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1,659 results for “Patient Data”
Pulmonary function in Thai patients with systemic sclerosis data
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raw data - Effects of preemptive acupuncture on cognitive function of the elderly patients after hip replacement: a randomized controlled trial
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Difference in rupture risk between familial and sporadic intracranial aneurysms: an individual patient data meta-analysis
<p><b>Objective:</b> We combined <span>individual patient data (IPD) from prospective cohorts of patients with </span>unruptured intracranial aneurysms (UIA) to assess to what extent patients with familial UIA have a higher rupture risk than those with sporadic UIA.<b> </b></p> <p><b>Methods:</b> For this IPD meta-analysis we performed an Embase and Pubmed search for studies published up to December 1, 2020. We included studies that 1) had a prospective study design; 2) included 50 or more patients with UIA; 3) studied the natural course of UIA and risk factors for aneurysm rupture including family history for aneurysmal subarachnoid haemorrhage and UIA; and 4) had aneurysm rupture as an outcome. Cohorts with available IPD were included. All studies included patients with newly diagnosed UIA visiting one of the study centers. The primary outcome was aneurysmal rupture. Patients with polycystic kidney disease and moyamoya disease were excluded. We compared rupture rates of familial versus sporadic UIA using a Cox proportional hazard regression model adjusted for the PHASES score and smoking. We performed two analyses: 1. only studies defining first-degree relatives as parents, children, and siblings and 2. all studies, thus both including and excluding siblings as first-degree relatives.</p> <p><b>Results:</b> We pooled IPD from eight cohorts with a low and moderate risk of bias. First-degree relatives were defined as parents, siblings and children in six cohorts (29% Dutch, 55% Finnish, 15% Japanese), totalling 2,297 patients (17% familial, 399 patients) with 3,089 UIA and 7,301 person-years follow-up. Rupture occurred in 10 familial patients (rupture rate: 0·89%/person-year; 95% CI:0·45-1·59) and 41 sporadic patients (0·66%/person-year; 95% CI:0·48-0·89); adjusted HR for familial patients 2·56 (95% CI: 1·18–5·56). After adding also the two cohorts excluding siblings as first-degree relatives resulting in 9,511 patients the adjusted HR was 1·44 (95% CI: 0·86–2·40).</p> <p><b>Conclusion:</b> The risk of rupture of UIA is two and a half times higher, with a range from a 1.2 to 5 times higher risk, in familial than in sporadic UIA. When assessing the risk of rupture in UIA, family history should be taken into account.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 04
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 04 contains a stitched image montage of the first and of the last semithin section from the analysis of patient C06, which was acquired by bright-field light microscopy.</p>
A detailed ultrastructural examination of lung cryobiopsy samples from a COVID-19 patient case series – Data set 16
<p>We investigated six cryobiopsy samples from six deceased patients (patients C03 to C08 from Barisione et al. 2020 <a href="https://doi.org/10.1007/s00428-020-02934-1">doi.org/10.1007/s00428-020-02934-1</a>) by using thin section electron microscopy (Cortese et al. 2022 <a href="http://doi.org/10.1007/s00428-022-03308-5">doi.org/10.1007/s00428-022-03308-5</a>). A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 16 contains stitched image montages of a thin section through the lung of patient C06 which were acquired by scanning electron microscopy. The file “Data_set_16.tif” contains a montage of the entire thin section while the other files contain selected areas of the section recorded at higher resolution.</p>
Raw data and SPSS analysis for the article Bacterial and fungal co-infections among ICU COVID-19 hospitalized patients in a Palestinian hospital: Incidence and antimicrobial stewardship
