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1,659 results for “Patient Data”
Tumor-induced Osteomalacia: A Systematic Review and Individual Patient's Data Analysis
<p>Abstract Context Tumor-induced osteomalacia (TIO) is a rare paraneoplastic syndrome, usually caused by small, benign, and slow-growing phosphaturic mesenchymal tumors. Clinically, TIO is characterized by renal phosphate leak, causing hypophosphatemia and osteomalacia. This review was performed to assess the clinical characteristics of TIO patients described worldwide so far. Evidence Acquisition On June 26, 2021, a systematic search was performed in Medline, Google Scholar, Google book, and Cochrane Library using the terms: "tumor induced osteomalacia," "oncogenic osteomalacia," "hypophosphatemia." There were no language restrictions. This review was performed according to Preferred Reporting Items for Systematic reviews and Meta-Analyses criteria. Evidence Results Overall, 1725 TIO cases were collected. TIO was more frequent in adult men, who showed a higher incidence of fractures compared with TIO women. The TIO-causing neoplasms were identified in 1493 patients. The somatostatin receptor-based imaging modalities have the highest sensitivity for the identification of TIO-causing neoplasms. TIO-causing neoplasms were equally located in bone and soft tissues; the latter showed a higher prevalence of fractures and deformities. The surgery is the preferred TIO definitive treatment (successful in &gt; 90% of patients). Promising nonsurgical therapies are treatments with burosumab in TIO patients with elevated fibroblast growth factor-23 levels, and with radiolabeled somatostatin analogs in patients with TIO-causing neoplasm identified by somatostatin receptor-based imaging techniques. Conclusion TIO occurs preferentially in adult men. The TIO clinical expressiveness is more severe in men as well as in patients with TIO-causing neoplasms located in soft tissues. Treatments with burosumab and with radiolabeled somatostatin analogs are the most promising nonsurgical therapies.</p>
Data from: Early prediction of in-hospital mortality in patients with congestive heart failure in intensive care unit: a retrospective observational cohort study
<p class="MsoNormal">Objective: Congestive heart failure (CHF) is a clinical syndrome in which heart disease progresses to a severe stage. Risk assessment and early diagnosis of death in patients with CHF are critical to patient prognosis and treatment. The purpose of this study was to establish a nomogram predicting in-hospital death for CHF patients in the ICU.</p> <p>Design: A retrospective observational cohort study.</p> <p>Setting and participants: The data of study from 30,411 CHF patients in the Medical Information Mart for Intensive Care (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD).</p> <p>Primary outcome: In-hospital mortality.</p> <p>Results: The inclusion criteria were met by 15983 subjects, whose in-hospital mortality rate was 12.4%. Multivariate analysis determined that the independent risk factors were age, race, norepinephrine, dopamine, phenylephrine, vasopressin, <a>mechanical</a> <a>ventilation</a>, intubation, HepF, heart rate, respiratory rate, temperature, SBP, AG, BUN, creatinine, chloride, MCV, RDW, and WBC. The C-index of the nomogram (0.767, 95%CI: 0.759–0.779) was <a>superior</a> to that of the traditional SOFA, APSIII and GWTGHF score, indicating its discrimination power. Calibration plots demonstrated that the predicted results are in good agreement with the observed results. The decision curves of the derivation and validation sets both had net benefits.</p> <p>Conclusion: The twenty independent risk factors for in-hospital mortality of CHF patients were age, race, norepinephrine, dopamine, phenylephrine, vasopressin, <a>mechanical</a> <a>ventilation</a>, intubation, HepF, heart rate, respiratory rate, temperature, SBP, AG, BUN, creatinine, chloride, MCV, RDW, and WBC. The nomogram that included these factors accurately predicted the in-hospital mortality of CHF patients. The novel nomogram has the potential to be a clinical practice aided predictive tool for predicting and assessing mortality in CHF patients in the ICU.</p>
