult heart failure patients who received invasive ventilation and divide them into quartile groups according to the level of the Systemic Heart Rate (SHR). The primary endpoints of the observation are the 30-day all-cause mortality rate and the all-cause mortality rate in the Intensive Care Unit (ICU), while the secondary endpoints are the 365-day all-cause mortality rate and the all-cause mortality rate during hospitalization. The Kaplan-Meier curve is used to compare the survival outcomes between groups. A Cox proportional hazards regression model that adjusts for demographic characteristics, underlying diseases, and the severity of critical illnesses is employed to evaluate the relationship between SHR and the mortality rate. The Restricted Cubic Spline (RCS) is utilized to test the nonlinear association between the two, and subgroup analysis is carried out to verify the consistency of the results across different groups. Results: Among the 1,038 eligible patients, the mean age was 68.50 years (range: 59.46 - 77.48 years), and 639 (61.56%) of them were male. The Kaplan-Meier curve showed that the higher the SHR index, the higher the risk of all-cause mortality in patients at 30 days (log-rank test, p = 0.011) and in the ICU (log-rank test, p = 0.0029). An increase in SHR was independently associated with an increased risk of 30-day and ICU mortality. Compared with the second quartile group Q2, the 30-day mortality rate in the group with the highest SHR was significantly higher (HR = 1.59, 95% CI 1.08, 2.33), and the ICU mortality rate in the group with the highest SHR was significantly higher (HR = 1.86, 95% CI 1.10, 3.14). The restricted cubic spline analysis showed a non-linear dose-response relationship between SHR and 30-day all-cause mortality (p for non-linearity &lt; 0.05), and the risk of 30-day and ICU all-cause mortality gradually increased with the increase of the SHR index. The risks of 30-day and all-cause mortality in the ICU gradually increased. The results of the subgroup analysis confirmed that it remained stable in the subgroup of patients with Coronary Heart Disease (CHD). Conclusion: In critically ill heart failure (HF) patients receiving invasive ventilation, a higher stress hyperglycemia ratio (SHR) index is significantly associated with an increased risk of 30-day and all-cause mortality in the intensive care unit (ICU). Meanwhile, the SHR index is an independent predictor of mortality in critically ill HF patients who require invasive ventilation.

Mortality prediction system for heart failure with orthogonal relief and dynamic radius means

Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): Grants from Region Östergötland County Medical Research Council of South East Sweden. Introduction The use of NT-proBNP is well established in the management of heart failure (HF), and as predictor of mortality in HF patients. However, on short-term mortality in acute decompensated heart failure the evidence is scarce. Purpose We aimed to evaluate NT-proBNP as an independent predictor of 30-day all-cause mortality in acute decompensated heart failure patients, seeking emergency medical care. Methods This was an observational study including all visits to Vrinnevisjukhuset hospital emergency department during two years, where the patient was above 18 years of age, and the main diagnosis was set to HF. Appropriate data for each visit were collected from the patients’ medical records retrospectively. The Cox proportional hazards model was applied to estimate hazard ratios (HR) for 30-day mortality. Results From the 459 emergency department visits included in the study 59 patients (12,9%) reached the primary end point of all-cause mortality in 30 days. Age, body mass index (BMI), mean arterial pressure (MAP), sodium, potassium, NT-proBNP, and the presence of atrial fibrillation or flutter were variables achieving P&amp;lt;0.1 on univariate Cox proportional hazards analysis and were subsequently introduced to the multivariate model. Age (HR: 1,05; p=0,008), sodium (HR: 0,925; p=0,012), potassium (HR: 1,95; p=0,002) and MAP (HR: 0,978; p=0,009) was found to be the only independent predictors of 30-day all-cause mortality. Conclusions Our study indicates that for short-term prediction of mortality, other markers than NT-proBNP are of significance. Measurement of serum potassium might be a better tool for clinicians in identifying patients at highest risk of short-term mortality.

