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Background 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. Main text 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. Conclusion 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.

Background Ventilator-free days (VFDs) are a composite outcome in critical care research, reflecting both survival and mechanical ventilation duration. However, analysis methods for VFDs are inconsistent, with some focusing on counts and others on time-to-event outcomes, while other approaches such as the multistate model and the win ratio have emerged. We aimed to evaluate various statistical models through simulations to identify the optimal approach for analysing VFDs. Methods First, 16 datasets of 300 individuals were simulated, comparing a control group to an intervention with varying survival rates and ventilation durations. Various statistical models were evaluated for statistical power and Type I error rate. Four clinical trial datasets (LIVE study, NCT02149589; ARMA study, NCT00000579; ACURASYS study, NCT00299650; COVIDICUS study, NCT04344730) were then used to apply the same statistical models to analyse VFDs. Twelve statistical methods were evaluated, including count-based, time-to-event approaches, and the win-ratio. Additionally, sensitivity analyses were conducted. Results Most statistical methods effectively controlled Type I error rate, except for the zero-inflated and hurdle Poisson/negative binomial count submodels, as well as the cause-specific Cox regression model for death. The power to detect survival benefit and ventilation duration effects varied, with time-to-event approaches, the Mann–Whitney test, the proportional odds model and the win ratio generally performing best. Similar results were observed in sensitivity analyses. In the real datasets, the multistate model, the Mann–Whitney test, the proportional odds model and the win ratio generally showed a significant association between VFDs and randomisation groups. Conclusions The multistate model could be recommended as the optimal approach for analysing VFDs, as it outperformed the other methods and offers a more interpretable effect size than the proportional odds model and the win ratio.

Previous studies have shown that plasma soluble receptor for advanced glycation end-products (sRAGE) and the radiographic assessment of lung edema (RALE) are associated with the severity of acute respiratory distress syndrome (ARDS) both at baseline and over time. This study aims to explore the causal relationships among sRAGE, the RALE score, their fluctuations, and 90-day survival. Causal mediation analysis was conducted as a secondary analysis of the randomized controlled lung imaging for ventilator setting in ARDS (LIVE) trial, which assessed a mechanical ventilation strategy based on lung morphology. The primary outcome was survival at day 90. We used group-based trajectory modeling to summarize the biomarker patterns over time, followed by mediation analysis with the sRAGE and RALE score as mediators. Out of 400 patients in the LIVE study, 115 were included, resulting in three trajectory groups for both sRAGE and RALE. The mechanical ventilation strategy appeared to influence survival directly and indirectly: one indirect effect was mediated by one RALE score trajectory (aligning with the direct effect), and another by one sRAGE trajectory (opposing the direct effect). Both plasma sRAGE and the RALE score appeared to exhibit a mediation effect on survival for specific patient clusters. Identifying these clusters and using biomarkers as surrogate endpoints warrants further investigation in precision ARDS trials.

Background Ventilator-free days (VFDs) are a composite endpoint increasingly used as the primary outcome in critical care trials. However, because of the skewed distribution and competitive risk between components, sample size estimation remains challenging. This systematic review was conducted to systematically assess whether the sample size was congruent, as calculated to evaluate VFDs in trials, with VFDs’ distribution and the impact of alternative methods on sample size estimation. Methods A systematic literature search was conducted within the PubMed and Embase databases for randomized clinical trials in adults with VFDs as the primary outcome until December 2021. We focused on peer-reviewed journals with 2021 impact factors greater than five. After reviewing definitions of VFDs, we extracted the sample size and methods used for its estimation. The data were collected by two independent investigators and recorded in a standardized, pilot-tested forms tool. Sample sizes were calculated using alternative statistical approaches, and risks of bias were assessed with the Cochrane risk-of-bias tool. Results Of the 26 clinical trials included, 19 (73%) raised “some concerns” when assessing risks of bias. Twenty-four (92%) trials were two-arm superiority trials, and 23 (89%) were conducted at multiple sites. Almost all the trials (96%) were unable to consider the unique distribution of VFDs and death as a competitive risk. Moreover, significant heterogeneity was found in the definitions of VFDs, especially regarding varying start time and type of respiratory support. Methods for sample size estimation were also heterogeneous, and simple models, such as the Mann–Whitney–Wilcoxon rank-sum test, were used in 14 (54%) trials. Finally, the sample sizes calculated varied by a factor of 1.6 to 17.4. Conclusions A standardized definition and methodology for VFDs, including the use of a core outcome set, seems to be required. Indeed, this could facilitate the interpretation of findings in clinical trials, as well as their construction, especially the sample size estimation which is a trade-off between cost, ethics, and statistical power. Systematic review registration PROSPERO ID: CRD42021282304. Registered 15 December 2021 ( https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021282304 ).

