Neutrophil to Lymphocyte Ratio and Outcomes in Patients with New-Onset or Worsening Heart Failure with Reduced and Preserved Ejection Fraction

Inflammation is thought to play a role in heart failure (HF) pathophysiology. Neutrophil-to-lymphocyte ratio (NLR) is a simple, routinely available measure of inflammation. Its relationship with other inflammatory biomarkers and its association with clinical outcomes in addition to other risk markers have not been comprehensively evaluated in HF patients. Methods We evaluated patients with worsening or new-onset HF from the BIOlogy Study to Tailored Treatment in Chronic Heart Failure (BIOSTAT-CHF) study who had available NLR at baseline. The primary outcome was time to all-cause mortality or HF hospitalization. Outcomes were validated in a separate HF population. Results 1622 patients were evaluated (including 523 ventricular ejection fraction [LVEF] < 40% and 662 LVEF ≥ 40%). NLR was significantly correlated with biomarkers related to inflammation as well as NT-proBNP. NLR was significantly associated with the primary outcome in patients irrespective of LVEF (hazard ratio [HR] 1.18 per standard deviation increase; 95% confidence interval [CI] 1.11–1.26, P < 0.001). Patients with NLR in the highest tertile had significantly worse outcome than those in the lowest independent of LVEF (<40%: HR 2.75; 95% CI 1.84–4.09, P < 0.001; LVEF ≥ 40%: HR 1.51; 95% CI 1.05–2.16, P = 0.026). When NLR was added to the BIOSTAT-CHF risk score, there were improvements in integrated discrimination index (IDI) and net reclassification index (NRI) for occurrence of the primary outcome (IDI + 0.009; 95% CI 0.00–0.019, P = 0.030; continuous NRI + 0.112, 95% CI 0.012–0.176, P = 0.040). Elevated NLR was similarly associated with adverse outcome in the validation cohort. Decrease in NLR at 6 months was associated with reduced incidence of the primary outcome (HR 0.75; 95% CI 0.57–0.98, P = 0.036). Conclusions Elevated NLR is significantly associated with elevated markers of inflammation in HF patients and is associated with worse outcome. Elevated NLR might potentially be useful in identifying high-risk HF patients and may represent a treatment target

Cardiac biomarkers of acute coronary syndrome: from history to high-sensitivity cardiac troponin

The role of cardiac troponins as diagnostic biomarkers of myocardial injury in the context of acute coronary syndrome (ACS) is well established. Since the initial 1st-generation assays, 5th-generation high-sensitivity cardiac troponin (hs-cTn) assays have been developed, and are now widely used. However, its clinical adoption preceded guidelines and even best practice evidence. This review summarizes the history of cardiac biomarkers with particular emphasis on hs-cTn. We aim to provide insights into using hs-cTn as a quantitative marker of cardiomyocyte injury to help in the differential diagnosis of coronary versus non-coronary cardiac diseases. We also review the recent evidence and guidelines of using hs-cTn in suspected ACS

Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms

Directed acyclic graphs (DAGs) play a large role in the modern approach to causal inference. DAGs describe the relationship between measurements taken at various discrete times including the effect of interventions. The causal mechanisms, on the other hand, would naturally be assumed to be a continuous process operating over time in a cause-effect fashion. How does such immediate causation, that is causation occurring over very short time intervals, relate to DAGs constructed from discrete observations? We introduce a time-continuous model and simulate discrete observations in order to judge the relationship between the DAG and the immediate causal model. We find that there is no clear relationship; indeed the Bayesian network described by the DAG may not relate to the causal model. Typically, discrete observations of a process will obscure the conditional dependencies that are represented in the underlying mechanistic model of the process. It is therefore doubtful whether DAGs are always suited to describe causal relationships unless time is explicitly considered in the model. We relate the issues to mechanistic modeling by using the concept of local (in)dependence. An example using data from the Swiss HIV Cohort Study is presented