Ethnic, racial and migrant inequalities in respiratory health

Disparities in the incidence, prevalence, morbidity and mortality rates of many respiratory diseases are evident between ethnic groups. Biological, cultural, and environmental factors related to ethnicity can all contribute to the differences in respiratory health observed between ethnic minority groups, but inequalities observed are most commonly due to lower socio-economic status. People who migrate within a country or across an international border may experience an improvement in respiratory health associated with improvements in socioeconomic status. However, migrants may also experience worse health outcomes in destination countries, as they are faced by barriers in language and culture, discrimination, exclusion, and limited access to health services. Whilst some high quality studies investigating ethnicity and respiratory health are available, further research into ethnic differences is needed. Improving the recording of ethnicity in health records, addressing barriers to accessing respiratory health care and improving cultural literacy more generally are some of the ways that inequalities can be tackled

Parameter-Efficient Fine-Tuning of LLaMA for the Clinical Domain

Adapting pretrained language models to novel domains, such as clinical applications, traditionally involves retraining their entire set of parameters. However, this approach is increasingly proven to be impractical owing to the substantial computational requirements associated with training such large language models. To address this issue, Parameter-Efficient Fine-Tuning (PEFT) techniques offer a viable solution by selectively fine-tuning a small subset of additional parameters, significantly reducing the computational requirements for domain adaptation. In this study, we propose Clinical LLaMA-LoRA, a PEFT adapter layer built upon the open-sourced LLaMA model. Clinical LLaMA-LoRA is trained using clinical notes obtained from the MIMIC-IV database, thereby creating a specialised adapter designed for the clinical domain. Additionally, we propose a two-step PEFT framework which fuses Clinical LLaMA-LoRA with Downstream LLaMA-LoRA, another PEFT adapter specialised for downstream tasks. We evaluate this framework on multiple clinical outcome prediction datasets, comparing it to clinically trained language models. Our proposed framework achieves a state-of-the-art AUROC score averaged across all clinical downstream tasks. We observe substantial improvements of 6-9% AUROC score in the large-scale multilabel classification tasks, such as diagnoses and procedures classification

Diagnostic accuracy of FeNO [fractional exhaled nitric oxide] and asthma symptoms increased when evaluated with a superior reference standard

Objectives: The objective of the study is to determine the impact of changing reference standards (RS), namely spirometry vs. whole-body plethysmography (WBP), on estimation of the diagnostic accuracy of fractional exhaled nitric oxide (FeNO) and clinical signs and symptoms (CSS) as index tests regarding asthma diagnosis. Study Design and Setting: This was a diagnostic study conducted in 393 patients attending a private practice of pneumologists with complaints suspicious of asthma. First, the index tests were compared with the diagnostic results of spirometry in terms of forced expiratory volume in the first second (FEV1) responsiveness. Second, the index tests were compared with the results of WBP in terms of specific airway resistance and FEV1 responsiveness. Areas under the curve (AUC) were compared with a generalized estimating equation approach based on binary logistic regression. Results: FeNO values and CSS ‘wheezing’ and ‘allergic rhinitis’ showed higher specificities (P < 0.001) and sensitivities (not significant) when evaluated with WBP; also, Youden indices increased in these CSS (P < 0.05). AUC of FeNO in combination with ‘wheezing’ and ‘allergic rhinitis’ when WBP was used as RS (AUC = 0.724; 95% confidence interval 0.672 to 0.776) was higher compared with spirometry as RS (AUC = 0.654; 95% confidence interval 0.585 to 0.722) (P < 0.001). Conclusion: In case of asthma, superior RS led to more favorable assessment of index tests. FeNO measurement might have been underestimated in some previous studies

ERS International congress, Madrid, 2019: Highlights from the General Pneumology Assembly

This article contains highlights and a selection of the scientific advances from the European Respiratory Society's General Pneumology Assembly that were presented at the 2019 European Respiratory Society International Congress in Madrid, Spain. The most relevant topics from the different groups will be discussed, covering a wide range of areas including rehabilitation and chronic care, general practice and primary care and M-health and E-health. In this review, the newest research and actual data as well as award-winning abstracts and highlight sessions will be discussed