Daily Respiratory Research Analysis
Three impactful respiratory studies advanced precision risk stratification and therapy optimization across critical care, imaging, and trauma. A two-cohort analysis showed that the prognostic value of troponin-I in sepsis/acute respiratory distress syndrome depends on inflammatory subphenotype. An AI-driven dual-energy CTPA tool quantified perfusion phenotypes in chronic thromboembolic disease and tracked response after balloon pulmonary angioplasty. Nationwide data supported early surgical stab
Summary
Three impactful respiratory studies advanced precision risk stratification and therapy optimization across critical care, imaging, and trauma. A two-cohort analysis showed that the prognostic value of troponin-I in sepsis/acute respiratory distress syndrome depends on inflammatory subphenotype. An AI-driven dual-energy CTPA tool quantified perfusion phenotypes in chronic thromboembolic disease and tracked response after balloon pulmonary angioplasty. Nationwide data supported early surgical stabilization of rib fractures, reducing pulmonary complications and mortality versus nonoperative care.
Research Themes
- Precision risk stratification in sepsis/ARDS
- AI-enabled pulmonary perfusion imaging in chronic thromboembolic disease
- Timing and benefits of surgical stabilization of rib fractures
Selected Articles
1. Heterogeneity in association of myocardial injury and mortality in sepsis or acute respiratory distress syndrome by subphenotype: a retrospective study.
Across two prospective ICU cohorts with sepsis or ARDS (急性呼吸窮迫症候群), peak troponin-I predicted 60-day mortality only in the hypoinflammatory subphenotype, despite higher absolute levels in the hyperinflammatory group. A parsimonious classifier (IL-8, sTNFR-1, vasopressors) assigned subphenotypes and revealed significant interaction between subphenotype and troponin prognostic value.
Impact: This study advances precision prognostication in sepsis/ARDS by demonstrating subphenotype-specific utility of a ubiquitous biomarker, informing tailored risk assessment and trial enrichment strategies.
Clinical Implications: Interpret troponin-I in the context of inflammatory subphenotype; hypoinflammatory patients with elevated troponin-I may merit heightened monitoring and targeted interventions, and future trials should stratify by subphenotype.
Key Findings
- Troponin-I levels were higher in the hyperinflammatory subphenotype, yet predicted 60-day mortality only in the hypoinflammatory subphenotype (EARLI aOR 1.14 per doubling; VALID aOR 1.11).
- A classifier using IL-8, sTNFR-1, and vasopressor use assigned subphenotypes and showed significant interaction between subphenotype and troponin-mortality association (p-interaction 0.004).
- Associations adjusted for age, labs, vasopressors, ventilation, and cardiac comorbidities, supporting robustness across cohorts.
Methodological Strengths
- Two prospective observational ICU cohorts with external validation (EARLI and VALID).
- Adjusted analyses with a parsimonious, biologically informed subphenotype classifier.
Limitations
- Observational design limits causal inference and clinical decision impact.
- Single time-window measurement (admission to 24 h) may miss temporal dynamics; subphenotype assigned by biomarker proxy rather than full LCA.
Future Directions: Prospective interventional trials should test subphenotype-guided management and evaluate whether troponin-I adds incremental value to risk scores within hypoinflammatory ARDS/sepsis.
RATIONALE: Myocardial injury is common in acute respiratory distress syndrome (ARDS) and sepsis and associated with increased mortality. Two latent class analysis derived subphenotypes are associated with differential risk of mortality in these populations, though the association of troponin-I with mortality within each subphenotype is unknown. METHODS: The derivation (n = 597 in EARLI) and validation (n = 452 in VALID) cohorts consisted of patients with sepsis or ARDS admitted to the ICU and enrolled in two separate prospective observational studies. Patients with troponin-I measured between hospital presentation and within 24 h of ICU admission were included. A parsimonious classifier model using interleukin-8, soluble tumor necrosis factor receptor-1, and vasopressor use assigned patients to subphenotype. Association between peak troponin-I concentration and 60-day in-hospital mortality within each subphenotype was assessed through logistic regression adjusting for age, admission laboratory values, vasopressor use, invasive ventilation use, and cardiac comorbidities. RESULTS: Median peak troponin-I was significantly higher in the hyperinflammatory vs hypoinflammatory subphenotype in both cohorts (0.07 vs 0.04 ng/mL and 0.17 vs 0.07 ng/mL, both p < 0.05). The association between peak troponin-I and mortality differed between inflammatory subphenotypes (p-interaction 0.004, EARLI). In EARLI, each doubling of peak troponin-I was associated with increased adjusted odds of 60-day mortality (aOR 1.14, 95% CI 1.02-1.28) in the hypoinflammatory subphenotype only. These findings were corroborated in VALID (aOR 1.11, 95% CI 1.03-1.21 in hypoinflammatory). CONCLUSIONS: Admission peak troponin-I is significantly associated with 60-day mortality in patients with sepsis or ARDS. This association was distinctly driven by the hypoinflammatory subphenotype.
