Daily Respiratory Research Analysis

BACKGROUND: Following a decline during the COVID-19 pandemic, Mycoplasma pneumoniae infections resurged in several countries. We aimed to characterise the clinical presentation of paediatric patients admitted to hospital for M pneumoniae during 2023 and 2024 in France. METHODS: We conducted a nationwide, multicentre, retrospective, and prospective observational study across 37 French paediatric hospitals (September, 2023-September, 2024). Children younger than 18 years who were hospitalised with laboratory-confirmed M pneumoniae infection (PCR or serology) were included. Demographics (excluding race), clinical features, laboratory and radiological findings, management, and outcomes data were described and analysed. Logistic regression was used to identify factors associated with paediatric intensive care unit (PICU) admission. The trial was registered at ClinicalTrials.gov (NCT06260371) and is complete. FINDINGS: We included 969 children and adolescents with M pneumoniae infection (7·3 years [SD 4·5], 426 [44%] of 966 patients were female and 540 [56%] of 966 were male). 936 (97%) of all patients were positive by PCR for M pneumoniae. Pneumonia was diagnosed in 628 (87%) of the 726 patients with respiratory involvement, and cutaneous manifestations were reported in 132 (14%) of 969 patients, including 56 (42%) of 132 who had erythema multiforme. Macrolides were prescribed in 884 (95%) of the 931 patients who were prescribed antibiotics, primarily azithromycin (563 [64%] of 884). Macrolide resistance was detected in one (5%) of the 21 tested samples. In total, 57 (6%) of 969 patients required PICU admission and four (<1%) died. Factors significantly associated with PICU admission included being older than 11 years (adjusted odds ratio 2·0 [95% CI 1·1-3·6]; p=0·023), asthma (2·2 [1·2-4·0]; p=0·0072), other underlying conditions (2·1 [1·2-3·7]; p=0·013), and erythema multiforme (3·7 [1·6-8·8]; 0·0025). INTERPRETATION: The 2023-2024 M pneumoniae epidemic in France resulted in a substantial paediatric hospitalisation burden. Although severe cases were uncommon, children older than 11 years, those with asthma, other comorbidities, and erythema multiforme were at increased risk of PICU admission. Ongoing surveillance and targeted management strategies are warranted for future epidemics. FUNDING: Association Clinique et Thérapeutique Infantile du Val de Marne (ACTIV).

BACKGROUND: The respiratory rate oxygenation (ROX) index, and machine learning (ML) models, are promising approaches to help clinicians identify earlier those patients at risk of failing high flow nasal cannula (HFNC) therapy. Respiratory rate (RR) and heart rate (HR) are key inputs to these models, but their measurement in a hospital environment may be subject to significant errors. The effect of these errors on the accuracy of HFNC outcome predictions is currently unknown. METHODS: We evaluated the capability of a recently-proposed ML model called Tabular Prior-data Fitted Network (TabPFN), a range of standard ML models, and the ROX index and its variants, to predict the outcome of HFNC therapy using measurements made within the first 2 h of treatment in patients with acute hypoxemic respiratory failure. 596 AHRF patients receiving HFNC (456 successes, 140 failures) from the RENOVATE trial in Brazil were used for model training. External validation was performed on a dataset on 241 AHRF patients (156 successes, 85 failures) from Italy and the US. During training and testing, we replicated RR and HR measurement errors that were consistent with those recorded in previously published studies employing 30-second and 15-second manual counting time-windows, respectively, and employed bootstrapping and Monte Carlo simulation to evaluate their effects on the accuracy of outcome predictions. RESULTS: The TabPFN model was more affected by the RR and HR measurement errors, but still provided more accurate predictions of HFNC outcome (Mean [95% CI] Accuracy 0.79 [0.73-0.84], AUC 0.86 [0.82-0.89] in external validation) than the ROX index and its variants (Accuracy 0.71 [0.68-0.75], AUC 0.78 [0.75-0.80]). Augmenting patient datasets with arterial blood gas measurements further improved the performance and robustness of the TabPFN model, but not the ROX index. CONCLUSIONS: In this multi-centre study, the recently introduced TabPFN ML model outperformed currently available methods for predicting the outcome of HFNC therapy even when realistic levels of measurement errors were included in the clinical data on RR and HR. The predictive performance of this ML model can be further improved by minimizing measurement errors using more advanced monitoring, and/or by additionally using arterial blood gas measurements.

BACKGROUND: Neoadjuvant immune checkpoint blockade (NA-ICB) shows promise in treating resectable and locally advanced non-small cell lung cancer (NSCLC), yet the specific T cell subtypes that expand and become functionally reactivated remain incompletely characterised. METHODS: We applied single-cell RNA sequencing, TCR repertoire analysis, and flow cytometry to tumour, paired normal lung tissue, and peripheral blood samples from 26 NA-ICB-treated and 14 treatment-naïve NSCLC patients to investigate responsive T cell subtypes, their tissue origins, migration patterns, and phenotype transitions. RESULTS: CD8 + CX3CR1 + T cells were significantly enriched in responsive tumours, as evidenced by increased proportions (p = 0.0027) and clonal expansion in scRNA-seq, and elevated protein-level frequencies detected by flow cytometry (p = 0.021). Longitudinal analysis revealed proliferation of these cells in peripheral blood post-treatment. Shared TCR clonotypes were identified across blood and tumour samples. Pseudotime analysis indicated differentiation of these cells into exhausted and cytotoxic NK-like CD8 + T cells upon tumour infiltration. CONCLUSION: These findings suggest that CD8 + CX3CR1 + T cells may represent circulating cytotoxic precursors associated with effective NA-ICB responses, suggesting their potential as predictive biomarkers and therapeutic targets for adoptive cell therapy.