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163 results for “mortality risk”
Data from: Non-steroidal anti-inflammatory drugs use in older adults decreases risk of Alzheimer’s disease mortality
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Humans drive spatial variation in mortality risk for a threatened wolf population in a Canis hybrid zone
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The mortality risk factor of severe community-acquired pneumonia (SCAP) patients with Sepsis: a retrospective study
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Data from: State transitions: a major mortality risk for seasonal species
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Data from: Season rather than habitat affects lynx survival and risk of mortality in the human-dominated landscape of southern Sweden
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Aetiology and prognostic risk factors of mortality in pneumonia patients receiving glucocorticoids alone or glucocorticoids and other immunosuppressants: a retrospective cohort study
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Data from: The landscape of anthropogenic mortality: how African lions respond to spatial variation in risk
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Data from: A dynamic framework for the study of optimal birth intervals reveals the importance of sibling competition and mortality risks
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Data: Upper thermal limits and risk of mortality of coastal Antarctic ectotherms
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Data from: A risk-based forecast of extreme mortality events in small cetaceans: using stranding data to inform conservation practice
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Development and validation of a machine learning model for use as an automated artificial intelligence tool to predict mortality risk in patients with COVID-19
<p><strong>Background</strong></p> <p>New York City quickly became an epicenter of the COVID-19 pandemic. Due to a sudden and massive increase in patients during COVID-19 pandemic, healthcare providers incurred an exponential increase in workload which created a strain on the staff and limited resources. As this is a new infection, predictors of morbidity and mortality are not well characterized.</p> <p><strong>Methods</strong></p> <p>We developed a prediction model to predict patients at risk for mortality using only laboratory, vital and demographic information readily available in the electronic health record on more than 3000 hospital admissions with COVID-19. A variable importance algorithm was used for interpretability and understanding of performance and predictors.</p> <p><strong>Findings</strong></p> <p>We built a model with 84-97% accuracy to identify predictors and patients with high risk of mortality, and developed an automated artificial intelligence (AI) notification tool that does not require manual calculation by the busy clinician. Oximetry, respirations, blood urea nitrogen, lymphocyte percent, calcium, troponin and neutrophil percentage were important features and key ranges were identified that contributed to a 50% increase in patients’ mortality prediction score. With an increasing negative predictive value (NPV) starting 0.90 after the second day of admission, we are able more confidently able identify likely survivors. This study serves as a use case of a model with visualizations to aide clinicians with a better understanding of the model and predictors of mortality. Additionally, an example of the operationalization of the model via an AI notification tool is illustrated.</p>
COVID-19 Mortality Risk Assessment Among Various Age Groups Using Phylogenetic Analysis
<p>The datasets used for the analysis titled <strong>Phylogenomic analysis of all available COVID-19 genomes and its influence on mortality</strong></p>
Influence of biological and environmental conditions on winter mortality risk of a northern ungulate: evidence for a late-winter survival bottleneck
<p><b>1.</b> A relationship between winter weather and survival of northern ungulates has long been established, yet the possible roles of biological (e.g., nutritional status) and environmental (e.g., weather) conditions make it important to determine which potential limiting factors are most influential.</p> <p><b>2</b>. Our objective was to examine the potential effects of individual (body mass and age) and extrinsic (winter severity and snow melt conditions) factors on the magnitude and timing of mortality for adult (>2.5 years old) female white-tailed deer (<i>Odocoileus virginianus </i>[Zimmerman, 1780] during February–May in the Upper Peninsula of Michigan, USA.</p> <p><b>3</b>. One hundred and fifty deer were captured and monitored during 2009–2015 in two areas with varying snowfall. February–May survival ranged from 0.24–0.89 (mean = 0.69) across years. Mortality risk increased 1.9% with each unit increase of cumulative winter severity index, decreased 8.2% with each cumulative snow free day, and decreased 4.3% with each kg increase in body mass. Age and weekly snow depth did not influence weekly deer survival. Predation, primarily from coyote (<i>Canis latrans </i>[Say, 1823]) and wolves (<i>Canis lupus </i>[L., 1758]), accounted for 78% of known-cause mortalities.</p> <p><b>4</b>. Our results suggest that cumulative winter severity, and possibly to a lesser degree deer condition entering winter, impacted deer winter survival. However, the timing of spring snow melt appeared to be the most influential factor determining late winter mortality of deer in our study. This supports the hypothesis that nutrition and energetic demands from weather conditions are both important to northern ungulate winter ecology. Under this model, a delay of several weeks in the timing of spring snow melt could exert a large influence on deer survival, resulting in a survival bottleneck.</p>
