A simple method for the analysis of clustered binary data.

A simple method for comparing independent groups of clustered binary data with group-specific covariates is proposed. It is based on the concepts of design effect and effective sample size widely used in sample surveys, and assumes no specific models for the intracluster correlations. It can be implemented using any standard computer program for the analysis of independent binary data after a small amount of preprocessing. The method is applied to a variety of problems involving clustered binary data: testing homogeneity of proportions, estimating dose-response models and testing for trend in proportions, and performing the Mantel-Haenszel chi-squared test for independence in a series of 2 x 2 tables and estimating the common odds ratio and its variance. Illustrative applications of the method are also presented.     

Mixed effects logistic regression models for multiple longitudinal binary functional limitation responses with informative drop-out and confounding by baseline outcomes

In the context of analyzing multiple functional limitation responses collected longitudinally from the Longitudinal Study of Aging (LSOA), we investigate the heterogeneity of these outcomes with respect to their associations with previous functional status and other risk factors in the presence of informative drop-out and confounding by baseline outcomes. We accommodate the longitudinal nature of the multiple outcomes with a unique extension of the nested random effects logistic model with an autoregressive structure to include drop-out and baseline outcome components with shared random effects. Estimation of fixed effects and variance components is by maximum likelihood with numerical integration. This shared parameter selection model assumes that drop-out is conditionally independent of the multiple functional limitation outcomes given the underlying random effect representing an individual's trajectory of functional status across time. Whereas it is not possible to fully assess the adequacy of this assumption, we assess the robustness of this approach by varying the assumptions underlying the proposed model such as the random effects structure, the drop-out component, and omission of baseline functional outcomes as dependent variables in the model. Heterogeneity among the associations between each functional limitation outcome and a set of risk factors for functional limitation, such as previous functional limitation and physical activity, exists for the LSOA data of interest. Less heterogeneity is observed among the estimates of time-level random effects variance components that are allowed to vary across functional outcomes and time. We also note that. under an autoregressive structure, bias results from omitting the baseline outcome component linked to the follow-up outcome component by subject-level random effects.     

Impact of functional status at six months on long term survival in patients with ischaemic stroke: prospective cohort studies

Objective To estimate the impact on long term survival of functional status at six months after ischaemic stroke.Design Prospective cohort study.Settings Three cohorts: Oxfordshire community stroke project, Lothian stroke register, and the first international stroke trial (in the United Kingdom).Participants 7710 patients with ischaemic stroke registered between 1981 and 2000 and followed up for a maximum of 19 years.Main outcome measures Functional status at six months after stroke assessed with modified Rankin scale or “two simple questions.” Mortality during follow-up. Survival analysis with Kaplan-Meier curves, log rank test, and Cox’s regression model.Results In a combined analysis of all three cohorts, among patients who survived to assessment six months after the index stroke, the subsequent median length of survival among those independent in daily living and those dependent was 9.7 years (95% confidence interval 8.9 to 10.6) and 6.0 years (5.7 to 6.4), respectively. In a combined analysis of the Oxfordshire and Lothian cohorts, subsequent median survival fell progressively from 12.9 years (10.0 to 15.9) for patients with a Rankin score of 0-1 at six months after the stroke to 2.5 years (1.4 to 3.5) for patients with a Rankin score of 5. All previously stated differences in median survival were significant (log rank test P<0.001). The influence of functional outcome on survival remained significant (P<0.05) in each cohort after adjustment for relevant covariates (such as age, presence of atrial fibrillation, visible infarct on computed tomography, subtype of stroke) in a Cox’s regression model.Conclusion Functional status six months after an ischaemic stroke is associated with long term survival. Early interventions that reduce dependency at six months might have positive effects on long term survival.     

Vitamin B12 and Progression of White Matter Lesions. A 2-Year Follow-Up Study in First-Ever Lacunar Stroke Patients

