Inflammation-based assessment for the risk stratification of mortality in patients with heart failure.
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ABSTRACT:
SUBMITTER: Itagaki T
PROVIDER: S-EPMC8298574 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Patients with acute heart failure (AHF) show various clinical courses during hospitalization. We aimed to identify time course predictors of in-hospital mortality and to establish a sequentially assessable risk model.We enrolled 1,035 consecutive AHF patients into derivation (n = 597) and validation (n = 438) cohorts. For risk assessments at admission, we utilized Get With the Guidelines-Heart Failure (GWTG-HF) risk scores. We examined significant predictors of in-hospital mortality from 11 variables obtained during hospitalization and developed a risk stratification model using multiple logistic regression analysis. Across both cohorts, 86 patients (8.3%) died during hospitalization. Using backward stepwise selection, we identified five time-course predictors: catecholamine administration, minimum platelet concentration, maximum blood urea nitrogen, total bilirubin, and C-reactive protein levels; and established a time course risk score that could sequentially assess a patient's risk status. The addition of a time course risk score improved the discriminative ability of the GWTG-HF risk score (c-statistics in derivation and validation cohorts: 0.776 to 0.888 [p = 0.002] and 0.806 to 0.902 [p<0.001], respectively). A calibration plot revealed a good relationship between observed and predicted in-hospital mortalities in both cohorts (Hosmer-Lemeshow chi-square statistics: 6.049 [p = 0.642] and 5.993 [p = 0.648], respectively). In each group of initial low-intermediate risk (GWTG-HF risk score <47) and initial high risk (GWTG-HF risk score ?47), in-hospital mortality was about 6- to 9-fold higher in the high time course risk score group than in the low-intermediate time course risk score group (initial low-intermediate risk group: 20.3% versus 2.2% [p<0.001], initial high risk group: 57.6% versus 8.5% [p<0.001]).A time course assessment related to in-hospital mortality during the hospitalization of AHF patients can clearly categorize a patient's on-going status, and may assist patients and clinicians in deciding treatment options.
| S-EPMC5667756 | biostudies-literature
Project description:Risk stratification in advanced heart failure (HF) is crucial for the individualization of therapeutic strategy, in particular for heart transplantation and ventricular assist device implantation. We tested the hypothesis that cardiac gene expression profiling can distinguish between HF patients with different disease severity. We obtained tissue samples from both left (LV) and right (RV) ventricle of explanted hearts of 44 patients undergoing cardiac transplantation or ventricular assist device placement. Gene expression profiles were obtained using an in-house microarray containing 4217 muscular organ-relevant genes. Based on their clinical status, patients were classified into three HF-severity groups: deteriorating (n= 12), intermediate (n= 19) and stable (n= 13). Two-class statistical analysis of gene expression profiles of deteriorating and stable patients identified a 170-gene and a 129-gene predictor for LV and RV samples, respectively. The LV molecular predictor identified patients with stable and deteriorating status with a sensitivity of 88% and 92%, and a specificity of 100% and 96%, respectively. The RV molecular predictor identified patients with stable and deteriorating status with a sensitivity of 100% and 96%, and a specificity of 100% and 100%, respectively. The molecular prediction was reproducible across biological replicates in LV and RV samples. Gene expression profiling has the potential to reproducibly detect HF patients with highest HF severity with high sensitivity and specificity. In addition, not only LV but also RV samples could be used for molecular risk stratification with similar predictive power.