Data for "Patient-Important Outcomes Other Than Mortality in Contemporary ICU Trials: A Scoping Review"
<p>This upload contains the final datasets analyzed in the manuscript "Patient-Important Outcomes Other Than Mortality in Contemporary ICU Trials: A Scoping Review", published in Critical Care Medicine, 2022 (https://doi.org/10.1097/CCM.0000000000005637). The Supplemental Digital Content included with the primary manuscript includes a data dictionary, additional details, and the analysis code used.</p>
Network Theme: Digital and data-driven blood monitoring and analytics for patient centred care pathways - Dr Weizi (Vicky) Li (Henley Business School, University of Reading)
<p>This video is the fifth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Network Theme: Digital and data-driven blood monitoring and analytics for patient centred care pathways - Dr Weizi (Vicky) Li (Henley Business School, University of Reading).</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/egINC9hJifI</p>
PDE12 expression data from diabetes patients and controls
<p><span>Type 1 diabetes (T1D) incidence is increased after COVID-19 infection in children under 18 years of age. Interferon-α-activated oligoadenylate synthetase and downstream RNAseL activation degrade pathogen RNA, but can also damage host RNA when RNAseL activity is poorly regulated. One such regulator is PDE12 which degrades 2′-5′ oligoadenylate units, thereby decreasing RNAseL activity. We analyzed <em>PDE12</em> expression in islets from non-diabetic donors, individuals with newly (median disease duration 35 days) and recently (5 years) diagnosed T1D, and individuals with type 2 diabetes (T2D). We also analyzed <em>PDE12</em> single-nucleotide polymorphisms (SNPs) relative to T1D incidence. <em>PDE12</em> expression was decreased in individuals with recently diagnosed T1D, in three of five individuals with newly diagnosed T1D, but not in individuals with T2D. Two rare <em>PDE12</em> SNPs were found to have odds ratios of 1.80 and 1.74 for T1D development. We discuss whether decreased <em>PDE12</em> expression after COVID-19 infection might be part of the up to 2.5-fold increase in T1D incidence.</span></p>
Transthoracic contrast echocardiography data of a patient for super-resolution ultrasound localisation microscopy
<p>We provide sample contrast echocardiography datasets used for generation of super-resolution ultrasound localisation microscopy (ULM) images as reported in the following paper:</p> <div> <div>Jipeng Yan, Biao Huang, Johanna Tonko, Matthieu Toulemonde, Joseph Hansen-Shearer, Qingyuan Tan, Kai Riemer, Konstantinos Ntagiantas, Rasheda A Chowdhury, Pier D Lambiase, Roxy Senior, Meng-Xing Tang. Transthoracic Ultrasound Localization Microscopy of Myocardial Vasculature in Patients, Nature Biomedical Engineering, 2024. DOI: (<a href="https://www.nature.com/articles/s41551-024-01206-6" target="_blank" rel="noopener noreferrer">10.1038/s41551-024-01206-6</a>).</div> <div> </div> <div>The sample datasets include:</div> <div> </div> <div>"LogCompressedCEUS.mp4' is a video of CEUS images gated within one cardiac cycle after motion correction and log compression (acquistion time: 0.36s).</div> <div> </div> <div>"LinearScaleCEUS.mat" is a Matlab data file containing CEUS images after motion correction.</div> <div> </div> <div>"LinearScaleCEUSAfterNoiseReduction.mat' is a Matlab data file containing above CEUS images with noise reduced, which can be processed with our SRUS software (<a href="https://github.com/JipengYan1995/SRUSSoftware">JipengYan1995/SRUSSoftware</a>) for localisation and tracking (A brief tutorial can be found in "Usage of sample data in SRUS Software.docx").