Dynamic assessment of prealbumin for nutrition support effectiveness in critically ill patients.

Biomarker Clinical nutrition Enteral Nitrogen balance Parenteral Prealbumin

Journal

Clinical nutrition (Edinburgh, Scotland)
ISSN: 1532-1983
Titre abrégé: Clin Nutr
Pays: England
ID NLM: 8309603

Informations de publication

Date de publication:
15 Apr 2024
Historique:
received: 17 01 2024
revised: 09 04 2024
accepted: 11 04 2024
medline: 28 4 2024
pubmed: 28 4 2024
entrez: 27 4 2024
Statut: aheadofprint

Résumé

Serum prealbumin is considered to be a sensitive predictor of clinical outcomes and a quality marker for nutrition support. However, its susceptibility to inflammation restricts its usage in critically ill patients according to current guidelines. We assessed the performance of the initial value of prealbumin and dynamic changes for predicting the ICU mortality and the effectiveness of nutrition support in critically ill patients. This monocentric study included patients admitted to the ICU between 2009 and 2016, having at least one initial prealbumin value available. Prospectively recorded data were extracted from the electronic ICU charts. We used both univariable and multivariable logistic regressions to estimate the performance of prealbumin for the prediction of ICU mortality. Additionally, the association between prealbumin dynamic changes and nutrition support was assessed via a multivariable linear mixed-effects model and multivariable linear regression. Performing subgroup analysis assisted in identifying patients for whom prealbumin dynamic assessment holds specific relevance. We included 3136 patients with a total of 4942 prealbumin levels available. Both prealbumin measured at ICU admission (adjusted odds-ratio (aOR) 0.04, confidence interval (CI) 95% 0.01-0.23) and its change over the first week (aOR 0.02, CI 95 0.00-0.19) were negatively associated with ICU mortality. Throughout the entire ICU stay, prealbumin dynamic changes were associated with both cumulative energy (estimate: 33.2, standard error (SE) 0.001, p < 0.01) and protein intakes (1.39, SE 0.001, p < 0.01). During the first week of stay, prealbumin change was independently associated with mean energy (6.03e-04, SE 2.32e-04, p < 0.01) and protein intakes (1.97e-02, SE 5.91e-03, p < 0.01). Notably, the association between prealbumin and energy intake was strongest among older or malnourished patients, those suffering from increased inflammation and those with high disease severity. Finally, prealbumin changes were associated with a positive mean nitrogen balance at day 7 only in patients with SOFA <4 (p = 0.047). Prealbumin measured at ICU admission and its change during the first-week serve as an accurate predictor of ICU mortality. Prealbumin dynamic assessment may be a reliable tool to estimate the effectiveness of nutrition support in the ICU, especially among high-risk patients.

Sections du résumé

BACKGROUND & AIMS OBJECTIVE
Serum prealbumin is considered to be a sensitive predictor of clinical outcomes and a quality marker for nutrition support. However, its susceptibility to inflammation restricts its usage in critically ill patients according to current guidelines. We assessed the performance of the initial value of prealbumin and dynamic changes for predicting the ICU mortality and the effectiveness of nutrition support in critically ill patients.
METHODS METHODS
This monocentric study included patients admitted to the ICU between 2009 and 2016, having at least one initial prealbumin value available. Prospectively recorded data were extracted from the electronic ICU charts. We used both univariable and multivariable logistic regressions to estimate the performance of prealbumin for the prediction of ICU mortality. Additionally, the association between prealbumin dynamic changes and nutrition support was assessed via a multivariable linear mixed-effects model and multivariable linear regression. Performing subgroup analysis assisted in identifying patients for whom prealbumin dynamic assessment holds specific relevance.
RESULTS RESULTS
We included 3136 patients with a total of 4942 prealbumin levels available. Both prealbumin measured at ICU admission (adjusted odds-ratio (aOR) 0.04, confidence interval (CI) 95% 0.01-0.23) and its change over the first week (aOR 0.02, CI 95 0.00-0.19) were negatively associated with ICU mortality. Throughout the entire ICU stay, prealbumin dynamic changes were associated with both cumulative energy (estimate: 33.2, standard error (SE) 0.001, p < 0.01) and protein intakes (1.39, SE 0.001, p < 0.01). During the first week of stay, prealbumin change was independently associated with mean energy (6.03e-04, SE 2.32e-04, p < 0.01) and protein intakes (1.97e-02, SE 5.91e-03, p < 0.01). Notably, the association between prealbumin and energy intake was strongest among older or malnourished patients, those suffering from increased inflammation and those with high disease severity. Finally, prealbumin changes were associated with a positive mean nitrogen balance at day 7 only in patients with SOFA <4 (p = 0.047).
CONCLUSION CONCLUSIONS
Prealbumin measured at ICU admission and its change during the first-week serve as an accurate predictor of ICU mortality. Prealbumin dynamic assessment may be a reliable tool to estimate the effectiveness of nutrition support in the ICU, especially among high-risk patients.

