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Erschienen in: Neurological Sciences 9/2020

07.07.2020 | COVID-19 | COVID-19 Zur Zeit gratis

Neurological diseases as mortality predictive factors for patients with COVID-19: a retrospective cohort study

verfasst von: Jong-moon Hwang, Ju-Hyun Kim, Jin-Sung Park, Min Cheol Chang, Donghwi Park

Erschienen in: Neurological Sciences | Ausgabe 9/2020

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Abstract

Introduction

In the current study, we evaluated factors that increase the coronavirus disease (COVID-19) patient death rate by analyzing the data from two cohort hospitals. In addition, we studied whether underlying neurological diseases are risk factors for death.

Methods

In this retrospective cohort study, we included 103 adult inpatients (aged ≥ 18 years). We evaluated differences in demographic data between surviving and non-surviving COVID-19 patients.

Results

In a multivariate logistic analysis, age and the presence of chronic lung disease and Alzheimer’s dementia (AD) were the only significant parameters for predicting COVID-19 non-survival (p < 0.05). However, hypertension, coronary vascular disease, dyslipidemia, chronic kidney disease, diabetes, and history of taking angiotensin II receptor blockers (ARBs) or angiotensin-converting enzyme (ACE) inhibitors, as well as nonsteroidal anti-inflammatory drugs (NSAIDs), were not significantly associated with the death of COVID-19 patients. The optimal cutoff value obtained from the maximum Youden index was 70 (sensitivity, 80.77%; specificity, 61.04%), and the odds ratio of non-survival increased 1.055 fold for every year of age.

Conclusions

Clinicians should closely monitor and manage the symptoms of COVID-19 patients who are over the age of 70 years or have chronic lung disease or AD.
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Metadaten
Titel
Neurological diseases as mortality predictive factors for patients with COVID-19: a retrospective cohort study
verfasst von
Jong-moon Hwang
Ju-Hyun Kim
Jin-Sung Park
Min Cheol Chang
Donghwi Park
Publikationsdatum
07.07.2020
Verlag
Springer International Publishing
Erschienen in
Neurological Sciences / Ausgabe 9/2020
Print ISSN: 1590-1874
Elektronische ISSN: 1590-3478
DOI
https://doi.org/10.1007/s10072-020-04541-z

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