Daily Ards 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.

BACKGROUND: Relapsing fever (RF) remains a major public health concern in Ethiopia, causing severe illness and high mortality despite global declines since the 1940s. Mortality may reach 70% without treatment, yet research on outcomes and risk factors is limited in resource-limited settings. This study, conducted at Yekatit-12 Hospital Medical College, aimed to evaluate the clinical features, complications, and determinants of death among patients with RF, addressing a critical knowledge gap in Ethiopia. METHODS: A retrospective cross-sectional study was conducted among patients aged 14 years and older with blood film-confirmed relapsing fever between September 2021 and February 2023. A structured data collection tool was used to extract clinical, demographic, and laboratory information. Patient characteristics were described using descriptive statistics, while multivariable logistic regression was used to determine the independent predictors of mortality, and results were presented as adjusted odds ratios (AOR) with 95% confidence intervals. RESULTS: A total of 119 male patients were included, with a mean age of 24.1 years. Most were daily laborers (42.0%) or homeless (39.5%). The leading symptoms were fever (85.5%), chills (67.2%), respiratory distress (54.6%), and altered mental status (46.2%). Complications were observed in 88.2% of cases, most commonly anemia (74.7%), acute respiratory distress syndrome (69.7%), shock (60.5%), and Jarisch-Herxheimer reaction (36.1%). Mortality was 45.4% (n=54), primarily from multi-organ failure (64.8%) and respiratory failure (25.9%). Independent predictors of death were symptom duration >5 days before admission (AOR = 3.1; 95% CI: 1.24-7.9), Jarisch-Herxheimer reaction (AOR = 2.8; 95% CI: 1.09-7.64), multi-organ failure (AOR = 3.8; 95% CI: 1.23-11.6), and requirement for mechanical ventilation (AOR = 2.7; 95% CI: 1.05-7.1). CONCLUSION: In Ethiopia, relapsing fever affects young, socioeconomically disadvantaged men, with high complication and mortality rates. Delayed treatment, Jarisch-Herxheimer reaction, multi-organ failure, and requiring mechanical ventilation during admission were predictors of mortality in mens, highlighting the importance of early diagnosis, antibiotics, and critical care.