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The number of children receiving domiciliary ventilatory support has grown over the last few decades driven largely by the introduction and widening applications of non-invasive ventilation. Ventilatory support may be used with the intention of increasing survival, or to facilitate discharge home and/or to palliate symptoms. However, the outcome of this intervention and the number of children transitioning to adult care as a consequence of longer survival is not yet clear. In this retrospective cohort study, we analysed the outcome in children (<17 years) started on home NIV at Royal Brompton Hospital over an 18 year period 1993-2011. The aim was to establish for different diagnostic groups: survival rate, likelihood of early death depending on diagnosis or discontinuation of ventilation, and the proportion transitioning to adult care. 496 children were commenced on home non invasive ventilation; follow-up data were available in 449 (91%). Fifty six per cent (n=254) had neuromuscular disease. Ventilation was started at a median age (IQR) 10 (3-15) years. Thirteen percent (n=59) were less than 1 year old. Forty percent (n=181) have transitioned to adult care. Twenty four percent (n=109) of patients have died, and nine percent (n=42) were able to discontinue ventilatory support. Long term ventilation is associated with an increase in survival in a range of conditions leading to ventilatory failure in children, resulting in increasing numbers surviving to adulthood. This has significant implications for planning transition and adult care facilities.

Background Chronic obstructive pulmonary disease (COPD) remains an underestimated and underdiagnosed condition due to low disease awareness. Generative Artificial Intelligence (AI) chatbots are convenient and accessible sources of medical information, but evaluation of the quality of answers provided by patient-generated questions about COPD has not been performed to date. Objective To assess and compare accuracy, comprehensiveness, understandability and reliability of different AI chatbots in response to patient-generated questions on the clinical management of COPD. Methods A cross-sectional study was conducted in collaboration with the European Respiratory Society (ERS), the European Lung Foundation (ELF), and the ERS CONNECT Clinical Research Collaboration (CRC). Fifteen real questions formulated by ELF COPD patient representatives were divided into three difficulty tiers (easy, medium, difficult) and submitted to ChatGPT (version 3.5), Bard, and Copilot. Experts assessed accuracy and comprehensiveness on a 0–10 scale; patients assessed understandability using the same scale. Reliability was assessed by two investigators. Reviewers were blinded to which AI system generated the answers, and only those who completed all evaluations were included in the analysis. Results ChatGPT responses were the most reliable (14/15), followed by Copilot (12/15) and Bard (11/15). ChatGPT scored higher for accuracy (8.0 [7.0 – 9.0]) and comprehensiveness (8.0 [6.8 – 9.0]) than Bard (6.0 [5.0 – 8.0] and 6.0 [5.0 – 7.0]) and Copilot (6.0 [5.0 – 7.3] and 6.0 [5.0 – 8.0]) (both P  < 0.001). Understandability was similar across all software (ChatGPT: 8.0 [8.0–10.0]; Bard: 9.0 [8.0–10.0]; Copilot: 9.0 [8.0–10.0]) ( P  = 0.53). No significant effect was detected according to the difficulty of the question. Conclusion Our findings suggest that AI chatbots, particularly ChatGPT, can provide accurate, comprehensive and understandable answers to patients’ questions.