<p>The attached data is related to a study with proposes to investigate the burden of bacterial and fungal co-infections outcomes on COVID-19 patients. It is a single-center cross-sectional study of hospitalized COVID-19 patients at Beit-Jala hospital in Palestine. The study included 321 hospitalized patients admitted to the ICU between June 2020 and March 2021 aged ≥20 years,</p> <p><b>Background:</b> Diagnosis of co-infections with multiple pathogens among hospitalized COVID-19 patients can be jointly challenging and very essential for appropriate treatment, shortening hospital stay and preventing antimicrobial resistance. This study proposes to investigate the burden of bacterial and fungal co-infections outcomes on COVID-19 patients. It is a single center cross-sectional study of hospitalized COVID-19 patients at Beit-Jala hospital in Palestine.</p> <p><b>Methods: </b>The study included 321 hospitalized patients admitted to the ICU between June 2020 and March 2021 aged ≥20 years, with a confirmed diagnosis of COVID-19 via RT-PCR conducted on a nasopharyngeal swab. The patient's information was gathered using graded data forms from electronic medical reports.</p> <p><b>Results:</b> The diagnosis of bacterial and fungal infection was proved through the patient`s clinical presentation and positive blood or sputum culture results. All cases had received empirical antimicrobial therapy before the ICU admission, and different regimens during the ICU stay. The rate of bacterial co-infection was 51.1%, mainly from gram-negative isolates (Enterobacter species and K.pneumoniae). The rate of fungal co-infection caused by A.fumigatus was 48.9%, and the mortality rate was 8.1%. However, it is unclear if it had been attributed to SARS-CoV-2 or coincidental.</p>
The survey data of patients, prescribers, and pharmacists' feedback on ePrescription systems security and privacy
<p><b>Objective: </b>To evaluate the attitudes of the parties (i.e. patients, prescribers, and pharmacists) involved in the ePrescription systems toward the new features and measure the potential benefits of introducing the use of blockchain and machine learning (ML) to strengthen the in-place methods for safely prescribing and dispensing<span><span> medication. </span></span></p> <p><b>Methods: </b>The survey contains questions about the features introduced in the proposed approach of using blockcahin and ML in the ePrescription system to evaluate the security, privacy, reliability, and availability of the ePrescription information in the system.</p> <p>The study population is comprised of 284 respondents in the patient group, 39 respondents in the pharmacist group, and 27 respondents in the prescriber group, all of whom met the inclusion criteria. The response rate was 80% (226/284) in the patient group, 87% (34/39) in the pharmacist group, and 96% (26/27) in the prescriber group. We removed the responses of participants who did not complete the surevy questions.</p> <p><b>Conclusion: </b>Our survey showed that a vast majority of respondents in all groups had positive attitudes towards using blockchain and ML algorithms to safely prescribe medications. However, a need for minor improvements regarding the proposed features was identified, and a post-implementation user study is needed to evaluate the proposed ePrescription system in depth.</p>
Predicting atrial fibrillation recurrence by combining population data and virtual cohorts of patient-specific left atrial models
<p><strong>Abstract</strong></p> <p><strong>Background: </strong>Current ablation therapy for atrial fibrillation is sub-optimal and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more inter-individual variability.</p> <p><strong>Methods: </strong>Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, 16 long-standing persistent), undergoing first ablation. Patients were followed for 1-year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fibre orientation maps, electrical properties and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were post-processed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging and atrial fibrillation simulation metrics.</p> <p><strong>Results: </strong>We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging and simulation stress tests (average ten-fold cross-validation area under the curve 0.85 ± 0.09, recall 0.80 ± 0.13, precision 0.74 ± 0.13) outperformed those trained to history and imaging (area under the curve 0.66 ± 0.17), or history alone (area under the curve 0.61 ± 0.14). </p> <p><strong>Conclusion: </strong>A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalise selection for atrial fibrillation ablation.</p> <p><strong>Dataset Description: </strong>We include surface meshes in vtk format, consisting of the nodes, triangular elements, the atrial coordinate fields defined on the nodes, and the endocardial and epicardial fibre fields defined on the elements. </p> <p>We also include universal atrial coordinate fields alpha and beta, which are a lateral-septal coordinate and posterior-anterior coordinate for the LA. More details on the coordinate construction are given in our manuscript and <a href="https://www.ncbi.nlm.nih.gov/pubmed/31026761">https://www.ncbi.nlm.nih.gov/pubmed/31026761</a>. These coordinates can be used for registering datasets. </p> <p><strong>Publication</strong>: https://pubmed.ncbi.nlm.nih.gov/35089057/</p>
