BackgroundTo describe the prevalence of multimorbidity and to study the association between acute and chronic diseases in acutely hospitalized older patientsMethodsProspective cohort study conducted between 2006 and 2008 in three teaching hospitals in the Netherlands. 639 patients aged 65 years and older, hospitalized for > 48 h were included. Two physicians scored diseases, using ICD-9 codes. Chronic multimorbidity was defined as the presence of ≥ 2 chronic diseases, and acute multimorbidity as ≥ 1 acute diseases upon pre-existent chronic diseases. Logistic regression analyses were conducted to analyse cluster associations between a chronic index disease and the concurrent chronic or acute disease, corrected for age and sex.ResultsThe mean age of patients was 78 years, over 50% had ADL impairments. Prevalence of chronic multimorbidity was 69%, and acute multimorbidity was present in 88%. Hypertension (OR 1.16; 95% CI 1.08–1.24), diabetes (type I or type 2) (OR 1.12; 95% CI 1.04–1.21), heart failure (OR 1.25; 95% CI 1.14–1.38) and COPD (OR 1.19; 95% CI 1.05–1.34) were associated with acute renal failure. Hypertension (OR 1.10; 95% CI 1.04–1.17) and atrial fibrillation (OR 1.17; 95% CI 1.08–1.27) were associated with an adverse drug event. Gastro-intestinal bleeding was clustered with atrial fibrillation (OR 1.11; 95% CI 1.04–1.19) and gastric ulcer (OR 1.16; 95% CI 1.07–1.25).ConclusionBoth acute and chronic multimorbidity was frequently present in hospitalized older patients. We identified specific associations between acute and chronic diseases. There is a need for strategies addressing multimorbidity during the exacerbation of chronic diseases.
DOCUMENT
Skeletal muscle-related symptoms are common in both acute coronavirus disease (Covid)-19 and post-acute sequelae of Covid-19 (PASC). In this narrative review, we discuss cellular and molecular pathways that are affected and consider these in regard to skeletal muscle involvement in other conditions, such as acute respiratory distress syndrome, critical illness myopathy, and post-viral fatigue syndrome. Patients with severe Covid-19 and PASC suffer from skeletal muscle weakness and exercise intolerance. Histological sections present muscle fibre atrophy, metabolic alterations, and immune cell infiltration. Contributing factors to weakness and fatigue in patients with severe Covid-19 include systemic inflammation, disuse, hypoxaemia, and malnutrition. These factors also contribute to post-intensive care unit (ICU) syndrome and ICU-acquired weakness and likely explain a substantial part of Covid-19-acquired weakness. The skeletal muscle weakness and exercise intolerance associated with PASC are more obscure. Direct severe acute respiratory syndrome coronavirus (SARS-CoV)-2 viral infiltration into skeletal muscle or an aberrant immune system likely contribute. Similarities between skeletal muscle alterations in PASC and chronic fatigue syndrome deserve further study. Both SARS-CoV-2-specific factors and generic consequences of acute disease likely underlie the observed skeletal muscle alterations in both acute Covid-19 and PASC.
DOCUMENT
Alongside the growing number of older persons, the prevalence of chronic diseases is increasing, leading to higher pressure on health care services. eHealth is considered a solution for better and more efficient health care. However, not every patient is able to use eHealth, for several reasons. This study aims to provide an overview of: (1) sociodemographic factors that influence the use of eHealth; and (2) suggest directions for interventions that will improve the use of eHealth in patients with chronic disease. A structured literature review of PubMed, ScienceDirect, Association for Computing Machinery Digital Library (ACMDL), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) was conducted using four sets of keywords: “chronic disease”, “eHealth”, “factors”, and “suggested interventions”. Qualitative, quantitative, and mixed-method studies were included. Four researchers each assessed quality and extracted data. Twenty-two out of 1639 articles were included. Higher age and lower income, lower education, living alone, and living in rural areas were found to be associated with lower eHealth use. Ethnicity revealed mixed outcomes. Suggested solutions were personalized support, social support, use of different types of Internet devices to deliver eHealth, and involvement of patients in the development of eHealth interventions. It is concluded that eHealth is least used by persons who need it most. Tailored delivery of eHealth is recommended
DOCUMENT
