Daily Respiratory Research Analysis

BACKGROUND: Early identification and monitoring of neonatal morbidities are critical for timely interventions that can prevent complications, optimise resource use, and support families. Although traditional tools based on tabular data and biomarkers are beneficial, they are restricted in assessing the risk of morbidities in newborns. In this study, we developed NeonatalBERT, a pre-trained large language model (LLM) that estimates the risk of neonatal morbidities from clinical notes. METHODS: This prognostic study investigated retrospective primary and external cohorts from two different quaternary-care academic medical centres in the USA: Stanford Health Care and Beth Israel Deaconess Medical Center. NeonatalBERT was initially pre-trained on clinical notes from the primary cohort and then fine-tuned separately for both cohorts. NeonatalBERT was also compared against other existing LLMs, such as BioBERT and Bio-ClinicalBERT, as well as traditional machine learning and logistic regression models using tabular features. NeonatalBERT was evaluated on 19 neonatal morbidities (respiratory distress syndrome, bronchopulmonary dysplasia, pulmonary haemorrhage, pulmonary hypertension, atelectasis, aspiration syndrome, intraventricular haemorrhage, periventricular leukomalacia, neonatal seizures, other CNS disorders, patent ductus arteriosus, cardiovascular instability, sepsis, candidiasis, anaemia, jaundice, necrotising enterocolitis, retinopathy of prematurity, and death) for the primary cohort and ten for the external cohort (respiratory distress syndrome, bronchopulmonary dysplasia, pulmonary haemorrhage, intraventricular haemorrhage, patent ductus arteriosus, sepsis, jaundice, necrotising enterocolitis, retinopathy of prematurity, and death). For each outcome, the area under the receiver operating characteristic curve, area under the precision-recall curve (AUPRC), and F1 scores were evaluated. FINDINGS: 32 321 newborns were included in the primary cohort, including 27 411 in the primary training set (mean gestational age 38·64 weeks [SD 2·30]; 13 056 [47·6%] female and 14 355 [52·4%] male newborns) and 4910 in the primary testing set (mean gestational age 38·64 [2·13] weeks; 2336 [47·6%] female and 2574 [52·4%] male newborns). Additionally, 7061 newborns were selected into the external cohort, including 5653 in the external training set (1567 [27·7%] premature and 4086 [72·3%] term births; 2614 [46·2%] female and 3039 [53·8%] male newborns) and 1408 in the external testing set (383 [27·2%] premature and 1025 [72·8%] term births; 624 [44·3%] female and 784 [55·7%] male newborns). In the primary cohort, the mean AUPRC over 19 outcomes was 0·291 (95% CI 0·268-0·314) for NeonatalBERT, 0·238 (0·217-0·259) for Bio-ClinicalBERT, 0·217 (0·197-0·236) for BioBERT, and 0·194 (0·177-0·211) for the traditional model using tabular data. In the external cohort, NeonatalBERT had a mean AUPRC of 0·360 (0·328-0·393), outperforming other models with the range of 0·224-0·333. INTERPRETATION: Based on validation using two large-scale US datasets, NeonatalBERT effectively estimates the risk of neonatal morbidities from unstructured clinical notes of newborns. The promising results from this study show the potential of NeonatalBERT to enhance neonatal care and streamline hospital operations. FUNDING: National Institutes of Health, Burroughs Wellcome Fund, March of Dimes Foundation, Alfred E Mann Foundation, Gates Foundation, Christopher Hess Research Fund, Roberts Foundation Research Fund, Prematurity Research Center, and Stanford Maternal & Child Health Research Institute Postdoctoral Support funds.

Acute hypoxemic respiratory failure (RF) occurs frequently in critically ill patients and is associated with substantial morbidity, mortality and resource use. We developed a comprehensive machine-learning-based monitoring system to support ICU physicians in managing RF through early detection, continuous monitoring, assessment of extubation readiness, and prediction of extubation failure (EF). In study patients, the model predicted 80% of RF events with 45% precision, identifying 65% of events more than 10 hours before, significantly outperforming standard clinical monitoring based on oxygenation index. The model was successfully validated in an external ICU cohort. We also demonstrated how predicted EF risk could help prevent extubation failure and unnecessarily prolonged ventilation. Lastly, we illustrated how prediction of RF risk, along with ventilator need and extubation readiness, helped ICU resource planning for mechanical ventilation. Our model predicted ICU-level ventilator demand 8-16 hours ahead, with a mean absolute error of 0.4 ventilators per 10 patients.

Asthma is a chronic inflammatory airway disease characterized by defective epithelial repair, resulting from metabolic dysregulation in facultative progenitor cells. Here, we investigate how pyruvate metabolism in airway club cells controls epithelial differentiation and allergic airway inflammation. Single-cell transcriptomics revealed elevated glycolytic activity in club and goblet cells from patients with asthma. In an ovalbumin (OVA)-induced asthma model, conditional deletion of Mpc2-but not Ldha-in club cells impaired club-to-goblet cell differentiation, reduced CLCA3 and Foxa3 expression, and attenuated eosinophilic inflammation and Il-13 expression. Mpc2 loss increased Cxcl17 expression in club cells, promoting Cxcl17-Cxcr4 signaling with alveolar macrophages that suppressed CCL17-mediated type 2 inflammation. Neutralizing CCL17 phenocopied the Mpc2 knockout by reducing airway inflammation and goblet cell differentiation. These findings reveal a metabolic-immune crosstalk underlying asthma pathogenesis and identify mitochondrial pyruvate metabolism as a therapeutic target to limit epithelial remodeling and type 2 inflammation.