O20-2 - Beneficial Impact of Direct Oral Anticoagulants on Mortality in Patients with Heart Failure and Atrial Fibrillation

Introduction: Inflammation plays a important role in determining the prognosis of heart failure. Recent studies have demonstrated that both established and novel inflammatory biomarkers—such as the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), Systemic Immune-Inflammation Index (SII), and, in particular, the Systemic Inflammation Response Index (SIRI)—serve as important predictors of mortality in heart failure patients. Nonetheless, data from developing countries like Vietnam remain limited. Hypothesis: Inflammatory markers are associated with 30-day all-cause mortality in patients hospitalized with acute heart failure (AHF). Methods: We performed a prospective cohort study at Can Tho Central General Hospital—a tertiary care center in Vietnam—from May 2024 through April 2025. Consecutive adults (≥18 years) admitted with acute heart failure (NT-proBNP &gt;300 pg/mL) who provided informed consent were enrolled, while those with active infection, immunosuppressant use within the prior three months, chronic liver disease, or active malignancy were excluded. Survivors were followed for 30 days after discharge, with all-cause mortality as the primary endpoint. Relative risks were derived from a modified Poisson log-linear regression using a robust (sandwich) variance estimator, adjusting for baseline demographics and comorbidities. Results: Among 411 AHF patients (mean age 69.6 ± 12.6 years; 47.4 % men), 262 (63.7 %) completed 30-day follow-up, during which 56 deaths occurred (all-cause mortality 21.4 %). Non-survivors were older, carried a greater comorbidity burden, and exhibited higher median levels of NLR, MLR, PLR, SII, and SIRI than survivors. In multivariable Poisson models adjusted for age, sex, coronary artery disease, prior heart failure, hypertension, and diabetes, individuals in the highest biomarker quartile had markedly increased 30-day mortality risk compared with those in the lowest quartile: NLR (RR 3.6; 95 % CI 1.6–8.3), MLR (RR 6.1; 95 % CI 2.2–16.5), PLR (RR 2.3; 95 % CI 1.2–4.4), SII (RR 2.4; 95 % CI 1.2–4.8), and SIRI (RR 4.3; 95 % CI 1.8–10.4). Conclusion: Inflammatory markers independently predict 30-day mortality in Vietnamese AHF patients; adding them to risk models may enhance short-term prognosis, while its longer-term predictive value warrants further investigation.

<b>Introduction:</b> Fluid resuscitation confers protection against in-hospital mortality in heart failure (HF) patients with severe sepsis. SARS COV-2 infection leads to cytokine storm that is clinically similar to severe sepsis.&nbsp;We aim to evaluate if positive fluid balance is associated with in-hospital mortality in HF patients with Covid-19. <b>Methods:</b> This single center retrospective cohort study was conducted in patients admitted in the ICU for Covid 19 from 10/2020 to 3/2021 in a community hospital in Newark. The primary outcome was survival to discharge. Clinical SAS 9.4 was used to obtain summary statistics, perform chi-squared test and multivariable logistic regression analysis. <b>Results:</b> We included 91 patients admitted in the ICU with covid 19, of which 33 were diagnosed with HF. Out of 33 people, majority were males. Most of the patients were hispanic. Diabetes and hypertension were the most common comorbidities. 60.61% of HF patients had multiple comorbidities. Odds of negative survival outcome in those with positive fluid balance after adjusting for HF as compared to those with negative fluid balance in patients of COVID 19 was 12.958 (P value= 0.0183). <b>Conclusion:</b> Positive fluid balance in HF patients admitted with Covid 19 may be associated with adverse outcomes. Larger, prospective studies are needed to investigate the correlation between covid 19 and fluid balance in HF patients.

The triglyceride-glucose (TyG) index is regarded as a surrogate marker of systemic insulin resistance (IR). Studies have substantiated the impact of IR on cardiovascular diseases. Nonetheless, the prognostic value of the TyG index in critical patients with heart failure (HF) with intensive care unit (ICU) admission remains unclear. This study aims to assess the association between the TyG index and all-cause mortality in critically ill patients with HF. Patients with HF requiring ICU admission were identified from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and subsequently stratified into quartiles based on their TyG index. The primary outcome was 30-day all-cause mortality, and the secondary outcome was 1-year all-cause mortality. The relationship between the TyG index and all-cause mortality in HF patients was analyzed using multivariable Cox proportional hazards models and restricted cubic splines. A total of 1220 patients (62.4% men) were enrolled, with a mean age of 70.6 years. The 30-day and one-year all-cause mortality rate were 15.7% and 34.6%, respectively. Multivariable Cox regression revealed that TyG index was significantly associated with an elevated risk of 30-day all-cause mortality (adjusted HR, 1.360; 95% CI, 1.093–1.694; P = 0.006), but not with one-year mortality (adjusted HR 1.046; 95% CI 0.895–1.222, P = 0.574). Restricted cubic splines showed a progressively increasing risk of 30-day mortality was linearly related to an elevated TyG index. Subgroup analyses indicated a more prominent association between TyG index and 30-day mortality in patients with age ≤ 65, female or BMI > 30 kg/m2. In critically ill patients with HF, the TyG index is significantly associated with short-term all-cause mortality. Our results highlight that the TyG index can be useful in identifying HF patients at high risk of all-cause mortality and require close follow-up after discharge.

