Efficacy of treatment for hyperglycemic crisis in elderly diabetic patients in a day hospital

The purpose of this prospective cohort study was to compare the costs of day hospital (DH) care for hyperglycemic crisis in elderly diabetic patients with those of conventional hospitalization (CH). Secondary objectives were to compare these two clinical scenarios in terms of glycemic control, number of emergency and outpatient visits, readmissions, hypoglycemic episodes, and nosocomial morbidity. The study population comprised diabetic patients aged >74 years consecutively admitted to a tertiary teaching hospital in Spain for hyperglycemic crisis (sustained hyperglycemia [>300 mg/dL] for at least 3 days with or without ketosis). The patients were assigned to DH or CH care according to time of admission and were followed for 6 months after discharge. Exclusion criteria were ketoacidosis, hyperosmolar crisis, hemodynamic instability, severe intercurrent illness, social deprivation, or Katz index >D. Sixty-four diabetic patients on DH care and 36 on CH care were included, with no differences in baseline characteristics. The average cost per patient was 1,345.1±793.6 € in the DH group and 2,212.4±982.5 € in the CH group (P <0.001). There were no differences in number of subjects with mild hypoglycemia during follow-up (45.3% DH versus 33.3% CH, P =0.24), nor in the percentage of patients achieving a glycated hemoglobin (HbA) <8% (67.2% DH versus 58.3% CH, P =0.375). Readmissions for hyperglycemic crisis and pressure ulcer rates were significantly higher in the CH group. DH care for hyperglycemic crises is more cost-effective than CH care, with a net saving of 1,418.4 € per case, lower number of readmissions and pressure ulcer rates, and similar short-term glycemic control and hypoglycemia rates

Seven-year mortality in heart failure patients with undiagnosed diabetes : an observational study

Background: Patients with type 2 diabetes mellitus and heart failure have adverse clinical outcomes, but the characteristics and prognosis of those with undiagnosed diabetes in this setting has not been established. Methods: In total, 400 patients admitted consecutively with acute heart failure were grouped in three glycaemic categories: no diabetes, clinical diabetes (previously reported or with hypoglycaemic treatment) and undiagnosed diabetes. The latter was defined by the presence of at least two measurements of fasting plasma glycaemia ≥ 7 mmol/L before or after the acute episode.Group differences were tested by proportional hazards models in all-cause and cardiovascular mortality during a 7-year follow-up. Results: There were 188 (47%) patients without diabetes, 149 (37%) with clinical diabetes and 63 (16%) with undiagnosed diabetes. Patients with undiagnosed diabetes had a lower prevalence of hypertension, dyslipidaemia, peripheral vascular disease and previous myocardial infarction than those with clinical diabetes and similar to that of those without diabetes. The adjusted hazards ratios for 7-year total and cardiovascular mortality compared with the group of subjects without diabetes were 1.69 (95% CI: 1.17-2.46) and 2.45 (95% CI: 1.58-3.81) for those with undiagnosed diabetes, and 1.48 (95% CI: 1.10-1.99) and 2.01 (95% CI: 1.40-2.89) for those with clinical diabetes. Conclusions: Undiagnosed diabetes is common in patients requiring hospitalization for acute heart failure. Patients with undiagnosed diabetes, despite having a lower cardiovascular risk profile than those with clinical diabetes, show a similar increased mortality

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

Efficacy of treatment for hyperglycemic crisis in elderly diabetic patients in a day hospital

