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Background: We aimed to develop and validate a new nomogram for predicting the risk of intracranial hemorrhage (ICH) in patients with acute ischemic stroke (AIS) after intravenous thrombolysis (IVT).
Methods: A retrospective study enrolled 553 patients with AIS treated with IVT. The patients were randomly divided into two cohorts: the training set (70%, = 387) and the testing set (30%, = 166). The factors in the predictive nomogram were filtered using multivariable logistic regression analysis. The performance of the nomogram was assessed based on the area under the receiver operating characteristic curve (AUC-ROC), calibration plots, and decision curve analysis (DCA).
Results: After multivariable logistic regression analysis, certain factors, such as smoking, National Institutes of Health of Stroke Scale (NIHSS) score, blood urea nitrogen-to-creatinine ratio (BUN/Cr), and neutrophil-to-lymphocyte ratio (NLR), were found to be independent predictors of ICH and were used to construct a nomogram. The AUC-ROC values of the nomogram were 0.887 (95% CI: 0.842-0.933) and 0.776 (95% CI: 0.681-0.872) in the training and testing sets, respectively. The AUC-ROC of the nomogram was higher than that of the Multicenter Stroke Survey (MSS), Glucose, Race, Age, Sex, Systolic blood Pressure, and Severity of stroke (GRASPS), and stroke prognostication using age and NIH Stroke Scale-100 positive index (SPAN-100) scores for predicting ICH in both the training and testing sets ( < 0.05). The calibration plot demonstrated good agreement in both the training and testing sets. DCA indicated that the nomogram was clinically useful.
Conclusions: The new nomogram, which included smoking, NIHSS, BUN/Cr, and NLR as variables, had the potential for predicting the risk of ICH in patients with AIS after IVT.
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http://dx.doi.org/10.3389/fneur.2022.774654 | DOI Listing |
Am J Emerg Med
September 2025
University of Toronto, Rotman School of Management, Canada.
Study Objective: Accurately predicting which Emergency Department (ED) patients are at high risk of leaving without being seen (LWBS) could enable targeted interventions aimed at reducing LWBS rates. Machine Learning (ML) models that dynamically update these risk predictions as patients experience more time waiting were developed and validated, in order to improve the prediction accuracy and correctly identify more patients who LWBS.
Methods: The study was deemed quality improvement by the institutional review board, and collected all patient visits to the ED of a large academic medical campus over 24 months.
JMIR Res Protoc
September 2025
Department of Medical Oncology, Early Phase Unit, Georges-François Leclerc Centre, Dijon, France.
Background: Sarcomas are rare cancer with a heterogeneous group of tumors. They affect both genders across all age groups and present significant heterogeneity, with more than 70 histological subtypes. Despite tailored treatments, the high metastatic potential of sarcomas remains a major factor in poor patient survival, as metastasis is often the leading cause of death.
View Article and Find Full Text PDFJMIR Res Protoc
September 2025
University of Nevada, Las Vegas, Las Vegas, NV, United States.
Background: In-hospital cardiac arrest (IHCA) remains a public health conundrum with high morbidity and mortality rates. While early identification of high-risk patients could enable preventive interventions and improve survival, evidence on the effectiveness of current prediction methods remains inconclusive. Limited research exists on patients' prearrest pathophysiological status and predictive and prognostic factors of IHCA, highlighting the need for a comprehensive synthesis of predictive methodologies.
View Article and Find Full Text PDFNeurol Neuroimmunol Neuroinflamm
November 2025
Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Background And Objectives: Myelitis is a relatively common clinical entity for neurologists, with diverse underlying causes. The aim of this study was to describe the incidence of myelitis, its causes, clinical presentation, and factors predicting functional outcomes and relapses.
Methods: Using the Swedish National Patient Registry, we identified all adult patients in Stockholm County between 2008 and 2018 using International Classification of Diseases, 10th Edition (ICD-10) codes likely to include myelitis.
Crit Care Explor
September 2025
Department of Biostatistics, University of Florida Colleges of Medicine and Public Health and Health Professions, Gainesville, FL.
Objectives Background: Monocyte anisocytosis (monocyte distribution width [MDW]) has been previously validated to predict sepsis and outcome in patients presenting in the emergency department and mixed-population ICUs. Determining sepsis in a critically ill surgical/trauma population is often difficult due to concomitant inflammation and stress. We examined whether MDW could identify sepsis among patients admitted to a surgical/trauma ICU and predict clinical outcome.
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