Improved 30-Day Survival Estimation in ICU Patients: A Comparative Analysis of Different Approaches With Real-World Data.


Journal

Critical care medicine
ISSN: 1530-0293
Titre abrégé: Crit Care Med
Pays: United States
ID NLM: 0355501

Informations de publication

Date de publication:
26 Oct 2023
Historique:
pubmed: 26 10 2023
medline: 26 10 2023
entrez: 26 10 2023
Statut: aheadofprint

Résumé

The objective of this study was to compare three different approaches for estimating 30-day survival in ICU studies, considering the issue of informative censoring that occurs when patients are lost to follow-up after discharge. A comparative analysis was conducted to evaluate the effect of different approaches on the estimation of 30-day survival. Three methods were compared: the classical approach using the Kaplan-Meier (KM) estimator and Cox regression modeling, the competing risk approach using the Fine and gray model, considering censoring as a competing event, and the logistic regression approach. The study was conducted in a university ICU and data from patients admitted between 2010 and 2020 were included. Patient characteristics were collected from electronic records. A total of 10,581 patients were included in the study. The true date of death for each patient, obtained from a national registry, allowed for an absence of censoring. All patients were censored at the time of discharge from the ICU, and the three different approaches were applied to estimate the mortality rate and the effects of covariates on mortality. Regression analyses were performed using five variables known to be associated with ICU mortality. The 30-day survival rate for the included patients was found to be 80.5% (95% CI, 79.7-81.2%). The KM estimator severely underestimated the 30-day survival (50.6%; 95% CI, 48.0-53.4%), while the competing risk and logistic regression approaches provided similar results, only slightly overestimating the survival rate (84.5%; 95% CI, 83.8-85.2%). Regression analyses showed that the estimates were not systematically biased, with the Cox and logistic regression models exhibiting greater bias compared with the competing risk regression method. The competing risk approach provides more accurate estimates of 30-day survival and is less biased compared with the other methods evaluated.

Identifiants

pubmed: 37882642
doi: 10.1097/CCM.0000000000006097
pii: 00003246-990000000-00218
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2023 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

Déclaration de conflit d'intérêts

Dr. Friggeri received funding from Merck Sharp & Dohme. The remaining authors have disclosed that they do not have any potential conflicts of interest.

Références

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Auteurs

Charles-Hervé Vacheron (CH)

PHE3ID, Centre International de Recherche en Infectiologie, Institut National de la Santé et de la Recherche Médicale U1111, CNRS Unité Mixte de Recherche 5308, École Nationale Supérieure de Lyon, Université Claude Bernard Lyon 1, Lyon, France.
Service d'Anesthésie Réanimation-Médecine Intensive, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, Lyon, France.
Hospices Civils de Lyon, Pôle Santé Publique, Service de Biostatistique-Bioinformatique, Lyon, France.
Service d'Anesthésie Réanimation-Médecine Intensive, Centre Hospitalier Lyon Sud Hospices Civils de Lyon, Pierre-Bénite, France.
Département d'information médicale, 3 quai des Célestins, Lyon, France.
Médecine Intensive Reanimation Infectieuse APHP Hopital Bichat, IAME UMR1137, Université De Paris, Paris, France.
Université Claude Bernard, Lyon, France.
Université de Lyon, VetAgro Sup, Campus Vétérinaire de Lyon, UPSP 2016.A101, Pulmonary and Cardiovascular Aggression in Sepsis, Marcy-l'Étoile, France.
CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Évolutive, Équipe Biostatistique-Santé, Villeurbanne, France.
Université de Lyon, Lyon, France.

Arnaud Friggeri (A)

PHE3ID, Centre International de Recherche en Infectiologie, Institut National de la Santé et de la Recherche Médicale U1111, CNRS Unité Mixte de Recherche 5308, École Nationale Supérieure de Lyon, Université Claude Bernard Lyon 1, Lyon, France.
Service d'Anesthésie Réanimation-Médecine Intensive, Centre Hospitalier Lyon Sud Hospices Civils de Lyon, Pierre-Bénite, France.

Chloe Gerbaud-Coulas (C)

Service d'Anesthésie Réanimation-Médecine Intensive, Centre Hospitalier Lyon Sud Hospices Civils de Lyon, Pierre-Bénite, France.

Tristan Dagonneau (T)

Département d'information médicale, 3 quai des Célestins, Lyon, France.

Jean Francois Timsit (JF)

Médecine Intensive Reanimation Infectieuse APHP Hopital Bichat, IAME UMR1137, Université De Paris, Paris, France.

Bernard Allaouchiche (B)

Service d'Anesthésie Réanimation-Médecine Intensive, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, Lyon, France.
Université Claude Bernard, Lyon, France.
Université de Lyon, VetAgro Sup, Campus Vétérinaire de Lyon, UPSP 2016.A101, Pulmonary and Cardiovascular Aggression in Sepsis, Marcy-l'Étoile, France.

Florent Wallet (F)

PHE3ID, Centre International de Recherche en Infectiologie, Institut National de la Santé et de la Recherche Médicale U1111, CNRS Unité Mixte de Recherche 5308, École Nationale Supérieure de Lyon, Université Claude Bernard Lyon 1, Lyon, France.
Service d'Anesthésie Réanimation-Médecine Intensive, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, Lyon, France.

Julien Bohe (J)

Service d'Anesthésie Réanimation-Médecine Intensive, Centre Hospitalier Lyon Sud Hospices Civils de Lyon, Pierre-Bénite, France.

Vincent Piriou (V)

Service d'Anesthésie Réanimation-Médecine Intensive, Centre Hospitalier Lyon Sud Hospices Civils de Lyon, Pierre-Bénite, France.

Delphine Maucort-Boulch (D)

Hospices Civils de Lyon, Pôle Santé Publique, Service de Biostatistique-Bioinformatique, Lyon, France.
CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Évolutive, Équipe Biostatistique-Santé, Villeurbanne, France.
Université de Lyon, Lyon, France.

Mathieu Fauvernier (M)

Hospices Civils de Lyon, Pôle Santé Publique, Service de Biostatistique-Bioinformatique, Lyon, France.

Classifications MeSH