Daily Ards Research Analysis

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a highly heterogeneous syndrome with substantial mortality, for which existing subphenotyping strategies based on single-modal data have limited ability to guide targeted therapies. A comprehensive, multimodal framework is urgently needed to decipher its complexity and advance precision care. METHODS: The BIOWARE study is a prospective, multicenter cohort aiming to enroll 2,000 ARDS patients across nine Chinese centers. The protocol integrates longitudinal data from clinical assessments, ventilator waveforms, advanced imaging (CT, EIT, lung ultrasound), and biospecimen analyses. FINDINGS: As of August 2025, 169 patients have been enrolled (median age 62 years, 74% male). Core clinical and imaging data were successfully acquired across all centers, confirming the feasibility of the multimodal collection protocol. While baseline biospecimen collection was complete (100% for plasma and BALF at Day 1), follow-up sampling decreased at later timepoints (Day 7 BALF: n = 24). Some specialized parameters (e.g., P0.1) showed higher missing rates due to clinical constraints, reflecting real-world implementation challenges. INTERPRETATION: The BIOWARE cohort provides a multidimensional framework that captures dynamic interactions among physiology, pulmonary structural changes, and host response, enabling identification of ARDS endotypes grounded in biological mechanisms. This integrative strategy represents a paradigm shift from syndrome-based classification toward mechanism-driven precision care, establishing a transformative platform for targeted therapeutic development.

BACKGROUND: Lung ultrasound is increasingly used as a bedside imaging modality in critically ill patients with acute respiratory failure (ARF), but its overall diagnostic performance across diverse settings remains uncertain. We conducted a diagnostic test accuracy meta-analysis to evaluate the accuracy of lung ultrasound in identifying the thoracic cause of ARF in critically ill patients. METHODS: We systematically searched MEDLINE, Embase, Web of Science, Scopus from inception to November 2025. Two reviewers independently performed study selection, data extraction, and QUADAS-2 quality assessment. Pooled sensitivity, specificity, and summary receiver operating characteristic (SROC) curves were estimated using a bivariate random-effects model; likelihood ratios, diagnostic odds ratio (DOR), Fagan nomogram, and Deek's funnel plot were derived. RESULTS: Twenty studies (3083 units; 1227 reference-positive, 1856 reference-negative) were included. Pooled sensitivity and specificity of lung ultrasound for thoracic cause of ARF were 0.89 (95% CI: 0.83-0.94) and 0.94 (95% CI: 0.90-0.97), respectively. Positive and negative likelihood ratios were 16.2 (95% CI: 9.1-28.9) and 0.11 (95% CI: 0.07-0.18), yielding DOR of 144 (95% CI: 63-330) and area under the SROC curve of 0.97 (95% CI: 0.95-0.98). Heterogeneity was substantial (I CONCLUSIONS: Lung ultrasound can be applied as rapid bedside adjunct to support early ARDS evaluation in ARF, potentially reducing delays associated with transport-dependent imaging. However, clinicians should interpret findings in context, as diagnostic performance may be attenuated when acoustic windows are limited (e.g., obesity) and when mechanical ventilation settings (e.g., high PEEP) alter lung aeration and artifact patterns.

PURPOSE: Despite extensive research, it remains unclear which patient-ventilator asynchronies are reliably detectable in clinical practice, most clinically relevant, and how they rank in severity. METHODS: Multiple-choice questions and 5-point Likert-scale statements were used in iterative Delphi rounds. Feedback was incorporated until stable consensus or dissensus was reached for all items. First series of rounds focused on identifying and classifying patient-ventilator asynchronies detectable from ventilator waveforms, second series assessed their associations with outcomes in three patient groups, and in the final rounds, asynchronies were ranked by severity within these patient groups and across three scenarios. RESULTS: In total, 11 panelists completed nine rounds. Consensus classified ineffective triggering, reverse triggering, double triggering, auto-triggering, insufficient flow, premature cycling, and delayed cycling as clinically relevant patient-ventilator asynchronies. Of these, auto-triggering and delayed cycling were deemed unlikely to be detectable using ventilator waveforms alone. Across all three patient groups, the panelists reached consensus that double triggering and ineffective triggering were the most clinically relevant. In acute respiratory distress syndrome, double triggering, ineffective triggering, and reverse triggering were all judged clinically relevant. In patients without acute respiratory distress syndrome and after cardiac surgery, asynchronies were classified as severe or mild and combined into two composite groups. CONCLUSION: This Delphi study provides a consensus-based framework for identifying and ranking patient-ventilator asynchronies at the bedside, highlighting those most likely to be clinically relevant and offering a structured approach to support monitoring, intervention, and future research.