| S-EPMC3829011 | biostudies-literature
Project description:BackgroundPredicting hospital mortality risk is essential for the care of heart failure patients, especially for those in intensive care units.MethodsUsing a novel machine learning algorithm, we constructed a risk stratification tool that correlated patients' clinical features and in-hospital mortality. We used the extreme gradient boosting algorithm to generate a model predicting the mortality risk of heart failure patients in the intensive care unit in the derivation dataset of 5676 patients from the Medical Information Mart for Intensive Care III database. The logistic regression model and a common risk score for mortality were used for comparison. The eICU Collaborative Research Database dataset was used for external validation.ResultsThe performance of the machine learning model was superior to that of conventional risk predictive methods, with the area under curve 0.831 (95% CI 0.820-0.843) and acceptable calibration. In external validation, the model had an area under the curve of 0.809 (95% CI 0.805-0.814). Risk stratification through the model was specific when the hospital mortality was very low, low, moderate, high, and very high (2.0%, 10.2%, 11.5%, 21.2% and 56.2%, respectively). The decision curve analysis verified that the machine learning model is the best clinically valuable in predicting mortality risk.ConclusionUsing readily available clinical data in the intensive care unit, we built a machine learning-based mortality risk tool with prediction accuracy superior to that of linear regression model and common risk scores. The risk tool may support clinicians in assessing individual patients and making individualized treatment.
| S-EPMC8932070 | biostudies-literature
Project description:Introduction and Objectives: Most multi-biomarker strategies in acute heart failure (HF) have only measured biomarkers in a single-point time. This study aimed to evaluate the prognostic yielding of NT-proBNP, hsTnT, Cys-C, hs-CRP, GDF15, and GAL-3 in HF patients both at admission and discharge. Methods: We included 830 patients enrolled consecutively in a prospective multicenter registry. Primary outcome was 12-month mortality. The gain in the C-index, calibration, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) was calculated after adding each individual biomarker value or their combination on top of the best clinical model developed in this study (C-index 0.752, 0.715-0.789) and also on top of 4 currently used scores (MAGGIC, GWTG-HF, Redin-SCORE, BCN-bioHF). Results: After 12-month, death occurred in 154 (18.5%) cases. On top of the best clinical model, the addition of NT-proBNP, hs-CRP, and GDF-15 above the respective cutoff point at admission and discharge and their delta during compensation improved the C-index to 0.782 (0.747-0.817), IDI by 5% (p < 0.001), and NRI by 57% (p < 0.001) for 12-month mortality. A 4-risk grading categories for 12-month mortality (11.7, 19.2, 26.7, and 39.4%, respectively; p < 0.001) were obtained using combination of these biomarkers. Conclusion: A model including NT-proBNP, hs-CRP, and GDF-15 measured at admission and discharge afforded a mortality risk prediction greater than our clinical model and also better than the most currently used scores. In addition, this 3-biomarker panel defined 4-risk categories for 12-month mortality.
| S-EPMC8569896 | biostudies-literature
Project description:BackgroundThe prevalence of depression is 4- to 5-fold higher in heart failure patients than in the general population. We examined the influence of depression on all-cause mortality in patients with heart failure.Methods and resultsUsing Danish medical registries, this nationwide population-based cohort study included all patients with a first-time hospitalization for heart failure (1995-2014). All-cause mortality risks and 19-year mortality rate ratios were estimated based on Cox regression analysis, adjusting for age, sex, time period, comorbidity, and socioeconomic status. The analysis included 9636 patients with and 194 887 patients without a diagnosis of depression. Compared with patients without a history of depression, those with depression had higher 1-year (36% versus 33%) and 5-year (68% versus 63%) mortality risks. Overall, the adjusted mortality rate ratio was 1.03 (95% CI 1.01-1.06). Compared with no depression, the adjusted mortality rate ratios for mild, moderate, and severe depression, as defined by diagnostic codes, were 1.06 (95% CI 1.00-1.13), 1.03 (95% CI 0.99-1.08), and 1.02 (95% CI 0.96-1.09), respectively. In a subcohort of patients, the mortality rate ratios were modified by left ventricular ejection fraction, with adjusted mortality rate ratios of 1.17 (95% CI, 1.05-1.31) for ?35%, 0.98 (95% CI 0.81-1.18) for 36% to 49%, and 0.96 (95% CI 0.74-1.25) for ?50%. Results were consistent after adjustment for alcohol abuse and smoking.ConclusionsA history of depression was an adverse prognostic factor for all-cause mortality in heart failure patients with left ventricular ejection fraction ?35% but not for other heart failure patients.
