Daily Ards Research Analysis
Analyzed 3 papers and selected 3 impactful papers.
Summary
Across three studies, a systematic review supports practical conversion of SpO2/FiO2 to PaO2/FiO2 with caveats at high saturation, a nationwide prospective cohort delineates seven Omicron ICU phenotypes with distinct outcomes, and a machine learning study proposes mitochondrial-related biomarkers for sepsis-induced ARDS. Together, these works advance noninvasive oxygenation assessment, phenotype-driven critical care, and biomarker-based diagnosis.
Research Themes
- Noninvasive oxygenation metrics (SF to PF conversion)
- Phenotype-driven critical care in COVID-19 Omicron
- Mitochondrial biomarkers in sepsis-induced ARDS
Selected Articles
1. Approaches to Converting Spo2/Fio2 Ratio to Pao2/Fio2 Ratio for Assessment of Respiratory Failure in Critically Ill Patients: A Systematic Review.
This systematic review of 45 observational studies found strong correlation between SF (SpO2/FiO2) and PF (PaO2/FiO2) ratios, though accuracy declines at SpO2 ≥ 97%. Four conversion equations were prioritized for bedside use, with a simple linear equation performing well and SF showing prognostic utility comparable to PF.
Impact: Provides clinically actionable guidance for using noninvasive oxygenation metrics when arterial blood gases are unavailable. The prioritized equations can standardize SF-to-PF conversion at the bedside.
Clinical Implications: Clinicians can estimate PF from SF using a linear equation when ABGs are not feasible, avoiding overreliance at SpO2 ≥ 97%. SF can support ARDS severity assessment and risk stratification within composite scores.
Key Findings
- Across 45 studies, SF and PF ratios showed strong correlation; accuracy drops when SpO2 ≥ 97%.
- Four equations (1 linear, 1 log-linear, 2 nonlinear) were prioritized based on usability and generalizability.
- A simple linear equation is easiest to apply and provides acceptable bedside performance.
- SF ratio demonstrated prognostic value comparable to PF, both alone and within composite scores.
Methodological Strengths
- Comprehensive multi-database search with QUADAS-2 risk-of-bias assessment
- Large cumulative measurements (up to 141,000) across diverse settings
Limitations
- Substantial heterogeneity across studies and no single best conversion equation
- Reduced accuracy at high oxygen saturation (SpO2 ≥ 97%) and limited data at extremes
Future Directions: Prospective validation of prioritized equations across ARDS severities and integration into electronic calculators and clinical decision support.
OBJECTIVE: The Pao2/Fio2 (PF) ratio is widely used as an assessment of respiratory failure in guiding ventilation strategies and prognostication in critically ill patients. However, given that it mandates invasive arterial access, the Spo2/Fio2 (SF) ratio has been suggested as a noninvasive and readily accessible alternative. What are the best ways to convert SF and PF ratios in critically ill patients, in terms of their diagnostic/prognostic accuracy and clinical utility? DATA SOURCES: We comprehensively searched databases (MEDLINE, Embase, Web of Science, Cochrane library) to identify relevant studies. STUDY SELECTION: Any observational studies that compared the SF to PF ratio in critically ill patients. We assessed individual study risk of bias (ROB) using the revised QUADAS II tool. DATA EXTRACTION: We included 45 observational studies, ranging from 61 to 141,000 measurements. DATA SYNTHESIS: SF to PF imputation was less accurate when the Spo2 was equal to or greater than 97%. Otherwise, all studies were able to establish strong correlational relationships between SF and PF ratios, but there was no clear best equation. Based on ease of use, size, generalizability and methodology, we were able to prioritize four equations (one linear, one logarithmic linear, and two nonlinear). All four equations showed strong correlation between SF and PF ratios, with the linear equation being easiest to apply. The SF ratio also correlated well with clinical outcomes when compared with the PF ratio, both as an individual value and as part of a comprehensive score, with more discriminating performance in some cases. CONCLUSIONS: SF and PF ratios demonstrate good correlation, and may have similar prognostic value. Although there is no clear optimal method to convert SF to PF ratios, linear equations show acceptable correlation and are most easily applied at the bedside.
2. Clinical Phenotypes of Critically Ill Patients with COVID-19 Infected with Omicron: A Nationwide Prospective Cohort Study.
In a multicenter prospective cohort of 777 Omicron-infected ICU patients with acute respiratory failure, seven phenotypic clusters were identified using unsupervised methods. Clusters differed in comorbidities, organ support needs, medication use, and 28-day mortality (13.1% to 41.1%), supporting phenotype-driven personalization and trial stratification.
Impact: First nationwide prospective delineation of Omicron ICU phenotypes with actionable differences in management and outcomes. Establishes a framework for precision trials and tailored therapies.
Clinical Implications: Use phenotype features to inform risk stratification, anticipate organ support needs, and tailor immunomodulatory strategies; supports stratified enrollment and endpoints in future trials.
Key Findings
- Seven clinical clusters were identified among 777 Omicron ICU patients with acute respiratory failure.
- Clusters 5 and 7 required the most organ support; cluster 6 had frequent vasopressor use, and cluster 7 had more renal replacement therapy.
- Dexamethasone and tocilizumab were most prescribed in cluster 4 (91.3% and 30.2%).
- 28-day mortality varied markedly by cluster, from 13.1% (cluster 3) to 41.1% (cluster 6).
