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Deep learning-based NT-proBNP prediction from the ECG for risk assessment in the community

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Deep learning-based NT-proBNP prediction from the ECG for risk assessment in the community. / Neyazi, Meraj; Bremer, Jan P; Knorr, Marius S; Gross, Stefan; Brederecke, Jan; Schweingruber, Nils; Csengeri, Dora; Schrage, Benedikt; Bahls, Martin; Friedrich, Nele; Zeller, Tanja; Felix, Stephan; Blankenberg, Stefan; Dörr, Marcus; Vollmer, Marcus; Schnabel, Renate B.

in: CLIN CHEM LAB MED, Jahrgang 62, Nr. 4, 25.03.2024, S. 740-752.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Neyazi, M, Bremer, JP, Knorr, MS, Gross, S, Brederecke, J, Schweingruber, N, Csengeri, D, Schrage, B, Bahls, M, Friedrich, N, Zeller, T, Felix, S, Blankenberg, S, Dörr, M, Vollmer, M & Schnabel, RB 2024, 'Deep learning-based NT-proBNP prediction from the ECG for risk assessment in the community', CLIN CHEM LAB MED, Jg. 62, Nr. 4, S. 740-752. https://doi.org/10.1515/cclm-2023-0743

APA

Neyazi, M., Bremer, J. P., Knorr, M. S., Gross, S., Brederecke, J., Schweingruber, N., Csengeri, D., Schrage, B., Bahls, M., Friedrich, N., Zeller, T., Felix, S., Blankenberg, S., Dörr, M., Vollmer, M., & Schnabel, R. B. (2024). Deep learning-based NT-proBNP prediction from the ECG for risk assessment in the community. CLIN CHEM LAB MED, 62(4), 740-752. https://doi.org/10.1515/cclm-2023-0743

Vancouver

Neyazi M, Bremer JP, Knorr MS, Gross S, Brederecke J, Schweingruber N et al. Deep learning-based NT-proBNP prediction from the ECG for risk assessment in the community. CLIN CHEM LAB MED. 2024 Mär 25;62(4):740-752. https://doi.org/10.1515/cclm-2023-0743

Bibtex

@article{cdedaa2044674384b9256a7c1b340d37,
title = "Deep learning-based NT-proBNP prediction from the ECG for risk assessment in the community",
abstract = "OBJECTIVES: The biomarker N-terminal pro B-type natriuretic peptide (NT-proBNP) has predictive value for identifying individuals at risk for cardiovascular disease (CVD). However, it is not widely used for screening in the general population, potentially due to financial and operational reasons. This study aims to develop a deep-learning model as an efficient means to reliably identify individuals at risk for CVD by predicting serum levels of NT-proBNP from the ECG.METHODS: A deep convolutional neural network was developed using the population-based cohort study Hamburg City Health Study (HCHS, n=8,253, 50.9 % women). External validation was performed in two independent population-based cohorts (SHIP-START, n=3,002, 52.1 % women, and SHIP-TREND, n=3,819, 51.2 % women). Assessment of model performance was conducted using Pearson correlation (R) and area under the receiver operating characteristics curve (AUROC).RESULTS: NT-proBNP was predictable from the ECG (R, 0.566 [HCHS], 0.642 [SHIP-START-0], 0.655 [SHIP-TREND-0]). Across cohorts, predicted NT-proBNP (pNT-proBNP) showed good discriminatory ability for prevalent and incident heart failure (HF) (baseline: AUROC 0.795 [HCHS], 0.816 [SHIP-START-0], 0.783 [SHIP-TREND-0]; first follow-up: 0.669 [SHIP-START-1, 5 years], 0.689 [SHIP-TREND-1, 7.3 years]), comparable to the discriminatory value of measured NT-proBNP. pNT-proBNP also demonstrated comparable results for other incident CVD, including atrial fibrillation, stroke, myocardial infarction, and cardiovascular death.CONCLUSIONS: Deep learning ECG algorithms can predict NT-proBNP concentrations with high diagnostic and predictive value for HF and other major CVD and may be used in the community to identify individuals at risk. Long-standing experience with NT-proBNP can increase acceptance of such deep learning models in clinical practice.",
author = "Meraj Neyazi and Bremer, {Jan P} and Knorr, {Marius S} and Stefan Gross and Jan Brederecke and Nils Schweingruber and Dora Csengeri and Benedikt Schrage and Martin Bahls and Nele Friedrich and Tanja Zeller and Stephan Felix and Stefan Blankenberg and Marcus D{\"o}rr and Marcus Vollmer and Schnabel, {Renate B}",
note = "{\textcopyright} 2023 Walter de Gruyter GmbH, Berlin/Boston.",
year = "2024",
month = mar,
day = "25",
doi = "10.1515/cclm-2023-0743",
language = "English",
volume = "62",
pages = "740--752",
journal = "CLIN CHEM LAB MED",
issn = "1434-6621",
publisher = "Walter de Gruyter GmbH & Co. KG",
number = "4",

