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147 results for “patient stratification”
Lung ultrasonography features and risk stratification in 80 patients with COVID-19: a prospective observational cohort study
<p><strong>Background</strong></p> <p>Point-of-care lung ultrasound (LUS) is a promising and pragmatic risk stratification tool in COVID-19. This study describes and compares early LUS characteristics across of range of clinical outcomes.</p> <p><strong>Method</strong></p> <p>Prospective observational study of PCR-confirmed COVID-19 patients in the emergency department (ED) of Lausanne University Hospital. A trained physician recorded LUS images using a standardized protocol. Two experts retrospectively reviewed images blinded to patient outcome. We describe and compare early LUS findings (acquired within 24hours of presentation at the ED) between patient groups based on their outcome at 7-days after inclusion: 1) self-resolving outpatients, 2) hospitalised and 3) intubated/death. The LUS score was used to discriminate between groups.</p> <p><strong>Findings</strong></p> <p>Between March 6 and April 3 2020, we included 80 patients (18 outpatients, 41 hospitalized and 21 intubated/dead). 73 patients (91%) had abnormal LUS (72% outpatients, 95% hospitalised and 100% intubated/death; p=0.004). The proportion of involved zones was lower in outpatients compared with other groups (median 30% [IQR 0-40%], 44% [33-70%] and 70% [50-88%], p<0.001). Predominant abnormal patterns were bilateral and multifocal spread thickening of the pleura with pleural line irregularities (77%), confluent B lines (66%) and pathologic B lines (55%). Posterior inferior zones were more often affected. Median LUS score had a good level of discrimination between outpatients and others with area under the ROC of 0.80 (95% CI 0.66-0.95).</p> <p><strong>Interpretation</strong></p> <p>Systematic LUS is a reliable, cheap and easy-to-use triage tool for the early stratification of risk in COVID-19 patients presenting at emergency departments.</p> <p><strong>Funding</strong></p> <p>Leenaards Foundation</p>
A potential patient stratification biomarker for Parkinson´s disease based on LRRK2 kinase-mediated centrosomal alterations in peripheral blood-derived cells
<p>Annotated WES VCF file and phenotype file used in Gene burden analysis of Naaldijk et al., "A potential patient stratification biomarker for Parkinson´s disease based on LRRK2 kinase-mediated centrosomal alterations in peripheral blood-derived cells", 2023. </p><p> </p>
Splicing-Aware Patient Stratification Using Different Biclustering Methods
<div> <div> <div> <p>Patient stratification refers to the process of categorizing patients into specific groups based on whether they exhibit certain characteristics of a disease. In this research, we investigated the feasibility of splicing-aware measurements for patient stratification in complex diseases like breast cancer and dilated cardiomyopathy.</p> <p>Splicing-aware patient stratification integrates the intricate dynamics of RNA splicing with the stratification of patients in clinical settings. The significance of this approach lies in its potential to revolutionize personalized medicine by offering more precise and effective treatment strategies. To execute our research, we collected datasets at three levels: genomic, transcrip- tomic, and isoform-specific expressions for both breast cancer and dilated cardiomyopathy. We applied network-constrained (BiCoN) and unconstrained (MoSBi) biclustering algorithms to classify individuals into subtypes. Here, we show that working not just with transcripts but with dominant isoforms of transcripts and different measurements can unveil new information. We took the dominant isoform of transcripts by a median of expression into account to examine the splicing effect. Despite various factors, we included in our research, both BiCoN and MoSBi appeared more efficient on the gene level. We observed that different biclustering methods or approaches work differently in both diseases and also on different levels in each disease.</p> <p>With our results, we aim to shed light on the potential applications of alternative splicing analysis for disease subtyping.</p> </div> </div> </div>
Benchmark cancer datasets for Clustering algorithms for Omics-based Patient Stratification (COPS)
<p>This repository contains seven multi-omic cancer datasets including several cancer types (breast, kidney, lung, ovary, prostate, and thyroid cancers as well as low grade gliomas) that were used for benchmarking several multi-view clustering algorithms implemented by COPS (https://github.com/UEFBiomedicalInformaticsLab/COPS). The datasets were originally compiled from The Cancer Genoma Atlas (TCGA) and downloaded using the <em>curatedTCGAData</em> R-package. The datasets include copy-number variations, methylomics as well as mRNA and miRNA transcriptomics. The methylomics data was mapped to genes by averaging methylation level of probes associated with the promoter regions of genes. Similarly the miRNA transcriptomics data was mapped to genes by using known and predicted miRNA -> gene interactions. Updated survival data was acquired from the Liu et al. 2018 paper. </p> <p>This repository also includes two sets of cancer associated pathway networks used by pathway-based multi-omic methods benchmarked in our study. NCI-PID pathways were downloaded using the <em>ndexr</em> R-package on December 22 2021. While KEGG pathways were downloaded using the <em>pathview</em> R-package on May 3 2022. </p> <p>More details on the processing can be found on the related publication.</p>
Early Risk Stratification in ED Chest Pain Patients
ClinicalTrials.gov study NCT02364271. IPD Sharing: Not stated. Countries: 1. Publications: 17.
