Brit J Hosp Med
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The rapidly developing field of artificial intelligence (AI) may soon equip clinicians with algorithms that model and predict perioperative problems with extreme accuracy. Here, we outline emerging AI applications in preoperative risk stratification and intraoperative event prediction, where algorithm performance has been shown to outstrip commonly used conventional risk prediction tools. While offering an enticing view of a novel perioperative practice with superhuman foresight, AI's limited scope and lack of transparency remain key challenges for widespread adoption. As yet it is unclear whether machine learning alone can influence human clinical practice to exert real-world effects on patient outcomes.
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Patients who discharge themselves against medical advice comprise 1%-2% of hospital admissions. Discharge against medical advice (DAMA) is defined as when a hospitalised patient chooses to leave the hospital before the treating medical team recommends discharge. The act of DAMA impacts on both the patient, the staff and their ongoing care. ⋯ Patients who decide to DAMA tend to be young males, from a lower socioeconomic background and with a history of mental health or substance misuse disorder. DAMA has an associated increased risk of morbidity and mortality. In this review of studies across Western healthcare settings, specifically adult medical inpatients, we will review the evidence and seek to address the causes, consequences and possible corrective measures in this common scenario.
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Survival of preterm-born infants, especially at extremes of prematurity (less than 28 weeks gestation), is now common, particularly in the developed world. Despite advances in neonatal care, short-term respiratory morbidity, termed bronchopulmonary dysplasia (also called chronic lung disease of prematurity), remains an important clinical outcome. ⋯ In addition, we shall review the emerging literature on the respiratory morbidity experienced in childhood, adolescence, and adulthood by preterm-born survivors, with reduced lung function and a risk of developing chronic obstructive pulmonary disease in early adult life. As this population of preterm-born individuals increases, an understanding of the respiratory consequences of preterm birth will become increasingly important not only for neonatologists, paediatricians and paediatric pulmonologists but also for physicians and healthcare professionals involved in the care of adults who were born preterm.
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Aims/Background The application of immunochemotherapy has significantly enhanced the quality of life and overall survival of patients with esophageal cancer. Sarcopenia, which is increasingly prevalent in these patients, markedly affects prognosis, but can be reversed by appropriate and effective treatment. Methods The narrative review was conducted on PubMed using the keywords ("esophageal" or "esophagus" and "sarcopenia"). ⋯ It summarizes the evaluation indicators of skeletal muscle loss in these patients, analyzes the barriers to intervention for frailty among esophageal cancer patients, and proposes corresponding countermeasures. Conclusion Patients with esophageal cancer often suffer from severe sarcopenia. Clinical intervention is crucial in addressing this issue.
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The potential applications of Artificial Intelligence (AI) in anaesthesia are expansive.~However, like any technological advancement, the integration of AI in anaesthetic practice comes with both benefits and potential risks. This article seeks to set out some of the advantages and disadvantages of the use of AI technologies within the field of anaesthesia. ⋯ Whilst AI within anaesthetic practice holds immense promise, there are substantial challenges which require careful consideration and ongoing evaluation. A collaborative approach will be required from healthcare staff, developers and regulators to promote the safe, responsible, and effective application of AI in anaesthesia practice.