SARS-CoV-2 infection and cardiovascular or pulmonary complications in ambulatory care: A risk assessment based on routine data

Karapetyan, Siranush; Schneider, Antonius; Linde, Klaus; Donnachie, Ewan; Hapfelmeier, Alexander

doi:10.1371/journal.pone.0258914

Cited by 7 publications

(5 citation statements)

References 28 publications

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“…В когорте из 99 811 пациентов в скорректированной многофакторной регрессионной модели Karapetyan S и соавт. (2021) определили значимые факторы риска сердечно-сосудистых или лёгочных осложнений COVID-19: ишемическая болезнь сердца (ИБС) (ОШ = 2,58), артериальная гипертензия (ОШ = 1,65), употребление табака (ОШ = 1,56), хроническая обструктивная болезнь лёгких (ХОБЛ) (ОШ = 1,53), предшествующая пневмония (ОШ = 1,53), хроническая болезнь почек (ХБП) (ОШ = 1,25) и диабет 2-го типа (ОШ = 1,23) [19].…”

Section: обсуждение / Discussionunclassified

Factors associated with adverse outcome among hospitalized patients with moderate to severe COVID-19

Mishinova

Gomon

Kolbin

et al. 2023

GCP

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Aims. To consider factors associated with adverse outcome among hospitalized patients with moderate and severe COVID-19.Materials and methods. Data from 345 case histories of adult patients hospitalized with moderate to severe COVID-19 were analyzed in a single-center retrospective study. Characteristics by sex, age, number of days from disease onset to admission to hospital, duration of hospitalization, duration of disease, population characteristics by medical history of comorbidities and self-medication, outcome of hospitalization and medical technologies used in hospital conditions were given for the whole cohort of patients. All parameters were analyzed using descriptive statistics methods. Qualitative variables are given in absolute (n) and relative (%) values with 95% confidence interval. Continuous variables were presented as median and quartiles. Risk factors for mortality were determined across groups using the χ2 criterion and odds ratio.Results. There was no demonstrated effect of gender, degree of pulmonary tissue lesions on computed tomography data, or time of initiation of respiratory support on disease outcome. At the same time, the age older than 65 years as well as neoplasms, type 2 diabetes mellitus, dementia, Stage 3 arterial hypertension, chronic heart failure, coronary heart disease, myocardial infarction and stroke history, chronic obstructive pulmonary disease, bronchiectatic disease, urogenital diseases were the predictors of unfavorable outcome in patients with moderate and severe COVID-19 form. Prehospital use of antiplatelet agents, direct and indirect oral anticoagulants, drugs affecting the renin-angiotensin system, systemic glucocorticosteroids, antibiotics, antiviral drugs, and analgesics was associated with a decrease in the mortality rate in patients with moderate-to-severe COVID-19, as well as timely use of pronposition and transfer to intensive care unit.Conclusions. Our findings are partially consistent with previous reports on the effect of risk factors on COVID-19 outcomes.

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Section: обсуждение / Discussionunclassified

Factors associated with adverse outcome among hospitalized patients with moderate to severe COVID-19

Mishinova

Gomon

Kolbin

et al. 2023

GCP

View full text Add to dashboard Cite

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“…In contrast, the most common source of the UK data were the Clinical Practice Research Datalink (CPRD), which is the largest patients' data registry in the UK [27]. The overall time span of health data across the studies ranged from 1982 [28] to 2020 [29]. The individual time span of the included studies varied between 2 months to 28 years.…”

Section: Geographical and Chronological Characteristicsmentioning

confidence: 99%

“…Data were extracted from the different data sources over time span one month to 23 years. Six circulatory health conditions were identified in 23 studies [29,36,37,40,45,[53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70]. These conditions were hypertension (I10-I15) (n = 5), heart failure (I50) (n = 5), atrial fibrillation (I48) (n = 2), stroke (I64) (n = 2), atherosclerosis (I70) (n = 1), myocardial infarction (I21)…”

Section: Health Conditionsmentioning

confidence: 99%

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

Abdulazeem,

Whitelaw,

Schauberger

et al. 2023

PLoS ONE

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With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models’ development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer’s disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.

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“…Multimorbidity with insulin resistance, older age, and obesity are the main risk factors not only for AD but also for severe COVID-19 infection [ 28 ], with type 2 diabetes and cardiovascular disease as the leading two comorbidities that worsen COVID-19 pathology. Cognitive impairment, depression, and delirium are possible neurological manifestations of viral infection [ 29 , 30 ], and the long-term consequences of this infection on the prevalence of dementia cannot be estimated yet.…”

Section: Comorbidities Of Admentioning

confidence: 99%

Why Is Iron Deficiency/Anemia Linked to Alzheimer’s Disease and Its Comorbidities, and How Is It Prevented?

Fehsel

2023

Biomedicines

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Impaired iron metabolism has been increasingly observed in many diseases, but a deeper, mechanistic understanding of the cellular impact of altered iron metabolism is still lacking. In addition, deficits in neuronal energy metabolism due to reduced glucose import were described for Alzheimer’s disease (AD) and its comorbidities like obesity, depression, cardiovascular disease, and type 2 diabetes mellitus. The aim of this review is to present the molecular link between both observations. Insufficient cellular glucose uptake triggers increased ferritin expression, leading to depletion of the cellular free iron pool and stabilization of the hypoxia-induced factor (HIF) 1α. This transcription factor induces the expression of the glucose transporters (Glut) 1 and 3 and shifts the cellular metabolism towards glycolysis. If this first line of defense is not adequate for sufficient glucose supply, further reduction of the intracellular iron pool affects the enzymes of the mitochondrial electron transport chain and activates the AMP-activated kinase (AMPK). This enzyme triggers the translocation of Glut4 to the plasma membrane as well as the autophagic recycling of cell components in order to mobilize energy resources. Moreover, AMPK activates the autophagic process of ferritinophagy, which provides free iron urgently needed as a cofactor for the synthesis of heme- and iron–sulfur proteins. Excessive activation of this pathway ends in ferroptosis, a special iron-dependent form of cell death, while hampered AMPK activation steadily reduces the iron pools, leading to hypoferremia with iron sequestration in the spleen and liver. Long-lasting iron depletion affects erythropoiesis and results in anemia of chronic disease, a common condition in patients with AD and its comorbidities. Instead of iron supplementation, drugs, diet, or phytochemicals that improve energy supply and cellular glucose uptake should be administered to counteract hypoferremia and anemia of chronic disease.

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SARS-CoV-2 infection and cardiovascular or pulmonary complications in ambulatory care: A risk assessment based on routine data

Cited by 7 publications

References 28 publications

Factors associated with adverse outcome among hospitalized patients with moderate to severe COVID-19

Factors associated with adverse outcome among hospitalized patients with moderate to severe COVID-19

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

Why Is Iron Deficiency/Anemia Linked to Alzheimer’s Disease and Its Comorbidities, and How Is It Prevented?

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