Methodological Strengths
- Multicenter prospective design across 39 ICUs with standardized clustering using SOM and ClinTrajan
- Registered protocol (NCT05162508) and predefined phenotyping approach
Limitations
- Observational design limits causal inference and may be affected by residual confounding
- Generalizability may be restricted to Omicron-era French ICUs and specific practice patterns
Future Directions: Prospective validation of phenotypes with targeted interventions and incorporation into adaptive, phenotype-stratified clinical trials.
INTRODUCTION: The clinical presentation of critically ill patients with coronavirus disease 2019 (COVID-19) has evolved significantly with the emergence of the Omicron variant. Current intensive care unit (ICU) admissions involve patients with diverse comorbidities and immune statuses, highlighting the need to redefine homogeneous phenotypic subgroups within this population. This study aimed to characterize distinct clinical phenotypes among critically ill patients with COVID-19 and acute respiratory failure. METHODS: This multicenter prospective substudy of the SEVARVIR cohort included adult patients from 39 French ICUs between December 2021 and October 2024 with acute respiratory failure and infected with the Omicron variant. Clustering analysis was conducted using Kohonen's self-organizing maps (SOMs) and validated with ClinTrajan, two unsupervised clustering methods, to identify homogeneous patient phenotypes. RESULTS: During the study period, 777 patients with Omicron infection were included, and 7 distinct clinical clusters were identified. Clusters 1 and 2 included patients with metabolic and cardiovascular comorbidities. Cluster 3 featured younger, mildly ill patients with isolated chronic respiratory failure, while cluster 4 comprised older male patients with isolated respiratory failure. Cluster 5 included patients with isolated hematologic malignancies, cluster 6 patients with multiorgan failure, and cluster 7 organ transplant recipients, with high severity scores and impaired renal function. ICU management varied substantially across clusters. Patients in clusters 5 and 7 had the highest requirements for organ support, with frequent use of invasive mechanical ventilation, vasopressors (cluster 6), and renal replacement therapy (cluster 7). Dexamethasone and tocilizumab were most commonly prescribed in cluster 4 (91.3% and 30.2%, respectively). Mortality at day 28 varied significantly across clusters, ranging from 13.1% in cluster 3 to 41.1% in cluster 6. CONCLUSIONS: This clustering analysis highlights, for the first time, the clinical heterogeneity of critically ill patients infected with Omicron, identifying seven distinct clusters with varying clinical presentations, management strategies and outcomes. These findings underscore the relevance of a phenotype-driven approach to support personalized treatment strategies and guide future clinical trials. TRIAL REGISTRATION: Clinicaltrials.gov, NCT05162508. A Graphical Abstract is available for this article.
3. Mitochondrial-related biomarkers as the diagnostic markers in sepsis induced acute respiratory distress syndrome.
Using integrative bioinformatics and machine learning, the study identified three mitochondrial-related biomarkers (ARID4B, RGS2, TGM2) for sepsis-induced ARDS. A nomogram based on these genes showed excellent diagnostic performance, supported by scRNA-seq profiling and experimental validation (qRT-PCR, western blot), with immune infiltration signals involving naive B cells and CD8+ T cells.
Impact: Proposes mitochondria-linked gene markers with multi-layer validation for diagnosing sepsis-induced ARDS, connecting pathophysiology to potential clinical tools.
Clinical Implications: Suggests a candidate gene panel for early diagnosis or risk stratification of sepsis-induced ARDS; could guide precision monitoring once externally validated.
Key Findings
- Three mitochondrial-related biomarkers (ARID4B, RGS2, TGM2) were identified for sepsis-induced ARDS via multi-algorithm machine learning.
- A biomarker-based nomogram demonstrated excellent diagnostic performance.
- scRNA-seq mapped biomarker expression across cell subpopulations, and qRT-PCR/western blot validated gene/protein expression.
- Immune infiltration analysis implicated naive B cells and CD8+ T cells.
Methodological Strengths
- Integrated multi-omic approach with machine learning feature selection and experimental validation
- Single-cell RNA sequencing to localize biomarker expression across cell types
Limitations
- Relies on retrospective public datasets with unspecified sample sizes and potential batch effects
- Lack of external prospective clinical validation; risk of overfitting of the diagnostic model
Future Directions: Prospective, multicenter validation with predefined endpoints; assessment of incremental value over clinical scores and PF/SF indices; exploration of mechanistic roles of ARID4B, RGS2, TGM2.
Previous study found that mitochondrial might correlate with sepsis-induced acute respiratory distress syndrome (ARDS). Thus, this study aimed to find the effect of mitochondrial-related biomarkers in sepsis-induced ARDS. Differentially expressed genes (DEGs) were firstly screened in sepsis-induced ARDS datasets and intersected with mitochondrial-related genes (MRGs) to create differentially expressed-MRGs (DE-MRGs). Based on DE-MRGs, three machine learning algorithms were further performed to yield feature genes, the intersections of feature genes were conducted expression analysis. Those genes with significant difference of expression were identified as biomarkers for nomogram construction and immune infiltration. Single cell RNA sequencing (scRNA-seq) analysis was processed to annotate cell subpopulations, the expression of biomarkers was evaluated in different cell subpopulations. Finally, the expression of biomarkers was validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western blotting. Relied on comprehensive analysis, three biomarkers (ARID4B, RGS2, TGM2) related to mitochondrial were acquired for sepsis-induced ARDS. A nomogram was then constructed relied on biomarkers, and verified to have an excellent performance. Analysis of immune infiltration showed that naive B cells and CD8