Data from: Lower education level is a risk factor for peritonitis and technique failure but not a risk for overall mortality in peritoneal dialysis under comprehensive training system
Background: Lower education level could be a risk factor for higher peritoneal dialysis (PD)-associated peritonitis, potentially resulting in technique failure. This study evaluated the influence of lower education level on the development of peritonitis, technique failure, and overall mortality. Methods: Patients over 18 years of age who started PD at Seoul National University Hospital between 2000 and 2012 with information on the academic background were enrolled. Patients were divided into three groups: middle school or lower (academic year ? 9, n = 102), high school (9 < academic year ? 12, n = 229), and higher than high school (academic year > 12, n = 324). Outcomes were analyzed using Cox proportional hazards models and competing risk regression. Results: A total of 655 incident PD patients (60.9% male, age 48.4 ± 14.1 years) were analyzed. During follow-up for 41 (interquartile range, 20-65) months, 255 patients (38.9%) experienced more than one episode of peritonitis, 138 patients (21.1%) underwent technique failure, and 78 patients (11.9%) died. After adjustment, middle school or lower education group was an independent risk factor for peritonitis (adjusted hazard ratio [HR], 1.61; 95% confidence interval [CI], 1.10-2.36; P = 0.015) and technique failure (adjusted HR, 1.87; 95% CI, 1.10-3.18; P = 0.038), compared with higher than high school education group. However, lower education was not associated with increased mortality either by as-treated (adjusted HR, 1.11; 95% CI, 0.53-2.33; P = 0.788) or intent-to-treat analysis (P = 0.726). Conclusions: Although lower education was a significant risk factor for peritonitis and technique failure, it was not associated with increased mortality in PD patients. Comprehensive training and multidisciplinary education may overcome the lower education level in PD.
Potential risk zone for anthropogenic mortality of carnivores in Gandaki province, Nepal
<p>Anthropogenic pressures in human-dominated landscapes often contribute to wildlife mortality. Carnivores are especially vulnerable to human-induced mortality due to the perceived threat to livestock and humans. Despite having widespread conservation implications, carnivore mortality data has been largely underutilized within Nepal. This study utilized Maxent to identify high-risk areas and explore the contribution of habitat attributes associated with carnivore mortality using the casualty database within the Gandaki province of central Nepal. We categorized the risk to carnivore species in three taxonomic groups: Felid, Viverridae, and Herpestidae, and identified a 3704 km2 area within the province at high risk for carnivore casualty. The middle mountains were the riskiest physiographic zone, and the Annapurna Conservation Area represented the largest risk zone among the four protected areas. Agricultural land was the most problematic area in terms of carnivore casualty. The human population was positively associated with high-risk areas and the number of casualties, whereas protected area cover had a negative association. This study identified that the common leopard was at the highest risk of mortality and therefore would benefit from the implementation of an action plan and species-specific conservation strategies, especially within identified high-risk zones. An expansion of protected areas in the middle mountain region would serve to greatly reduce carnivore casualty. Species distribution modeling can be further used with national-level spatial and temporal mortality data to identify the most prominent casualty times and pinpoint potential casualty locations throughout the country.</p>
Assessment of the 90-day Mortality Risk Score After VATS Lobectomy
ClinicalTrials.gov study NCT04799509. IPD Sharing: NO. Countries: 0. Publications: 1.
The Psychosocial Effect of Thoughts of Personal Mortality on Cardiac Risk Assessment by Medical Students
ClinicalTrials.gov study NCT00500136. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Morbidity, Mortality And Risk Factors of Mpox in HIV Negative High Risk Sexual Health Clinic Attenders and People Living With HIV
ClinicalTrials.gov study NCT05965427. IPD Sharing: UNDECIDED. Countries: 4. Publications: 0.
Mortality Risk Prediction Tool in Hospitalized Dementia Patients
ClinicalTrials.gov study NCT05371782. IPD Sharing: NO. Countries: 0. Publications: 1.
Body Mass Index and Obesity Surgery Mortality Risk Score in Perioperative Complications of Laparoscopic Sleeve Gastrectomy
ClinicalTrials.gov study NCT04427644. IPD Sharing: UNDECIDED. Countries: 0. Publications: 11.
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.