Risk Stratification in Patients With Preserved Ejection Fraction
ClinicalTrials.gov study NCT02124018. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Thromboembolic Risk Stratification by TRiP(Cast) Score to Guide Physicians in Preventive Treatment Prescriptions for Patients With Lower Limb Trauma Requiring Brace or castING.
ClinicalTrials.gov study NCT04064489. IPD Sharing: NO. Countries: 2. Publications: 2.
Data from: A risk stratification tool for prehospital triage of patients exposed to a whiplash trauma
Objective: Our aim was to develop a risk stratification model to predict the presence of a potentially more sinister injury in patients exposed to a whiplash trauma. Methods: The study base comprised of 3,115 residents who first sought healthcare contact within one week after being exposed to a whiplash trauma between 1999-2008, from within a defined geographical area, Skaraborg County in south-western Sweden. Information about gender, age, time elapsed prior to seeking care, type of health care contact, and hospitalization was retrieved. Eighteen potential risk factors were identified and evaluated using multivariate logistic regression. Results: Of 3,115 patients, 215 (6.9%) required hospital admission so theoretically 93% could have been initially assessed by primary health care. However, only 46% had their first contact in primary health care. All patients had symptoms resulting in a diagnosis of whiplash injury. Four risk factors were found to be associated with hospital admission: commotio cerebri (OR 31, 19-51), fracture / luxation (OR 11, 5.1-22), serious injury (OR 41, 8.0-210), and the patient sought care during the same day as the trauma (OR 5.9, 3.7- 9.5). These four risk factors explained 27 % of the variation for hospital admission and the area under curve (AUC) was 0.77 (0.74-0.80). Ninety-six percent of patients (2,985) had only a whiplash injury with none of the other four risk factors. These could be split into those attending health care the same day as the trauma, 1,737 (56%) with a 7.1% risk for hospital admission, and those attending health care later, 1,248 (40%) with a 1.3% risk for hospital admission. Conclusion: Patients with no signs of commotio cerebri, no fracture/luxation injury, no serious injury, comprising 96% of all patients exposed to a whiplash trauma can initially be referred to primary health care for initial assessment. However, those contacting the health care the same day as the trauma should be referred to a hospital for evaluation if they can't get an appointment with a general practitioner the same day.
Denoising Autoencoders for Phenotype Stratification (DAPS) Sample Simulated Patient Data
<p>Generated with https://github.com/greenelab/DAPS/</p>
Dataset for paper: Prognostic Stratification by the Meet-URO Score in Real-World Older Patients With Metastatic Renal Cell Carcinoma (mRCC) Receiving Cabozantinib: A Subanalysis of the Prospective ZEBRA Study (Meet-URO 9)
<p>Dataset for paper: Prognostic Stratification by the Meet-URO Score in Real-World Older Patients With Metastatic Renal Cell Carcinoma (mRCC) Receiving Cabozantinib: A Subanalysis of the Prospective ZEBRA Study (Meet-URO 9)</p>
Dataset for "Moving towards genome-wide data integration for patient stratification with Integrate Any Omics"
Open the record for dataset details and reuse information.
