Should data monitoring committees assess efficacy when considering safety in trails in acute stroke?

The primary role of a trials Data Monitoring Committee (DMC) is to ensure the safety of enrolled patients. In stroke trials, safety is monitored typically by comparing death and stroke specific events between treatment groups. DMCs may also have the remit for monitoring efficacy depending on the aims of the trial. We hypothesised that functional outcome at end of follow-up, a measure of efficacy, is also a powerful measure of safety and tested this in a systematic review

Sex differences in quality of life in stroke survivors. Data from the Tinzaparin in Acute Ischaemic Stroke Trial (TAIST)

Introduction: Female sex is predictive of poor functional outcome in stroke, even after correction for prognostic factors. Poor quality of life (QoL) is observed in stroke survivors, with lower scores seen in the most disabled patients. We used data from the TAIST trial to assess the relationship between sex and QoL after ischaemic stroke. Methods: TAIST was a randomised controlled trial assessing the safety and efficacy of tinzaparin versus aspirin in 1,484 patients with acute ischaemic stroke. QoL was measured at 180 days post randomisation using the short-form 36 health survey which assesses QoL across eight domains. The relationship between sex and each domain was assessed using ordinal regression, both unadjusted and adjusted for key prognostics factors. Results: Of the 1,484 patients randomised into TAIST, 216 had died at 180 days post randomisation. 1,268 survivors were included in this analysis, 694 males (55%), 574 females (45%). Females tended to score lower than males across all QoL domains (apart from general health); statistically significant lower scores were seen for physical functioning (odds ratio (OR) 0.58, 95% confidence interval (CI) 0.47-0.72), vitality (OR 0.79, 95% CI 0.64-0.98) and mental health (OR 0.75, 95% CI 0.61-0.93). The results for physical functioning and mental health remained significant after adjustment for prognostic variables (OR 0.73, 95% CI 0.58-0.92; OR 0.76, 95% CI 0.60-0.95 respectively). Conclusions: QoL, in particular physical function and mental health domains, is lower in female patients after stroke. This difference persists even after correction for known prognostic factors such as age and stroke severity.

Stroke severity, early recovery and outcome are each related with clinical classification of stroke: data from the 'Tinzaparin in Acute Ischaemic Stroke Trial (TAIST)

Introduction: Baseline severity and clinical stroke syndrome (Oxford Community Stroke Project, OCSP) classification are predictors of outcome in stroke. We used data from the ‘Tinzaparin in Acute Ischaemic Stroke Trial’ (TAIST) to assess the relationship between stroke severity, early recovery, outcome and OCSP syndrome. Methods: TAIST was a randomised controlled trial assessing the safety and efficacy of tinzaparin versus aspirin in 1,484 patients with acute ischaemic stroke. Severity was measured as the Scandinavian Neurological Stroke Scale (SNSS) at baseline and days 4, 7 and 10, and baseline OCSP clinical classification recorded: total anterior circulation infarct (TACI), partial anterior circulation infarct (PACI), lacunar infarct (LACI) and posterior circulation infarction (POCI). Recovery was calculated as change in SNSS from baseline at day 4 and 10. The relationship between stroke syndrome and SNSS at days 4 and 10, and outcome (modified Rankin scale at 90 days) were assessed. Results: Stroke severity was significantly different between TACI (most severe) and LACI (mildest) at all four time points (p<0.001), with no difference between PACI and POCI. The largest change in SNSS score occurred between baseline and day 4; improvement was least in TACI (median 2 units), compared to other groups (median 3 units) (p<0.001). If SNSS did not improve by day 4, then early recovery and late functional outcome tended to be limited irrespective of clinical syndrome (SNSS, baseline: 31, day 10: 32; mRS, day 90: 4); patients who recovered early tended to continue to improve and had better functional outcome irrespective of syndrome (SNSS, baseline: 35, day 10: 50; mRS, day 90: 2). Conclusions: Although functional outcome is related to baseline clinical syndrome (best with LACI, worst with TACI), patients who improve early have a more favourable functional outcome, irrespective of their OCSP syndrome. Hence, patients with a TACI syndrome may still achieve a reasonable outcome if early recovery occurs.

