Estimation of B-type Natriuretic Peptide Values from N-Terminal proBNP Levels

Nabeshima, Yosuke; Sakanishi, Youko; Otani, Kyoko; Higa, Yukie; Honda, Masahisa; Takeuchi, Masaaki

doi:10.7888/juoeh.42.1

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…N-terminal BNP (NT-proBNP) is also one of the natriuretic peptides for which clinical usefulness has been established [ 12 , 13 ]. NT-proBNP is well correlated with BNP and is useful for diagnosing HF and predicting future cardiac events [ 17 , 18 ]. NT-proBNP is usually measured using a general-purpose, automatic biochemical analyzer, which is used in most hospitals in Japan.…”

Section: Discussionmentioning

confidence: 99%

Brain natriuretic peptide measurements using standard biochemical equipment: Comparisons with conventional immunoassays

et al. 2022

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Background Brain natriuretic peptide (BNP) is an essential cardiac biomarker for diagnosing heart failure and for prognoses in patients with various cardiac diseases. However, measurement requires immunological assays that are not available in every hospital. Recently, a novel BNP kit (Nanopia BNP-A, Sekisui Inc.; BNPn) that uses general-purpose, automated, biochemical analyzers has become commercially available. We assessed how its accuracy and utility compare with those of conventional immunological tests. Methods and results We retrospectively collected 1491 conventional BNP measurements (BNPc), which had been clinically indicated for BNP testing and for which residual samples were still stored in the laboratory. We measured BNP using the novel kit and determined the correlation of BNP levels between the two methods. We also assessed the predictive value of both BNP measurements for major cardiac events (MACEs). The analytical performance of both measuring methods was similar. Log-transformed BNP measured by both methods showed strong correlation (r = 0.92); however, log-transformed BNPn was significantly higher than log-transformed BNPc (p<0.001). BNPc of 200 ng/L was used to stratify patients into two groups. According to the regression formula between the two methods, we determined a cut-off value of BNPn as 250 ng/L. During a median of 15 months of follow-up, 43 MACEs developed. Both BNPc and BNPn were associated with MACEs. Kaplan-Meier survival analysis indicated that both BNPc and BNPn cut-off values stratified the high-risk group for prognostication. The diagnostic and prognostic utilities were proven even if the lower cut-off values (BNPc = 100 ng/L, BNPn = 130 ng/L) were employed. Conclusions A new BNP measurement using biochemical equipment provides prognostic value similar to that of conventional BNP analysis; thus, it should prove useful in hospitals in which conventional immunological examinations are not available.

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Section: Discussionmentioning

confidence: 99%

Brain natriuretic peptide measurements using standard biochemical equipment: Comparisons with conventional immunoassays

et al. 2022

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“…Furthermore, the following blood examinations were performed at admission: blood urea nitrogen (BUN), creatinine, estimated glomerular filtration rate (eGFR), and either B‐type natriuretic peptide (BNP) or N‐terminal pro‐B‐type natriuretic peptide (NT‐ proBNP). BNP was converted into a logarithmic scale (Log BNP) according to its distribution, while NT‐proBNP was converted into log BNP using the formula proposed by Nabeshima et al 12 …”

Section: Methodsmentioning

confidence: 99%

Optimal timing of echocardiography for heart failure inpatients in Japanese institutions: OPTIMAL Study

