Nutritional status of patients with COVID-19 1-y post-ICU stay: A prospective observational study.

OBJECTIVES: Patients discharged from the intensive care unit (ICU) often experience physical complaints and poor nutritional intake, which negatively affect their nutritional status (NS). The aim of this study was to describe the NS of patients with COVID-19 1-y post-ICU stay. METHODS: This was an observational study of adult patients with COVID-19 1-y post-ICU stay. NS assessment (nutrient balance, body composition, and physical status) was performed. We examined nutritional intake and nutrition-related complaints. Nutritional requirements were determined with indirect calorimetry and body composition with bioelectrical impedance. Fat-free mass index (FFMI) and fat mass index (FMI) were calculated. Physical status was determined using handgrip strength, the 6-min walk test, and the 1-min sit-to-stand test. Descriptive statistics and paired sample t tests were used for analysis. RESULTS: We included 48 patients (73% men; median age 60 y [IQR 52;65]). Median weight loss during the ICU stay was 13%. One-y post-ICU stay, 12% of weight was regained. Median body mass index was 26 kg/m2 and 23% of the patients were obese (body mass index >30 kg/m2 and high FMI). Of the patients, 50% had high FMI and 19% had low FFMI. Median reported nutritional intake was 90% of measured resting energy expenditure. Nutrition-related complaints were seen in 16%. Percentages of normal values reached in physical tests were 92% of handgrip strength, 95% of 6-min walking distance, and 79% of 1-min sit-to-stand test. CONCLUSIONS: Despite almost fully regained weight and good physical recovery in adult patients 1-y post-ICU stay, NS remained impaired because of elevated FMI, even though reported nutritional intake was below the estimated requirements.

Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter.

OBJECTIVE: Although the role of artificial intelligence (AI) in medicine is increasingly studied, most patients do not benefit because the majority of AI models remain in the testing and prototyping environment. The development and implementation trajectory of clinical AI models are complex and a structured overview is missing. We therefore propose a step-by-step overview to enhance clinicians' understanding and to promote quality of medical AI research. METHODS: We summarised key elements (such as current guidelines, challenges, regulatory documents and good practices) that are needed to develop and safely implement AI in medicine. CONCLUSION: This overview complements other frameworks in a way that it is accessible to stakeholders without prior AI knowledge and as such provides a step-by-step approach incorporating all the key elements and current guidelines that are essential for implementation, and can thereby help to move AI from bytes to bedside.