2. Development of a lung perfusion automated quantitative model based on dual-energy CT pulmonary angiography in patients with chronic pulmonary thromboembolism.
PerAIDE, an AI-driven DE-CTPA tool, rapidly quantified perfusion phenotypes with strong agreement to expert readers and a 30-fold time reduction. Perfusion defects correlated with pulmonary vascular resistance and mean pulmonary artery pressure, distinguished CTEPH from CTEPD (AUC 0.809), and decreased after BPA, enabling noninvasive disease phenotyping and treatment monitoring.
Impact: Introduces a clinically relevant AI workflow that standardizes DE-CTPA perfusion quantification, aligns with hemodynamics, and captures therapeutic response, potentially reducing reliance on invasive right heart catheterization for serial assessment.
Clinical Implications: Use automated perfusion metrics to differentiate CTEPD vs CTEPH, triage for invasive testing, and monitor BPA response. Integrating PerAIDE could shorten reporting time and support treatment decisions in pulmonary hypertension programs.
Key Findings
- High agreement with radiologists (ICC 0.778) and markedly reduced analysis time (31±3 s vs. 15±4 min, p<0.001).
- Perfusion defects correlated with PVR (ρ=0.534) and mPAP (ρ=0.482), and inversely with oxygenation index (ρ=-0.441).
- Differentiated CTEPH from CTEPD (AUC 0.809) and detected significant perfusion defect reduction 3 months after BPA.
Methodological Strengths
- Prospective cohort with invasive hemodynamic correlation and pre/post therapeutic assessment.
- Automated U-Net-based segmentation with benchmarking against expert readers.
Limitations
- Single-center study; external validation and outcome prognostication not yet established.
- Details of AI training/generalizability across vendors and acquisition protocols require further study.
Future Directions: Multicenter validation, integration with clinical risk models, and trials to test AI-guided management pathways (e.g., imaging-triggered BPA or RHC decisions) and impact on outcomes.
OBJECTIVE: To develop PerAIDE, an AI-driven system for automated analysis of pulmonary perfusion blood volume (PBV) using dual-energy computed tomography pulmonary angiography (DE-CTPA) in patients with chronic pulmonary thromboembolism (CPE). MATERIALS AND METHODS: In this prospective observational study, 32 patients with chronic thromboembolic pulmonary disease (CTEPD) and 151 patients with chronic thromboembolic pulmonary hypertension (CTEPH) were enrolled between January 2022 and July 2024. PerAIDE was developed to automatically quantify three distinct perfusion patterns-normal, reduced, and defective-on DE-CTPA images. Two radiologists independently assessed PBV scores. Follow-up imaging was conducted 3 months after balloon pulmonary angioplasty (BPA). RESULTS: PerAIDE demonstrated high agreement with the radiologists (intraclass correlation coefficient = 0.778) and reduced analysis time significantly (31 ± 3 s vs. 15 ± 4 min, p < 0.001). CTEPH patients had greater perfusion defects than CTEPD (0.35 vs. 0.29, p < 0.001), while reduced perfusion was more prevalent in CTEPD (0.36 vs. 0.30, p < 0.001). Perfusion defects correlated positively with pulmonary vascular resistance (ρ = 0.534) and mean pulmonary artery pressure (ρ = 0.482), and negatively with oxygenation index (ρ = -0.441). PerAIDE effectively differentiated CTEPH from CTEPD (AUC = 0.809, 95% CI: 0.745-0.863). At the 3-month post-BPA, a significant reduction in perfusion defects was observed (0.36 vs. 0.33, p < 0.01). CONCLUSION: CTEPD and CTEPH exhibit distinct perfusion phenotypes on DE-CTPA. PerAIDE reliably quantifies perfusion abnormalities and correlates strongly with clinical and hemodynamic markers of CPE severity. TRIAL REGISTRATION: ClinicalTrials.gov, NCT06526468. Registered 28 August 2024- Retrospectively registered, https://clinicaltrials.gov/study/NCT06526468?cond=NCT06526468&rank=1 . CRITICAL RELEVANCE STATEMENT: PerAIDE is a dual-energy computed tomography pulmonary angiography (DE-CTPA) AI-driven system that rapidly and accurately assesses perfusion blood volume in patients with chronic pulmonary thromboembolism, effectively distinguishing between CTEPD and CTEPH phenotypes and correlating with disease severity and therapeutic response. KEY POINTS: Right heart catheterization for definitive diagnosis of chronic pulmonary thromboembolism (CPE) is invasive. PerAIDE-based perfusion defects correlated with disease severity to aid CPE-treatment assessment. CTEPH demonstrates severe perfusion defects, while CTEPD displays predominantly reduced perfusion. PerAIDE employs a U-Net-based adaptive threshold method, which achieves alignment with and faster processing relative to manual evaluation.