The neutrophil-to-lymphocyte ratio (NLR) is an emerging biomarker reflecting systemic inflammation, playing a critical role in heart failure (HF) prognosis. Elevated NLR, indicative of neutrophilia and lymphocytopenia, correlates with worsened outcomes, including higher mortality and adverse cardiac events. Studies highlight its utility as a robust indicator for risk stratification and management in both acute and chronic HF conditions. This study aims to analyze the correlation of NLR as a predictor of mortality in acute heart failure patients. This systematic review and meta-analysis explored the relationship between the neutrophil-to-lymphocyte ratio (NLR) and clinical outcomes in heart failure (HF) patients, including mortality, rehospitalization, disease progression, and functional capacity. A comprehensive search of PubMed, Scopus, Embase, and Web of Science identified 38 studies meeting the inclusion criteria. Quality and bias were assessed using established tools, and statistical analyses evaluated pooled effect sizes, heterogeneity, and optimal NLR cutoffs, evaluating their prognostic accuracy for HF outcomes. The findings highlight that heart failure (HF) patients who survived had significantly lower neutrophil-to-lymphocyte ratio (NLR) values compared to those who died (pooled standardized mean difference {SMD} = -0.48; 95% confidence interval {CI}: -0.54, -0.43; p < 0.05). Elevated NLR is significantly associated with increased mortality in heart failure patients, with most studies showing strong inverse associations and odds ratios (ORs) below 1. Odds ratios further supported this, with higher NLR linked to increased mortality risk (e.g., OR = 0.38). The area under the curve (AUC) of 0.834 indicates strong predictive accuracy for mortality, with optimal NLR cutoffs of 5.91 and 6.18 balancing sensitivity (84.6%) and specificity (84.6%). High heterogeneity (I² = 90%) shows the variability among studies. The study has concluded that an elevated neutrophil-to-lymphocyte ratio (NLR) is consistently associated with increased mortality in patients with heart failure (HF). Thus, the neutrophil-to-lymphocyte ratio (NLR) can be used as a reliable prognostic marker in patients with heart failure.

Clinical Outcomes With Nurse-Coordinated Multidisciplinary Care in Patients With Heart Failure: A Systematic Review and Meta-analysis

Galectin-3 (Gal-3) is a new biomarker for assessing prognosis of heart failure (HF) patients. This systemic review and meta-analysis aims to examine Gal-3's ability in assessing prognosis of HF patients. We searched MEDLINE and Embase up to November 2014. Test performance characteristics were summarized using forest plots and hierarchical summary receiver operating characteristic curves. The diagnostic odds ratio of Gal-3 in predicting mortality in chronic HF patients was 2.36 (95% CI: 1.71-3.26) and 2.30 (95% CI: 1.76-3.01) in acute HF patients. Elevated levels of Gal-3 are associated with mortality in both acute and chronic HF patients. However, current evidence does not support sole use of Gal-3 for prognosis evaluation of HF patients.