</div> <div> </div> <div>"RcvDataSample.mat' is a Matlab data file containing RF data in channels;</div> <div> </div> <div>"BFInformation.mat' is a Matlab data file containing parameters for beamforming;</div> <div> </div> <div>"Data Description.txt" contains more detailed descriptions of above data.</div> <div> </div> </div> <p><strong>Data from all the 10 cardiac cycles of this patient will be available in the future.</strong></p> <p>If you have any questions, please contact Meng-Xing Tang (email: mengxing.tang@imperial.ac.uk).</p>
Multi-antigen imaging data of skin tissue samples from cutaneous T cell lymphoma, atopic dermatitis, and psoriasis patients
<h1>Data</h1> <p>Multi-antigen imaging data for 69 skin tissue samples (21 cutaneous T cell lymphoma (CTCL), 23 atopic dermatitis (AD), and 25 pseudolymphoma (PSO)), obtained from 27 patients (8 CTCL, 7 AD, 12 PSO) treated at the University Hospital Erlangen. Each sample contains at least 36 protein channels, each of resolution 512 X 512 pixels. The data were generated using multi-epitope ligand cartography (MELC) (https://doi.org/10.1038/nbt1250, https://doi.org/10.1007/3-540-36459-5_8).</p> <h1>Metadata</h1> <p>For each sample, we information on sex, age, and condition of the corresponding patient. Samples and patients are numbered as S01 through S69 and P01 through P27, respectively.</p> <h1>Structure of the repository</h1> <ul> <li>The file <strong>metadata.csv</strong> contains the metadata.</li> <li>The archive <strong>data.zip</strong> contains one directory for each sample, named with the sample numbers S01 through S69.</li> <li>The directories for the individual samples contain images named<strong> <marker>-<flourescent protein>.tif</strong>, where <marker> is the name of the quantified protein marker and <flourescent protein> is the name of the flourescent protein that was used for image acquisition.</li> </ul> <p> </p> <p> </p>
Data underlying the article: "A patient-centric knowledge graph approach to prioritize mutants for selective anti-cancer targeting"
<p>This repository contains the data and code underlying the article: "A patient-centric knowledge graph approach to prioritize mutants for selective anti-cancer targeting" available on BioRxiv. </p>
raw data Figure 1 of the paper entitled "Plasma metabolic signature predictive of developing cancer in patients with Lynch syndrome "
Open the record for dataset details and reuse information.
HOMI Patient data
<p>EEG exam from an HOMI patient</p> <p>Annoation can be found in:</p> <p>https://github.com/SELF-Software-Evolution-Lab/AI-Epilepsy/tree/main/mlAIEp/EEGPaper/Scripts/Others</p>
Metadata from database covering longitudinal data from patients with subarachnoid hemorrhage
<p>Data from patient history, lab values (low-resolution data) and from multiple biosensors (high-resolution data) were prospectively collected from patients with aneurysmal subarachnoid hemorrhage (aSAH) who were admitted to the neurosurgical intensive care unit at the University Hospital of Zurich since October 2016. The cohort covers data from about 300 patients. The data collection was carried out via a CNS data collector (Moberg ICU Solutions, Ambler, PA) and the processing and storage of the high-resolution data was realized on the dedicated information technology infrastructure "ICU Cockpit" [doi.org/10.1093/jamia/ocac064].</p> <p><span>With the present project, highly sensitive patient data in need of protection is collected. Data cannot be made publicly available. However, for specific research questions, data may be made available upon request with the appropriate ethics committee approval and after completion of a DTA.</span></p>
Dataset for Spence et al., "Patient consent to publication and data sharing in industry and NIH-funded clinical trials" (Trials 2018 19:269).
