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Multimarker approach for the prediction of heart failure incidence in the community.

ABSTRACT: BACKGROUND:Several biological pathways are activated in ventricular remodeling and in overt heart failure (HF). There are no data, however, on the incremental utility of a parsimonious set of biomarkers (reflecting pathways implicated in HF) for predicting HF risk in the community. METHODS AND RESULTS:We related a multibiomarker panel to the incidence of a first HF event in 2754 Framingham Heart Study participants (mean age, 58 years; 54 women) who were free of HF and underwent routine assays for 6 biomarkers (C-reactive protein, plasminogen activator inhibitor-1, homocysteine, aldosterone-to-renin ratio, B-type natriuretic peptide, and urinary albumin-to-creatinine ratio). We estimated model c statistic, calibration, and net reclassification improvement to assess the incremental predictive usefulness of biomarkers. We also related biomarkers to the incidence of nonischemic HF in participants without prevalent coronary heart disease. On follow-up (mean, 9.4 years), 95 first HF events occurred (54 in men). In multivariable-adjusted models, the biomarker panel was significantly related to HF risk (P=0.00005). On backward elimination, B-type natriuretic peptide and urinary albumin-to-creatinine ratio emerged as key biomarkers predicting HF risk; hazards ratios per 1-SD increment in log marker were 1.52 (95 confidence interval, 1.24 to 1.87) and 1.35 (95 confidence interval, 1.11 to 1.66), respectively. B-type natriuretic peptide and urinary albumin-to-creatinine ratio significantly improved the model c statistic from 0.84 (95 confidence interval, 0.80 to 0.88) in standard models to 0.86 (95 confidence interval, 0.83 to 0.90), enhanced risk reclassification (net reclassification improvement=0.13; P=0.002), and were independently associated with nonischemic HF risk. CONCLUSION:Using a multimarker strategy, we identified B-type natriuretic peptide and urinary albumin-to-creatinine ratio as key risk factors for new-onset HF with incremental predictive utility over standard risk factors.

SUBMITTER: Velagaleti RS

PROVIDER: S-EPMC2993157 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

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Multimarker approach for the prediction of heart failure incidence in the community.

Velagaleti Raghava S RS Gona Philimon P Larson Martin G MG Wang Thomas J TJ Levy Daniel D Benjamin Emelia J EJ Selhub Jacob J Jacques Paul F PF Meigs James B JB Tofler Geoffrey H GH Vasan Ramachandran S RS

Circulation 20101011 17

<h4>Background</h4>Several biological pathways are activated in ventricular remodeling and in overt heart failure (HF). There are no data, however, on the incremental utility of a parsimonious set of biomarkers (reflecting pathways implicated in HF) for predicting HF risk in the community.<h4>Methods and results</h4>We related a multibiomarker panel to the incidence of a first HF event in 2754 Framingham Heart Study participants (mean age, 58 years; 54 women) who were free of HF and underwent ro ...[more]

PMID: 20937976

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Project description:ObjectivePrimarily to develop a multimarker score for prediction of 3-year mortality in older patients with decompensated heart failure (HF).DesignProspective cohort study.SettingSecondary care. Single centre. PATIENTS AND BIOMARKERS: 131 patients, aged ?65 years, with decompensated HF were included. Assessment of biomarkers was performed at discharge.Primary outcome measure3-year mortality.ResultsMean age was 73±11 years; mean left ventricular ejection fraction , 43±14%; 53% were male. The 3-year mortality was 53.4%. The following N-terminal brain natriuretic peptide (NTproBNP) levels could optimally stratify mortality: <2000 ng/l (n=39), 30.8% mortality; 2000-8000 ng/l (n=58), 51.7% mortality; and >8000 ng/l (n=34), 82.4% mortality. However, in the 2000-8000 ng/l range, NTproBNP levels had low-prognostic capacity, based on the area under the receiver operating characteristic curve (AUC=0.53; 95% CI 0.40 to 0.67). In this group, multivariate analysis identified age, cystatin C (CysC), and troponin T (TnT) levels as independent risk factors. A risk score based on these three risk factors separated a high-risk and low-risk groups within the NTproBNP range of 2000-8000 ng/l. The score exhibited a significantly higher AUC (0.75; 95% CI 0.62 to 0.86) than NTproBNP alone (p=0.03) in this NTproBNP group and had similar prognostic capacity as NTproBNP in patients below or above this NTproBNP range (p=0.57). Net reclassification improvement and integrated discriminatory improvement in the group with NTproBNP levels between 2000 and 8000 ng/l was 54% and 23%, respectively, and in the whole cohort 22% and 11%, respectively.ConclusionsOur results suggested that, to assess risk in HF, older patients required significantly higher levels of NTproBNP than younger patients. Furthermore, a risk score that included TnT and CysC at discharge, and age could improve risk stratification for mortality in older patients with HF in particular when NTproBNP was moderately elevated.

