Daily Respiratory Research Analysis
Three studies advance respiratory medicine across diagnosis, mechanisms, and data science: a multicenter trial shows stool Xpert Ultra can augment tuberculosis detection in people with HIV, particularly with low CD4 counts; a mechanistic investigation identifies ICOS+ CD4+ T cells in aged hosts as drivers of anti–PD-1-related lung toxicity, suggesting actionable biomarkers and targets; and an externally validated EHR/radiology machine-learning model accurately identifies ARDS across institutions
Summary
Three studies advance respiratory medicine across diagnosis, mechanisms, and data science: a multicenter trial shows stool Xpert Ultra can augment tuberculosis detection in people with HIV, particularly with low CD4 counts; a mechanistic investigation identifies ICOS+ CD4+ T cells in aged hosts as drivers of anti–PD-1-related lung toxicity, suggesting actionable biomarkers and targets; and an externally validated EHR/radiology machine-learning model accurately identifies ARDS across institutions.
Research Themes
- Stool-based molecular testing to enhance TB diagnosis in people with HIV
- Mechanisms and biomarkers of immunotherapy-related lung toxicity in aging
- Cross-institutional machine learning for ARDS phenotyping using EHR and radiology
Selected Articles
1. ICOS+CD4+ T cells define a high susceptibility to anti-PD-1 therapy-induced lung pathogenesis.
In aged tumor-bearing mice, anti–PD-(L)1 therapy induces ICOS+ CD4+ T cell activation that drives germinal center B cell responses and lung injury mimicking irAEs; blocking ICOS–ICOSL attenuates damage, while local IL-21 restores it. Adoptive transfer and single-cell data show both aged host milieu and pathogenic CD4+ T cells are required. In patients, CD4+ T-cell ICOS upregulation correlates with later irAE incidence.
Impact: This study uncovers an age-amplified, ICOS+ CD4+ T cell–centric mechanism for anti–PD-1 lung toxicity and validates a translational biomarker/target, informing risk stratification and preventive strategies for irAEs in the elderly.
Clinical Implications: Monitoring ICOS expression on CD4+ T cells could anticipate lung irAEs in patients on anti–PD-1 therapy, especially the elderly. Therapeutically, modulating the ICOS–ICOSL axis or downstream IL-21 signaling may mitigate lung toxicity without broadly suppressing antitumor immunity.
Key Findings
- Anti–PD-(L)1 therapy induced ICOS+ CD4+ T cell activation and ectopic T/B cell infiltration with antibody deposition in aged, not young, mouse lungs.
- Blocking ICOS–ICOSL reduced germinal center B cell differentiation and lung injury; local IL-21 administration reversed protection.
- Adoptive transfer showed both pathogenic aged lung CD4+ T cells and an aged host environment were required for irAE-like responses.
- In cancer patients, increased ICOS expression on CD4+ T cells associated with later irAE incidence.
Methodological Strengths
- Integrated in vivo aging models with adoptive transfer and single-cell transcriptomics to dissect mechanisms.
- Translational validation in patient samples linking ICOS upregulation to irAE incidence.
Limitations
- Predominantly preclinical murine data; generalizability across tumor types and human heterogeneity remains to be established.
- Clinical cohort details (sample size, timing, confounders) for ICOS association were limited in the abstract.
Future Directions: Prospective studies to validate ICOS+ CD4+ T cells as predictive biomarkers and trials testing ICOS–ICOSL modulation or IL-21 pathway interventions to prevent/manage lung irAEs, with stratification by age.
2. Performance of stool Xpert MTB/RIF Ultra for detection of Mycobacterium tuberculosis among adults living with HIV: a multicentre, prospective diagnostic study.
In 677 adults with HIV across three African countries, stool Xpert Ultra showed overall sensitivity of 23.7% and specificity of 94.0% versus a composite reference; sensitivity rose to 45.5% when CD4 ≤200 cells/μL. Stool Ultra added 23–33% extra cases compared with sputum Ultra, sputum culture, or urine TB-LAM, supporting its role as an adjunct diagnostic in PLHIV.
Impact: Provides prospective, multicountry evidence that stool Ultra augments TB detection in PLHIV—especially the immunosuppressed—filling a critical gap where sputum is unobtainable or paucibacillary.
Clinical Implications: Incorporate stool Xpert Ultra as an adjunct test in TB diagnostic algorithms for PLHIV—prioritizing those with CD4 ≤200 cells/μL—and combine with urine LAM and sputum testing to maximize yield.
Key Findings
- Overall stool Ultra sensitivity 23.7% (95% CI 16.4–32.4) and specificity 94.0% (95% CI 91.7–95.9) versus composite reference.
- Higher sensitivity in advanced immunosuppression: 45.5% with CD4 ≤200 cells/μL vs 21.3% with CD4 >200.
- Stool Ultra added 23–33% additional cases compared with sputum Ultra, sputum culture, and urine TB-LAM across all tested.
- Diagnostic yield among all treated: stool Ultra 9%, urine TB-LAM 12%, sputum Ultra 6%, sputum culture 4%.
Methodological Strengths
- Prospective, multicenter design across three countries with standardized processing.
- Use of composite microbiologic reference including WHO-recommended tests; CD4-stratified analyses and yield comparisons.
Limitations
- Overall sensitivity was modest; performance may vary with stool processing and bacillary load.
- Study population limited to adults with HIV; generalizability to HIV-negative populations not assessed.
Future Directions: Optimize stool processing workflows and evaluate integration with triage tools (e.g., clinical scores, digital CXR) in implementation studies; assess cost-effectiveness and impact on time-to-treatment.
3. Development and External Validation of a Detection Model to Retrospectively Identify Patients With Acute Respiratory Distress Syndrome.
Using structured EHR data plus radiology reports, a regularized logistic model achieved AUROC 0.91 (internal) and 0.88 (external) with good calibration (ICI 0.13). At a set threshold, sensitivity/specificity were both 80%, PPV 64%, and cases were identified a median 2.2 hours after Berlin criteria, enabling robust retrospective ARDS phenotyping across health systems.
Impact: The model standardizes retrospective ARDS identification using routinely collected data and generalizes across systems, enabling reproducible cohort building, quality measurement, and multicenter research.
Clinical Implications: Hospitals and researchers can apply this validated EHR+radiology model to consistently identify ARDS cases for quality improvement, surveillance, and research; prospective adaptation could support earlier recognition and trial enrollment.
Key Findings
- Regularized logistic EHR+radiology model achieved AUROC 0.91 (internal) and 0.88 (external), with external ICI 0.13.
- At a prespecified threshold, sensitivity and specificity were both 80%, with PPV 64%.
- Model identified ARDS a median 2.2 hours after meeting Berlin criteria, enabling timely retrospective capture.
- Physician-adjudicated labels and cross-system validation support generalizability.
Methodological Strengths
- Physician-adjudicated ARDS labels with internal and external validation.
- Integration of structured EHR features with radiology reports; calibration assessed.
Limitations
- Retrospective design; model identifies ARDS shortly after criteria are met rather than predicting pre-onset.
- External validation limited to two health systems; performance in broader settings and languages requires testing.
Future Directions: Prospective implementation for early ARDS recognition, integration with bedside alerts, and testing across diverse health systems and international datasets.