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Thirty-Day Post-Discharge Outcomes Following COVID-19 Infection.

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TLDR
In this paper, the authors describe post-hospitalization outcomes of adults following COVID-19 infection are limited to electronic medical record review, which may underestimate theÂincidence of outcomes.
Abstract
The clinical course of COVID-19 includes multiple disease phases. Data describing post-hospital discharge outcomes may provide insight into disease course. Studies describing post-hospitalization outcomes of adults following COVID-19 infection are limited to electronic medical record review, which may underestimate the incidence of outcomes. To determine 30-day post-hospitalization outcomes following COVID-19 infection. Retrospective cohort study Quaternary referral hospital and community hospital in New York City. COVID-19 infected patients discharged alive from the emergency department (ED) or hospital between March 3 and May 15, 2020. Outcomes included return to an ED, re-hospitalization, and mortality within 30 days of hospital discharge. Thirty-day follow-up data were successfully collected on 94.6% of eligible patients. Among 1344 patients, 16.5% returned to an ED, 9.8% were re-hospitalized, and 2.4% died. Among patients who returned to the ED, 50.0% (108/216) went to a different hospital from the hospital of the index presentation, and 61.1% (132/216) of those who returned were re-hospitalized. In Cox models adjusted for variables selected using the lasso method, age (HR 1.01 per year [95% CI 1.00–1.02]), diabetes (1.54 [1.06–2.23]), and the need for inpatient dialysis (3.78 [2.23–6.43]) during the index presentation were independently associated with a higher re-hospitalization rate. Older age (HR 1.08 [1.05–1.11]) and Asian race (2.89 [1.27–6.61]) were significantly associated with mortality. Among patients discharged alive following their index presentation for COVID-19, risk for returning to a hospital within 30 days of discharge was substantial. These patients merit close post-discharge follow-up to optimize outcomes.

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Hospital readmissions and post-discharge all-cause mortality in COVID-19 recovered patients; A systematic review and meta-analysis.

TL;DR: In this article, the authors performed a systematic review and meta-analysis on the prevalence of one-year hospital readmissions and post-discharge all-cause mortality in recovered COVID-19 patients.
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Hospital readmissions and post-discharge all-cause mortality in COVID-19 recovered patients; A systematic review and meta-analysis

TL;DR: A systematic review and meta-analysis on the prevalence of one-year hospital readmissions and post-discharge all-cause mortality in recovered COVID-19 patients was performed in this paper .
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Age‐related differences in symptoms in older emergency department patients with COVID‐19: Prevalence and outcomes in a multicenter cohort

TL;DR: Older adults represent a disproportionate share of severe COVID‐19 presentations and fatalities, but there is limited understanding of the differences in presentation by age and the association between less typical emergency department presentations and clinical outcomes.
References
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Research electronic data capture (REDCap)-A metadata-driven methodology and workflow process for providing translational research informatics support

TL;DR: Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data Capture tools to support clinical and translational research.
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mice: Multivariate Imputation by Chained Equations in R

TL;DR: Mice adds new functionality for imputing multilevel data, automatic predictor selection, data handling, post-processing imputed values, specialized pooling routines, model selection tools, and diagnostic graphs.
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An interactive web-based dashboard to track COVID-19 in real time.

TL;DR: The outbreak of the 2019 novel coronavirus disease (COVID-19) has induced a considerable degree of fear, emotional stress and anxiety among individuals around the world.
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Multiple imputation using chained equations: Issues and guidance for practice

TL;DR: The principles of the method and how to impute categorical and quantitative variables, including skewed variables, are described and shown and the practical analysis of multiply imputed data is described, including model building and model checking.
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