Calculation of sample size for stroke trials assessing functional outcome: comparison of binary and ordinal approaches

Background Many acute stroke trials have given neutral results. Sub-optimal statistical analyses may be failing to detect efficacy. Methods which take account of the ordinal nature of functional outcome data are more efficient. We compare sample size calculations for dichotomous and ordinal outcomes for use in stroke trials. Methods Data from stroke trials studying the effects of interventions known to positively or negatively alter functional outcome – Rankin Scale and Barthel Index – were assessed. Sample size was calculated using comparisons of proportions, means, medians (according to Payne), and ordinal data (according to Whitehead). The sample sizes gained from each method were compared using Friedman 2 way ANOVA. Results Fifty-five comparisons (54 173 patients) of active vs. control treatment were assessed. Estimated sample sizes differed significantly depending on the method of calculation (Po00001). The ordering of the methods showed that the ordinal method of Whitehead and comparison of means produced significantly lower sample sizes than the other methods. The ordinal data method on average reduced sample size by 28% (inter-quartile range 14–53%) compared with the comparison of proportions; however, a 22% increase in sample size was seen with the ordinal method for trials assessing thrombolysis. The comparison of medians method of Payne gave the largest sample sizes. Conclusions Choosing an ordinal rather than binary method of analysis allows most trials to be, on average, smaller by approximately 28% for a given statistical power. Smaller trial sample sizes may help by reducing time to completion, complexity, and financial expense. However, ordinal methods may not be optimal for interventions which both improve functional outcome

Can we improve the statistical analysis of stroke trials? Statistical re-analysis of functional outcomes in stroke trials

Background: Most large acute stroke trials have been neutral. Functional outcome is usually analysed using a yes or no answer, e.g. death or dependency vs. independence. We assessed which statistical approaches are most efficient in analysing outcomes from stroke trials. Methods: Individual patient data from acute, rehabilitation and stroke unit trials studying the effects of interventions which alter functional outcome were assessed. Outcomes included modified Rankin Scale, Barthel Index, and ‘3 questions’. Data were analysed using a variety of approaches which compare two treatment groups. The results for each statistical test for each trial were then compared. Results: Data from 55 datasets were obtained (47 trials, 54,173 patients). The test results differed substantially so that approaches which use the ordered nature of functional outcome data (ordinal logistic regression, t-test, robust ranks test, bootstrapping the difference in mean rank) were more efficient statistically than those which collapse the data into 2 groups (chi square) (ANOVA p<0.001). The findings were consistent across different types and sizes of trial and for the different measures of functional outcome. Conclusions: When analysing functional outcome from stroke trials, statistical tests which use the original ordered data are more efficient and more likely to yield reliable results. Suitable approaches included ordinal logistic regression, t-test, and robust ranks test.

Can we improve the statistical analysis of stroke trials? Statistical reanalysis of functional outcomes in stroke trials

Background and Purpose—Most large acute stroke trials have been neutral. Functional outcome is usually analyzed using a yes or no answer, eg, death or dependency versus independence. We assessed which statistical approaches are most efficient in analyzing outcomes from stroke trials. Methods—Individual patient data from acute, rehabilitation and stroke unit trials studying the effects of interventions which alter functional outcome were assessed. Outcomes included modified Rankin Scale, Barthel Index, and “3 questions”. Data were analyzed using a variety of approaches which compare 2 treatment groups. The results for each statistical test for each trial were then compared. Results—Data from 55 datasets were obtained (47 trials, 54 173 patients). The test results differed substantially so that approaches which use the ordered nature of functional outcome data (ordinal logistic regression, t test, robust ranks test, bootstrapping the difference in mean rank) were more efficient statistically than those which collapse the data into 2 groups (2; ANOVA, P0.001). The findings were consistent across different types and sizes of trial and for the different measures of functional outcome. Conclusions—When analyzing functional outcome from stroke trials, statistical tests which use the original ordered data are more efficient and more likely to yield reliable results. Suitable approaches included ordinal logistic regression, test, and robust ranks test.