Data from: Effects of age and disease duration on excess mortality in patients with multiple sclerosis from a French nationwide cohort
<p><strong>Objective: </strong>To determine the effects of current age and disease duration on excess mortality in multiple sclerosis, we described the dynamics of excess deaths rates over these two time scales and studied the impact of age at multiple sclerosis clinical onset on these dynamics, separately in each initial phenotype.</p> <p><strong>Methods: </strong>We used data from 18 French multiple sclerosis expert centers participating in the Observatoire Français de la Sclérose en Plaques. Patients with multiple sclerosis living in metropolitan France and having a clinical onset between 1960 and 2014 were included. Vital status was updated on January 1st, 2016. For each multiple sclerosis phenotype separately (relapsing onset (R-MS) or primary progressive (PPMS)), we used an innovative statistical method to model the logarithm of excess death rates by a multidimensional penalized spline of age and disease duration.</p> <p><strong>Results: </strong>Among 37524 patients (71% women, mean age at multiple sclerosis onset ± standard deviation 33.0 ± 10.6 years), 2883 (7.7%) deaths were observed and 7.8% of patients were lost-to-follow-up. For R-MS patients, there was no excess mortality during the first 10 years after disease onset; afterwards, whatever age at onset, excess death rates increased with current age. From current age 70, the excess death rates values converged and became identical whatever the age at disease onset, which means that disease duration had no more impact. Excess death rates were higher in men with an excess hazard ratio of 1.46 (95% confidence interval 1.25-1.70). In contrast, in PPMS patients, excess death rates rapidly increased from disease onset, and were associated with age at onset, but not with sex.</p> <p><strong>Conclusions:</strong> In R-MS, current age has a stronger impact on multiple sclerosis mortality than disease duration while their respective effects are not so clear in PPMS. </p>
SKIRT data (1945 patients)
<p>- Basic information of the 1945 patients</p> <p>- Two-dimensional embedding of the 11150 immune profiles</p>
Reanalysis of Single-Cell Sequencing Data from Healthy and Periodontitis Patients: Characterization of Oral Epithelial Cells
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CTMM Data for: 'Sex-specific differences in cytokine signaling pathways in circulating monocytes of cardiovascular disease patients'
<p><span>Data from the publication 'Sex-specific differences in cytokine signaling pathways in circulating monocytes of cardiovascular disease patients' <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.atherosclerosis.2023.04.005" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.atherosclerosis.2023.04.005</span></a> </span></p> <p><span>We generated sex-biased gene expression signatures by comparing male versus female monocytes of coronary artery disease (CAD) patients (n = 450) from the Center for Translational Molecular Medicine-Circulating Cells Cohort. </span></p> <p><span>the data includes 3 dataframes : </span></p> <p><span>clin_cvd: patient info</span></p> <p><span>geneexprs_hgnc: gene expression after batch correction</span></p> <p><span>GES_cvdmf_HGNC: limma results (male vs female)</span></p>
Clinical data of COVID-19 infected patients
<p>Objectives</p> <p>This study aimed to evaluate the clinical efficacy of Paxlovid in patients hospitalized with severe/critical COVID-19.</p> <p>Methods</p> <p>Data were acquired from patients with severe/critical COVID-19 diagnosed between December 2022 and January 2023 at a medical center in China. Patients were divided into the Paxlovid treatment group and the conventional treatment group. The association between Paxlovid and all-cause mortality of patients during hospitalization was evaluated using the COX regression model and inverse probability weighting method, respectively. The secondary endpoint was the improvement in patients' lung imaging findings 1 week later. The odds ratio (OR) was estimated using logistic regression.</p> <p>Results</p> <p>A total of 158 eligible patients were enrolled, including 50 in-hospital deaths (50/98) in the Paxlovid group and 28 (28/60) in the conventional treatment group. The corrected hazard ratio for death was 0.51 (95% CI: 0.28–0.94, p = 0.031) and the inverse probability-weighted hazard ratio was 0.42 (95% CI: 0.24–0.75). The secondary endpoint analysis revealed that Paxlovid was associated with improved lung imaging findings 1 week later (adjusted OR: 0.35, 95% CI: 0.16–0.77).</p> <p>Conclusion</p> <p>Treatment with Paxlovid is associated with a significantly reduced risk of death and improved lung imaging findings in patients with severe/critical COVID-19.</p>
