Predicting mortality in hospitalized COVID-19 patients

Tirandi, Amedeo; Ramoni, Davide; Montecucco, Fabrizio; Liberale, Luca

doi:10.1007/s11739-022-03017-6

Cited by 4 publications

(4 citation statements)

References 42 publications

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“…Clinical cohort includes selected patients from the ‘Genoa‐BASH SEPSIS’ study. Original study design, inclusion and exclusion criteria have been reported elsewhere 10 . (See the Appendix S1).…”

Section: Methodsmentioning

confidence: 99%

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Osteopontin is associated with neutrophil extracellular trap formation in elderly patients with severe sepsis

Bertolotto,

Verzola,

Contini

et al. 2024

Eur J Clin Investigation

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Section: Methodsmentioning

confidence: 99%

“…Original study design, inclusion and exclusion criteria have been reported elsewhere. 10 (See the Appendix S1).…”

Section: Clinical Studymentioning

confidence: 99%

Osteopontin is associated with neutrophil extracellular trap formation in elderly patients with severe sepsis

Bertolotto,

Verzola,

Contini

et al. 2024

Eur J Clin Investigation

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“…Clinical evidence from hospitalized patients has demonstrated that COVID-19, by promoting the evolution toward a prothrombotic state, increases the susceptibility to AF during the infective stages and presumably during post-recovery ( 34 ). Meta-analysis studies revealed that pre-existing AF in patients with COVID-19 is associated with increased in-hospital mortality, post-discharge mortality and mechanical ventilation use ( 35 ).…”

Section: The Outline Of Af Pathophysiologymentioning

confidence: 99%

COVID-19 and atrial fibrillation: Intercepting lines

Donniacuo

Angelis

Rafaniello

et al. 2023

Front. Cardiovasc. Med.

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Almost 20% of COVID-19 patients have a history of atrial fibrillation (AF), but also a new-onset AF represents a frequent complication in COVID-19. Clinical evidence demonstrates that COVID-19, by promoting the evolution of a prothrombotic state, increases the susceptibility to arrhythmic events during the infective stages and presumably during post-recovery. AF itself is the most frequent form of arrhythmia and is associated with substantial morbidity and mortality. One of the molecular factors involved in COVID-19-related AF episodes is the angiotensin-converting enzyme (ACE) 2 availability. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uses ACE2 to enter and infect multiple cells. Atrial ACE2 internalization after binding to SARS-CoV-2 results in a raise of angiotensin (Ang) II, and in a suppression of cardioprotective Ang(1–7) formation, and thereby promoting cardiac hypertrophy, fibrosis and oxidative stress. Furthermore, several pharmacological agents used in COVID-19 patients may have a higher risk of inducing electrophysiological changes and cardiac dysfunction. Azithromycin, lopinavir/ritonavir, ibrutinib, and remdesivir, used in the treatment of COVID-19, may predispose to an increased risk of cardiac arrhythmia. In this review, putative mechanisms involved in COVID-19-related AF episodes and the cardiovascular safety profile of drugs used for the treatment of COVID-19 are summarized.

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“…Most previous studies on this topic have shown which factors have an effect on mortality or survival time through survival analysis [9][10][11] or logistic regression analysis [12][13][14]. These studies described their results in terms of "people with risk factors have [several times] the risk of death compared to those without them."…”

Section: Introductionmentioning

confidence: 99%

Long-Term Survival Prediction Model for Community-Dwelling Elderly Using Deep Learning Method

Cho,

Paek,

2023

Preprint

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In a super-aged society, maintaining healthy aging, preventing death, and enabling a continua-tion of economic activities are crucial. This study sought to develop a model for predicting the survival time in community-dwelling older individuals by using a deep learning method, and to identify the level of influence of various risk factors on the survival period, so that older in-dividuals can manage their own health. This study used the Korean National Health Insurance Service claims data. We observed community-dwelling older people, aged 66 years, for 11 years and developed a survival time prediction model. Of the 189,697 individuals enrolled at baseline, 180,235 (95.0%) survived from 2009 to 2019, while 9,462 (5.0%) died. Using deep learning based models (C statistics = 0.7011), we identified Charlson’s comorbidity index; the frailty index; long-term care benefit grade; disability grade; income level; a combination of diabetes mellitus, hypertension, and dyslipidemia; sex; smoking status; and alcohol consumption habit as factors impacting survival. In particular, Charlson’s comorbidity index (SHAP value: 0.0445) and frailty index (SHAP value: 0.0443) were strong predictors of survival time. Older individuals should rec-ognize modifiable risk factors that affect survival period in order to manage their own health.

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Predicting mortality in hospitalized COVID-19 patients

Cited by 4 publications

References 42 publications

Osteopontin is associated with neutrophil extracellular trap formation in elderly patients with severe sepsis

Osteopontin is associated with neutrophil extracellular trap formation in elderly patients with severe sepsis

COVID-19 and atrial fibrillation: Intercepting lines

Long-Term Survival Prediction Model for Community-Dwelling Elderly Using Deep Learning Method

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