Heart failure is a significant global health challenge with high mortality rates. This study examines the association between glycemic variability and short-term mortality in critically ill heart failure patients. Data from the eICU Collaborative Research Database (eICU-CRD) and the Medical Information Mart for Intensive Care (MIMIC-IV) database were analyzed, including 23,744 heart failure patients. Glycemic variability, measured by the coefficient of variation of glucose levels during ICU stay, was categorized into quartiles. Multivariable logistic regression and Cox proportional hazards models were used to assess associations with in-hospital and 30-day mortality. Linear regression models were employed to evaluate the association with ICU length of stay. Dose-response relationships were explored using restricted cubic splines. The in-hospital and 30-day mortality rates were 15.0% and 17.6%, respectively. The median ICU length of stay was 3.1 days (IQR: 1.9-5.4). Patients in the highest glycemic variability quartile had a significantly higher risk of in-hospital mortality (OR: 1.77, 95% CI: 1.54-2.04) and 30-day mortality (HR: 1.37, 95% CI: 1.23-1.53) compared to the lowest quartile. Additionally, higher glycemic variability was associated with prolonged ICU stays, with each unit increase resulting in a 2.57-day extension (95% CI: 2.03-3.10, P < 0.001) after adjustment for covariates. A U-shaped association was observed for in-hospital mortality, while a linear relationship was seen for 30-day mortality. Sensitivity and subgroup analyses confirmed the robustness of these findings. Elevated glycemic variability is independently associated with increased short-term mortality and prolonged ICU stays in critically ill heart failure patients, highlighting the importance of managing blood glucose fluctuations to improve outcomes and reduce healthcare resource utilization.

The relationship between glycemic control and outcome in patients with heart failure (HF) remains contentious. A recent study showed that patients with HF with mid-range ejection fraction (HFmrEF) more frequently had comorbid diabetes relative to other patients. Herein, we examined the association between glycosylated hemoglobin (HbA1c) and in-hospital mortality in acute HF patients with reduced, mid-range, and preserved EF. A multicenter retrospective study was conducted on 5205 consecutive patients with acute HF. Potential risk factors for in-hospital mortality were selected by univariate analyses; then, multivariate Cox regression analysis with backward stepwise selection was performed to identify significant factors. Kaplan-Meier survival curves and log-rank testing were used to compare in-hospital mortality between groups. Across the study cohort, 44% (2288 patients) had reduced EF, 20% had mid-range EF, and 36% had preserved EF. The overall in-hospital mortality rate was 4.6%, with no significant differences among the HF patients with reduced, mid-range, and preserved EF groups. For patients with HFmrEF, higher HbA1c level was a significant risk factor for in-hospital mortality (hazard ratio 1.387; 95% confidence interval 1.014-1.899; P = 0.041). In contrast, HbA1c was not an independent risk factor for in-hospital mortality in HF patients with preserved or reduced EF. In conclusion, HbA1c is an independent risk factor for in-hospital mortality in acute HF patients with mid-range EF, but not in those with preserved or reduced EF. Elucidation of the pathophysiological mechanisms behind these findings could facilitate the development of more effective individualized therapies for acute HF.

BackgroundThe platelet-albumin-bilirubin (PALBI) score has shown prognostic value across multiple medical conditions; nevertheless, its effectiveness in forecasting prognoses among severely ill heart failure (HF) patients treated in Intensive Care Unit (ICU) has yet to be fully established. This study explores the relationship between PALBI scores at ICU admission and all-cause mortality in HF patients admitted to the ICU.MethodsDrawing on records from the MIMIC-IV version 3.1 critical care database, we included ICU-admitted HF patients and calculated their PALBI scores at admission. Kaplan–Meier survival curves and log-rank tests were used to assess differences in overall mortality at 30 and 360 days across the PALBI tertile groups. Cox regression models based on the proportional hazards assumption were utilized to control for possible confounding variables. In addition, predictive models based on machine learning were constructed using PALBI alongside other clinical features to estimate 30-day mortality, with model performance evaluated through the area under the ROC curve (AUC).ResultsA total of 4,318 participants were included in the study cohort (57% male; median age 73 years). The cumulative incidence of all-cause mortality was 24% at 30 days and 44% at 360 days. Individuals in the top PALBI tertile exhibited markedly higher mortality rates compared to those in the lowest tertile (30% vs. 20% at 30 days and 52% vs. 39% at 360 days). Multivariate Cox regression analysis revealed significant associations of elevated PALBI scores with higher mortality risk at both 30 days (HR: 1.36; 95% CI: 1.12–1.64; p = 0.002) and 360 days (HR: 1.22; 95% CI: 1.03–1.44; p = 0.019). Machine learning models effectively discriminated patients at risk of 30-day mortality, with the best performance achieved by Ridge regression (AUC = 0.76).ConclusionThe PALBI score independently predicts 30-day and 360-day all-cause mortality among ICU-admitted HF patients. These findings suggest that the PALBI score has potential utility for risk stratification and for guiding treatment decisions in the intensive care management of HF.