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Aim. Study the impact of various combinations of comorbid original diseases in patients infected with COVID-19 later on the disease progression and outcomes of the new coronavirus infection. Materials and methods. The ACTIV registry was created on the Eurasian Association of Therapists initiative. 5,808 patients have been included in the registry: men and women with COVID-19 treated at hospital or at home. ClinicalTrials.gov ID NCT04492384. Results. Most patients with COVID-19 have original comorbid diseases (oCDs). Polymorbidity assessed by way of simple counting of oCDs is an independent factor in negative outcomes of COVID-19. Search for most frequent combinations of 2, 3 and 4 oCDs has revealed absolute domination of cardiovascular diseases (all possible variants). The most unfavorable combination of 2 oCDs includes atrial hypertension (AH) and chronic heart failure (CHF). The most unfavorable combination of 3 oCDs includes AH, coronary heart disease (CHD) and CHF; the worst combination of 4 oCDs includes AH, CHD, CHF and diabetes mellitus. Such combinations increased the risk of lethal outcomes 3.963, 4.082 and 4.215 times respectively. Conclusion. Polymorbidity determined by way of simple counting of diseases may be estimated as a factor in the lethal outcome risk in the acute phase of COVID-19 in real practice. Most frequent combinations of 2, 3 and 4 diseases in patients with COVID-19 primarily include cardiovascular diseases (AH, CHD and CHF), diabetes mellitus and obesity. Combinations of such diseases increase the COVID-19 lethal outcome risk.

Background: Multimorbidity is highly prevalent in the elderly and relates to many adverse outcomes, such as higher mortality, increased disability and functional decline. Many studies tried to reduce the heterogeneity of multimorbidity by identifying multimorbidity clusters or disease combinations, however, the internal structure of multimorbidity clusters and the linking between disease combinations and clusters are still unknown. The aim of this study was to depict which diseases were associated with each other on person-level within the clusters and which ones were responsible for overlapping multimorbidity clusters.Methods: The study analyses insurance claims data of the Gmünder ErsatzKasse from 2006 with 43,632 female and 54,987 male patients who were 65 years and older. The analyses are based on multimorbidity clusters from a previous study and combinations of three diseases ("triads") identified by observed/expected ratios ≥ 2 and prevalence rates ≥ 1%. In order to visualise a "disease network", an edgelist was extracted from these triads, which was analysed by network analysis and graphically linked to multimorbidity clusters.Results: We found 57 relevant triads consisting of 31 chronic conditions with 200 disease associations ("edges") in females and 51 triads of 29 diseases with 174 edges in males. In the disease network, the cluster of cardiovascular and metabolic disorders comprised 12 of these conditions in females and 14 in males. The cluster of anxiety, depression, somatoform disorders, and pain consisted of 15 conditions in females and 12 in males.Conclusions: We were able to show which diseases were associated with each other in our data set, to which clusters the diseases were assigned, and which diseases were responsible for overlapping clusters. The disease with the highest number of associations, and the most important mediator between diseases, was chronic low back pain. In females, depression was also associated with many other diseases. We found a multitude of associations between disorders of the metabolic syndrome of which hypertension was the most central disease. The most prominent bridges were between the metabolic syndrome and musculoskeletal disorders. Guideline developers might find our approach useful as a basis for discussing which comorbidity should be addressed. [ABSTRACT FROM AUTHOR]

Aim: Study the impact of various combinations of comorbid original diseases in patients infected with COVID-19 later on the disease progression and outcomes of the new coronavirus infection., Materials and Methods: The ACTIV registry was created on the Eurasian Association of Therapists initiative. 5,808 patients have been included in the registry: men and women with COVID-19 treated at hospital or at home., Clinicaltrials: gov ID NCT04492384., Results: Most patients with COVID-19 have original comorbid diseases (oCDs). Polymorbidity assessed by way of simple counting of oCDs is an independent factor in negative outcomes of COVID-19. Search for most frequent combinations of 2, 3 and 4 oCDs has revealed absolute domination of cardiovascular diseases (all possible variants). The most unfavorable combination of 2 oCDs includes atrial hypertension (AH) and chronic heart failure (CHF). The most unfavorable combination of 3 oCDs includes AH, coronary heart disease (CHD) and CHF; the worst combination of 4 oCDs includes AH, CHD, CHF and diabetes mellitus. Such combinations increased the risk of lethal outcomes 3.963, 4.082 and 4.215 times respectively., Conclusion: Polymorbidity determined by way of simple counting of diseases may be estimated as a factor in the lethal outcome risk in the acute phase of COVID-19 in real practice. Most frequent combinations of 2, 3 and 4 diseases in patients with COVID-19 primarily include cardiovascular diseases (AH, CHD and CHF), diabetes mellitus and obesity. Combinations of such diseases increase the COVID-19 lethal outcome risk.

Background: Multimorbidity, the co-occurrence of two or more chronic medical conditions within a single individual, is increasingly becoming part of daily care of general medical practice. Literature-based discovery may help to investigate the patterns of multimorbidity and to integrate medical knowledge for improving healthcare delivery for individuals with co-occurring chronic conditions., Objective: To explore the usefulness of literature-based discovery in primary care research through the key-case of finding associations between psychiatric and somatic diseases relevant to general practice in a large biomedical literature database (Medline)., Methods: By using literature based discovery for matching disease profiles as vectors in a high-dimensional associative concept space, co-occurrences of a broad spectrum of chronic medical conditions were matched for their potential in biomedicine. An experimental setting was chosen in parallel with expert evaluations and expert meetings to assess performance and to generate targets for integrating literature-based discovery in multidisciplinary medical research of psychiatric and somatic disease associations., Results: Through stepwise reductions a reference set of 21,945 disease combinations was generated, from which a set of 166 combinations between psychiatric and somatic diseases was selected and assessed by text mining and expert evaluation., Conclusions: Literature-based discovery tools generate specific patterns of associations between psychiatric and somatic diseases: one subset was appraised as promising for further research; the other subset surprised the experts, leading to intricate discussions and further eliciting of frameworks of biomedical knowledge. These frameworks enable us to specify targets for further developing and integrating literature-based discovery in multidisciplinary research of general practice, psychology and psychiatry, and epidemiology.