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

Charles-Hervé Vacheron^{1,2,3,4,5,6,7,8,9,10}, Arnaud Friggeri^1,4, Chloe Gerbaud-Coulas⁴

¹PHE3ID, 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.

Critical Care Medicine

|October 26, 2023

Abstract

Objectives:

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.

Design:

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.

Setting:

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.

Patients:

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.

Interventions:

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.

Measurements And Main Results:

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.

Conclusions:

The competing risk approach provides more accurate estimates of 30-day survival and is less biased compared with the other methods evaluated.

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