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147
datasets available to search
ShareScore release 0.9.0
Dataset results
147 results for “patient stratification”
Use of CPETarm for Risk Stratification of Patients With CLTI
ClinicalTrials.gov study NCT06404229. IPD Sharing: NO. Countries: 1. Publications: 0.
Improving the Criteria for Selecting Patients for Primary Prevention of Sudden Cardiac Death by Arrhythmic Risk Stratification
ClinicalTrials.gov study NCT05539898. IPD Sharing: NO. Countries: 1. Publications: 1.
CoMPaSS-NMD - Computational Models for New Patients Stratification Strategies of HNMD
ClinicalTrials.gov study NCT06734949. IPD Sharing: UNDECIDED. Countries: 2. Publications: 7.
Risk Stratification and Minimally Invasive Surgery in Acute ICH Patients
ClinicalTrials.gov study NCT03862729. IPD Sharing: NO. Countries: 1. Publications: 4.
Early Risk Stratification of Patient Hospitalized for SARS-CoV2 Infection: Critical COVID-19 France CCF
ClinicalTrials.gov study NCT04344327. IPD Sharing: NO. Countries: 1. Publications: 1.
Assessing Antibiotic Induced Liver Injury for Stratification of Tuberculosis Patients
ClinicalTrials.gov study NCT03211208. IPD Sharing: NO. Countries: 1. Publications: 1.
Risk Stratification for Venous Thromboembolism in Hospitalized Medical Patients
ClinicalTrials.gov study NCT04439383. IPD Sharing: NO. Countries: 1. Publications: 1.
Utility of ROTEM® for Risk Stratification of Post-Invasive Procedure Hemorrhage in Patients With Cirrhosis (CIR-ROTEM)
ClinicalTrials.gov study NCT06908785. IPD Sharing: NO. Countries: 1. Publications: 23.
ExosoMe as Integrative Tool for pRognostic Stratification of Adverse Cardiac remodeLing in stEmi Patients: the MIRACLE Study
ClinicalTrials.gov study NCT06070974. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Luxembourg Cohort of Positive Patients for COVID-19: a Stratification Study to Predict Patient Prognosis
ClinicalTrials.gov study NCT04380987. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Cardiovascular Risk Stratification in Patients With Obstructive Sleep Apnea
ClinicalTrials.gov study NCT00768625. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Risk Stratification of Hepatocarcinogenesis Using a Deep Learning Based Clinical, Biological and Ultrasound Model in High-risk Patients
ClinicalTrials.gov study NCT04802954. IPD Sharing: NO. Countries: 1. Publications: 14.
MASTER I - Microvolt T Wave Alternans Testing for Risk Stratification of Post MI Patients
ClinicalTrials.gov study NCT00305240. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Non-contrast Cardiac CT as a Risk Stratification Tool in Patients With Non-cardiac Chest Pain
ClinicalTrials.gov study NCT02422316. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: A risk stratification tool for prehospital triage of patients exposed to a whiplash trauma
Open the record for dataset details and reuse information.
Data from: Combination of the National Early Warning Score (NEWS) and inflammatory biomarkers for early risk stratification in emergency department patients: results of a multi-national, observational study
Open the record for dataset details and reuse information.
Denoising Autoencoders for Phenotype Stratification (DAPS) Sample Trained Simulated Patient Data
<p>DAPS Trained data</p>
Denoising Autoencoders for Phenotype Stratification (DAPS) Sample Trained Patient Data
<p>Data for: https://github.com/greenelab/DAPS</p>
ChatGPT Provides Inconsistent Risk-Stratification of Patients With Atraumatic Chest Pain - Dataset
<p>Dataset for analysis of the ability of ChatGPT to risk-stratify patients with atraumatic chest pain</p>
Risk stratification and prognostic value of basic coagulation parameters among COVID-19 patients
<p class="MsoNormal"><strong><span>Background</span></strong><span>:<strong> </strong></span><span>COVID-19 is a viral disease caused by a new strain of coronaviru<span>s</span><span><span>.</span></span> Currently, prognosis and risk stratification of COVID-19 patients is done by the disease's clinical presentation. Therefore, identifying laboratory biomarkers for disease prognosis and risk stratification of COVID-19 patients is critical for prompt treatment. Therefore, the main objective of this study was to </span><span>assess the risk stratification and prognostic value of basic coagulation parameters and factors associated with disease severity among COVID-19 patients, Northwest Ethiopia.</span></p> <p class="MsoNormal"><strong><span>Methods</span></strong><span>:<strong> </strong></span><span>A follow-up study was conducted among conveniently recruited COVID-19 patients attended from March to June 2021. Socio-demographic and clinical data were collected using a structured questionnaire and checklist, respectively. </span><span>Prothrombin time (PT) and activated partial thromboplastin time (APTT)</span><span> were analyzed by the HUMACLOT DUE PLUS<sup>®</sup> machine. Descriptive statistics were used to summarize the socio-demographic and clinical characteristics of study participants. Kruskal Wallis tests were used to compare the difference between parametric and non-parametric continuous variables, respectively. The area under the receiver operating characteristic curve (AUC) was used to evaluate the value of PT and APTT in the risk stratification and disease prognosis of COVID-19 patients. </span><span>Ordinal</span><span> logistic regression<span> was used to identify the factors associated with disease severity and prognosis</span>. </span><span>A P-value < 0.05 was defined as statistically significant for all results.</span></p> <p class="MsoNormal"><strong><span>Result</span></strong><span>:<strong> </strong></span><span>Baseline PT at a cut-off value <span class="red-underline">≥ </span>16.25 seconds differentiated severe COVID-19 patients from mild and moderate patients (AUC: 0.89, 95% CI: 0.83</span><span><span>–</span>0.95). PT also differentiated mild COVID-19 patients from moderate and severe patients at a cut-off value <span class="red-underline">≤ </span>15.35 seconds (AUC: 0.90, 95% CI: 0.84–0.96). Moreover, alcohol drinkers were 3.52 times more likely chance of having severe disease than non-drinkers (95% CI: 1.41–8.81). A one-year increment in age also increased the odds of disease severity by 6% (95% CI: 3</span><span><span>–</span>9%). An increment of </span><span>≥ 0.65 </span><span>seconds from the baseline<span> </span>PT predicted poor prognosis (AUC:</span><span> 0.93, 0.87–0.99)</span><span>. </span></p> <p class="MsoNormal"><strong><span>Conclusions and recommendations</span></strong><span>:</span><strong><span> </span></strong><span>Prolonged baseline PT was observed in severe COVID-19 patients. <span>Prolonged baseline PT was also predicted to worsen prognosis. An increase from the baseline PT was associated with worsen prognosis. Therefore, </span></span><span>PT can be used as a risk stratification and prognostic marker in COVID-19 patients.</span></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.