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Mechanical ventilation (MV) is a core life-support therapy for patients suffering from respiratory failure or acute respiratory distress syndrome (ARDS). Respiratory failure is a secondary outcome of a range of injuries and diseases, and results in almost half of all intensive care unit (ICU) patients receiving some form of MV. Funding the increasing demand for ICU is a major issue and MV, in particular, can double the cost per day due to significant patient variability, over-sedation, and the large amount of clinician time required for patient management. Reducing cost in this area requires both a decrease in the average duration of MV by improving care, and a reduction in clinical workload.Both could be achieved by safely automating all or part of MV care via model-based dynamic systems modelling and control methods are ideally suited to address these problems. This paper presents common lung models, and provides a vision for a more automated future and explores predictive capacity of some current models. This vision includes the use of model-based methods to gain real-time insight to patient condition, improve safety through the forward prediction of outcomes to changes in MV, and develop virtual patients for in-silico design and testing of clinical protocols. Finally, the use of dynamic systems models and system identification to guide therapy for improved personalised control of oxygenation and MV therapy in the ICU will be considered. Such methods are a major part of the future of medicine, which includes greater personalisation and predictive capacity to both optimise care and reduce costs. This review thus presents the state of the art in how dynamic systems and control methods can be applied to transform this core area of ICU medicine.

Introduction: Mental health, physical health and cognitive skills have been scarcely investigated in the same sample of adults with PKU (AwPKU). This is striking since emotional difficulties may potentially contribute to cognitive impairments and vice-versa. Here we aim to fill this gap.Method: Thirty-six early-treated AwPKU and 40 controls were given an extensive battery of cognitive tasks assessing complex executive functions, inhibitory control, short -term-memory, sustained attention, visuospatial attention, language production (reading and naming), visuomotor coordination, spoken language and orthographic processing. In addition, participants were given tasks tapping emotion recognition and completed questionnaires to assess depression (BDI-II), empathy (IRI) and mental/physical health-related quality of life (SF-36).Results: As a group, AwPKU performed significantly worse than controls especially in tasks tapping complex executive functions and across tasks when speed was measured but did not differ for emotional-health and physical health. In the PKU group, cognitive measures and measures of physical health-related quality of life were inter-correlated (differently than in the control group), and both measures were associated with metabolic control: better metabolic control, better cognition and better physical health. Instead, cognitive measures and measures of emotional-health/ mental-health related quality of life did not correlate with one another and better metabolic control was not associated with better emotional health. Instead, some negative correlations were found. Better metabolic control was associated with worse perspective taking and more distress in socially stressful situations. Furthermore, difficulties in keeping the diet were associated with less emotional well-being.Conclusions: Taken together, these results indicate the advantages, but also possible emotional difficulties related to maintain a PKU diet, suggesting the importance of developing alternative therapy options.

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BackgroundIn the treatment of phenylketonuria (PKU), there was disparity between UK dietitians regarding interpretation of how different foods should be allocated in a low phenylalanine diet (allowed without measurement, not allowed, or allowed as part of phenylalanine exchanges). This led to variable advice being given to patients.MethodologyIn 2015, British Inherited Metabolic Disease Group (BIMDG) dietitians (n = 70) were sent a multiple-choice questionnaire on the interpretation of protein from food-labels and the allocation of different foods. Based on majority responses, 16 statements were developed. Over 18-months, using Delphi methodology, these statements were systematically reviewed and refined with a facilitator recording discussion until a clear majority was attained for each statement. In Phase 2 and 3 a further 7 statements were added.ResultsThe statements incorporated controversial dietary topics including: a practical ‘scale’ for guiding calculation of protein from food-labels; a general definition for exchange-free foods; and guidance for specific foods. Responses were divided into paediatric and adult groups. Initially, there was majority consensus (≥86%) by paediatric dietitians (n = 29) for 14 of 16 statements; a further 2 structured discussions were required for 2 statements, with a final majority consensus of 72% (n = 26/36) and 64% (n = 16/25). In adult practice, 75% of dietitians agreed with all initial statements for adult patients and 40% advocated separate maternal-PKU guidelines. In Phase 2, 5 of 6 statements were agreed by ≥76% of respondents with one statement requiring a further round of discussion resulting in 2 agreed statements with a consensus of ≥71% by dietitians in both paediatric and adult practice. In Phase 3 one statement was added to elaborate further on an initial statement, and this received 94% acceptance by respondents. Statements were endorsed by the UK National Society for PKU.ConclusionsThe BIMDG dietitians group have developed consensus dietetic statements that aim to harmonise dietary advice given to patients with PKU across the UK, but monitoring of statement adherence by health professionals and patients is required.