The utility of temporal trends of blood biomarkers as predictors for bloodstream infections in left ventricular assist device recipients.

biomarker dynamics biomarker kinetics bloodstream infections left ventricular assist device prediction of bloodstream infections temporal patterns temporal trends

Journal

Artificial organs
ISSN: 1525-1594
Titre abrégé: Artif Organs
Pays: United States
ID NLM: 7802778

Informations de publication

Date de publication:
06 Aug 2024
Historique:
revised: 31 05 2024
received: 21 03 2024
accepted: 22 07 2024
medline: 6 8 2024
pubmed: 6 8 2024
entrez: 6 8 2024
Statut: aheadofprint

Résumé

Temporal trends of routinely obtained parameters may provide valuable information for predicting BSIs, but this association has not yet been established in LVAD patients. This retrospective analysis included data from 347 consecutive recipients of three rotary LVAD types. Study endpoints included the incidence of BSI, the association of temporal trends of routinely obtained blood biomarkers with the development of BSIs, the incidence of BSIs, and survival on LVAD support. During follow-up, 47.8% (n = 166) of the patients developed BSI. In multivariate analyses, the development of BSI was a significant predictor of mortality (HR 5.78, 95% CI 4.08-8.19, p < 0.0001). In univariate analyses, after adjusting for potential confounders, albumin (SHR 0.94, 95% CI 0.91-0.97, p < 0.00010), creatinine (SHR 1.49, 95% CI 1.03-2.15, p = 0.033), and C-reactive protein (SHR 1.19, 95% CI 1.08-1.32, p = 0.0007) significantly predicted the development of BSIs during LVAD support. Notably, the strength of the association of parameter changes with the prediction of BSIs demonstrated a time-dependent correlation in the cases of albumin (p = 0.045) and creatinine (p = 0.003). Bloodstream infections are highly prevalent among LVAD recipients and are independent predictors of mortality. Temporal biomarker trends significantly predict the development of BSIs. These findings suggest opportunities for interventions aiming to reduce the incidence of BSIs.

Sections du résumé

BACKGROUND BACKGROUND
Temporal trends of routinely obtained parameters may provide valuable information for predicting BSIs, but this association has not yet been established in LVAD patients.
METHODS METHODS
This retrospective analysis included data from 347 consecutive recipients of three rotary LVAD types. Study endpoints included the incidence of BSI, the association of temporal trends of routinely obtained blood biomarkers with the development of BSIs, the incidence of BSIs, and survival on LVAD support.
RESULTS RESULTS
During follow-up, 47.8% (n = 166) of the patients developed BSI. In multivariate analyses, the development of BSI was a significant predictor of mortality (HR 5.78, 95% CI 4.08-8.19, p < 0.0001). In univariate analyses, after adjusting for potential confounders, albumin (SHR 0.94, 95% CI 0.91-0.97, p < 0.00010), creatinine (SHR 1.49, 95% CI 1.03-2.15, p = 0.033), and C-reactive protein (SHR 1.19, 95% CI 1.08-1.32, p = 0.0007) significantly predicted the development of BSIs during LVAD support. Notably, the strength of the association of parameter changes with the prediction of BSIs demonstrated a time-dependent correlation in the cases of albumin (p = 0.045) and creatinine (p = 0.003).
CONCLUSION CONCLUSIONS
Bloodstream infections are highly prevalent among LVAD recipients and are independent predictors of mortality. Temporal biomarker trends significantly predict the development of BSIs. These findings suggest opportunities for interventions aiming to reduce the incidence of BSIs.

Identifiants

pubmed: 39105561
doi: 10.1111/aor.14839
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024 The Author(s). Artificial Organs published by International Center for Artificial Organ and Transplantation (ICAOT) and Wiley Periodicals LLC.

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Auteurs

Kamen Dimitrov (K)

Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria.

Alexandra Kaider (A)

Institute of Clinical Biometrics, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.

Christoph Gross (C)

Ludwig-Boltzmann-Institute for Cardiovascular Research, Vienna, Austria.
Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.

Selma Rizvanovic (S)

Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria.

Flogin Pepa (F)

Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria.

Marcus Granegger (M)

Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria.

Johanna Schlein (J)

Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Division of Cardiac Thoracic Vascular Anaesthesia and Intensive Care Medicine, Medical University of Vienna, Vienna, Austria.

Philipp Angleitner (P)

Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria.

Dominik Wiedemann (D)

Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria.

Julia Riebandt (J)

Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria.

Thomas Schlöglhofer (T)

Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria.
Ludwig-Boltzmann-Institute for Cardiovascular Research, Vienna, Austria.
Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.

Günther Laufer (G)

Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria.

Daniel Zimpfer (D)

Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria.

Classifications MeSH