In cross-sectional studies periventricular white matter lesions (WML) were related to low plasma levels of vitamin B12. Whether low vitamin B12 levels are also related to progression of WML is still unknown. We studied baseline vitamin B12 levels and its association with progression of WML over 2 years of follow-up in first-ever lacunar stroke patients. In 107 first-ever lacunar stroke patients in whom baseline brain MRI and vitamin B12 status were available, we obtained a follow-up brain MRI after 2 years. We assessed progression of periventricular WML (pWML) and deep WML (dWML) using a visual WML change scale. We studied the relationship between baseline levels of plasma vitamin B12 and progression of WML after 2 years of follow-up by binary logistic regression analyses. Vitamin B12 deficiency was more frequent in patients with progression of pWML compared to those without progression (41.9% and 19.7% respectively, p = 0.02). Corrected for sex and age, progression of pWML was associated with lower baseline levels of vitamin B12 (OR 1.42 per 50 unit decrease, 95% CI 1.00-1.92). Vitamin B12 levels were not associated with progression of dWML. In conclusion progression of pWML after 2 years of follow-up relates to low levels of vitamin B12 at baseline in first-ever lacunar stroke patients. Whether this population could benefit from vitamin B12 supplementation is unknown and requires further investigation.     

Genetic Variation at the BDNF Locus: Evidence for Association with Long-Term Outcome after Ischemic Stroke

Background and Purpose

Rates and extent of recovery after stroke vary considerably between individuals and genetic factors are thought to contribute to post-stroke outcome. Brain-derived neurotrophic factor (BDNF) plays important roles in brain plasticity and repair and has been shown to be involved in stroke severity, recovery, and outcome in animal models. Few clinical studies on BDNF genotypes in relation to ischemic stroke have been performed. The aims of the present study are therefore to investigate whether genetic variation at the BDNF locus is associated with initial stroke severity, recovery and/or short-term and long-term functional outcome after ischemic stroke.

Methods

Four BDNF tagSNPs were analyzed in the Sahlgrenska Academy Study on Ischemic Stroke (SAHLSIS; 600 patients and 600 controls, all aged 18–70 years). Stroke severity was assessed using the NIH Stroke Scale (NIHSS). Stroke recovery was defined as the change in NIHSS over a 3-month period. Short- and long-term functional outcome post-stroke was assessed using the modified Rankin Scale at 3 months and at 2 and 7 years after stroke, respectively.

Results

No SNP was associated with stroke severity or recovery at 3 months and no SNP had an impact on short-term outcome. However, rs11030119 was independently associated with poor functional outcome 7-years after stroke (OR 0.66, 95% CI 0.46–0.92; P =  0.006).

Conclusions

BDNF gene variants were not major contributors to ischemic stroke severity, recovery, or short-term functional outcome. However, this study suggests that variants in the BDNF gene may contribute to poor long-term functional outcome after ischemic stroke.     

Comparisons of Prediction Models of Quality of Life after Laparoscopic Cholecystectomy: A Longitudinal Prospective Study

Background

Few studies of laparoscopic cholecystectomy (LC) outcome have used longitudinal data for more than two years. Moreover, no studies have considered group differences in factors other than outcome such as age and nonsurgical treatment. Additionally, almost all published articles agree that the essential issue of the internal validity (reproducibility) of the artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR) and multiple linear regression (MLR) models has not been adequately addressed. This study proposed to validate the use of these models for predicting quality of life (QOL) after LC and to compare the predictive capability of ANNs with that of SVM, GPR and MLR.

Methodology/Principal Findings

A total of 400 LC patients completed the SF-36 and the Gastrointestinal Quality of Life Index at baseline and at 2 years postoperatively. The criteria for evaluating the accuracy of the system models were mean square error (MSE) and mean absolute percentage error (MAPE). A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and to rank the variables in order of importance. Compared to SVM, GPR and MLR models, the ANN model generally had smaller MSE and MAPE values in the training data set and test data set. Most ANN models had MAPE values ranging from 4.20% to 8.60%, and most had high prediction accuracy. The global sensitivity analysis also showed that preoperative functional status was the best parameter for predicting QOL after LC.

Conclusions/Significance

Compared with SVM, GPR and MLR models, the ANN model in this study was more accurate in predicting patient-reported QOL and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data.     

Estimating marginal and incremental effects on health outcomes using flexible link and variance function models

We propose an extension to the estimating equations in generalized linear models to estimate parameters in the link function and variance structure simultaneously with regression coefficients. Rather than focusing on the regression coefficients, the purpose of these models is inference about the mean of the outcome as a function of a set of covariates, and various functionals of the mean function used to measure the effects of the covariates. A commonly used functional in econometrics, referred to as the marginal effect, is the partial derivative of the mean function with respect to any covariate, averaged over the empirical distribution of covariates in the model. We define an analogous parameter for discrete covariates. The proposed estimation method not only helps to identify an appropriate link function and to suggest an underlying distribution for a specific application but also serves as a robust estimator when no specific distribution for the outcome measure can be identified. Using Monte Carlo simulations, we show that the resulting parameter estimators are consistent. The method is illustrated with an analysis of inpatient expenditure data from a study of hospitalists.     