Obesity is a growing public health issue associated with increased peri-induction risk during general anesthesia, particularly airway-related complications. Substantial variability in national and international guidelines may contribute to heterogeneous clinical practice. This study aimed to describe anesthetic induction practices in patients with obesity in France and to explore the influence of body mass index and obesity-related comorbidities on the decision for or against rapid sequence induction. This nationwide declarative survey used an anonymous online questionnaire distributed to French anesthesiologists between May and December 2024. Using standardized theoretical clinical scenarios, induction strategies were classified as rapid sequence induction or conventional induction, as reported by participants. Data were analyzed descriptively, with group comparisons performed using Pearson's chi-square test. Of the 665 responses collected, 652 were analyzed, revealing wide variability in practices. For a body mass index of 43 kg/m2, 57% of participants would not perform rapid sequence induction. Among respondents, 33% regarded a body mass index threshold of ≥40 kg/m2 as appropriate for initiating rapid sequence induction, whereas 24% did not define any specific threshold. Rapid sequence induction was primarily selected for patients with daily gastroesophageal reflux disease, gastroesophageal reflux disease associated with a hiatal hernia, or a history of bariatric surgery. Anesthesiologist experience was associated with differences in the choice of induction sequence (p = 0.003). Considerable heterogeneity remained in the calculation of neuromuscular blocking agent doses: 28% of practitioners used total body weight for non-depolarizing agents. Apneic oxygenation remained underused, with 32% of respondents reporting that they never used it. This nationwide survey highlights substantial variability in anesthetic induction practices for patients with obesity, reflecting the absence of consensus. These findings underscore the need for further evidence to inform future recommendations and optimize anesthetic management in this high-risk population. •National survey describing anesthetic induction practices in patients with obesity in France.•Marked heterogeneity observed in induction sequences and neuromuscular drug dosing.•Among respondents, 33% reported systematic rapid sequence induction from BMI 40 kg/m2, while 24% reported no defined threshold.•Comorbidities and practitioner experience strongly influenced induction technique.•Airway management practices during induction varied, with apneic oxygenation and videolaryngoscopy not routinely used in patients with obesity.

Background Increasing transparency in clinical research is crucial to avoid misleading conclusions. Registering clinical trials prior to participant enrolment is mandatory, and the publication of trial protocols could further enhance transparency. However, the impact of protocol publication on primary outcomes (PO) and sample sizes (SS) remains unclear. This study aimed to determine the rates of trial protocol publication and registration for a sample of randomized controlled trials (RCTs) and to compare the consistency of published and registered PO and SS. Methods A search was conducted in MEDLINE via PubMed ® for RCT reports indexed in May and June 2023 across various medical specialties, focusing on general and high-impact factor journals. Data were extracted regarding trial registration, protocol publication, and comparisons were made between PO and SS in articles, registries, and published protocols. Results Out of 1119 references, 589 (52.6%) were RCTs. The corresponding protocol was published for 146 RCTs (24.8%) including 40 over 140 (28.6%) (6 without end date available) after the trial had ended. Sixty-two (42.4%) protocols were published before the trial conclusion, with no significant differences between PO and SS in published protocols and their corresponding articles. Five hundred and twenty-eight (89.6%) RCTs were registered, 225 over 510 (44%) were registered before the study start with no differences in PO and SS between article and registry. Articles published in generalist or high impact factor journals were associated with higher frequencies of published protocols and trial registration and a lower frequency of difference in PO and SS between articles, registries, and published protocols. Conclusions While publishing trial protocols may enhance transparency in peer-review process, the initial registered protocol alone appears sufficient for ensuring consistency in primary outcomes and sample sizes. Protocol publication does not seem to provide additional significant benefits in terms of outcome reporting.

Volatile anesthetics have demonstrated anti-inflammatory and epithelial-protective effects in several sterile experimental models of acute lung injury (ALI). However, their effects in endotoxin-induced ALI remain unclear, and recent clinical data have raised concerns regarding their potential impact on patient outcomes. We investigated whether sevoflurane restores alveolar fluid clearance (AFC) and preserves epithelial integrity in a murine model of lipopolysaccharide (LPS)-induced ALI. Wild-type (WT) and receptor for advanced glycation end-products–deficient (RAGE−/−) mice received intratracheal lipopolysaccharide (LPS) and were exposed to sevoflurane (1 vol %) or control gas for 1 h. Our primary outcome, the net AFC rate was measured 48 h after injury. Secondary outcomes included lung histology, bronchoalveolar lavage (BAL) protein and cytokine levels, and lung expression of epithelial sodium channel (ENaC), water transporter aquaporin-5 (AQP5), and adherens junction protein E-cadherin. LPS induced significant weight loss and severe lung injury, with impaired AFC and downregulation of ENaC and AQP5. Sevoflurane did not restore AFC, reduce alveolar-capillary permeability, or preserve epithelial junctional integrity. RAGE−/− mice exhibited attenuated lung injury and partial preservation of epithelial marker expression, without a statistically significant interaction with sevoflurane exposure. BAL cytokine levels were largely unaffected by sevoflurane. These findings suggest context-dependent effects of a 1-h exposure to 1.0 vol% sevoflurane in experimental ALI, with absence of epithelial benefit in a mouse model of endotoxin-induced ALI, in contrast with previously reported effects in a sterile model. This work may provide mechanistic insight into the differential translational impact of inhaled sedation strategies.

We introduce the first generative foundation model for chest radiograph synthesis trained from scratch at the billion-parameter scale. Existing radiographic AI models often suffer from poor generalisation across patient subpopulations, institutions, and acquisition settings, resulting in limited real-world clinical utility. Controlled, high-fidelity synthesis of chest radiographs is a promising path toward diversifying clinical datasets and evaluating the robustness of diagnostic models. Therefore, we present the largest specialist generative foundation model for chest radiographs to date, with over 1.3B parameters, trained for 1.6T tokens on a curated, heterogeneous dataset comprising 1.2M radiographs and clinical expert-guided metadata. Our model supports controllable radiograph generation and editing across multiple demographic subgroups, acquisition views, and a dozen pathologies. Moreover, we significantly advance the state of the art in radiograph synthesis fidelity, producing images that are indistinguishable from real radiographs to clinical experts.