Respiratory Medicine Libguide

Thorax. 2025 Jun 15:thorax-2024-222953. doi: 10.1136/thorax-2024-222953. Online ahead of print.

NO ABSTRACT

PMID:40523674 | DOI:10.1136/thorax-2024-222953

Thorax. 2025 Jun 15:thorax-2024-222306. doi: 10.1136/thorax-2024-222306. Online ahead of print.

ABSTRACT

RATIONALE: Inhalational exposures are associated with risk of developing interstitial lung disease (ILD), yet the relationship between specific exposures and ILD is poorly characterised.

OBJECTIVE: Identify inhalational exposures associated with ILD and estimate the effects of exposures on ILD risk.

METHODS: MEDLINE and EMBASE databases were searched from 1990 to 2022 to identify inhalational exposures associated with ILD diagnosis. ILDs where causality is well-established (hypersensitivity pneumonitis, pneumoconiosis) and sarcoidosis were excluded. Two independent reviewers screened abstracts with full-text review and data extraction of eligible studies. Where possible, data were pooled and multilevel meta-analysis was specified using a random effects model. Sources of heterogeneity and risk of bias were assessed.

MAIN RESULTS: 96 studies were included in the systematic review, representing 40 819 116 subjects (295 167 had ILD, 40 523 949 controls). For the meta-analysis, 54 studies were included (40 490 793 subjects: 273 899 ILD, 40 216 894 controls). Exposures associated with significantly increased ILD risk included smoking (OR 1.69, 95% CI 1.47 to 1.94), organic exposures (OR 1.56, 95% CI 1.12 to 2.16), metals (OR 1.52, 95% CI 1.07 to 2.16), dust (OR 1.45, 95% CI 1.20 to 1.76) and asbestos (OR 1.53, 95% CI 1.08 to 2.15). Silica and fumes had positive associations with ILD that trended towards significance.

CONCLUSIONS: This systematic review and multilevel meta-analysis is the first to comprehensively assess the effect of inhalational exposures on overall risk of ILD, with multiple putative exposures identified. Future work should investigate novel occupational exposures associated with ILD, characterise the gene-environment interaction and develop preventative strategies.

PROSPERO REGISTRATION NUMBER: CRD42022292908.

PMID:40518258 | DOI:10.1136/thorax-2024-222306

Thorax. 2025 Jun 15:thorax-2024-222125. doi: 10.1136/thorax-2024-222125. Online ahead of print.

ABSTRACT

BACKGROUND: The integration of artificial intelligence (AI) into critical care offers significant potential to enhance early diagnosis, predict patient deterioration, personalise treatment and inform clinical decision-making. Despite this promise, AI adoption in the intensive care unit (ICU) faces challenges, as illustrated by the limited number of AI tools which have been approved for clinical use and/or successfully deployed in critical care.

METHODS: Aims of the review are to provide a synthesis of research on AI in critical care; assess approved tools; and consider challenges and opportunities, focusing on the different phases of the AI algorithm lifecycle in the ICU, including data collection, modelling, validation, implementation and post-deployment monitoring. Peer-reviewed publications were searched using terms relevant to AI and critical care spanning the years 2000-2025.

RESULTS: Research on AI applications in the ICU is characterised by significant limitations including suboptimal data quality, retrospective analyses and a paucity of prospective validation studies. The few AI algorithms that have received Food and Drug Administration approval for use in the ICU have not gained widespread clinical adoption due, in part, to issues such as lack of user trust, integration challenges, unclear clinical impact or performance drift. Overcoming these barriers will require a structured approach that addresses the key challenges identified in the AI lifecycle, including the integration of real-world data, post-deployment performance monitoring, governance and ethical considerations. A successful implementation pathway should consider realistic goal-setting, greater model explainability, improved workflow integration and active end-user involvement.

CONCLUSIONS: Advancing critical care with AI will require special attention to interdisciplinary collaboration, robust validation frameworks and adaptive governance models. The need for rigorous scientific evaluation needs to be balanced with the pressure for rapid deployment. Ensuring transparency, safety and alignment with clinical workflows will be critical to achieving meaningful AI integration in critical care.

PMID:40518257 | DOI:10.1136/thorax-2024-222125

Thorax. 2025 Jun 8:thorax-2024-222168. doi: 10.1136/thorax-2024-222168. Online ahead of print.

ABSTRACT

Climate change is altering ecosystems worldwide. While shifting environmental conditions are complex, it has been hypothesised that the impact of climate change is directly leading to increases in fungal infections across the globe. Rising temperatures, changing precipitation patterns and extreme weather events are thought to be driving the adaptation of fungal pathogens to new climates, expanding their geographical range and posing a growing threat to human health and agriculture. This review highlights how climate change may impact key pathogens, including Candida auris, Candida orthopsilosis, Cryptococcus deuterogattii and resistant strains of Aspergillus fumigatus, which have emerged as significant public health concerns. Their spread is accelerated by globalisation, urbanisation and the intensifying use of agricultural fungicides, which further increase antifungal resistance. The growing prevalence of resistant strains and emergence of novel fungal pathogens is likely linked to anthropogenic climate change, underscoring the urgent need for action and for more robust data collection.

PMID:40484641 | DOI:10.1136/thorax-2024-222168

Thorax. 2025 Jun 8:thorax-2025-223403. doi: 10.1136/thorax-2025-223403. Online ahead of print.

NO ABSTRACT

PMID:40484640 | DOI:10.1136/thorax-2025-223403