Impact of brain natriuretic peptide reduction on the worsening renal function in patients with acute heart failure

Brain natriuretic peptide in acute heart failure and its association with glomerular filtration rate: A systematic review and meta-analysis

BACKGROUND

Acute heart failure (AHF) is one of the most common cardiovascular diseases. Early diagnosis and prognosis are essential, as they can eventually lead to a fatal condition. Recently, brain natriuretic peptide (BNP) has been recognized as one of the most popular biomarkers for AHF. Changes in glomerular filtration rate (GFR) are often observed in AHF.

METHODS

We searched PubMed, Google Scholar, and ScienceDirect between March and June 2023. Original case control studies written in English that assessed levels oh BNP in AHF were included. Systematic reviews, letters to editor, correspondence, comprehensive reviews, and duplicated studies were excluded. Funnel plots were constructed to assess publication bias.

RESULTS

A total of 9 studies were selected and we obtained the mean difference (MD) of BNP level to be 2.57 (95% CI: 1.35, 3.78), and GFR to be -15.52, (95% CI: -23.35, -7.70) in AHF patients. Sensitivity analyses supported the robustness of the outcome.

CONCLUSION

Results indicated that BNP was a promising prognostic biomarker of AHF, whereas GFR was found to be negatively correlated with AHF.

Impact of the degree of worsening renal function and B-type natriuretic peptide on the prognosis of patients with acute heart failure

Background

The impact of the degree of worsening renal function (WRF) and B-type natriuretic peptide (BNP) on the prognosis of patients with acute heart failure (AHF) is still debatable. The present study investigated the influence of different degrees of WRF and BNP levels at discharge on 1-year all-cause mortality in AHF.

Methods

Hospitalized AHF patients diagnosed with acute new-onset/worsening of chronic heart failure (HF) between January 2015 and December 2019 were included in this study. Patients were assigned into high and low BNP groups based on the median BNP level at discharge (464 pg/ml). According to serum creatinine (Scr) levels, WRF was divided into non-severe WRF (nsWRF) (Scr increased ≥0.3 mg/dl and <0.5 mg/dl) and severe WRF (sWRF) (Scr increased ≥0.5 mg/dl); non-WRF (nWRF) was defined as Scr increased of <0.3 mg/dl). Multivariable cox regression was used to evaluate the association of low BNP value and different degrees of WRF with a all-cause death, as well as testing for an interaction between the two.

Results

Among 440 patients in the high BNP group, there was a significant difference in WRF on mortality (nWRF vs. nsWRF vs. sWRF: 22% vs. 23.8% vs. 58.8%, P < 0.001). Yet, mortality did not significantly differ across the WRF subgroups in the low BNP group (nWRF vs. nsWRF vs. sWRF: 9.1% vs. 6.1% vs. 15.2%, P = 0.489). In multivariate Cox regression analysis, low BNP group at discharge (HR, 0.265; 95%CI, 0.162–0.434; P < 0.001) and sWRF (HR, 2.838; 95%CI, 1.756–4.589; P < 0.001) were independent predictors of 1-year mortality in AHF.There was a significant interaction between low BNP group and sWRF(HR, 0.225; 95%CI, 0.055–0.918; P < 0.05).

Conclusions

nsWRF does not increase the 1-year mortality in AHF patients, whereas sWRF does. A low BNP value at discharge is associated with better long-term outcomes and mitigates the adverse effects of sWRF on prognosis.

Fibrosis-4 index identifies worsening renal function associated with adverse outcomes in acute heart failure

AIMS

Worsening renal function (WRF) often develops during heart failure (HF) treatment. However, prognostic implications of WRF in acute HF remain controversial, and risk stratification of WRF is challenging. Although the fibrosis-4 index (FIB-4) was initially established as a liver fibrosis marker, recent studies show that high FIB-4 is associated with venous congestion and poor prognosis in acute HF. This study aimed to evaluate whether FIB-4 could identify prognostically relevant and non-relevant WRF in patients with acute HF.