A Likelihood-Based Approach with Shared Latent Random Parameters for the Longitudinal Binary and Informative Censoring Processes

Longitudinal studies with binary outcomes characterized by informative right censoring are commonly encountered in clinical, basic, behavioral, and health sciences. Approaches developed to analyze data with binary outcomes were mainly tailored to clustered or longitudinal data with missing completely at random or at random. Studies that focused on informative right censoring with binary outcomes are characterized by their imbedded computational complexity and difficulty of implementation. Here we present a new maximum likelihood-based approach with repeated binary measures modeled in a generalized linear mixed model as a function of time and other covariates. The longitudinal binary outcome and the censoring process determined by the number of times a subject is observed share latent random variables (random intercept and slope) where these subject-specific random effects are common to both models. A simulation study and sensitivity analysis were conducted to test the model under different assumptions and censoring settings. Our results showed accuracy of the estimates generated under this model when censoring was fully informative or partially informative with dependence on the slopes. A successful implementation was undertaken on a cohort of renal transplant patients with blood urea nitrogen as a binary outcome measured over time to indicate normal and abnormal kidney function until the emanation of graft rejection that eventuated in informative right censoring. In addition to its novelty and accuracy, an additional key feature and advantage of the proposed model is its viability of implementation on available analytical tools and widespread application on any other longitudinal dataset with informative censoring.

     

Predicting Cognitive Function from Clinical Measures of Physical Function and Health Status in Older Adults

Introduction

Current research suggests that the neuropathology of dementia—including brain changes leading to memory impairment and cognitive decline—is evident years before the onset of this disease. Older adults with cognitive decline have reduced functional independence and quality of life, and are at greater risk for developing dementia. Therefore, identifying biomarkers that can be easily assessed within the clinical setting and predict cognitive decline is important. Early recognition of cognitive decline could promote timely implementation of preventive strategies.

Methods

We included 89 community-dwelling adults aged 70 years and older in our study, and collected 32 measures of physical function, health status and cognitive function at baseline. We utilized an L1–L2 regularized regression model (elastic net) to identify which of the 32 baseline measures were strongly predictive of cognitive function after one year. We built three linear regression models: 1) based on baseline cognitive function, 2) based on variables consistently selected in every cross-validation loop, and 3) a full model based on all the 32 variables. Each of these models was carefully tested with nested cross-validation.

Results

Our model with the six variables consistently selected in every cross-validation loop had a mean squared prediction error of 7.47. This number was smaller than that of the full model (115.33) and the model with baseline cognitive function (7.98). Our model explained 47% of the variance in cognitive function after one year.

Discussion

We built a parsimonious model based on a selected set of six physical function and health status measures strongly predictive of cognitive function after one year. In addition to reducing the complexity of the model without changing the model significantly, our model with the top variables improved the mean prediction error and R-squared. These six physical function and health status measures can be easily implemented in a clinical setting.     

A Prospective Study of the Incidence and Correlated Factors of Post-Stroke Depression in China

Background

Post-stroke depression (PSD) is commonly observed among stroke survivors. However, statistical analysis of such data is scarce in developing countries. The purpose of this study is to examine the incidence of PSD and its relationship with stroke characteristics in China.

Methods

This was a prospective hospital-based study. Stroke patients were assessed within two weeks after acute ischemic stroke onset and then reevaluated at three months. Hamilton Depression Scale (HAMD) was used for screening depression (PSD). Subjects with HAMD score of ≥7 were further assessed with the World Health Organization Composite International Diagnostic Interview. Stroke severity was measured by the National Institutes of Health Stroke Scale (NIHSS). Stroke outcome was measured by the modified Rankin Scale (mRS).

Results

One hundred and two stroke patients were recruited, only ninety-one patients completed del period (men = 53, 63.74%), with mean age 60.0±10.4 years (range, 34–82 years). The incidence of PSD was 27.47% two weeks after stroke. The occurrence of PSD was unrelated with age, stroke type, stroke lesion and the history of disease. In univariate analysis gender, PSD was correlated with female gender. In multivariate logistic regression analysis, poor stroke outcome (mRS≥3) (OR 12.113, CI 1.169 to 125.59, P<0.05) was the important predictors of PSD.