| S-EPMC5079053 | biostudies-literature
Project description:AimsRisk changes with the progression of disease and the impact of treatment. We developed a dynamic risk stratification Markov chain model using artificial intelligence in patients with chronic heart failure (CHF).Methods and resultsWe described the pattern of behaviour among 7496 consecutive patients assessed for suspected HF. The following mutually exclusive health states were defined and assessed every 4 months: death, hospitalization, outpatient visit, no event, and leaving the service altogether (defined as no event at any point following assessment). The observed figures at the first transition (4 months) weres 427 (6%), 1559 (21%), 2254 (30%), 1414 (19%), and 1842 (25%), respectively. The probabilities derived from the first two transitions (i.e. from baseline to 4 months and from 4 to 8 months) were used to construct the model. An example of the model's prediction is that at cycle 4, the cumulative probability of death was 14%; leaving the system, 37%; being hospitalized between 12 and 16 months, 10%; having an outpatient visit, 8%; and having no event, 31%. The corresponding observed figures were 14%, 41%, 10%, 15%, and 21%, respectively. The model predicted that during the first 2 years, a patient had a probability of dying of 0.19, and the observed value was 0.18.ConclusionsA model derived from the first 8 months of follow-up is strongly predictive of future events in a population of patients with chronic heart failure. The course of CHF is more linear than is commonly supposed, and thus more predictable.
| S-EPMC9715820 | biostudies-literature
Project description:Right ventricular dysfunction (RVD) parameters are increasingly important features in heart failure with preserved ejection fraction (HFpEF). We sought to evaluate the prognostic impact of a progressive RVD staging system by combining the tricuspid annular plane systolic excursion (TAPSE) to pulmonary artery systolic pressure (TAPSE/PASP) ratio with functional tricuspid regurgitation (TR) severity. We prospectively included 1355 consecutive HFpEF patients discharged for acute heart failure (HF). Of them, in 471 (34.7%) patients, PASP could not be accurately measured, leaving the final sample size to be 884 patients. Patients were categorized as Stage 1: TAPSE/PASP ? 0.36 without significant TR; stage 2: TAPSE/PASP ? 0.36 with significant TR; stage 3: TAPSE/PASP < 0.36 without significant TR; and stage 4: TAPSE/PASP < 0.36 with significant TR. By the 1 year follow-up, 207 (23.4%) patients had died. We found a significant and graded association between RVD stages and mortality rates (15.8%, 25%, 31.2%, and 45.4% from stage 1 to stage 4, respectively; log-rank test, p < 0.001). After multivariable adjustment, and compared to stage 1, stages 3 and 4 were independently associated with mortality risk (HR: 1.8219; 95% CI 1.308-2.538; p < 0.001 and HR = 2.2632; 95% CI 1.540-3.325; p < 0.001, respectively). A RVD staging system, integrating TAPSE/PASP and TR, provides a comprehensive and widely available tool for risk stratification in HFpEF.
| S-EPMC7141269 | biostudies-literature
Project description:BackgroundHeart failure (HF) is a complex clinical syndrome with persistently high mortality. High-throughput proteomic technologies offer new opportunities to improve HF risk stratification, but their contribution remains to be clearly defined. We aimed to systematically review prognostic studies using high-throughput proteomics to identify protein signatures associated with HF mortality.MethodsWe searched four databases and two clinical trial registries for articles published from 2012 to 2023. HF proteomics studies measuring high numbers of proteins using aptamer or antibody-based affinity platforms on human plasma or serum with outcomes of all-cause or cardiovascular death were included. Two reviewers independently screened articles, extracted data, and assessed the risk of bias. A third reviewer resolved conflicts. We assessed the risk of bias using the Risk Of Bias In Non-randomized Studies-of Exposure tool.ResultsOut of 5131 unique articles identified, nine articles were included in the review. The nine studies were observational; three used the aptamer platform, and six used the antibody platform. We found considerable heterogeneity across studies in measurement panels, HF definitions, ejection fraction categorization, follow-up duration, and outcome definitions, and a lack of risk estimates for most protein associations. Hence, we proceeded with a systematic review rather than a meta-analysis. In two comparable aptamer studies in patients with HF with reduced ejection fraction, 21 proteins were identified in common for the association with all-cause death. Among these, one protein, WAP four-disulfide core domain protein 2 was also reported in an antibody study on HFrEF and for the association with CV death. We proposed standardized reporting criteria to facilitate the interpretation of future studies.ConclusionsIn this systematic review of nine studies evaluating the association of proteomics with mortality in HF, we identified a limited number of proteins common across several studies. Heterogeneity across studies compromised drawing broad inferences, underscoring the importance of standardized approaches to reporting.