Online Tables for the thesis "Clustering approaches for patient stratification in psychiatry" by Jonas Hagenberg
<p>This repository contains the online tables that accompany my thesis "Clustering approaches for patient stratification in psychiatry".</p> <p>In the following, I list the description of all tables:</p> <p> </p> <div> <div> <p>1: Information about somatic diseases and medication separated by status (participants labeled as cases and used in the clustering as well as controls without a DSM-IV diagnosis). N data denotes the number of individuals who had the information available. The medication information was not available for the OPTIMA cohort. N denotes the individuals who were affected by the disease or took the medication.</p> <p>2: Missingness and coefficient of variation information of the immune marker data for 237 participants used in the clustering and 36 controls without a DSM-IV diagnosis. The coefficient of variation was calculated from three internal controls that were run in duplicates.</p> <p>3: Variable importance of all variables the initial clustering. The variable importance is calculated as the F-value from an ANOVA model with the clusters as independent variables. The variable importance cannot be interpreted as a p-value as the variables were already used in the clustering, thereby inflating the p-values. Full version of supplementary table 3.</p> <p>4: Variable importance of all gene sets in the initial clustering. Full version of supplementary table 8.</p> <p>5: Variable importance of all variables the secondary analysis corrected for age, sex and BMI. Full version of supplementary table 9.</p> <p>6: Variable importance of all variables the exploratory analysis including cell type proportions. Full version of supplementary table 10.</p> <p>7: Variable importance of all gene sets in the exploratory analysis including cell type proportions. Full version of supplementary table 11.</p> <p>8: Differentially expressed genes with regard to the CRP concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP, IL-6 and BMI. Full version of supplementary table 12.</p> <p>9: Differentially expressed genes with regard to the IL-6 concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP, IL-6 and BMI. Full version of supplementary table 13.</p> <p>10: Enriched hallmark gene sets with regard to CRP calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type.</p> <p>11: Enriched hallmark gene sets with regard to IL-6 calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type.</p> <p>12: Enriched hallmark gene sets with regard to BMI calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type.</p> <p>13: Enriched GO biological pathway gene sets with regard to CRP calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type. Full version of supplementary table 15.</p> <p>14: Enriched GO biological pathway gene sets with regard to IL-6 calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type.</p> <p>15: Enriched GO biological pathway gene sets with regard to BMI calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type.</p> <p>16: Differentially expressed genes with regard to the CRP concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP and IL-6.</p> <p>17: Differentially expressed genes with regard to the IL-6 concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP and IL-6.</p> <p>18: Differentially expressed genes with regard to the CRP concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP and BMI.</p> <p>19: Differentially expressed genes with regard to BMI separated by cell type. The analysis was performed with DESeq2 and the model contained CRP and BMI.</p> <p>20: Differentially expressed genes with regard to the IL-6 concentration separated by cell type. The analysis was performed with DESeq2 and the model contained IL-6 and BMI.</p> <p>21: Differentially expressed genes with regard to BMI separated by cell type. The analysis was performed with DESeq2 and the model contained IL-6 and BMI.</p> <p>22: Differentially expressed genes with regard to the CRP concentration separated by cell type. The analysis was performed with DESeq2 and the model contained only CRP.</p> <p>23: Differentially expressed genes with regard to the IL-6 concentration separated by cell type. The analysis was performed with DESeq2 and the model contained only IL-6.</p> <p>24: Differentially expressed genes with regard to BMI separated by cell type. The analysis was performed with DESeq2 and the model contained only BMI.</p> <p>25: Variable importance of initial multi-omics clustering of the DEGs identified in the single cell data set.</p> </div> </div>
Data for dissertation titled 'Topic modelling for the stratification of neurological patients'