<p>Dataset for our journal publication (Spence et al. 2018 <a href="https://doi.org/10.1186/s13063-018-2651-2">https://doi.org/10.1186/s13063-018-2651-2</a>). This dataset contains</p> <ul> <li>Informed consent forms (ICFs) for 98 industry-funded clinical trials</li> <li>Informed consent forms (ICFs) for 46 NIH-funded clinical trials</li> <li>Our extraction datasheet</li> </ul>
Missing data of Patient F for Chaudhary et al 2017 PloS Biology Publication
<p>The folder contains the missing data set of patient F for Chaudhary et al 2017 PloS Biology Publication</p>
Data for publication ''Interactive nutrition education is more effective in terms of improved levels of glycated hemoglobin in adolescent patients with poorly controlled type 1 diabetes – a randomized study''
<p>Data present changes in the most important clinical parameters after 3 and 6 months from the applied educational intervention in the group of children and adolescents with type 1 diabetes.</p>
MATLAB Code for "Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients"
<p>This MATLAB code is part of the study titled <em>"Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients"</em>, which has been accepted for publication in the <em>Journal of Imaging (MDPI)</em>. The code supports image processing, feature extraction, and deep learning model training (including LSTM and RexNet) to classify pediatric patients as anemic or non-anemic based on palm, conjunctival, and fingernail images. Full study details are available in this paper:</p> <p>Berghout T. Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients. <em>Journal of Imaging</em>. 2024; 10(10):245. <a href="https://doi.org/10.3390/jimaging10100245">https://doi.org/10.3390/jimaging10100245 </a></p> <p>The datsets use in this work are:</p> <p>Asare, J. W., Appiahene, P. & Donkoh, E. (2022). Anemia Detection using Palpable Palm Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/ccr8cm22vz.1<br>Asare, J. W., Appiahene, P. & Donkoh, E. (2023). CP-AnemiC (A Conjunctival Pallor) Dataset from Ghana. Mendeley Data. https://doi.org/10.17632/m53vz6b7fx.1<br>Asare, J. W., Appiahene, P. & Donkoh, E. (2020). Detection of Anemia using Colour of the Fingernails Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/2xx4j3kjg2.1</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.
Patient reviews data by MPU team
<p>This dataset is privacy-treated patient review data and is restricted to scientific research only. We publicize more than 100,000 real patient review data, and the review language is Chinese. In addition, we provide an extra 500 sample data, which is used to help researchers understand the content of our articles. We publicize such a large amount of dataset in order to facilitate all researchers to be able to conduct scientific research in any direction in order to advance science.</p>
Data from: Understanding the role of physician attire on patient perceptions: a systematic review of the literature— targeting attire to improve likelihood of rapport (TAILOR) investigators
Objectives: Despite a growing body of literature, uncertainty regarding the influence of physician dress on patients' perceptions exists. Therefore, we performed a systematic review to examine the influence of physician attire on patient perceptions including trust, satisfaction and confidence. Setting, participants, interventions and outcomes: We searched MEDLINE, Embase, Biosis Previews and Conference Papers Index. Studies that: (1) involved participants ≥18 years of age; (2) evaluated physician attire; and (3) reported patient perceptions related to attire were included. Two authors determined study eligibility. Studies were categorised by country of origin, clinical discipline (eg, internal medicine, surgery), context (inpatient vs outpatient) and occurrence of a clinical encounter when soliciting opinions regarding attire. Studies were assessed using the Downs and Black Scale risk of bias scale. Owing to clinical and methodological heterogeneity, meta-analyses were not attempted. Results: Of 1040 citations, 30 studies involving 11 533 patients met eligibility criteria. Included studies featured patients from 14 countries. General medicine, procedural (eg, general surgery and obstetrics), clinic, emergency departments and hospital settings were represented. Preferences or positive influence of physician attire on patient perceptions were reported in 21 of the 30 studies (70%). Formal attire and white coats with other attire not specified was preferred in 18 of 30 studies (60%). Preference for formal attire and white coats was more prevalent among older patients and studies conducted in Europe and Asia. Four of seven studies involving procedural specialties reported either no preference for attire or a preference for scrubs; four of five studies in intensive care and emergency settings also found no attire preference. Only 3 of 12 studies that surveyed patients after a clinical encounter concluded that attire influenced patient perceptions. Conclusions: Although patients often prefer formal physician attire, perceptions of attire are influenced by age, locale, setting and context of care. Policy-based interventions that target such factors appear necessary.
