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1,823 results for “intensive care”
DATA SET: Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in COVID-19 patients admitted to intermediate respiratory and intensive care units
<p>This repository contains the data sets of the article:</p> <p>Mesquida, J., Caballer, A., Cortese, L. <em>et al.</em> Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in COVID-19 patients admitted to intermediate respiratory and intensive care units. <em>Crit Care</em> <strong>25, </strong>381 (2021). https://doi.org/10.1186/s13054-021-03803-2</p>
Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities
<p>Simulation output files for 'Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities'.</p> <p>Simulating COVID-19 transmission and hospital burden to assess at which intensive care unit (ICU) occupancies mitigation, that reduces transmission, needs to be triggered to avoid exceeding ICU capacity limits, using the city of Chicago, Illinois as an example.</p> <p>Manuscript is under review for scientific publication, (see <a href="https://www.medrxiv.org/content/10.1101/2021.06.27.21259530v1">preprint on medRxiv</a>) and scripts are available from the GitHub repository at https://github.com/numalariamodeling/ICUtrigger_covid_chicago_paper_2021. </p> <p>Simulation output files uploaded per scenario including projected COVIID-19 transmission and burden trajectories for Chicago city for March 2020 to May 2021 per day.</p> <p>Simulation scenarios:</p> <p><reopening % above ICU capacity>_<delay after reaching ICU threshold>_<%mitigation>_<common simulation name> i.e. `50perc_1daysdelay_pr6_triggeredrollback_reopen`</p> <ul> <li>`emodl` file <ul> <li>required file for COVID-19 transmission model in the <a href="https://docs.idmod.org/projects/cms/en/latest/index.html">Compartmental Modeling Software</a> (see <a href="https://github.com/numalariamodeling/ICUtrigger_covid_chicago_paper_2021">GitHub repository</a> for details)</li> </ul> </li> <li>sampled_parameters.csv <ul> <li>simulation input and scenario parameters, (nrow=4400, 400 unique parameter combinations * 11 scenario values)</li> </ul> </li> <li>rt_trajectoriescovidregion_11.csv <ul> <li>estimated reproductive numbers per trajectory for complete timeline per day</li> </ul> </li> <li>trajectoriesDat_region_11_traces.csv <ul> <li>filtered to include top 100 trajectories fitted to ICU data</li> </ul> </li> <li>trajectoriesDat_region_trimfut.csv <ul> <li>truncated to only include projections after September 1st 2020</li> </ul> </li> </ul> <p>The folder `mainfigures_csvs.zip` includes processed simulation output data for the publication figures.</p>
Identifying Episodes of Hypovigilance in Intensive Care Units Using Routine Physiological Parameters and Artificial Intelligence: a Derivation Study. Open Code and Dataset
<p>The purpose of this project is to detect hypogilance using the EVEILS database.</p> <p>Database is ICU data from Hôtel-Dieu De Lévis , Québec, Canada. Please cite us if you use either the data or code. </p> <p>This code was written during Raphaëlle Giguère Msc in Computer Science. The goal of her project is to detect hypovigilance using machine learning in the ICU. In this repository, you have the data set before preprocessing:</p> <ul> <li>df_hypovigilance : Contains the hours, date and value of the vigilance level, using either the RASS or Ramsay and already converted using the thresholds shown in the paper.</li> <li>raw_df : Contains the raw values from the gateway for each participant. All of the identifying values have been removed.</li> </ul> <p>At the end of the preprocessing_anonymous script, you should generate a new dataset called "df_final". This dataset is used for the training_model script.</p> <p>The cross validation employs groups of random size meaning the results might differ from time to time but should stay consistent.</p> <p> </p>
Myeloperoxidase can differentiate between sepsis and non-infectious SIRS and predicts mortality in intensive care patients with SIRS.
