Posts Tagged predictor

[Abstract] Cognitive and Motor Recovery and Predictors of Long-Term Outcome in Patients with Traumatic Brain Injury

Abstract

Objective

To explore the patterns of cognitive and motor recovery at four time points from admission to nine months post-discharge from IR and to investigate the association of therapeutic factors and pre- and post-discharge conditions with long-term outcomes.

Design

Secondary analysis of traumatic brain injury-practice based evidence (TBI-PBE) dataset.

Settings

Inpatient rehabilitation (IR) in Ontario, Canada.

Participants

A total of 85 patients with TBI consecutively admitted for IR between 2008 and 2011 and had data available from admission to nine months follow-up.

Interventions

Not applicable.

Main outcome measure

Functional Independence Measure-Rasch cognitive and motor scores at admission, discharge, three, and nine months post-discharge.

Results

Cognitive and motor recovery showed similar patterns of improvement with recovery up to three months but no significant change from three to nine months. Having fewer post-discharge health conditions was associated with better long-term cognitive scores (95% CI -13.06, -1.2) and added 9.9 % to the explanatory power of the model. More therapy time in complex occupational therapy activities (95% CI .02, .09) and fewer post-discharge health conditions (95% CI -19.5, -3.8) were significant predictors of better long-term motor function and added 14.3% and 7.2% to the explanatory power of the model, respectively.

Conclusion

Results of this study inform health care providers about the influence of the timing of IR on cognitive and motor recovery. In addition, it underlines the importance of making patients and families aware of residual health conditions following discharge from IR.

via Cognitive and Motor Recovery and Predictors of Long-Term Outcome in Patients with Traumatic Brain Injury – Archives of Physical Medicine and Rehabilitation

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[Abstract] Functional visual fields: relationship of visual field areas to self-reported function

Abstract

Purpose

The aim of this study is to relate areas of the visual field to functional difficulties to inform the development of a binocular visual field assessment that can reflect the functional consequences of visual field loss.

Methods

Fifty-two participants with peripheral visual field loss undertook binocular assessment of visual fields using the 30-2 and 60-4 SITA Fast programs on the Humphrey Field Analyser, and mean thresholds were derived. Binocular visual acuity, contrast sensitivity and near reading performance were also determined. Self-reported overall and mobility function were assessed using the Dutch ICF Activity Inventory.

Results

Greater visual field loss (0–60°) was associated with worse self-reported function both overall (R2 = 0.50; p < 0.0001), and for mobility (R2 = 0.64; p < 0.0001). Central (0–30°) and peripheral (30–60°) visual field areas were similarly related to mobility function (R2 = 0.61, p < 0.0001 and R2 = 0.63, p < 0.0001 respectively), although the peripheral (30–60°) visual field was the best predictor of mobility self-reported function in multiple regression analyses. Superior and inferior visual field areas related similarly to mobility function (R2 = 0.56, p < 0.0001 and R2 = 0.67, p < 0.0001 respectively). The inferior field was found to be the best predictor of mobility function in multiple regression analysis.

Conclusion

Mean threshold of the binocular visual field to 60° eccentricity is a good predictor of self-reported function overall, and particularly of mobility function. Both the central (0–30°) and peripheral (30–60°) mean threshold are good predictors of self-reported function, but the peripheral (30–0°) field is a slightly better predictor of mobility function, and should not be ignored when considering functional consequences of field loss. The inferior visual field is a slightly stronger predictor of perceived overall and mobility function than the superior field.

Source: Functional visual fields: relationship of visual field areas to self-reported function – Subhi – 2017 – Ophthalmic and Physiological Optics – Wiley Online Library

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[WEB SITE] 6-minute walk test helps predict walking activity for stroke survivors.

Feb 01, 2017 | Tim Casey

A cross-sectional analysis of two trials found that the six-minute walk test was the strongest predictor of walking activity for stroke survivors.

Lead researcher George D. Fulk, PhD, of Clarkson University in Potsdam, New York, and colleagues published their results online in Stroke on Jan. 5.

The researchers evaluated 441 adults who survived a stroke and enrolled in the LEAPS (Locomotor Experience Applied Post-Stroke) and FASTEST (Functional Ambulation: Standard Treatment vs Electrical Stimulation) trials. The participants could walk at least 10 meters with at most maximal assistance and had a gait speed of less than 0.80 m/s at baseline.