et al. 2020

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Aims Guidelines for the diagnosis and treatment of acute and chronic heart failure (HF) provided by the European Society of Cardiology state that echocardiography is recommended for the assessment of the myocardial structure and function of subjects with suspected HF including HF with reduced (HFrEF), mid-range (HFmrEF), and preserved ejection fraction (HFpEF) as class I of recommendation and level C of evidence. However, the impact of timing of echocardiography on survival for hospitalized HF patients or the prevalence of echocardiography during their stay has not yet been fully investigated. Therefore, we designed and conducted a prospective multicentre study, Optimal Timing of Echocardiography for Heart Failure Inpatients in Japanese Institutions (OPTIMAL) study, to investigate and evaluate the prevalence of echocardiography during the in-hospital stay of HF patients, and the impact of timing of echocardiography on their survival. Methods and results OPTIMAL was based on a nationwide, prospective, multicentre registry at 10 institutions in Japan endorsed by the Japanese Society of Echocardiography. A total of 601 patients hospitalized with HF were enrolled between August 2016 and July 2018 at the participating centres. Their mean age was 73.9 ± 13.0 years, left ventricular ejection fraction was 37.0% (26.0-50.0), and 256 patients (42.6%) were female. Admission echocardiography (admission echo) was categorized as either standard or point-of-care echocardiography performed within 3 days of admission, as was pre-discharge echocardiography (pre-discharge echo) within 3 days of discharge. The primary endpoint was defined as cardiovascular death over a median follow-up period of 18.9 months (9.3-26.5 months). Admission echo was performed for 476 patients (79.2%) and pre-discharge echo for 216 patients (35.9%). The primary endpoint of cardiovascular death occurred in 65 patients (10.8%). Kaplan-Meier curve findings indicated that survival of patients with pre-discharge echo was significantly better than that of patients without it (log-rank P < 0.001), and the same findings were obtained for patients with HFrEF, HFmrEF, and HFpEF. However, survival of patients with and without admission echo was similar (log-rank P = 0.33). Conclusions This OPTIMAL study prospectively showed the importance of pre-discharge echo for hospitalized HF patients. Careful attention is needed regarding the haemodynamic status of HF patients by administering pre-discharge echo to avoid HF re-hospitalization after discharge, and pre-discharge echo may provide additional information for deciding the appropriate discharge time. Our findings may thus offer a new insight into the management of hospitalized HF patients.

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“…Two methods were applied: firstly, with the help of our clinical partners, we recovered some missing BNP values for affected patients by assigning values acquired within two months from the time of data analysis; secondly, some incorrectly assigned values were removed upon critical examination of the remaining records, leaving a total of 641 records with a BNP or proBNP value assigned. For those cases where only proBNP values were available, we applied a change factor of four, as suggested by our clinical partners after discovering from a literature analysis [50][51][52] that conversion factors and formulas may vary as a function of the data source, nationality, and some other environmental issues.…”

Section: Datasetmentioning

confidence: 99%

Deep Learning for Predicting Congestive Heart Failure

et al. 2022

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Congestive heart failure (CHF) is one of the most debilitating cardiac disorders. It is a costly disease in terms of both lives and financial outlays, given the high rate of hospital re-admissions and mortality. Heart failure (HF) is notoriously difficult to identify on time, and is frequently accompanied by additional comorbidities that further complicate diagnosis. Many decision support systems (DSS) have been developed to facilitate diagnosis and to raise the standard of screening and monitoring operations, even for non-expert staff. This is confirmed in the literature by records of highly performing diagnosis-aid systems, which are unfortunately not very relevant to expert cardiologists. In order to assist cardiologists in predicting the trajectory of HF, we propose a deep learning-based system which predicts severity of disease progression by employing medical patient history. We tested the accuracy of four models on a labeled dataset, composed of 1037 records, to predict CHF severity and progression, achieving results comparable to studies based on much larger datasets, none of which used longitudinal multi-class prediction. The main contribution of this work is that it demonstrates that a fairly complicated approach can achieve good results on a medium size dataset, providing a reasonably accurate means of determining the evolution of CHF well in advance. This potentially constitutes a significant aid for healthcare managers and expert cardiologists in designing different therapies for medication, healthy lifestyle changes and quality of life (QoL) management, while also promoting allocation of resources with an evidence-based approach.

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Estimation of B-type Natriuretic Peptide Values from N-Terminal proBNP Levels

Cited by 4 publications

References 21 publications

Brain natriuretic peptide measurements using standard biochemical equipment: Comparisons with conventional immunoassays

Brain natriuretic peptide measurements using standard biochemical equipment: Comparisons with conventional immunoassays

Optimal timing of echocardiography for heart failure inpatients in Japanese institutions: OPTIMAL Study

Deep Learning for Predicting Congestive Heart Failure

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