METHODS AND RESULTS

We retrospectively analysed data from a single-centre registry on acute HF at our university hospital between January 2015 and June 2021. This study included patients with acute HF aged ≥20 years who were immediately hospitalized and had brain natriuretic peptide levels ≥100 pg/mL at admission. WRF was defined as increases of ≥0.3 mg/dL and >25% in serum creatinine level from admission to discharge. FIB-4 scores were calculated before discharge. The primary endpoint was all-cause mortality within 1 year of discharge. Based on the presence of WRF and whether FIB-4 scores were above the median, patients were stratified into four groups: no WRF and lower FIB-4 scores, no WRF and higher FIB-4 scores, WRF and lower FIB-4 scores, and WRF and higher FIB-4 scores. The patients were followed up via clinical visits or telephone interviews. Clinical outcomes were collected from the electronic medical records.

RESULTS

Of the 969 patients hospitalized for acute HF (76 ± 11 years, 59% men), 118 patients (12%) had WRF at discharge. The median (interquartile range) FIB-4 score at discharge was 2.36 (1.55-3.25). The primary endpoint occurred in 136 patients (14.0%). The 1 year mortality rates were 10.5% in the no WRF and lower FIB-4 scores (≤2.36) group (n = 428), 16.1% in the no WRF and higher FIB-4 scores (>2.36) group (n = 423), 12.5% in the WRF and lower FIB-4 scores group (n = 56), and 25.8% in the WRF and higher FIB-4 scores group (n = 62) (P = 0.005). Kaplan-Meier analysis demonstrated higher all-cause mortality in the WRF and higher FIB-4 group (log-rank P = 0.003). In the Cox regression analysis, only the WRF and higher FIB-4 scores group was associated with an increased risk of mortality compared with the no WRF and lower FIB-4 scores group (hazard ratio = 2.11, 95% confidence interval: 1.07-4.18, P = 0.032), despite adjusting for other confounding factors.

CONCLUSIONS

FIB-4 is a valuable risk stratification marker for WRF in patients with acute HF. The underlying mechanism and potential clinical importance of these observations require further investigation.

Acute kidney injury in the critically ill: an updated review on pathophysiology and management

Acute kidney injury (AKI) is now recognized as a heterogeneous syndrome that not only affects acute morbidity and mortality, but also a patient’s long-term prognosis. In this narrative review, an update on various aspects of AKI in critically ill patients will be provided. Focus will be on prediction and early detection of AKI (e.g., the role of biomarkers to identify high-risk patients and the use of machine learning to predict AKI), aspects of pathophysiology and progress in the recognition of different phenotypes of AKI, as well as an update on nephrotoxicity and organ cross-talk. In addition, prevention of AKI (focusing on fluid management, kidney perfusion pressure, and the choice of vasopressor) and supportive treatment of AKI is discussed. Finally, post-AKI risk of long-term sequelae including incident or progression of chronic kidney disease, cardiovascular events and mortality, will be addressed.

Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data

Background

Acute heart failure (AHF) is associated with significant morbidity and mortality. Effective patient risk stratification is essential to guiding hospitalization decisions and the clinical management of AHF. Clinical decision support systems can be used to improve predictions of mortality made in emergency care settings for the purpose of AHF risk stratification. In this study, several models for the prediction of seven-day mortality among AHF patients were developed by applying machine learning techniques to retrospective patient data from 236,275 total emergency department (ED) encounters, 1881 of which were considered positive for AHF and were used for model training and testing. The models used varying subsets of age, sex, vital signs, and laboratory values. Model performance was compared to the Emergency Heart Failure Mortality Risk Grade (EHMRG) model, a commonly used system for prediction of seven-day mortality in the ED with similar (or, in some cases, more extensive) inputs. Model performance was assessed in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity.

Results

When trained and tested on a large academic dataset, the best-performing model and EHMRG demonstrated test set AUROCs of 0.84 and 0.78, respectively, for prediction of seven-day mortality. Given only measurements of respiratory rate, temperature, mean arterial pressure, and FiO₂, one model produced a test set AUROC of 0.83. Neither a logistic regression comparator nor a simple decision tree outperformed EHMRG.

Conclusions

A model using only the measurements of four clinical variables outperforms EHMRG in the prediction of seven-day mortality in AHF. With these inputs, the model could not be replaced by logistic regression or reduced to a simple decision tree without significant performance loss. In ED settings, this minimal-input risk stratification tool may assist clinicians in making critical decisions about patient disposition by providing early and accurate insights into individual patient’s risk profiles.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13040-021-00255-w.