Using BNP to develop a risk score for heart failure in primary care

The MECKI score initiative: Development and state of the art

The high morbidity and poor survival rates associated with chronic heart failure still represent a big challenge, despite improvements in treatments and the development of new therapeutic opportunities. The prediction of outcome in heart failure is gradually moving towards a multiparametric approach in order to obtain more accurate models and to tailor the prognostic evaluation to the individual characteristics of a single subject. The Metabolic Exercise test data combined with Cardiac and Kidney Indexes (MECKI) score was developed 10 years ago from 2715 patients and subsequently validated in a different population. The score allows an accurate evaluation of the risk of heart failure patients using only six variables that include the evaluation of the exercise capacity (peak oxygen uptake and ventilation/CO₂ production slope), blood samples (haemoglobin, Na⁺, Modification of Diet in Renal Disease) and echocardiography (left ventricular ejection fraction). Over the following years, the MECKI score was tested taking into account therapies and specific markers of heart failure, and it proved to be a simple, useful tool for risk stratification and for therapeutic strategies in heart failure patients. The close connection between the centres involved and the continuous updating of the data allow the participating sites to propose substudies on specific subpopulations based on a common dataset and to put together and develop new ideas and perspectives.

An Appraisal of Biomarker-Based Risk-Scoring Models in Chronic Heart Failure: Which One Is Best?

PURPOSE OF REVIEW

While prediction models incorporating biomarkers are used in heart failure, these have shown wide-ranging discrimination and calibration. This review will discuss externally validated biomarker-based risk models in chronic heart failure patients assessing their quality and relevance to clinical practice.

RECENT FINDINGS

Biomarkers may help in determining prognosis in chronic heart failure patients as they reflect early pathologic processes, even before symptoms or worsening disease. We present the characteristics and describe the performance of 10 externally validated prediction models including at least one biomarker among their predictive factors. Very few models report adequate discrimination and calibration. Some studies evaluated the additional predictive value of adding a biomarker to a model. However, these have not been routinely assessed in subsequent validation studies. New and existing prediction models should include biomarkers, which improve model performance. Ongoing research is needed to assess the performance of models in contemporary patients.

Multistate Model to Predict Heart Failure Hospitalizations and All-Cause Mortality in Outpatients With Heart Failure With Reduced Ejection Fraction: Model Derivation and External Validation

BACKGROUND

Outpatients with heart failure (HF) who are at high risk for HF hospitalization and death may benefit from early identification. We sought to develop and externally validate a model to predict both HF hospitalization and mortality that accounts for the semicompeting nature of the 2 outcomes and captures the risk associated with the transition from the stable outpatient state to the post-HF hospitalization state.

METHODS AND RESULTS

A multistate model to predict HF hospitalization and all-cause mortality was derived using data (n=3834) from the HEAAL study (Heart Failure Endpoint evaluation of Angiotensin II Antagonist Losartan), a multinational randomized trial in symptomatic patients with reduced left ventricular ejection fraction. Twelve easily and reliably obtainable demographic and clinical predictors were prespecified for model inclusion. Model performance was assessed in the SCD-HeFT cohort (Sudden Cardiac Death in Heart Failure Trial; n=2521). At 1 year, the probability of being alive without HF hospitalization was 94% for a typical patient in the lowest risk quintile and 77% for a typical patient in the highest risk quintile and this variability in risk continued through 7 years of follow-up. The model c-index was 0.72 in the derivation cohort, 0.66 in the validation cohort, and 0.69 in the implantable cardiac defibrillator arm of the validation cohort. There was excellent calibration across quintiles of predicted risk.

CONCLUSIONS

Our findings illustrate the advantages of a multistate modeling approach, providing estimates of HF hospitalization and death in the same model, comparison of predictors for the different outcomes and demonstrating the different trajectories of patients based on baseline characteristics and intermediary events.

CLINICAL TRIAL REGISTRATION

URL: http://www.clinicaltrials.gov. Unique identifiers: NCT00000609 and NCT00090259.

Prediction of higher cost of antiretroviral therapy (ART) according to clinical complexity. A validated clinical index

BACKGROUND

The financing of antiretroviral therapy (ART) is generally determined by the cost incurred in the previous year, the number of patients on treatment, and the evidence-based recommendations, but not the clinical characteristics of the population.

OBJECTIVE

To establish a score relating the cost of ART and patient clinical complexity in order to understand the costing differences between hospitals in the region that could be explained by the clinical complexity of their population.

