Evidence map›Paper›PMID 32576328›Full record

ArticleDisaster medicine and public health preparedness2020

Prediction of the Rehabilitation Duration and Risk Management for Mild-Moderate COVID-19.

Qiong-Na Zheng, Mei-Yan Xu, Yong-Le Zheng, Xiu-Ying Wang, Hui Zhao

Open access · hybridAbstract read
In one paragraph

Article in Disaster medicine and public health preparedness, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 40% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Qiong-Na ZhengDepartment of Infectious Diseases, Affiliated Yueqing Hospital, Wenzhou Medical University, Yueqing, China.
Mei-Yan XuDepartment of Infectious Diseases, Affiliated Yueqing Hospital, Wenzhou Medical University, Yueqing, China.
Yong-Le ZhengDepartment of Infectious Diseases, Affiliated Yueqing Hospital, Wenzhou Medical University, Yueqing, China.
Xiu-Ying WangDepartment of Infectious Diseases, Affiliated Yueqing Hospital, Wenzhou Medical University, Yueqing, China.
Hui ZhaoDepartment of Infectious Diseases, Affiliated Yueqing Hospital, Wenzhou Medical University, Yueqing, China.
Yueqing People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesMore than 80% of coronavirus disease 2019 (COVID-19) cases are mild or moderate. In this study, a risk model was developed for predicting rehabilitation duration (the time from hospital admission to discharge) of the mild-moderate COVID-19 cases and was used to conduct refined risk management for different risk populations.

methodsA total of 90 consecutive patients with mild-moderate COVID-19 were enrolled. Large-scale datasets were extracted from clinical practices. Through the multivariable linear regression analysis, the model was based on significant risk factors and was developed for predicting the rehabilitation duration of mild-moderate cases of COVID-19. To assess the local epidemic situation, risk management was conducted by weighing the risk of populations at different risk.

resultsTen risk factors from 44 high-dimensional clinical datasets were significantly correlated to rehabilitation duration (P < 0.05). Among these factors, 5 risk predictors were incorporated into a risk model. Individual rehabilitation durations were effectively calculated. Weighing the local epidemic situation, threshold probability was classified for low risk, intermediate risk, and high risk. Using this classification, risk management was based on a treatment flowchart tailored for clinical decision-making.

conclusionsThe proposed novel model is a useful tool for individualized risk management of mild-moderate COVID-19 cases, and it may readily facilitate dynamic clinical decision-making for different risk populations.

Indexed as

Time FactorsAdultChinaCOVID-19Disease ProgressionFemaleHumansMaleMiddle AgedRehabilitationRisk FactorsRisk Managementmild-moderate COVID-19predictive modelrefined risk managementrehabilitation durationtailored clinical decisions-making

Identifiers

PMID32576328
PMCPMC7369334
OpenAlexW3036623533

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.