Renard Triché Laurent, Jabaudon Matthieu, Chevret Sylvie
Department of Perioperative Medicine, CHU Clermont-Ferrand, 58 rue Montalembert, Clermont-Ferrand, 63 000, France.
iGReD, INSERM, CNRS, Université Clermont Auvergne, Clermont-Ferrand, France.
Crit Care. 2025 Aug 4;29(1):343. doi: 10.1186/s13054-025-05593-3.
Mortality is a critical endpoint in clinical research, but identifying meaningful differences necessitates large sample sizes. Consequently, composite outcomes such as ventilator-free days (VFDs) have been developed, combining survival and ventilation duration into a single measure. Different statistical methods used to analyse VFDs lead to different estimands. Traditionally, VFDs are treated as a count; however, some models consider time to death and time to extubation separately. This review explores the applicability of several time-to-event models and innovative approaches.
The first model to consider is the competing risks approach using the Fine-Gray model. This approach focuses solely on the initial extubation event and considers death as a competing event. Second, to incorporate all extubation and reintubation events, multistate models can be employed. Specifically, the multiple-event framework, which allows for multiple transitions between intubation and extubation, while the recurrent events framework, focuses on extubation recurrence. However, these models require complete data and a sufficient number of events for analysis. Third, current ventilation-free survival estimates use methods adapted from leukaemia-free survival to evaluate the probability of remaining extubated and alive over time. Finally, the mixture cure model distinguishes between deceased and extubated individuals within the non-deceased population. It models death through logistic regression and extubation timing through survival regression among living patients.
In critical care, especially for acute respiratory distress syndrome, three key states are intubation, extubation, and death. We do not advocate a one-size-fits-all model because the choice depends heavily on the specific goals. The key is to decide which estimand the study will target in the statistical plan, before initiating the study, and to ensure the analysis model is the most appropriate for addressing the research question. .
死亡率是临床研究中的关键终点，但要识别有意义的差异需要大样本量。因此，诸如无呼吸机天数（VFDs）等复合结局被开发出来，将生存和通气持续时间合并为一个单一指标。用于分析VFDs的不同统计方法会导致不同的估计量。传统上，VFDs被视为一个计数指标；然而，一些模型分别考虑死亡时间和拔管时间。本综述探讨了几种生存时间模型和创新方法的适用性。
首先要考虑的模型是使用Fine-Gray模型的竞争风险方法。这种方法仅关注初始拔管事件，并将死亡视为竞争事件。其次，为了纳入所有拔管和重新插管事件，可以采用多状态模型。具体来说，多事件框架允许在插管和拔管之间进行多次转换，而复发事件框架则关注拔管复发。然而，这些模型需要完整的数据和足够数量的事件用于分析。第三，当前的无通气生存估计使用从无白血病生存改编而来的方法来评估随时间保持拔管和存活的概率。最后，混合治愈模型区分未死亡人群中的死亡个体和拔管个体。它通过逻辑回归对死亡进行建模，并通过生存回归对存活患者的拔管时间进行建模。
在重症监护中，特别是对于急性呼吸窘迫综合征，三个关键状态是插管、拔管和死亡。我们不主张采用一刀切的模型，因为选择很大程度上取决于具体目标。关键是在研究开始前在统计计划中确定研究将针对的估计量，并确保分析模型最适合解决研究问题。