Data from: Axial symptoms predict mortality in patients with Parkinson disease with subthalamic stimulation
Objective: To characterize how disease progression is associated with mortality in a large cohort of PD patients with long-term follow-up after STN-DBS. Methods: Motor and cognitive disabilities were assessed before, and 1, 2, 5 and 10 years after STN-DBS in 143 consecutive PD patients. We measured motor symptoms Off and On levodopa and STN-DBS, and recorded causes of death. We used linear mixed-models to characterize symptom progression, including interactions between treatment conditions and time to determine how treatments changed efficacy. We used joint models to link progression to mortality. Results: Median observation time was 12 years after surgery, during which akinesia, rigidity and axial symptoms worsened, with mean increases of 8.8 (SD 6.5), 1.8 (3.1) and 5.4 (4.1) points from year 1 to 10 after surgery (On dopamine/On STN-DBS), respectively. Responses to dopaminergic medication and STN-DBS were attenuated with time, but remained effective for all except axial symptoms, for which both treatments and their combination were predicted to be ineffective 20 years after surgery. Cognitive status significantly declined. Forty-one patients died with a median time to death of 9 years after surgery. The current level of axial disability was the only symptom that significantly predicted death (HR=4.30 [SE 1.50] per unit of square-root transformed axial score). Conclusions: We quantified long-term symptom progression and attenuation of dopaminergic medication and STN-DBS treatment efficacy in PD patients, and linked symptom progression to mortality. Axial disability significantly predicts individual risk of death after surgery, which may be useful for planning therapeutic strategies in PD.
Data from: Incidence of cancer-associated thromboembolism in Japanese gastric and colorectal cancer patients receiving chemotherapy: a single-institutional retrospective cohort analysis (Sapporo CAT study)
Objective: Few data regarding the incidence of cancer-associated thromboembolism (TE) are available for Asian populations. We investigated the incidence of TE (TEi) and its risk factors among gastric and colorectal cancer (GCC) patients who received chemotherapy in a daily practice setting. Design: A retrospective cohort study. Setting: A single institutional study that used data from Sapporo City General Hospital, Japan, on patients treated between January 2008 and May 2015. Participants: Five hundred Japanese GCC patients who started chemotherapy from January 2008 to May 2015. Primary and secondary outcome measures: TE was diagnosed by reviewing all the reports of contrast-enhanced computed tomography (CT) performed during the follow-up period. All types of thrombosis detected by CT or additional imaging tests, such as venous TE, arterial TE, and cerebral infarction, were defined as TE. Medical records of all identified patients were reviewed and potential risk factors for TE including clinicopathological backgrounds were collected. We defined the following patients as 'active cancer'; patients with unresectable advanced GCC, cancer recurrence during or after completing adjuvant (Adj) chemotherapy, and/or presence of other malignant tumours. Results: Of the 500 patients, 70 patients (14.0%) developed TE during the follow-up period. TEi was 9.2% and 17.3% in gastric and colorectal cancer patients, 18.1% and 3.5% in active and non-active cancer patients, and 24.0% and 12.9% in multiple and single primary, respectively. Multivariate logistic regression analysis showed that colorectal cancer (odds ratio [OR], 2.371; 95% confidence interval [CI], 1.328 to 4.233), active cancer (OR 7.593; 95% CI 2.950 to 19.543), and multiple primary (OR 2.527; 95% CI 1.189 to 5.370) were independently associated with TEi. Conclusion: TEi was 14.0% among Japanese GCC patients received chemotherapy, and was significantly higher among patients with colorectal cancer, active cancer, and multiple primary than among those with gastric cancer, non-active cancer, and single primary, respectively.
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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.