Data set for article''Digital health intervention in patients undergoing cardiac rehabilitation: systematic review and meta-analysis''
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Reduction mammoplasty patient data
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Data - Effectiveness of treatments for oral mucositis in pediatric patients with cancer
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Patient records from the simulation model and raw data
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Hand Function, Pain, and Grip Strength Data in Rheumatoid Arthritis Patients
<p>This dataset contains comprehensive information collected from rheumatoid arthritis (RA) patients, including sociodemographic, clinical, and study-specific variables. The data are organized into an Excel file and include the following key components:</p> <ul> <li>Sociodemographic Data: Includes variables such as age, gender, education level, marital status, and occupation.</li> <li>Clinical Data: Contains details on disease duration, disease activity (measured by Clinical Disease Activity Index, CDAI), rheumatoid arthritis articular damage (Rheumatoid Arthritis Articular Damage Score, RAAD), and the presence of hand deformities.</li> <li>Study Variables: <ul> <li>Hand Function: Assessed using the Cochin Hand Functional Scale (CHFS).</li> <li>Hand Pain: Measured with the Numeric Pain Scale (NPS).</li> <li>Grip Strength: Recorded using appropriate measuring tools.</li> <li>Disability: Evaluated by the Health Assessment Questionnaire (HAQ).</li> <li>Quality of Life: Assessed with the 12-Item Short Form Survey (SF-12).</li> <li>Tactile Sensitivity: Data on impaired hand sensitivity.</li> </ul> </li> </ul> <p>Purpose:</p> <p>The dataset is designed to facilitate analysis of the relationships between hand function, pain, and grip strength with various clinical outcomes and quality of life measures in RA patients. It provides valuable insights into how these factors interact and impact overall patient well-being.</p> <p>Usage:</p> <p>Researchers and clinicians can use this dataset for further analysis, validation of study findings, and exploration of correlations between different variables. The data are provided to support transparency, reproducibility, and advancement in the understanding of rheumatoid arthritis.</p>
Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial (Data for interventional clinical trial)
<p>Digital technologies and advanced analytics have drastically improved our ability to capture and interpret health relevant data from patients. However, to date, limited data and results have been published detailing real-world patient compliance, demonstrating accuracy in target indications or examining what novel insights and clinical value can be derived. Here we present novel, digital mobility data from two studies: an independent, non-interventional validation study with elderly, naturally slow walking subjects, and a global, multi-site phase IIb clinical trial involving patients with age-related muscle loss and slow walking speed (sarcopenia). Based on these data, we validate the accuracy of a novel algorithm for capturing in-clinic and real-world gait speed in frail, slow-walking adults. We demonstrate the feasibility of continuous monitoring with a wearable inertial sensor in elderly adults in real-world settings, and propose minimum thresholds for compliance required for robust capture of gait behaviors in this population. We also show how simple, inferred contextual information, describing the length of a given walking bout, can explain some of the variation in real-world gait speed, and use this information to demonstrate for the first time a relationship between in-clinic performance and real-world gait speed behavior. This work lays a foundation for exploration of the clinical relevance and value of such measures and is a first step in building a more complete chain of evidence between standardized physical performance assessment, real-world behavior, and subjective perceptions of mobility, independence and health.</p> <p>This dataset contains data collected during the interventional clinical trial: derived data from raw accelerometry data, and summary performance data.</p> <p>The full dataset, including raw accelerometry data, is available here: <a href="https://mueller-et-al-2019.s3.amazonaws.com/index.html">https://mueller-et-al-2019.s3.amazonaws.com/index.html</a></p>
Diabetic Retinopathy Patient Survey Data
<p>Patient survey data</p>
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
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.