3. Early surgical stabilization of multiple rib fractures and flail chest is associated with better outcomes compared with nonoperative management.
In a large TQIP cohort, SSRF was associated with lower mortality versus weighted NOM, with pronounced benefit in flail chest. Early fixation within 82 hours reduced ARDS (急性呼吸窮迫症候群) and ventilator-associated pneumonia and shortened hospital stay compared with delayed SSRF, though overall resource use was higher than NOM.
Impact: Provides robust nationwide evidence supporting SSRF and defining an actionable timing threshold (~82 hours) linked to lower pulmonary morbidity, informing trauma pathways and quality metrics.
Clinical Implications: Consider SSRF, particularly for flail chest, and prioritize early fixation (≤82 hours) to reduce ARDS and VAP. Balance survival benefits against increased ICU/hospital resource utilization and plan perioperative pathways accordingly.
Key Findings
- After IPTW, SSRF showed lower in-hospital mortality vs NOM (1.5% vs 2.7%, p<0.001); flail chest subgroup 4.2% vs 10.1% (p=0.002).
- Early SSRF within 82 hours decreased ARDS (0.5% vs 1.5%) and VAP (0.9% vs 2.3%) and shortened hospital stay compared with delayed SSRF.
- SSRF patients had longer hospital and ICU stays than NOM, indicating higher resource utilization.
Methodological Strengths
- Large national registry with inverse probability treatment weighting and subgroup/timing analyses.
- Clinically relevant endpoints including ARDS and VAP with spline modeling to define timing.
Limitations
- Retrospective registry with potential residual confounding and selection bias.
- Limited granularity on fracture morphology, analgesia protocols, and center-level practice variation.
Future Directions: Pragmatic randomized or quasi-experimental studies to confirm the optimal timing, refine patient selection criteria, and assess cost-effectiveness and long-term functional outcomes.
BACKGROUND: Surgical stabilization of rib fractures (SSRF) is increasingly performed. Nationwide data comparing its outcomes with nonoperative management (NOM) and defining the best timing for SSRF are scarce. METHODS: We analyzed data from the American College of Surgeons Trauma Quality Improvement Program, 2017-2021. Adults with three or more blunt rib fractures and no major extrathoracic injury were included. Surgical fixation was compared with risk-weighted NOM using inverse probability of treatment weighting. Primary outcome was in-hospital mortality. Secondary outcomes were hospital and intensive care length of stay, ventilator duration, ventilator-free days, acute respiratory distress syndrome, and ventilator-associated pneumonia. Subgroup analyses examined flail chest and the impact of timing of fixation, which was modeled as a continuous exposure with a generalized additive spline; its discriminatory performance was evaluated with receiver-operating-characteristic curve analysis to calculate the Youden's index. RESULTS: A total of 3,806 patients underwent SSRF, and 3,753 weighted controls received NOM. After weighting, an association of SSRF with lower mortality (1.5% vs. 2.7%, p < 0.001) but longer hospital (median, 10 vs. 5 days) and intensive care stays (5 vs. 3 days, both p < 0.001) were observed. In the flail chest subgroup, SSRF was associated with a mortality of 4.2% compared with 10.1% with NOM ( p = 0.002). In the nonflail group, mortality was 1.3% after SSRF versus 2.0% in NOM ( p = 0.003). Early SSRF within 82 hours had similar mortality to delayed fixation (1.6% vs. 1.4%, p = 0.647). However, early SSRF was associated with lower rates of acute respiratory distress syndrome (0.5% vs. 1.5%), ventilator-associated pneumonia (0.9% vs. 2.3%), and shorter hospital stays compared with delayed SSRF. CONCLUSION: Nationwide data demonstrated that SSRF is associated with higher survival, particularly in patients with flail chest, at the cost of increased resource utilization. Surgical stabilization of rib fractures performed within 82 hours is associated with higher survival, lower pulmonary morbidity, and additional resource utilization. LEVEL OF EVIDENCE: Therapeutic/Care Management; Level III.