Identifiants

pubmed: 38677045
pii: S0261-5614(24)00121-3
doi: 10.1016/j.clnu.2024.04.015
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1343-1352

Informations de copyright

Copyright © 2024 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Déclaration de conflit d'intérêts

Conflict of interest E.P. received a research grant from Nestle Health Science, speaking honoraria from Fresenius Kabi & Baxter, and congressional reimbursement from Fresenius Kabi and Nutricia.

Auteurs

Emmanuel Pardo (E)

Sorbonne Université, GRC 29, AP-HP, DMU DREAM, Département d'Anesthésie-Réanimation, Hôpital Saint-Antoine, Assistance Publique-hôpitaux de Paris, 75012, Paris, France. Electronic address: emmanuel.pardo@aphp.fr.

Matthieu Jabaudon (M)

Department of Perioperative Medicine, CHU Clermont-Ferrand, 58 Rue Montalembert, 63000, Clermont-Ferrand, France; iGReD, CNRS, INSERM, Université Clermont Auvergne, Clermont-Ferrand, France.

Thomas Godet (T)

Department of Perioperative Medicine, CHU Clermont-Ferrand, 58 Rue Montalembert, 63000, Clermont-Ferrand, France; Université Clermont Auvergne, Department of Healthcare Simulation, Clermont-Ferrand, F-63000, France; Université Clermont Auvergne, Inserm, Neuro-Dol, Clermont-Ferrand, F-63000, France.

Bruno Pereira (B)

Biostatistics and Data Management Unit, Department of Clinical Research and Innovation, CHU Clermont-Ferrand, Clermont-Ferrand, France.

Dominique Morand (D)

Direction de la Recherche Clinique (DRCI), CHU de Clermont-Ferrand, Clermont-Ferrand, F-63003, France.

Emmanuel Futier (E)

Department of Perioperative Medicine, CHU Clermont-Ferrand, 58 Rue Montalembert, 63000, Clermont-Ferrand, France; iGReD, CNRS, INSERM, Université Clermont Auvergne, Clermont-Ferrand, France.

Gauthier Arpajou (G)

Department of Perioperative Medicine, CHU Clermont-Ferrand, 58 Rue Montalembert, 63000, Clermont-Ferrand, France.

Elena Le Cam (E)

Sorbonne Université, GRC 29, AP-HP, DMU DREAM, Département d'Anesthésie-Réanimation, Hôpital Saint-Antoine, Assistance Publique-hôpitaux de Paris, 75012, Paris, France.

Marie-Pierre Bonnet (MP)

Sorbonne Université, Département Anesthésie-Réanimation, Hôpital Armand Trousseau, DMU DREAM, GRC 29, AP-HP, Paris, France; Université Paris Cité, INSERM, INRA, Centre for Epidemiology and Statistics Sorbonne Paris Cité (CRESS), Obstetrical Perinatal and Pediatric Epidemiology Research Team, EPOPé, Maternité Port Royal, 53 Avenue de l'Observatoire, F-75014, Paris, France.

Jean-Michel Constantin (JM)

Sorbonne Université, GRC 29, AP-HP, DMU DREAM, Département d'Anesthésie-Réanimation, Hôpital Pitié-Salpêtrière, Assistance Publique-hôpitaux de Paris, 75013, Paris, France.

Classifications MeSH