Corpus overview


Overview

MeSH Disease

Human Phenotype

Transmission

Seroprevalence
    displaying 1 - 2 records in total 2
    records per page




    Evaluation of the Efficacy of Methylprednisolone Pulse Therapy in Treatment of Covid-19 Adult TRANS Patients with Severe Respiratory Failure MESHD Respiratory Failure HP: Randomized, Clinical Trial

    Authors: Ramin Hamidi Farahani; Reza Mosaed; Amir Nezami-Asl; Mohsen chamanara; Saeed Soleiman-Meigooni; Shahab Kalantar; Mojtaba Yousefi zoshk; Ebad Shiri Malekabad; Ebrahim Hazrati

    doi:10.21203/rs.3.rs-66909/v1 Date: 2020-08-27 Source: ResearchSquare

    Background: Covid-19 is now global concern and widely spread to the world due to high mortality among the nations we tried to evaluate the efficacy of methylprednisolone pulse in COVID-19 patients with severe respiratory failure HP respiratory failure MESHD.Methods: This study was phase2, double-blind, randomized, clinical trial in adults TRANS with COVID-19 ( aged TRANS ≥18 years old) admitted to the intensive care unit (ICU) of *. Patients with intermediate or severe COVID-19 with PaO2/FiO2 less than 300 and progressive disease unresponsive to standard treatments admitted to ICU. Patients were randomly allocated in either control or investigation group. The control group received recommended regimen for COVID-19. The investigation group received the recommended regimen plus Methylprednisolone (1000mg/day for three days) and oral prednisolone 1mg/kg with tapering of dose within ten days. Results: A total of 29 ICU patients with intermediate or severe COVID-19 pneumonia HP pneumonia MESHD recruited in this study. Fourteen patients (4 female TRANS, ten male TRANS) allocated in the investigation group, and 15 patients (6 female TRANS, nine male TRANS) assigned to the control group. The participant’s average age TRANS was 64.03±13.545 (case: 61.07±12.83, control: 66.80±14.03). The patients with methylprednisolone pulse had significantly higher systolic (P=0.018) and diastolic (P=0.001) blood SERO pressure, meanwhile, the Glasgow coma HP coma MESHD scale (GCS) of methylprednisolone group was considerably (P<0.001) higher, and by the improvement in SpO2 of methylprednisolone group none of these patients needed mechanical ventilation.Conclusion: This study demonstrated methylprednisolone pulse in COVID-19 severe respiratory failure HP respiratory failure MESHD dramatically improves the clinical condition of patients including, GCS, and SpO2 of patients.Clinical Trial Registration Number: IRCT20200406046963N1

    Machine learning based prognostic model for predicting infection MESHD susceptibility of COVID-19 using health care data 

    Authors: R Srivatsan; Prithviraj N Indi; Swapnil Agrahari; Siddharth Menon; Dr. S. Denis Ashok

    doi:10.21203/rs.3.rs-46681/v1 Date: 2020-07-21 Source: ResearchSquare

    From public health perspectives of COVID-19 pandemic, accurate estimates of i nfection MESHDseverity of individuals are extremely valuable for the informed decision making and targeted response to an emerging pandemic.  This paper presents machine learning based prognostic model for providing early warning to the individuals for COVID-19 infection using the health care data set. In the present work, a prognostic model using Random Forest classifier and support vector regression is developed for predicting the susceptibility of COVID-19 i nfection MESHDand it is applied on an open health care data set containing 27 field values. The typical fields of the health care data set include basic personal details such as age TRANS, gender TRANS, number of children TRANS in the household, marital status along with medical data like Coma HP oma MESHDscore, Pulmonary score, Blood SERO Glucose level, HDL cholesterol etc. An effective preprocessing method is carried out for handling the numerical, categorical values (non-numerical), missing data in the health care data set. Principal component analysis is applied for dimensionality reduction of the health care data set. From the classification results, it is noted that the random forest classifier provides a higher accuracy as compared to Support vector regression for the given health data set. Proposed machine learning approach can help the individuals to take additional precautions for protecting against COVID-19 i nfection. MESHD Based on the results of the proposed method, clinicians and government officials can focus on the highly susceptible people for limiting the pandemic spread. Methods In the present work, Random Forest classifier and support vector regression techniques are applied to a medical health care dataset containing 27 variables for predicting the susceptibility score of an individual towards COVID-19 i nfection MESHDand the accuracy of prediction is compared. An effective preprocessing is carried for handling the missing data in the health care data set. Principal Component Analysis is carried out on the data set for dimensionality reduction of the feature vectors. Results From the classification results, it is noted that the Random Forest classifier provides an accuracy of 90%, sensitivity SERO of 94% and specificity of 81% for the given medical data set.Conclusion Proposed machine learning approach can help the individuals to take additional precautions for protecting people from the COVID-19 i nfection, MESHD clinicians and government officials can focus on the highly susceptible people for limiting the pandemic spread. 

The ZB MED preprint Viewer preVIEW includes all COVID-19 related preprints from medRxiv and bioRxiv, from ChemRxiv, from ResearchSquare, from arXiv and from Preprints.org and is updated on a daily basis (7am CET/CEST).
The web page can also be accessed via API.

Sources


Annotations

All
None
MeSH Disease
Human Phenotype
Transmission
Seroprevalence


Export subcorpus as...

This service is developed in the project nfdi4health task force covid-19 which is a part of nfdi4health.

nfdi4health is one of the funded consortia of the National Research Data Infrastructure programme of the DFG.