| S-EPMC10809595 | biostudies-literature
Project description:ObjectivesHeart failure (HF) management has significantly improved over the past two decades, leading to better survival. This study aimed to assess changes in predicted mortality risk after 12 months of management in a multidisciplinary HF clinic.Materials and methodsOut of 1,032 consecutive HF outpatients admitted from March-2012 to November-2018, 357 completed the 12-months follow-up and had N-terminal pro-B-type natriuretic peptide (NTproBNP), high sensitivity troponin T (hs-TnT), and interleukin-1 receptor-like-1 (known as ST2) measurements available both at baseline and follow-up. Three contemporary risk scores were used: MAGGIC-HF, Seattle HF Model (SHFM), and the Barcelona Bio-HF (BCN Bio-HF) calculator, which incorporates the three above mentioned biomarkers. The predicted risk of all-cause death at 1 and 3 years was calculated at baseline and re-evaluated after 12 months.ResultsA significant decline in predicted 1-and 3-year mortality risk was observed at 12 months: MAGGIC ~16%, SHFM ~22% and BCN Bio-HF ~15%. In the HF with reduced ejection fraction (HFrEF) subgroup guideline-directed medical therapy led to a complete normalization of left ventricular ejection fraction (≥50%) in almost a third of the patients and to a partial normalization (41-49%) in 30% of them. Repeated risk assessment after 12 months with SHFM and BCN Bio-HF provided adequate discrimination for all-cause 3-year mortality (C-Index: MAGGIC-HF 0.762, SHFM 0.781 and BCN Bio-HF 0.791).ConclusionMortality risk declines in patients with HF managed for 12 months in a multidisciplinary HF clinic. Repeating the mortality risk assessment after optimizing the HF treatment is recommended, particularly in the HFrEF subgroup.
| S-EPMC9039357 | biostudies-literature
Project description:AimsThe timely selection of severe heart failure (HF) patients for cardiac transplantation and advanced HF therapy is challenging. Peak oxygen consumption (VO2 ) values obtained by the cardiopulmonary exercise testing are used to determine the transplant recipient list. This study reassessed the prognostic predictability of peak VO2 and compared it with the Heart Failure Survival Score (HFSS) in the modern optimized guideline-directed medical therapy (GDMT) era.Methods and resultsWe retrospectively selected 377 acute HF patients discharged from the hospital. The primary outcome was a composite of all-cause mortality, or urgent cardiac transplantation. We divided these patients into the more GDMT (two or more types of GDMT) and less GDMT groups (fewer than two types of GDMT) and compared the performance of their peak VO2 and HFSS in predicting primary outcomes. The median follow-up period was 3.3 years. The primary outcome occurred in 57 participants. Peak VO2 outperformed HFSS when predicting 1 year (0.81 vs. 0.61; P = 0.017) and 2 year (0.78 vs. 0.58; P < 0.001) major outcomes. The cutoff peak VO2 for predicting a 20% risk of a major outcome within 2 years was 10.2 (11.8-7.0) for the total cohort. Multivariate Cox regression analyses showed that peak VO2 , sodium, previous implantable cardioverter defibrillator (ICD) implantation, and estimated glomerular filtration rate were significant predictors of major outcomes.ConclusionsOptimizing the cutoff value of peak VO2 is required in the current GDMT era for advanced HF therapy. Other clinical factors such as ICD use, hyponatraemia, and chronic kidney disease could also be used to predict poor prognosis. The improvement of resource allocation and patient outcomes could be achieved by careful selection of appropriate patients for advanced HF therapies, such as cardiac transplantation.
| S-EPMC10053263 | biostudies-literature