Measures of wave intensity as a non-invasive surrogate for cardiac function predicts mortality in haemodialysis patients.

ESKD blood pressure cardiac function haemodialysis wave intensity analysis

Journal

Clinical kidney journal
ISSN: 2048-8505
Titre abrégé: Clin Kidney J
Pays: England
ID NLM: 101579321

Informations de publication

Date de publication:
Jul 2024
Historique:
received: 21 11 2023
medline: 26 7 2024
pubmed: 26 7 2024
entrez: 26 7 2024
Statut: epublish

Résumé

Risk prediction in haemodialysis (HD) patients is challenging due to the impact of the dialysis regime on the patient's volume status and the complex interplay with cardiac function, comorbidities and hypertension. Cardiac function as a key predictor of cardiovascular (CV) mortality in HD patients is challenging to assess in daily routine. Thus the aim of this study was to investigate the association of a novel, non-invasive relative index of systolic function with mortality and to assess its interplay with volume removal. A total of 558 (373 male/185 female) HD patients with a median age of 66 years were included in this analysis. They underwent 24-hour ambulatory blood pressure monitoring, including wave intensity analysis [i.e. S:D ratio (SDR)]. All-cause and CV mortality served as endpoints and multivariate proportional hazards models were used for risk prediction. Intradialytic changes were analysed in tertiles according to ultrafiltration volume. During a follow-up of 37.8 months, 193 patients died (92 due to CV reasons). The SDR was significantly associated with all-cause {univariate hazard ratio [HR] 1.36 [95% confidence interval (CI) 1.20-1.54], This study provides well-powered evidence for the independent association of a novel index of systolic function with mortality. Furthermore, it revealed a significant association between intradialytic changes of the measure and intradialytic volume removal.

Sections du résumé

Background UNASSIGNED
Risk prediction in haemodialysis (HD) patients is challenging due to the impact of the dialysis regime on the patient's volume status and the complex interplay with cardiac function, comorbidities and hypertension. Cardiac function as a key predictor of cardiovascular (CV) mortality in HD patients is challenging to assess in daily routine. Thus the aim of this study was to investigate the association of a novel, non-invasive relative index of systolic function with mortality and to assess its interplay with volume removal.
Methods UNASSIGNED
A total of 558 (373 male/185 female) HD patients with a median age of 66 years were included in this analysis. They underwent 24-hour ambulatory blood pressure monitoring, including wave intensity analysis [i.e. S:D ratio (SDR)]. All-cause and CV mortality served as endpoints and multivariate proportional hazards models were used for risk prediction. Intradialytic changes were analysed in tertiles according to ultrafiltration volume. During a follow-up of 37.8 months, 193 patients died (92 due to CV reasons).
Results UNASSIGNED
The SDR was significantly associated with all-cause {univariate hazard ratio [HR] 1.36 [95% confidence interval (CI) 1.20-1.54],
Conclusion UNASSIGNED
This study provides well-powered evidence for the independent association of a novel index of systolic function with mortality. Furthermore, it revealed a significant association between intradialytic changes of the measure and intradialytic volume removal.

Identifiants

pubmed: 39056069
doi: 10.1093/ckj/sfae172
pii: sfae172
pmc: PMC11270016
doi:

Types de publication

Journal Article

Langues

eng

Pagination

sfae172

Informations de copyright

© The Author(s) 2024. Published by Oxford University Press on behalf of the ERA.

Déclaration de conflit d'intérêts

S.W. and C.C.M. are the inventors (not holders) of a patent that is partly used in the ARCSolver algorithm in the Mobil-O-Graph 24-hour PWA. The remaining authors declare no conflicts of interest.

Auteurs

Christopher C Mayer (CC)

AIT Austrian Institute of Technology, Center for Health & Bioresources, Medical Signal Analysis, Vienna, Austria.
TU Wien, Institute for Analysis and Scientific Computing, Vienna, Austria.

Pantelis A Sarafidis (PA)

Aristotle University of Thessaloniki, Department of Nephrology, Hippokration Hospital, Thessaloniki, Greece.

Julia Matschkal (J)

Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Nephrology, Munich, Germany.

Marieta Theodorakopoulou (M)

Aristotle University of Thessaloniki, Department of Nephrology, Hippokration Hospital, Thessaloniki, Greece.

Georg Lorenz (G)

Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Nephrology, Munich, Germany.

Artemios Karagiannidis (A)

Aristotle University of Thessaloniki, Department of Nephrology, Hippokration Hospital, Thessaloniki, Greece.

Susanne Angermann (S)

Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Nephrology, Munich, Germany.

Fotini Iatridi (F)

Aristotle University of Thessaloniki, Department of Nephrology, Hippokration Hospital, Thessaloniki, Greece.

Matthias C Braunisch (MC)

Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Nephrology, Munich, Germany.

Antonios Karpetas (A)

Therapeutiki Hemodialysis Unit, Thessaloniki, Greece.

Marcus Baumann (M)

Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Nephrology, Munich, Germany.

Eva Pella (E)

Aristotle University of Thessaloniki, Department of Nephrology, Hippokration Hospital, Thessaloniki, Greece.

Uwe Heemann (U)

Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Nephrology, Munich, Germany.

Siegfried Wassertheurer (S)

AIT Austrian Institute of Technology, Center for Health & Bioresources, Medical Signal Analysis, Vienna, Austria.
TU Wien, Institute for Analysis and Scientific Computing, Vienna, Austria.

Christoph Schmaderer (C)

Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Nephrology, Munich, Germany.

Classifications MeSH