Project description:Background and importanceDiagnosing acute heart failure (AHF) is difficult in elderly patients presenting with acute dyspnea to the emergency department.ObjectivesTo assess the diagnostic accuracy of NT-proBNP, high-sensitivity cardiac troponin-I (Hs-cTnI), soluble ST2 (ST2), galectin-3 and CD146 alone and in combination for diagnosing AHF in elderly patients presenting with acute dyspnea to the emergency department.Design, settings and participantsThis was a prospective, multicenter study performed between September 2016 and January 2020, including elderly patients presenting with acute dyspnea to the emergency department of 6 French hospitals.InterventionMeasurement of NT-proBNP, hs-cTnI, ST2, galectin-3 and CD146.Outcome measure and analysisThe reference standard, AHF, was adjudicated by two independent physicians based on ED and hospitalization clinical, biological (excluding biomarkers), radiological and echocardiography data (performed by a cardiologist in the cardiology department specifically for this study). Three exploratory methods (two using a cross-sectional approach with logistic regression and counting all biomarker combinations, and one using a sequential approach with gray zone optimizations) were applied to create comprehensive combinations of the 5 biomarkers for measuring diagnostic accuracy.Main resultsTwo hundred thirty-eight patients (median age of 85 years, IQR = 8) were analyzed, and 110 (46%) were diagnosed with AHF. The accuracies of NT-proBNP, CD146, hs-cTnI, galectin-3, and ST2 were 0.72 [95% confidence interval (CI) 0.66-0.77], 0.63 (95% CI 0.57-0.69), 0.59 (95% CI 0.53-0.65), 0.55 (95% CI 0.49-0.61) and 0.51 (95% CI 0.45-0.57), respectively. Regardless of the approach used or how the 5 biomarkers were combined, the best accuracy for diagnosing AHF (0.73, 95% CI 0.67-0.78) did not differ from that of NT-proBNP alone.ConclusionIn this study, NT-proBNP alone exhibited the best diagnostic accuracy for diagnosing AHF in elderly patients presenting with acute dyspnea to the emergency departments. None of the other biomarkers alone or combined improved the accuracy compared to NT-proBNP, which is the only biomarker to use in this setting.

Project description:AimsThe objective of the present study is to assess the bidirectional association between heart failure (HF) and atrial fibrillation (AF) using real-world data.Methods and resultsFrom an electronic health recording with a population of 3 799 885 adult subjects, those with prevalent or incident HF were selected and followed throughout a study period of 5 years. Prevalence and incidence of AF, and their impact in the risk for acute HF hospitalization, worsening renal function, ischaemic and haemorrhagic stroke, and all-cause mortality were identified. We analysed all incident and prevalent patients with HF and AF, 128 086 patients (S1), and subsequently analysed a subset of patients with incident HF and AF, 57 354 patients (S2). We analysed all incident and prevalent patients with HF and AF, 128 086 patients (S1), and subsequently a subset of patients with incident HF and AF, 57 354 patients (S2). The prevalence of AF was 59 906 (46.7%) of the HF patients, while incidence in the S2 was 231/1000 patients/year. In both cohorts, S1 and S2, AF significantly increases the risk of acute heart failure hospitalization [incidence 79.1/1000 and 97.5/1000 patients/year; HR 1.53 (1.48-1.59 95% CI) and HR 1.32 (1.24-1.41 95% CI), respectively], risk of decreased renal function (eGFR reduced by >20%) [66.2/1000 and 94.0/1000 patients/year; HR 1.13 (1.09-1.18 95% CI) and HR 1.22 (1.14-1.31 95% CI), respectively] and all-cause mortality [203/1000 and 294/1000 patients/year; HR 1.62 (1.58-1.65 95% CI) and HR 1.65 (1.59-1.70 95% CI), respectively]. The number of episodes of hospitalization for acute heart failure was also significantly higher in the AF patients (27 623 vs. 10 036, P < 0.001). However, the risk for ischaemic stroke was reduced in the AF subjects [HR 0.66 (0.63-0.74 95% CI)], probably due to the anticoagulant treatment.ConclusionsAF is associated with an increment in the risk of episodes of acute heart failure as well as decline of renal function and increment of all-cause mortality.

Project description:BACKGROUND:Patients hospitalized with heart failure suffer the highest rates of 30-day readmission among other clinically defined patient populations in the United States. Investigation into the predictability of 30-day readmissions can lead to clinical decision support tools and targeted interventions that can help care providers to improve individual patient care and reduce readmission risk. OBJECTIVE:This study aimed to develop a dynamic readmission risk prediction model that yields daily predictions for patients hospitalized with heart failure toward identifying risk trajectories over time and identifying clinical predictors associated with different patterns in readmission risk trajectories. METHODS:A two-stage predictive modeling approach combining logistic and beta regression was applied to electronic health record data accumulated daily to predict 30-day readmission for 534 hospital encounters of patients with heart failure over 2750 patient days. Unsupervised clustering was performed on predictions to uncover time-dependent trends in readmission risk over the patient's hospital stay. We used data collected between September 1, 2013, and August 31, 2015, from a community hospital in Maryland (United States) for patients with a primary diagnosis of heart failure. Patients who died during the hospital stay or were transferred to other acute care hospitals or hospice care were excluded. RESULTS:Readmission occurred in 107 (107/534, 20.0%) encounters. The out-of-sample area under curve for the 2-stage predictive model was 0.73 (SD 0.08). Dynamic clinical predictors capturing laboratory results and vital signs had the highest predictive value compared with demographic, administrative, medical, and procedural data included. Unsupervised clustering identified four risk trajectory groups: decreasing risk (131/534, 24.5% encounters), high risk (113/534, 21.2%), moderate risk (177/534, 33.1%), and low risk (113/534, 21.2%). The decreasing risk group demonstrated change in average probability of readmission from admission (0.69) to discharge (0.30), whereas the high risk (0.75), moderate risk (0.61), and low risk (0.39) groups maintained consistency over the hospital course. A higher level of hemoglobin, larger decrease in potassium and diastolic blood pressure from admission to discharge, and smaller number of past hospitalizations are associated with decreasing readmission risk (P<.001). CONCLUSIONS:Dynamically predicting readmission and quantifying trends over patients' hospital stay illuminated differing risk trajectory groups. Identifying risk trajectory patterns and distinguishing predictors may shed new light on indicators of readmission and the isolated effects of the index hospitalization.

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Multimarker approach for the prediction of heart failure incidence in the community.

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Multimarker approach for the prediction of heart failure incidence in the community.

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