Daily Respiratory Research Analysis
Three impactful respiratory studies emerged. A Nature Communications correlates analysis identified day-29 neutralizing antibodies and prefusion F IgG as consistent correlates of protection for an RSV mRNA vaccine in older adults. A global synthesis and Bayesian model mapped RSV hospitalization risk by infant month-of-age and birth month to optimize timing of passive immunization, while a Thorax causal machine-learning analysis pinpointed COPD subgroups most likely to benefit from prophylactic a
Summary
Three impactful respiratory studies emerged. A Nature Communications correlates analysis identified day-29 neutralizing antibodies and prefusion F IgG as consistent correlates of protection for an RSV mRNA vaccine in older adults. A global synthesis and Bayesian model mapped RSV hospitalization risk by infant month-of-age and birth month to optimize timing of passive immunization, while a Thorax causal machine-learning analysis pinpointed COPD subgroups most likely to benefit from prophylactic azithromycin.
Research Themes
- RSV vaccine correlates of protection and immunobridging
- Age- and birth-month–specific RSV hospitalization risk for immunization planning
- Precision medicine in COPD: individual treatment effect modeling for azithromycin
Selected Articles
1. Immune correlates analysis of mRNA-1345 RSV vaccine efficacy clinical trial.
In a correlates analysis of the pivotal mRNA-1345 trial in older adults, day-29 RSV-A/B neutralizing titers and prefusion F IgG inversely correlated with RSV-LRTD and ARD. A 10-fold increase in RSV-A nAb reduced event hazards by ~55–60%, supporting these markers as correlates of protection.
Impact: Defines robust immune correlates for an RSV vaccine, enabling immunobridging, accelerated decision-making, and potential use of surrogate endpoints.
Clinical Implications: Day-29 nAb and preF IgG can inform booster timing, immunobridging for new populations or formulations, and may guide regulatory acceptance of surrogate endpoints in RSV vaccine development.
Key Findings
- Day-29 RSV-A nAb: 10-fold increase associated with HR 0.44 for RSV-LRTD-2+, 0.41 for RSV-LRTD-3+, and 0.45 for RSV-ARD.
- Consistent inverse correlations were observed for RSV-B nAb and prefusion F IgG.
- Findings designate day-29 nAb and preF IgG as correlates of risk and protection for clinically relevant RSV endpoints.
Methodological Strengths
- Correlates analysis anchored in a pivotal phase 3 efficacy trial
- Multiple endpoints and antigens (RSV-A/B nAbs and preF IgG) showing concordant results
Limitations
- Correlational nature; thresholds for protection and durability not fully defined
- Generalizability limited to older adults; timing beyond day 29 not assessed in this analysis
Future Directions: Define protective thresholds and durability, validate correlates across age groups and vaccine platforms, and assess utility for regulatory immunobridging.
2. Respiratory syncytial virus hospitalisation by chronological month of age and by birth month in infants.
A global synthesis and Bayesian modeling estimated infant RSV hospitalization risk by month-of-age and developed a web tool to project risk by birth month and local seasonality. Risk peaks at 2 months and concentrates under 6 months, but varies by birth month, informing timing of passive immunization.
Impact: Provides actionable, granular risk estimates to time maternal vaccination or monoclonal antibody administration for maximal per-dose effectiveness.
Clinical Implications: Programs can schedule nirsevimab or maternal RSV vaccination aligned with local seasonality and infant birth month to cover the highest-risk window (around 2 months of age).
Key Findings
- Global infant RSV hospitalization burden peaks at month 2 and is concentrated under 6 months of age.
- Hospitalization risk varies substantially by birth month due to local seasonality.
- A validated web-based tool estimates hospitalization distribution by birth month to guide immunization timing.
Methodological Strengths
- Bayesian modeling synthesizing systematic review and collaborator datasets
- External validation and practical web-based decision tool linked to local seasonality
Limitations
- Data heterogeneity across sources and countries may introduce bias
- Modeling is ecological; individual-level risk factors are not incorporated
Future Directions: Integrate individual-level predictors and evaluate real-world outcomes when aligning passive immunization with birth-month–based risk windows.
3. Identifying azithromycin responders with an individual treatment effect model in COPD.
Using a Causal Forest on MACRO (n=1025) with validation in COLUMBUS (n=83), a responder tertile had markedly greater reductions in COPD exacerbations with azithromycin, while two-thirds showed no significant benefit. Predictors included symptoms, WBC, hemoglobin, CRP, and FVC; smoking status was not predictive.
Impact: Enables precision deployment of azithromycin, reducing unnecessary exposure and resistance risk by targeting those most likely to benefit.
Clinical Implications: Clinicians can use simple parameters (symptoms, WBC, Hb, CRP, FVC) to identify likely responders to prophylactic azithromycin and avoid prescribing to non-responders.
Key Findings
- Responder tertile achieved larger exacerbation reductions (MACRO: rate ratio 0.70; COLUMBUS: 0.43) compared to cohort averages.
- No significant benefit in approximately two-thirds of patients, indicating strong treatment effect heterogeneity.
- Top predictors: respiratory symptoms, WBC, hemoglobin, CRP, FVC; smoking status not predictive.
Methodological Strengths
- Causal machine learning with external validation
- Post hoc analysis of RCT data with clinically interpretable predictors
Limitations
- Post hoc modeling; prospective clinical utility testing is needed
- Generalizability beyond trial populations and health systems remains to be shown
Future Directions: Prospective impact studies integrating the model into clinical workflows and assessing exacerbation, safety, and resistance outcomes.