}

RIS

TY - JOUR

T1 - Deep learning-based NT-proBNP prediction from the ECG for risk assessment in the community

AU - Neyazi, Meraj

AU - Bremer, Jan P

AU - Knorr, Marius S

AU - Gross, Stefan

AU - Brederecke, Jan

AU - Schweingruber, Nils

AU - Csengeri, Dora

AU - Schrage, Benedikt

AU - Bahls, Martin

AU - Friedrich, Nele

AU - Zeller, Tanja

AU - Felix, Stephan

AU - Blankenberg, Stefan

AU - Dörr, Marcus

AU - Vollmer, Marcus

AU - Schnabel, Renate B

N1 - © 2023 Walter de Gruyter GmbH, Berlin/Boston.

PY - 2024/3/25

Y1 - 2024/3/25

N2 - OBJECTIVES: The biomarker N-terminal pro B-type natriuretic peptide (NT-proBNP) has predictive value for identifying individuals at risk for cardiovascular disease (CVD). However, it is not widely used for screening in the general population, potentially due to financial and operational reasons. This study aims to develop a deep-learning model as an efficient means to reliably identify individuals at risk for CVD by predicting serum levels of NT-proBNP from the ECG.METHODS: A deep convolutional neural network was developed using the population-based cohort study Hamburg City Health Study (HCHS, n=8,253, 50.9 % women). External validation was performed in two independent population-based cohorts (SHIP-START, n=3,002, 52.1 % women, and SHIP-TREND, n=3,819, 51.2 % women). Assessment of model performance was conducted using Pearson correlation (R) and area under the receiver operating characteristics curve (AUROC).RESULTS: NT-proBNP was predictable from the ECG (R, 0.566 [HCHS], 0.642 [SHIP-START-0], 0.655 [SHIP-TREND-0]). Across cohorts, predicted NT-proBNP (pNT-proBNP) showed good discriminatory ability for prevalent and incident heart failure (HF) (baseline: AUROC 0.795 [HCHS], 0.816 [SHIP-START-0], 0.783 [SHIP-TREND-0]; first follow-up: 0.669 [SHIP-START-1, 5 years], 0.689 [SHIP-TREND-1, 7.3 years]), comparable to the discriminatory value of measured NT-proBNP. pNT-proBNP also demonstrated comparable results for other incident CVD, including atrial fibrillation, stroke, myocardial infarction, and cardiovascular death.CONCLUSIONS: Deep learning ECG algorithms can predict NT-proBNP concentrations with high diagnostic and predictive value for HF and other major CVD and may be used in the community to identify individuals at risk. Long-standing experience with NT-proBNP can increase acceptance of such deep learning models in clinical practice.

AB - OBJECTIVES: The biomarker N-terminal pro B-type natriuretic peptide (NT-proBNP) has predictive value for identifying individuals at risk for cardiovascular disease (CVD). However, it is not widely used for screening in the general population, potentially due to financial and operational reasons. This study aims to develop a deep-learning model as an efficient means to reliably identify individuals at risk for CVD by predicting serum levels of NT-proBNP from the ECG.METHODS: A deep convolutional neural network was developed using the population-based cohort study Hamburg City Health Study (HCHS, n=8,253, 50.9 % women). External validation was performed in two independent population-based cohorts (SHIP-START, n=3,002, 52.1 % women, and SHIP-TREND, n=3,819, 51.2 % women). Assessment of model performance was conducted using Pearson correlation (R) and area under the receiver operating characteristics curve (AUROC).RESULTS: NT-proBNP was predictable from the ECG (R, 0.566 [HCHS], 0.642 [SHIP-START-0], 0.655 [SHIP-TREND-0]). Across cohorts, predicted NT-proBNP (pNT-proBNP) showed good discriminatory ability for prevalent and incident heart failure (HF) (baseline: AUROC 0.795 [HCHS], 0.816 [SHIP-START-0], 0.783 [SHIP-TREND-0]; first follow-up: 0.669 [SHIP-START-1, 5 years], 0.689 [SHIP-TREND-1, 7.3 years]), comparable to the discriminatory value of measured NT-proBNP. pNT-proBNP also demonstrated comparable results for other incident CVD, including atrial fibrillation, stroke, myocardial infarction, and cardiovascular death.CONCLUSIONS: Deep learning ECG algorithms can predict NT-proBNP concentrations with high diagnostic and predictive value for HF and other major CVD and may be used in the community to identify individuals at risk. Long-standing experience with NT-proBNP can increase acceptance of such deep learning models in clinical practice.

U2 - 10.1515/cclm-2023-0743

DO - 10.1515/cclm-2023-0743

M3 - SCORING: Journal article

C2 - 37982681

VL - 62

SP - 740

EP - 752

JO - CLIN CHEM LAB MED

JF - CLIN CHEM LAB MED

SN - 1434-6621

IS - 4

ER -