METHODS

Retrospective analysis of patients receiving ART in a tertiary hospital between 2009 and 2011. Factors potentially associated with a higher cost of ART were assessed by bivariate and multivariate analysis. Two predictive models of "high-cost" were developed. The normalized estimated (adjusted for the complexity scores) costs were calculated and compared with the normalized real costs.

RESULTS

In the Hospital Index, 631 (16.8%) of the 3758 patients receiving ART were responsible for a "high-cost" subgroup, defined as the highest 25% of spending on ART. Baseline variables that were significant predictors of high cost in the Clinic-B model in the multivariate analysis were: route of transmission of HIV, AIDS criteria, Spanish nationality, year of initiation of ART, CD4+ lymphocyte count nadir, and number of hospital admissions. The Clinic-B score ranged from 0 to 13, and the mean value (5.97) was lower than the overall mean value of the four hospitals (6.16).

CONCLUSIONS

The clinical complexity of the HIV patient influences the cost of ART. The Clinic-B and Clinic-BF scores predicted patients with high cost of ART and could be used to compare and allocate costs corrected for the patient clinical complexity.

Short-term outcomes after hospital discharge in patients admitted with heart failure in Abeokuta, Nigeria: data from the Abeokuta Heart Failure Registry

Background

Compared to other regions of the world, there is a paucity of data on the short-term outcome of acute heart failure (AHF) in Africa’s most populous country, Nigeria. We examined the six-month outcomes (including case fatalities) in 285 of 309 AHF subjects admitted with HF to a tertiary hospital in Abeokuta, Nigeria.

Methods

The study cohort of 285 subjects comprised 150 men (52.6%) and 135 women (47.4%) with a mean age of 56.3 ± 15.6 years and the majority in NYHA class III (75%).

Results

There were a number of differences according to the subject’s gender; men being older and more likely to present with hypertensive heart disease (with greater left ventricular mass) while also having greater systolic dysfunction. Mean length of stay was 10.5 ± 5.9 days. Mean follow up was 205 days, with 23 deaths and 20 lost to follow up. At 30 days, 4.2% (95% CI: 2.4–7.3%) had died and by 180 days this had increased to 7.5% (95% CI: 4.7–11.2%); with those subjects with pericardial disease demonstrating the highest initial mortality rate. Over the same period, 13.9% of the cohort was re-admitted at least once.

Conclusions

The characteristics of this AHF cohort in Nigeria were different from those reported in high-income countries. Cases were relatively younger and presented with non-ischaemic aetiological risk factors for HF, especially hypertensive heart disease. Moreover, mortality and re-admission rates were relatively lower, suggesting region-specific strategies are required to improve health outcomes.

Clinical uses of brain natriuretic peptide in diagnosing and managing heart failure

PURPOSE

To review current issues in the diagnosis, prognosis, and management of heart failure (HF), focusing on the clinical use of brain natriuretic peptide (BNP) as a diagnostic marker.

DATA SOURCES

Selective review of scientific literature and clinical practice guidelines.

CONCLUSIONS

BNP is a useful clinical tool for the diagnosis, prognosis, and management of HF patients. Studies have consistently demonstrated high sensitivity, specificity, and negative predictive value of BNP levels in diagnostic situations. BNP cannot differentiate between systolic and diastolic HF.

IMPLICATIONS FOR PRACTICE

BNP can be used to assist in diagnosing HF in emergency and outpatient situations, particularly when the presenting symptom is dyspnea; determining HF prognosis, including predicting death and cardiac events; and potentially managing individuals with HF by determining safe discharge levels from acute care to avoid readmissions. BNP levels can vary depending on multiple confounders; therefore, clinical interpretation can be difficult.

The emerging role of echocardiography in the screening of patients at risk of heart failure

A large number of patients without symptoms of heart failure (HF) have asymptomatic left ventricular (LV) dysfunction owing to the compensatory mechanisms acting through the autonomic nervous system and neurohormones. In the setting of screening for prevention, one must identify the subgroup of these patients at high risk for symptomatic HF to establish appropriate therapy. As a first step to identify the subgroup of patients at high risk, clinical screening scores and natriuretic peptide measurements are used. Second, the definite diagnosis of asymptomatic LV dysfunction must be confirmed with echocardiography, occasionally with the help of new technologic developments to establish prompt, appropriate treatment to prevent disease progression. Therefore, the screening role of echocardiography is the early identification of patients with structural cardiopathy who are at risk of developing symptomatic HF and detection of those without LV dysfunction (diabetic and hypertensive) whose condition is prone to advance rapidly to structural cardiopathy or to symptomatic HF.