Relationship between hyperacute blood pressure and outcome after ischemic stroke: data from the VISTA Collaboration

Background and Purpose—High blood pressure (BP) is associated independently with poor outcome after acute ischemic stroke, although in most analyses “baseline” BP was measured 24 hours or more postictus, and not during the hyperacute period. Methods—Analyses included 1722 patients in hyperacute trials (recruitment 8 hours) from the Virtual Stroke International Stroke Trial Archive (VISTA) Collaboration. Data on BP at enrolment and after 1, 2, 16, 24, 48, and 72 hours, neurological impairment at 7 days (NIHSS), and functional outcome at 90 days (modified Rankin scale) were assessed using logistic regression models, adjusted for confounding variables; results are for 10-mm Hg change in BP. Results—Mean time to enrolment was 3.7 hours (range 1.0 to 7.9). High systolic BP (SBP) was significantly associated with increased neurological impairment (odds ratio, OR 1.06, 95% confidence interval, 95% CI 1.01 to 1.12), and poor functional outcome; odds ratios for both increased with later BP measurements made at up to 24 hours poststroke. Smaller (versus larger) declines in SBP over the first 24 hours were significantly associated with poor NIHSS scores (OR 1.16, 95% CI 1.05 to 1.27) and functional outcome (OR 1.23, 95% CI 1.13 to 1.34). A large variability in SBP was also associated with poor functional outcome. Conclusions—High SBP and large variability in SBP in the hyperacute stages of ischemic stroke are associated with increased neurological impairment and poor functional outcome, as are small falls in SBP over the first 24 hours.

The bacterial helicase-primase interaction: a common structural/functional module

The lack of a high-resolution structure for the bacterial helicase-primase complex and the fragmented structural information for the individual proteins have been hindering our detailed understanding of this crucial binary protein interaction. Two new structures for the helicase-interacting domain of the bacterial primases from Escherichia coli and Bacillus stearothermophilus have recently been solved and both revealed a unique and surprising structural similarity to the amino-terminal domain of the helicase itself. In this minireview, the current data are discussed and important new structural and functional aspects of the helicase-primase interaction are highlighted. An attractive structural model with direct biological significance for the function of this complex and also for the development of new antibacterial compounds is examined.

Granulocyte-colony stimulating factor in experimental stroke and its effects on infarct size and functional outcome: a systematic review

Background Granulocyte-colony stimulating factor (G-CSF) shows promise as a treatment for stroke. This systematic review assesses G-CSF in experimental ischaemic stroke. Methods Relevant studies were identified with searches of Medline, Embase and PubMed. Data were extracted on stroke lesion size, neurological outcome and quality, and analysed using Cochrane Review Manager using random effects models; results are expressed as standardised mean difference (SMD) and odds ratio (OR). Results Data were included from 19 publications incorporating 666 animals. G-CSF reduced lesion size significantly in transient (SMD -1.63, p<0.00001) but not permanent (SMD -1.56, p=0.11) focal models of ischaemia. Lesion size was reduced at all doses and with treatment commenced within 4 hours of transient ischaemia. Neurological deficit (SMD -1.37, p=0.0004) and limb placement (SMD -1.88, p=0.003) improved with G-CSF; however, locomotor activity (>4 weeks post ischaemia) was not (SMD 0.76, p=0.35). Death (OR 0.27, p<0.0001) was reduced with G-CSF. Median study quality was 4 (range 0-7/8); Egger’s test suggested significant publication bias (p=0.001). Conclusions G-CSF significantly reduced lesion size in transient but not permanent models of ischaemic stroke. Motor impairment and death were also reduced. Further studies assessing dose-response, administration time, length of ischaemia and long-term functional recovery are needed.

Parcellation of fMRI datasets with ICA and PLS: a data driven approach

Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI) data based on a standard General Linear Model (GLM) and spectral clustering was recently proposed as a means to alleviate the issues associated with spatial normalization in fMRI. However, for all its appeal, a GLM-based parcellation approach introduces its own biases, in the form of a priori knowledge about the shape of Hemodynamic Response Function (HRF) and task-related signal changes, or about the subject behaviour during the task. In this paper, we introduce a data-driven version of the spectral clustering parcellation, based on Independent Component Analysis (ICA) and Partial Least Squares (PLS) instead of the GLM. First, a number of independent components are automatically selected. Seed voxels are then obtained from the associated ICA maps and we compute the PLS latent variables between the fMRI signal of the seed voxels (which covers regional variations of the HRF) and the principal components of the signal across all voxels. Finally, we parcellate all subjects data with a spectral clustering of the PLS latent variables. We present results of the application of the proposed method on both single-subject and multi-subject fMRI datasets. Preliminary experimental results, evaluated with intra-parcel variance of GLM t-values and PLS derived t-values, indicate that this data-driven approach offers improvement in terms of parcellation accuracy over GLM based techniques.