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Dataset results
109 results for “Covid-19 mortality”
People critically ill with COVID-19 exhibit peripheral immune profiles predictive of mortality and reflective of SARS-CoV-2 lung viral burden
GEO Series GSE180578. Homo sapiens. 86 samples. Type: Expression profiling by high throughput sequencing; Other.
A 50-gene high-risk profile predictive of COVID-19 and Idiopathic Pulmonary Fibrosis mortality originates from a molecular imbalance in monocyte and T-cell subsets that reverses in survivors with post
GEO Series GSE264196. Homo sapiens. 20 samples. Type: Expression profiling by high throughput sequencing.
Th2/Th1 cytokine imbalance is associated with higher COVID-19 risk mortality
GEO Series GSE178399. Homo sapiens. 288 samples. Type: Other.
Dataset related to article "An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study "
<p>This record contains raw data related to article “An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study"</p> <p>Abstract:</p> <p><strong>Introduction: </strong> Identifying SARS-CoV-2 patients at higher risk of mortality is crucial in the management of a pandemic. Artificial intelligence techniques allow one to analyze large amounts of data to find hidden patterns. We aimed to develop and validate a mortality score at admission for COVID-19 based on high-level machine learning.</p> <p><strong>Material and methods: </strong> We conducted a retrospective cohort study on hospitalized adult COVID-19 patients between March and December 2020. The primary outcome was in-hospital mortality. A machine learning approach based on vital parameters, laboratory values and demographic features was applied to develop different models. Then, a feature importance analysis was performed to reduce the number of variables included in the model, to develop a risk score with good overall performance, that was finally evaluated in terms of discrimination and calibration capabilities. All results underwent cross-validation.</p> <p><strong>Results: </strong> 1,135 consecutive patients (median age 70 years, 64% male) were enrolled, 48 patients were excluded, and the cohort was randomly divided into training (760) and test (327) groups. During hospitalization, 251 (22%) patients died. After feature selection, the best performing classifier was random forest (AUC 0.88 ±0.03). Based on the relative importance of each variable, a pragmatic score was developed, showing good performances (AUC 0.85 ±0.025), and three levels were defined that correlated well with in-hospital mortality.</p> <p><strong>Conclusions: </strong> Machine learning techniques were applied in order to develop an accurate in-hospital mortality risk score for COVID-19 based on ten variables. The application of the proposed score has utility in clinical settings to guide the management and prognostication of COVID-19 patients.</p>
Mortality in an Italian nursing home during COVID-19 pandemic: correlation with gender, age, ADL, vitamin D supplementation, and limitations of the diagnostic tests
<p><strong>Introduction: </strong> The COVID-19 pandemic caused an increased mortality in nursing homes due to its quick spread and the age-related high lethality.</p> <p><strong>Results: </strong> We observed a two-month mortality of 40%, compared to 6.4% in the previous year. This increase was seen in both COVID-19 positive (43%) and negative (24%) residents, but 8 patients among those testing negative on the swab, tested positive on serological tests. Increased mortality was associated with male gender, older age, no previous vitamin D supplementation and worse "activities of daily living (ADL)" scores, such as Barthel index, Tinetti scale and S.OS.I.A.</p> <p><strong>Conclusion: </strong> Our data confirms a higher geriatric mortality due to COVID-19. Negative residents also had higher mortality, which we suspect is secondary to preanalytical error and a low sensitivity of the swab test in poorly compliant subjects. Male gender, older age and low scores on ADL scales (probably due to immobility) are risk factors for COVID-19 related mortality. Finally, mortality was inversely associated with vitamin D supplementation.</p> <p><strong>Design: </strong> In this observational study, we described the two-month mortality among the 157 residents (age 60-100) of a nursing home after Sars-CoV-2 spreading, reporting the factors associated with the outcome. We also compared the diagnostic tests for Sars-CoV-2.</p> <p> </p><p>https://pubmed.ncbi.nlm.nih.gov/33353888/</p> <p></p>
Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features
<p>Dataset from Schiaffino S, Codari M, Cozzi A, Albano D, Alì M, Arioli R, Avola E, Bnà C, Cariati M, Carriero S, Cressoni M, Danna PSC, Della Pepa G, Di Leo G, Dolci F, Falaschi Z, Flor N, Foà RA, Gitto S, Leati G, Magni V, Malavazos AE, Mauri G, Messina C, Monfardini L, Paschè A, Pesapane F, Sconfienza LM, Secchi F, Segalini E, Spinazzola A, Tombini V, Tresoldi S, Vanzulli A, Vicentin I, Zagaria D, Fleischmann D, Sardanelli F. Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features. J Pers Med. 2021 Jun 3;11(6):501. doi: 10.3390/jpm11060501. PMID: 34204911; PMCID: PMC8230339.</p> <p>Abstract</p> <p>Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann-Whitney <em>U</em> test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset split. Out of 897 patients, the 229 (26%) patients who died during hospitalization had higher median pulmonary artery diameter (29.0 mm) than patients who survived (27.0 mm, <em>p</em> < 0.001) and higher median ascending aortic diameter (36.6 mm versus 34.0 mm, <em>p</em> < 0.001). SVM and MLP best models considered the same ten input features, yielding a 0.747 (precision 0.522, recall 0.800) and 0.844 (precision 0.680, recall 0.567) area under the curve, respectively. In this model integrating clinical and radiological data, pulmonary artery diameter was the third most important predictor after age and parenchymal involvement extent, contributing to reliable in-hospital mortality prediction, highlighting the value of vascular metrics in improving patient stratification.</p>
Excess Mortality Resulting from COVID-19 in Turkey during 2020-2021
<p>Dataset about Excess Mortality Resulting from COVID-19 in Turkey during 2020-2021.</p>
Data set from Machine learning to predict in-hospital mortality in covid-19 patients using computed tomography-derived pulmonary and vascular features
<p>Data set from Machine learning to predict in-hospital mortality in covid-19 patients using computed tomography-derived pulmonary and vascular features</p>
Data set from Machine learning to predict in-hospital mortality in covid-19 patients using computed tomography-derived pulmonary and vascular features
<p>Data Set from the study Machine learning to predict in-hospital mortality in covid-19 patients using computed tomography-derived pulmonary and vascular features</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.
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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.