D Benaiges,1&ndash;3 JJ Chillar&oacute;n,1&ndash;3 MJ Carrera,1,3 F Cots,3,4 J Puig de Dou,1 E Corominas,1 J Pedro-Botet,1&ndash;3 JA Flores-Le Roux,1&ndash;3 C Claret,1 A Goday,1&ndash;3 JF Cano1&ndash;3 1Department of Endocrinology and Nutrition, Hospital del Mar, 2Department of Medicine, Universitat Aut&ograve;noma de Barcelona, 3Institut Hospital del Mar d&rsquo;Investigacions M&egrave;diques, 4Epidemiology and Evaluation Department, Parc de Salut Mar, Barcelona, Spain Background:&nbsp;The purpose of this prospective cohort study was to compare the costs of&nbsp;day hospital (DH) care for hyperglycemic crisis in elderly diabetic patients with those of conventional hospitalization (CH). Secondary objectives were to compare these two clinical scenarios in terms of glycemic control, number of emergency and outpatient visits, readmissions, hypoglycemic episodes, and nosocomial morbidity. Methods: The study population comprised diabetic patients aged &gt;74&nbsp;years consecutively admitted to a tertiary teaching hospital in Spain for hyperglycemic crisis (sustained hyperglycemia [&gt;300&nbsp;mg/dL] for at least&nbsp;3&nbsp;days with or without ketosis). The patients were assigned to DH or CH care according to time of admission and were followed for&nbsp;6&nbsp;months after discharge. Exclusion criteria were ketoacidosis, hyperosmolar crisis, hemodynamic instability, severe intercurrent illness, social deprivation, or Katz index &gt;D.Results: Sixty-four diabetic patients on DH care and&nbsp;36&nbsp;on CH care were included, with no differences in baseline characteristics. The average cost per patient was&nbsp;1,345.1&plusmn;793.6&nbsp;&euro; in the DH group and&nbsp;2,212.4&plusmn;982.5&nbsp;&euro; in the CH group (P&lt;0.001). There were no differences in number of subjects with mild hypoglycemia during follow-up (45.3% DH versus&nbsp;33.3% CH, P=0.24), nor in the percentage of patients achieving a glycated hemoglobin (HbA1c) &lt;8% (67.2% DH versus&nbsp;58.3% CH, P=0.375). Readmissions for hyperglycemic crisis and pressure ulcer rates were significantly higher in the CH group.Conclusion: DH care for hyperglycemic crises is more cost-effective than CH care, with a net saving of&nbsp;1,418.4&nbsp;&euro; per case, lower number of readmissions and pressure ulcer rates, and similar short-term glycemic control and hypoglycemia rates. Keywords: day hospital, conventional hospitalization, hyperglycemic crisi

Efficacy of treatment for hyperglycemic crisis in elderly diabetic patients in a day hospital

The purpose of this prospective cohort study was to compare the costs of day hospital (DH) care for hyperglycemic crisis in elderly diabetic patients with those of conventional hospitalization (CH). Secondary objectives were to compare these two clinical scenarios in terms of glycemic control, number of emergency and outpatient visits, readmissions, hypoglycemic episodes, and nosocomial morbidity. The study population comprised diabetic patients aged >74 years consecutively admitted to a tertiary teaching hospital in Spain for hyperglycemic crisis (sustained hyperglycemia [>300 mg/dL] for at least 3 days with or without ketosis). The patients were assigned to DH or CH care according to time of admission and were followed for 6 months after discharge. Exclusion criteria were ketoacidosis, hyperosmolar crisis, hemodynamic instability, severe intercurrent illness, social deprivation, or Katz index >D. Sixty-four diabetic patients on DH care and 36 on CH care were included, with no differences in baseline characteristics. The average cost per patient was 1,345.1±793.6 € in the DH group and 2,212.4±982.5 € in the CH group (P <0.001). There were no differences in number of subjects with mild hypoglycemia during follow-up (45.3% DH versus 33.3% CH, P =0.24), nor in the percentage of patients achieving a glycated hemoglobin (HbA) <8% (67.2% DH versus 58.3% CH, P =0.375). Readmissions for hyperglycemic crisis and pressure ulcer rates were significantly higher in the CH group. DH care for hyperglycemic crises is more cost-effective than CH care, with a net saving of 1,418.4 € per case, lower number of readmissions and pressure ulcer rates, and similar short-term glycemic control and hypoglycemia rates