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00004 Accuracy of the PREP2 algorithm for predicting Three Month Upper Limb Functional Capacity within a United States population of Persons with Stroke
- Jessica Barth, Kimberly Waddell, Marghuretta D. Bland, Catherine E. Lang
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- Journal:
- Journal of Clinical and Translational Science / Volume 5 / Issue s1 / March 2021
- Published online by Cambridge University Press:
- 30 March 2021, p. 113
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ABSTRACT IMPACT: Evaluate the accuracy of applying a predictive algorithm using clinical measures only in persons with stroke in the US. OBJECTIVES/GOALS: PREP2 is an algorithm, that predicts UL functional capacity at 3 months post stroke from measures taken within the first week.(1, 2) Despite its accuracy and ease of use, challenges arise of applying PREP2 in the US. The objective of this study was to evaluate the accuracy of PREP2 using only clinical measures in persons with stroke in the US. METHODS/STUDY POPULATION: Individuals with first-ever stroke were recruited from a local hospital and followed longitudinally, as part of an ongoing observational cohort. Variables captured within two weeks of stroke and entered into the algorithm were: age, SAFE score(1-3) and NIH Stroke Scale(4) total score. The algorithm classifies individuals into one of four expected categories: excellent, good, limited, or poor. The dependent variable was the predicted category of UL functional capacity as defined by ranges of the 3-month Action Research Arm Test score.(5) Accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) of the algorithm, were calculated using a 4x4 contingency table. Other statistics analyzed include demographic characteristics and a weighted kappa for the algorithm. RESULTS/ANTICIPATED RESULTS: Data from 49 individuals were analyzed (57% male, 88% ischemic stroke, age = 65±8.56 years). Expected categorization matched observed categorization in 29/49 subjects, with the overall accuracy of the algorithm of 59% (95% CI = 0.44-0.73). The sensitivity of the algorithm was low except for the excellent category (0.95). Specificity was moderate to high for good (0.81), limited (0.98), and poor (0.95) categories. PPV was low for all categories and NPV was high for all categories except the good category. Additional results including weighted kappa and inaccuracy of predictions to be presented. DISCUSSION/SIGNIFICANCE OF FINDINGS: PREP2 algorithm, with clinical measures only, is better than chance (chance = 25% for each of the 4 categories) alone at predicting a category of UL capacity at 3 months post stroke. PREP2 is a simple tool that facilitates evaluation of eventual UL outcome from measures routinely captured after a stroke within most healthcare settings in the US.
2326: Successful hand function recovery after stroke
- Shashwati Geed, Peter S. Lum, Michelle L. Harris-Love, Jessica Barth, Peter E. Turkeltaub, Alexander W. Dromerick
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- Journal:
- Journal of Clinical and Translational Science / Volume 1 / Issue S1 / September 2017
- Published online by Cambridge University Press:
- 10 May 2018, p. 62
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OBJECTIVES/SPECIFIC AIMS: Upper-extremity (UE) impairment affects 88% of stroke survivors due to dysfunctional shoulder-hand coordination. Patients may be able to grasp with the arm at rest, but unable to grasp in a functional context (eg, from a high shelf) because shoulder use elicits involuntary hand muscle activity. Further, much rehabilitation research is directed at unsuccessful stroke recovery (patients with persistent UE impairment) but very little towards patients who show successful clinical recovery (such as those with mild UE impairment) even though these patients have attained the desired rehabilitation outcome. We examined the neurophysiological trajectory of successful compared to unsuccessful post-stroke recovery in the context of functional UE movements to clearly identify what factors are necessary for successful recovery of functional UE movements after stroke. METHODS/STUDY POPULATION: We studied 3 populations: (1) mildly-impaired patients, early (at <17 d, 30 d, 90 d, and 180 d) after stroke as a model of successful post-stroke recovery, (2) moderately-impaired, chronic patients (>6-months post stroke) with persistent hand function impairment, as a model of incomplete post-stroke recovery (unsuccessful recovery), and (3) Healthy age-range matched controls. We used transcranial magnetic stimulation (TMS) in all 3 groups at the given time points to measure corticomotor excitability (motor evoked potentials, recruitment curve), corticomotor inhibition (short-interval intracortical inhibition, long-interval intracortical inhibition), and intracortical facilitation of hand muscles with the shoulder positioned in different degrees of flexion or abduction (these shoulder positions are known to elicit involuntary, undesired hand muscle activation, which leads to UE dysfunction and impairment in individuals with stroke). RESULTS/ANTICIPATED RESULTS: Data collection are in process and will be presented. Preliminary data from controls shows that corticomotor excitability of selected hand muscles is affected by changes in shoulder position. Preliminary findings in controls are consistent with clinical findings in stroke that certain shoulder positions elicit involuntary and undesired hand muscle activation, leading to UE dysfunction and disability. Findings from the stroke groups will be presented. DISCUSSION/SIGNIFICANCE OF IMPACT: We hypothesize that this centrally-facilitated coupling between shoulder and hand muscles is disrupted after stroke, which may play a central role in the inability of patients to perform functional UE movements. By comparing the TMS metrics in mildly-impaired Versus moderately-impaired chronic patients, we will be able to identify the longitudinal change in neurophysiology underlying shoulder-hand coordination that is associated with successful or unsuccessful clinical recovery of UE function after stroke. Thus, these findings will help us distinguish between the neurophysiology underlying successful from unsuccessful UE recovery leading to more mechanism-based interventions for UE dysfunction post stroke in the future.
Detection Thresholds of Archaeological Features in Airborne Lidar Data from Central Yucatán
- Aline Magnoni, Travis W. Stanton, Nicolas Barth, Juan Carlos Fernandez-Diaz, José Francisco Osorio León, Francisco Pérez Ruíz, Jessica A. Wheeler
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- Journal:
- Advances in Archaeological Practice / Volume 4 / Issue 3 / August 2016
- Published online by Cambridge University Press:
- 16 January 2017, pp. 232-248
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In this article we evaluate ∼48km2 of airborne lidar data collected at a target density of 15 laser shots/m in central Yucatán, Mexico. This area covers parts of the sites of Chichén Itzá and Yaxuná, a kilometer-wide transect between these two sites, and a transect along the first few kilometers of Sacbé 1 from Yaxuná to Cobá. The results of our ground validation and mapping demonstrate that not all sizable archaeological features can be detected in the lidar images due to: (1) the slightly rolling topography interspersed with 1-6 m-high bedrock hummocks, which morphologically mimic house mounds, further complicated by the presence of low foundations; (2) the complex forest structure in central Yucatán, which has particularly dense near-ground understory resulting in a high number of mixed-signal ground and low vegetation returns which reduces the fidelity and accuracy of the bare-earth digital elevation models; and (3) the predominance of low archaeological features difficult to discern from the textural noise of the near-ground vegetation. In this article we explore different visualization techniques to increase the identification of cultural features, but we conclude that, in this portion of the Maya region, lidar should be used as a complement to traditional on-the-ground survey techniques.