Conclusions

The study indicated that gender, functional dependence and stroke outcome are determinants of PSD occurrence during the first 2 weeks after stroke in China.     

Association between Statin Use and Short-Term Outcome Based on Severity of Ischemic Stroke: A Cohort Study

Background

Statins reportedly improve clinical outcomes for ischemic stroke patients. However, it is unclear whether the contribution of statin treatment varies depending on the severity of stroke. We sought to investigate the relationship between statin use and the outcome of acute first-ever ischemic stroke patients stratified by stroke severity.

Methods

A total of 7,455 acute first-ever ischemic stroke patients without statin treatment before onset were eligible from the China National Stroke Registry. A National Institutes of Health Stroke Scale (NIHSS) score of 0 to 4 was defined as minor stroke, and a NIHSS score of >4 was defined as non-minor stroke. We analyzed the association between statin use during hospitalization and mortality as well as functional outcome (measured by a modified Rankin Scale score of 0–5) at 3 months after onset using multivariable logistic regression models.

Results

A total of 3,231 (43.3%) patients received statin treatment during hospitalization. Multivariable analysis showed that statin use during hospitalization decreased mortality of ischemic stroke patients (OR, 0.51; 95%CI, 0.38–0.67), but did not improve poor functional outcomes (OR, 0.95; 95CI%, 0.81–1.11) at 3 months. The interaction between statin use and stroke severity was significant both in dependence and death outcome (P = 0.04 for dependence outcome, P = 0.03 for death outcome). After stratification by stroke severity, statin use during hospitalization decreased the mortality of stroke (OR, 0.44; 95%CI, 0.31–0.62) and poor functional outcome (OR, 0.73; 95%CI, 0.57–0.92) at 3 months in the non-minor stroke group.

Conclusions

Statin use during hospitalization may improve the clinical outcome of acute first-ever ischemic stroke depending on the severity of stroke. Non-minor stroke patients may obtain benefit from statin treatment with improvements in poor functional outcomes and mortality.     

Analysis of functional abilities for elderly Danish twins using GEE models.

In this work we present a new method for genetic analysis of twin data which is based on generalized estimating equations and allows for analysis of various response types (e.g., continuous, binary, counts) combined with estimation of residual correlations. The new approach allows for control of covariates of any kind (e.g., continuous, counts) by modeling the dependence of mean and variance on background variables. The proposed method was applied to identify the covariates that have a significant influence on elderly people's functional abilities, and find the estimates for the correlation coefficients of residuals for MZ and DZ twins in a sample of 2401 Danish twin 75 years of age or older. The bootstrap method was used to obtain standard errors for correlation coefficients. It was shown, that the chosen covariates have similar effects on MZ and DZ twins, and that the residual correlation in MZ twins is significantly higher than in DZ twins, which indicates that genetic factors play an etiological role in the determination of physical status of elderly people, controlled for 10 background variables.     

Functional Status Predicts Acute Care Readmissions from Inpatient Rehabilitation in the Stroke Population

Objective

Acute care readmission risk is an increasingly recognized problem that has garnered significant attention, yet the reasons for acute care readmission in the inpatient rehabilitation population are complex and likely multifactorial. Information on both medical comorbidities and functional status is routinely collected for stroke patients participating in inpatient rehabilitation. We sought to determine whether functional status is a more robust predictor of acute care readmissions in the inpatient rehabilitation stroke population compared with medical comorbidities using a large, administrative data set.

Methods

A retrospective analysis of data from the Uniform Data System for Medical Rehabilitation from the years 2002 to 2011 was performed examining stroke patients admitted to inpatient rehabilitation facilities. A Basic Model for predicting acute care readmission risk based on age and functional status was compared with models incorporating functional status and medical comorbidities (Basic-Plus) or models including age and medical comorbidities alone (Age-Comorbidity). C-statistics were compared to evaluate model performance.

Findings

There were a total of 803,124 patients: 88,187 (11%) patients were transferred back to an acute hospital: 22,247 (2.8%) within 3 days, 43,481 (5.4%) within 7 days, and 85,431 (10.6%) within 30 days. The C-statistics for the Basic Model were 0.701, 0.672, and 0.682 at days 3, 7, and 30 respectively. As compared to the Basic Model, the best-performing Basic-Plus model was the Basic+Elixhauser model with C-statistics differences of +0.011, +0.011, and + 0.012, and the best-performing Age-Comorbidity model was the Age+Elixhauser model with C-statistic differences of -0.124, -0.098, and -0.098 at days 3, 7, and 30 respectively.