<p>The uploaded zip-file entails the data needed for and obtained through the dissertation titled 'Topic modelling for the stratification of neurological patients' as part of the programme 'MSc. in Statistical Data Analysis' at Ghent University. The study aimed at exploring the applicability of hierarchical stochastic block models on resting-state functional magnetic resonance imaging (RS-fMRI) to cluster participants with known neurological disorders.</p> <p>The data is structured in different folders and aligns with the folder structure of the GitHub-repository that contains the analysis scripts (https://github.com/wvechelp/hsbm_on_fmri). The GitHub-repository already covers some example data, while additional data and results can be found in this zip-file. Additional comments on the analyses are also provided in the analysis scripts.</p> <p>The original raw data is obtained through the OpenFMRI project (<a href="http://openfmri.org/">http://openfmri.org/</a>, with label <em>ds000030</em>) and as a Stanford Digital Repository (<a href="https://purl.stanford.edu/mg599hw5271">https://purl.stanford.edu/mg599hw5271</a>) (Bilder<em> et al.</em>, 2016). It is obtained from the NIH Roadmap Initiative, as a result of the Consortium for Neuropsychiatric Phenomics (CNP) study (Poldrack<em> et al.</em>, 2016). Throughout the study, data was collected through interviews and rating scales, self-report measures, neurocognitive exams (using both paper-pencil and computerised tests), and a variety of neuroimaging data. More specific information on the selection procedure of the participants can be found in the description of the data (Bilder<em> et al.</em>, 2016) and the associated article (Poldrack<em> et al.</em>, 2016). Among the available neuroimaging data, the RS-fMRI data have been collected by asking participants to remain relaxed, while keeping their eyes open (with scans lasting 304 s and an image being collected every 2 seconds (Poldrack<em> et al.</em>, 2016)). This raw data was pre-processed by Rasero<em> et al.</em> (2019) to correct for motion and temporal alignment. Smoothing (6-mm full width at half-maximum Gaussian kernel), intensity normalisation, and a band-pass filter (between 0.01 and 0.08 Hz) were applied prior to the removal of linear and quadratic trends. Motion time courses, average CSF signal, and the average white matter signal were regressed out prior to data transformation into voxels with a volume of 3 mm x 3 mm x 3 mm. Ultimately, the functional atlas of Shen<em> et al.</em> (2013) was used to average the voxel signals per anatomical region of interest (ROI), resulting in a parcellation of 278 ROIs (and associated time series consisting of 152 measurements). From these ROI-specific time series, Rasero<em> et al.</em> (2019) generated 278 x 278 matrices with Pearson coefficients.</p> <p>References:<br> Bilder, R. M., Poldrack, R. A., Cannon, T., London, E., Freimer, N., Congdon, E., Karlsgodt, K., & Sabb, F. W. (2016). <em>UCLA Consortium for Neuropsychiatric Phenomics LA5c Study</em> Stanford Digital Repository. <a href="http://purl.stanford.edu/mg599hw5271">http://purl.stanford.edu/mg599hw5271</a> and <a href="https://openfmri.org/dataset/ds000030/">https://openfmri.org/dataset/ds000030/</a><br> Poldrack, R. A., Congdon, E., Triplett, W., Gorgolewski, K. J., Karlsgodt, K. H., Mumford, J. A., Sabb, F. W., Freimer, N. B., London, E. D., Cannon, T. D., & Bilder, R. M. (2016). A phenome-wide examination of neural and cognitive function. <em>Scientific Data</em>,<em> 3</em>(1), 160110. <a href="https://doi.org/10.1038/sdata.2016.110">https://doi.org/10.1038/sdata.2016.110</a><br> Rasero, J., Diez, I., Cortes, J. M., Marinazzo, D., & Stramaglia, S. A.-O. (2019). Connectome sorting by consensus clustering increases separability in group neuroimaging studies. <em>Network Neuroscience</em>,<em> 3</em>(2), 325-343. <a href="https://doi.org/https:/doi.org/10.1162/netn_a_00074">https://doi.org/https://doi.org/10.1162/netn_a_00074</a><br> Shen, X., Tokoglu, F., Papademetris, X., & Constable, R. T. (2013). Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. <em>NeuroImage</em>,<em> 82</em>, 403-415. <a href="https://doi.org/https:/doi.org/10.1016/j.neuroimage.2013.05.081">https://doi.org/https://doi.org/10.1016/j.neuroimage.2013.05.081</a></p> <p> </p>
Precision Risk Stratification in Kidney Transplant Patients - EU-TRAIN
ClinicalTrials.gov study NCT03652402. IPD Sharing: Not stated. Countries: 4. Publications: 15.
Stratification of Patients in Oncogeriatrics and Personalization of Interventions
ClinicalTrials.gov study NCT05131113. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Liquid Biopsy Using Exosomal miRNA Enables Risk Stratification of Potential Metastasis in Patients With Intrahepatic Cholangiocarcinoma.
ClinicalTrials.gov study NCT07224737. IPD Sharing: NO. Countries: 1. Publications: 13.
Point Of Care UltraSonography for Risk-stratification of COVID-19 Patients
ClinicalTrials.gov study NCT04338100. IPD Sharing: Not stated. Countries: 2. Publications: 5.
RISK Stratification Using a Combination of Cardiac Troponin T and Brain Natriuretic Peptide in Patients Receiving CRT-D
ClinicalTrials.gov study NCT00453622. IPD Sharing: Not stated. Countries: 1. Publications: 1.
SEPSIS 3 Critera for Risk Stratification in Emergency Patients
ClinicalTrials.gov study NCT02738164. IPD Sharing: UNDECIDED. Countries: 4. Publications: 1.
Thromboembolic and Bleeding Risk Stratification in Patients With Non-valvular Atrial Fibrillation
ClinicalTrials.gov study NCT02741349. IPD Sharing: NO. Countries: 1. Publications: 1.
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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.