Both trials used an activity monitor to measure comfortable gait speed, fast gait speed, six-minute walk test, lower extremity Fugl Meyer, Berg Balance scale, SIS-mobility, SIS-participation, functional ambulation category, mini mental state exam, age, sex, marital status and average steps per day.

For this analysis, the researchers developed analogous functional walking categories based on participants’ daily walking activity. They considered walking activity of 100 to 2,499 steps per day as household ambulatory (the home group), walking activity of 2,500 to 4,999 steps per day as a most limited community ambulatory (the most limited group), walking activity of 5,000 to 7,499 steps per day as a least limited community ambulatory (the least limited group) and walking activity 7,500 or more steps per day as an unlimited community ambulatory (the full community group).

The mean age of participants was 61.4 years old and the mean comfortable gait speed was 0.6 m/s. In addition, 49 percent of participants were female and 80 percent were independent ambulators.

Of the participants, 43.08 percent were household ambulators, 30.39 percent were most limited community ambulators, 14.29 percent were least limited community ambulators and 12.24 percent were unlimited community ambulators.

Based on a model, the researchers found that a combination of walking endurance, balance and motor function were the strongest predictors of community walking activity. They added that walking endurance and motor function helped discriminate between home and community walking activity and limited and unlimited community walking ambulators and that balance helped discriminate between home and community ambulators.

For instance, a comfortable gait speed of 0.49 m/s discriminated between home and community and a comfortable gait speed of 0.93 m/s discriminated between limited community and full community ambulators.

The researchers also found that measuring walking endurance with the 6-minute walk test was the strongest individual predictor of community walking activity. For instance, a six-minute walking distance of 205 meters or longer discriminated between home and community ambulators, while a distance of 288 meters or longer discriminated between limited and unlimited community ambulators.

They said the six-minute walk test is effective because it measures functional walking endurance and is also related to other body structure/function, activity and participation constructs.

The analysis had a few limitations, according to the researchers, including that the model did not account for balance, motor function and other factors that play a role in community mobility. They also noted this was a secondary analysis of data from two different studies.

“Walking endurance, motor function, and balance play an important role in home and community walking activity post-stroke,” the researchers wrote. “Rehabilitation interventions that target these areas may be beneficial for people with stroke in order to improve their ability to walk in their community. Although [comfortable gait speed] can predict home and community ambulators, cutoff values commonly used to discriminate between home and community ambulators may overestimate actual walking activity.”

Source: 6-minute walk test helps predict walking activity for stroke survivors | Cardiovascular Business

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[Abstract] Factors affecting post-stroke motor recovery: Implications on neurotherapy after brain injury

Highlights

  • Motor recovery after stroke is a multifactorial and dynamic process.
  • Advanced age, African American race, and female gender are major socioeconomic factors affecting stroke recovery.
  • Extent of initial injury after stroke is a major independent predictor of recovery.
  • Neurorehabilitation strategies provide a unique opportunity for enhancing recovery.
  • Genetic polymorphisms especially in BDNF may influence post-stroke recovery process.

Abstract

Neurological disorders are a major cause of chronic disability globally among which stroke is a leading cause of chronic disability. The advances in the medical management of stroke patients over the past decade have significantly reduced mortality, but at the same time increased numbers of disabled survivors. Unfortunately, this reduction in mortality was not paralleled by satisfactory therapeutics and rehabilitation strategies that can improve functional recovery of patients.

Motor recovery after brain injury is a complex, dynamic, and multifactorial process in which an interplay among genetic, pathophysiologic, sociodemographic and therapeutic factors determines the overall recovery trajectory. Although stroke recovery is the most well-studied form of post-injury neuronal recovery, a thorough understanding of the pathophysiology and determinants affecting stroke recovery is still lacking.

Understanding the different variables affecting brain recovery after stroke will not only provide an opportunity to develop therapeutic interventions but also allow for developing personalized platforms for patient stratification and prognosis. We aim to provide a narrative review of major determinants for post-stroke recovery and their implications in other forms of brain injury.

Source: Factors affecting post-stroke motor recovery: Implications on neurotherapy after brain injury

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