Conclusions

Readmission models for the inpatient rehabilitation stroke population based on functional status and age showed better predictive ability than models based on medical comorbidities.     

Missing covariates in longitudinal data with informative dropouts: bias analysis and inference

We consider estimation in generalized linear mixed models (GLMM) for longitudinal data with informative dropouts. At the time a unit drops out, time-varying covariates are often unobserved in addition to the missing outcome. However, existing informative dropout models typically require covariates to be completely observed. This assumption is not realistic in the presence of time-varying covariates. In this article, we first study the asymptotic bias that would result from applying existing methods, where missing time-varying covariates are handled using naive approaches, which include: (1) using only baseline values; (2) carrying forward the last observation; and (3) assuming the missing data are ignorable. Our asymptotic bias analysis shows that these naive approaches yield inconsistent estimators of model parameters. We next propose a selection/transition model that allows covariates to be missing in addition to the outcome variable at the time of dropout. The EM algorithm is used for inference in the proposed model. Data from a longitudinal study of human immunodeficiency virus (HIV)-infected women are used to illustrate the methodology.     

Case‐control data analysis for randomly pooled biomarkers

Pooled study designs, where individual biospecimens are combined prior to measurement via a laboratory assay, can reduce lab costs while maintaining statistical efficiency. Analysis of the resulting pooled measurements, however, often requires specialized techniques. Existing methods can effectively estimate the relation between a binary outcome and a continuous pooled exposure when pools are matched on disease status. When pools are of mixed disease status, however, the existing methods may not be applicable. By exploiting characteristics of the gamma distribution, we propose a flexible method for estimating odds ratios from pooled measurements of mixed and matched status. We use simulation studies to compare consistency and efficiency of risk effect estimates from our proposed methods to existing methods. We then demonstrate the efficacy of our method applied to an analysis of pregnancy outcomes and pooled cytokine concentrations. Our proposed approach contributes to the toolkit of available methods for analyzing odds ratios of a pooled exposure, without restricting pools to be matched on a specific outcome.     

Effect of X-Ray Attenuation of Arterial Obstructions on Intravenous Thrombolysis and Outcome after Ischemic Stroke

Objective

To assess whether the x-ray attenuation of intra-arterial obstruction measured on non-contrast CT in ischemic stroke can predict response to thrombolysis and subsequent functional outcome.

Methods

The Third International Stroke Trial (IST-3) was a multicenter randomized-controlled trial of intravenous thrombolysis (rt-PA) given within six hours of ischemic stroke. Ethical approval and informed consent were obtained. In a subgroup of 109 IST-3 patients (38 men, median age 82 years), a single reader, masked to all clinical and other imaging data, manually measured x-ray attenuation (Hounsfield Units, HU) on non-contrast CT at the location of angiographically-proven intra-arterial obstructions, pre-randomization and at 24–48 hour follow-up. We calculated change in attenuation between scans. We assessed the impact of pre-randomization arterial obstruction attenuation on six-month functional outcome.

Results

Most arterial obstructions (64/109, 59%) were hyperattenuating (mean 51.0 HU). Compared with control, treatment with rt-PA was associated with a greater, but non-significant, reduction in obstruction attenuation at follow-up (-8.0 HU versus -1.4 HU in patients allocated control, p = 0.117). In multivariable ordinal regression analysis controlled for patient age, stroke severity, location and extent of obstruction, time from stroke onset to baseline scan and rt-PA treatment allocation, the attenuation of pre-randomization arterial obstruction was not independently associated with six-month outcome (odds ratio = 0.99, 95% confidence interval = 0.94–1.03, p = 0.516).

Conclusions

In ischemic stroke, the x-ray attenuation of the arterial obstruction may decline more rapidly from baseline to 24–48 hours following treatment with thrombolysis but we found no evidence that baseline arterial obstruction attenuation predicts six-month outcome.     

