Web-based tool for dynamic functional outcome after acute ischemic stroke and comparison with existing models

BACKGROUND: Acute ischemic stroke (AIS) is one of the leading causes of death and adult disability worldwide. In the present study, we aimed to develop a web-based risk model for predicting dynamic functional status at discharge, 3-month, 6-month, and 1-year after acute ischemic stroke (Dynamic Functional Status after Acute Ischemic Stroke, DFS-AIS). METHODS: The DFS-AIS was developed based on the China National Stroke Registry (CNSR), in which eligible patients were randomly divided into derivation (60%) and validation (40%) cohorts. Good functional outcome was defined as modified Rankin Scale (mRS) score ≤ 2 at discharge, 3-month, 6-month, and 1-year after AIS, respectively. Independent predictors of each outcome measure were obtained using multivariable logistic regression. The area under the receiver operating characteristic curve (AUROC) and plot of observed and predicted risk were used to assess model discrimination and calibration. RESULTS: A total of 12,026 patients were included and the median age was 67 (interquartile range: 57–75). The proportion of patients with good functional outcome at discharge, 3-month, 6-month, and 1-year after AIS was 67.9%, 66.5%, 66.9% and 66.9%, respectively. Age, gender, medical history of diabetes mellitus, stroke or transient ischemic attack, current smoking and atrial fibrillation, pre-stroke dependence, pre-stroke statins using, admission National Institutes of Health Stroke Scale score, admission blood glucose were identified as independent predictors of functional outcome at different time points after AIS. The DFS-AIS was developed from sets of predictors of mRS ≤ 2 at different time points following AIS. The DFS-AIS demonstrated good discrimination in the derivation and validation cohorts (AUROC range: 0.837-0.845). Plots of observed versus predicted likelihood showed excellent calibration in the derivation and validation cohorts (all r = 0.99, P < 0.001). When compared to 8 existing models, the DFS-AIS showed significantly better discrimination for good functional outcome and mortality at discharge, 3-month, 6-month, and 1-year after AIS (all P < 0.0001). CONCLUSION: The DFS-AIS is a valid risk model to predict functional outcome at discharge, 3-month, 6-month, and 1-year after AIS. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12883-014-0214-z) contains supplementary material, which is available to authorized users

Mechanistic, mechanistic-based empirical, and continuum-based concepts and models for the transport of polyelectrolyte-modified nanoscale zerovalent iron (NZVI) in saturated porous media

Controlled emplacement of polyelectrolyte-modified NZVI at a high particle concentration (1–10 g/L) is needed for effective in situ subsurface remediation. For this reason, a modeling tool capable of predicting polyelectrolyte-modified NZVI transport is imperative. However, the deep bed filtration theory is invalid for this purpose because several phenomena governing the transport of polyelectrolyte-modified NZVI in saturated porous media, including detachment, particle agglomeration, straining, and porous media ripening, violate the fundamental assumption of such a classical theory. Thus, this chapter critically reviews the literature of each phenomenon with various kinds of nanoparticles with a special focus on polyelectrolyte-modified NZVI. Then, each phenomenon is elaborated using three kinds of mathematical models, including mechanistic (such as extended DLVO theory), mechanistic-based empirical (correlations to predict NZVI agglomeration and deposition), and continuum-based (Eulerian continuum-based models). These proposed modeling tools can be applied at various scales from column experiments (1-D) to field-scaled operations (3-D) for designing NZVI injection and emplacement in the subsurface