Impact of perinatal factors on biomarkers of cardiovascular disease risk in preadolescent children.

BACKGROUND: This review aims to summarize associations of the perinatal environment with arterial biophysical properties in childhood, to elucidate possible perinatal origins of adult cardiovascular disease (CVD). METHODS: A systematic search of PubMed database was performed (December 2020). Studies exploring associations of perinatal factors with arterial biophysical properties in children 12 years old or less were included. Properties studied included: pulse wave velocity; arterial stiffness or distensibility; augmentation index; intima-media thickness of aorta (aIMT) or carotids; endothelial function (laser flow Doppler, flow-mediated dilatation). Two reviewers independently performed study selection and data extraction. RESULTS: Fifty-two of 1084 identified records were included. Eleven studies explored associations with prematurity, 14 explored maternal factors during pregnancy, and 27 explored effects of low birth weight, small-for-gestational age and foetal growth restriction (LBW/SGA/FGR). aIMT was consistently higher in offspring affected by LBW/SGA/FGR in all six studies examining this variable. The cause of inconclusive or conflicting associations found with other arterial biophysical properties and perinatal factors may be multifactorial: in particular, measurements and analyses of related properties differed in technique, equipment, anatomical location, and covariates used. CONCLUSION: aIMT was consistently higher in LBW/SGA/FGR offspring, which may relate to increased long-term CVD risk. Larger and longer term cohort studies may help to elucidate clinical significance, particularly in relation to established CVD risk factors. Experimental studies may help to understand whether lifestyle or medical interventions can reverse perinatal changes aIMT. The field could be advanced by validation and standardization of techniques assessing arterial structure and function in children.

AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units.

INTRODUCTION: Growing demand for mental health services, coupled with funding and resource limitations, creates an opportunity for novel technological solutions including artificial intelligence (AI). This study aims to identify issues in patient flow on mental health units and align them with potential AI solutions, ultimately devising a model for their integration at service level. METHOD: Following a narrative literature review and pilot interview, 20 semi-structured interviews were conducted with AI and mental health experts. Thematic analysis was then used to analyse and synthesise gathered data and construct an enhanced model. RESULTS: Predictive variables for length-of-stay and readmission rate are not consistent in the literature. There are, however, common themes in patient flow issues. An analysis identified several potential areas for AI-enhanced patient flow. Firstly, AI could improve patient flow by streamlining administrative tasks and optimising allocation of resources. Secondly, real-time data analytics systems could support clinician decision-making in triage, discharge, diagnosis and treatment stages. Finally, longer-term, development of solutions such as digital phenotyping could help transform mental health care to a more preventative, personalised model. CONCLUSIONS: Recommendations were formulated for NHS trusts open to adopting AI patient flow enhancements. Although AI offers many promising use-cases, greater collaborative investment and infrastructure are needed to deliver clinically validated improvements. Concerns around data-use, regulation and transparency remain, and hospitals must continue to balance guidelines with stakeholder priorities. Further research is needed to connect existing case studies and develop a framework for their evaluation.

Applications of artificial intelligence to improve patient flow on mental health inpatient units - Narrative literature review.

BACKGROUND: Despite a growing body of research into both Artificial intelligence and mental health inpatient flow issues, few studies adequately combine the two. This review summarises findings in the fields of AI in psychiatry and patient flow from the past 5 years, finds links and identifies gaps for future research. METHODS: The OVID database was used to access Embase and Medline. Top journals such as JAMA, Nature and The Lancet were screened for other relevant studies. Selection bias was limited by strict inclusion and exclusion criteria. RESEARCH: 3,675 papers were identified in March 2020, of which a limited number focused on AI for mental health unit patient flow. After initial screening, 323 were selected and 83 were subsequently analysed. The literature review revealed a wide range of applications with three main themes: diagnosis (33%), prognosis (39%) and treatment (28%). The main themes that emerged from AI in patient flow studies were: readmissions (41%), resource allocation (44%) and limitations (91%). The review extrapolates those solutions and suggests how they could potentially improve patient flow on mental health units, along with challenges and limitations they could face. CONCLUSION: Research widely addresses potential uses of AI in mental health, with some focused on its applicability in psychiatric inpatients units, however research rarely discusses improvements in patient flow. Studies investigated various uses of AI to improve patient flow across specialities. This review highlights a gap in research and the unique research opportunity it presents.