Multiple imputation in the presence of an incomplete binary variable created from an underlying continuous variable

Multiple imputation (MI) is used to handle missing at random (MAR) data. Despite warnings from statisticians, continuous variables are often recoded into binary variables. With MI it is important that the imputation and analysis models are compatible; variables should be imputed in the same form they appear in the analysis model. With an encoded binary variable more accurate imputations may be obtained by imputing the underlying continuous variable. We conducted a simulation study to explore how best to impute a binary variable that was created from an underlying continuous variable. We generated a completely observed continuous outcome associated with an incomplete binary covariate that is a categorized version of an underlying continuous covariate, and an auxiliary variable associated with the underlying continuous covariate. We simulated data with several sample sizes, and set 25% and 50% of data in the covariate to MAR dependent on the outcome and the auxiliary variable. We compared the performance of five different imputation methods: (a) Imputation of the binary variable using logistic regression; (b) imputation of the continuous variable using linear regression, then categorizing into the binary variable; (c, d) imputation of both the continuous and binary variables using fully conditional specification (FCS) and multivariate normal imputation; (e) substantive-model compatible (SMC) FCS. Bias and standard errors were large when the continuous variable only was imputed. The other methods performed adequately. Imputation of both the binary and continuous variables using FCS often encountered mathematical difficulties. We recommend the SMC-FCS method as it performed best in our simulation studies.     

Stroke outcomes in Malawi, a country with high prevalence of HIV: a prospective follow-up study

Background

Stroke contributes significantly to disability and mortality in developing countries yet little is known about the determinants of stroke outcomes in such countries. 12% of Malawian adults have HIV/AIDS. It is not known whether having HIV-infection alters the outcome of stroke. The aim of this study was to document the functional outcome and mortality at 1 year of first-ever acute stroke in Malawi. Also to find out if the baseline variables, including HIV-infection, affect the outcome of stroke.

Methods and Findings

147 adult patients with first-ever acute stroke were prospectively followed up for 12 months. Conventional risk factors and HIV-infection were assessed at baseline. Stroke severity was evaluated with modified National Institute of Health Stroke Scale (mNIHSS) and functional outcome with modified Rankin scale (mRS). Fifty (34%) of patients were HIV-seropositive. 53.4% of patients had a poor outcome (severe disability or death, mRS 4–6) at 1 year. Poor outcome was related to stroke severity and female gender but not to presence of HIV-infection. HIV-seropositive patients were younger and had less often common risk factors for stroke. They suffer more often ischemic stroke than HIV-seronegative patients.

Conclusions

Mild stroke and male gender were associated with favourable outcome. HIV-infection is common in stroke patients in Malawi but does not worsen the outcome of stroke. However, it may be a risk factor for ischemic stroke for young people, who do not have the common stroke risk factors. Our results are significant, because stroke outcome in HIV-seropositive patients has not been studied before in a setting such as ours, with very limited resources and a high prevalence of HIV.     

A marginalized conditional linear model for longitudinal binary data when informative dropout occurs in continuous time

Within the pattern-mixture modeling framework for informative dropout, conditional linear models (CLMs) are a useful approach to deal with dropout that can occur at any point in continuous time (not just at observation times). However, in contrast with selection models, inferences about marginal covariate effects in CLMs are not readily available if nonidentity links are used in the mean structures. In this article, we propose a CLM for long series of longitudinal binary data with marginal covariate effects directly specified. The association between the binary responses and the dropout time is taken into account by modeling the conditional mean of the binary response as well as the dependence between the binary responses given the dropout time. Specifically, parameters in both the conditional mean and dependence models are assumed to be linear or quadratic functions of the dropout time; and the continuous dropout time distribution is left completely unspecified. Inference is fully Bayesian. We illustrate the proposed model using data from a longitudinal study of depression in HIV-infected women, where the strategy of sensitivity analysis based on the extrapolation method is also demonstrated.     

Spinal reflex properties in the long term after stroke

In the long term after stroke, secondary functional deterioration may be observed while patients also get older. Possible underlying mechanisms are largely unknown. We aimed to assess neuromuscular degeneration represented by alterations in peripheral reflex loop characteristics as a function of follow-up time after stroke, controlled for age. Twenty-one stroke survivors within a small age range (62-67 years) but large variance in follow-up time after stroke (1-15 years) and both five age matched (59-62 years) and young subjects (28-36 years) participated. Short and long latency reflexes evoked by ramp and hold stretches were identified from EMG traces of the m. flexor carpi radialis. Short latency reflex onset time was not enhanced (mean difference 1.6ms compared to age matched controls) and did not relate to follow-up time after stroke (p=0.81). Young controls showed significantly lower reflex delay times (mean difference 7.2ms with respect to older subjects, p=0.009). No evidence was found for peripheral neuromuscular deterioration as a function of follow up time after stroke. Functional deterioration as a result of ageing of stroke patients that may interact with post stroke follow-up time is of further interest.