<p>Dataset of "Myeloperoxidase can differentiate between sepsis and non-infectious SIRS and predicts mortality in intensive care patients with SIRS."</p>
Raw Data for the article: Impact of the Organizational Model Adopted during the COVID-19 Pandemic on the Perceived Safety of Intensive Care Unit Staff
<p><strong>Background: </strong>The SARS-CoV-2 pandemic had a devastating health, social, and economic effect on the population. Organizational, technical and structural operations aimed at protecting staff, outpatients and inpatients were implemented in an Italian hospital with a COVID-19 dedicated intensive care unit. The impact of the organizational model adopted on the perceived safety among staff was evaluated.</p> <p><strong>Methods: </strong>Descriptive, structured and voluntary, anonymous, non-funded, self-administered cross-sectional surveys on the impact of the organizational model adopted during COVID-19 on the perceived safety among staff.</p> <p><strong>Results: </strong>Response rate to the survey was 67.4% (153 completed surveys). A total of 91 (59%) of respondents had more than three years of ICU experience, while 16 (10%) were employed for less than one year. Group stratification according to profession: 74 nurses (48%); 12 medical-doctors (7%); 11 physiotherapists (7%); 35 nurses-aides (22%); 5 radiology-technicians (3%); 3 housekeeping (1%); 13 other (8%). The organizational model implemented at ISMETT made them feel safe during their workday. A total of 113 (84%) agreed or strongly agreed with the sense of security resulting from the implemented measures. A vast majority of respondents perceived COVID-19 as a dangerous and deadly disease (94%) not only for themselves but even more as vectors towards their families (79%). A total of 55% of staff took isolation measures and moved away from their home by changing personal habits. The organizational model was perceived overall as appropriate (91%) to guarantee their health.</p> <p><strong>Conclusion: </strong>The vast majority of respondents perceived the overall model applied during an unexpected, emergency situation as appropriate.</p>
Standard Issue Transfusion Versus Fresher Red Blood Cell Use in Intensive Care- A Randomised Controlled Trial
ClinicalTrials.gov study NCT01638416. IPD Sharing: UNDECIDED. Countries: 5. Publications: 4.
Program of Intensive Support in Emergency Departments for Care Partners of Cognitively Impaired Patients
ClinicalTrials.gov study NCT03325608. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Stress Ulcer Prophylaxis in the Intensive Care Unit
ClinicalTrials.gov study NCT02467621. IPD Sharing: YES. Countries: 6. Publications: 10.
Intravenous Exenatide in Coronary Intensive Care Unit (ICU) Patients
ClinicalTrials.gov study NCT00736229. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Drainage Of Pleural Effusions in the Intensive Care Unit (DOPE-ICU) - Feasibility Trial
ClinicalTrials.gov study NCT06709456. IPD Sharing: YES. Countries: 1. Publications: 0.
Intensive Models of HCV Care for Injection Drug Users
ClinicalTrials.gov study NCT01857245. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Safety and Efficacy of Acetaminophen in the Intensive Care Unit.
ClinicalTrials.gov study NCT02280239. IPD Sharing: UNDECIDED. Countries: 1. Publications: 16.
Addressing Post-Intensive Care Syndrome Among Survivors of COVID (APICS-COVID)
ClinicalTrials.gov study NCT03738774. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Nicotine Replacement Therapy in the Intensive Care Unit
ClinicalTrials.gov study NCT01362959. IPD Sharing: Not stated. Countries: 1. Publications: 16.
Handling Oxygenation Targets in the Intensive Care Unit
ClinicalTrials.gov study NCT03174002. IPD Sharing: YES. Countries: 7. Publications: 13.
Group Medical Appointments for Intensive Lifestyle Treatment for Obesity in Cleveland Clinic Primary Care Practices
ClinicalTrials.gov study NCT07268417. IPD Sharing: NO. Countries: 1. Publications: 4.
Precision Sedation in Intensive Care
ClinicalTrials.gov study NCT06991777. IPD Sharing: NO. Countries: 1. Publications: 20.
Facilitating EndotracheaL Intubation by Laryngoscopy Technique and Apneic Oxygenation Within the Intensive Care Unit: The FELLOW Study
ClinicalTrials.gov study NCT02051816. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Family Support Intervention in Intensive Care Units
ClinicalTrials.gov study NCT05280691. IPD Sharing: NO. Countries: 1. Publications: 7.
Second Survey of Intensive Care in India
ClinicalTrials.gov study NCT03631927. IPD Sharing: NO. Countries: 1. Publications: 2.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.