The effect of covariate adjustment for baseline severity in acute stroke clinical trials with responder analysis outcomes

Sex Differences in Severity and Risk Factors for Ischemic Stroke in Patients With Hyperlipidemia

Objective

This study aims to determine sex differences in poststroke hypertriglyceridemia (serum triglyceride levels ⩾ 200 mg/dl) and high stroke severity in ischemic stroke patients.

Method

Our study analyzed data from 392 males and 373 females with hypertriglyceridemia. Stroke severity on admission was measured using the National Institute of Health Stroke Scale (NIHSS) with a value ⩽7 indicating a more favorable post-stroke prognosis while a score of >7 indicates poorer post-stroke outcomes. Logistic regression models adjusted for demographic and risk factors. The adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for each clinical risk factor were used to predict the increasing odds of an association of a specific clinical baseline risk factor with the male or female AIS with hypertriglyceridemia.

Results

In the adjusted analysis, male patients with hypertriglyceridemia, diastolic blood pressure (OR = 1.100, 95% CI, 1.034-1.171, P = .002), and Ischemic stroke mortality (OR = 6.474, 95% CI, 3.262-12.847, P < .001) were significantly associated with increased stroke severity. In female patients with hypertriglyceridemia, age (OR = 0.920, 95% CI, 0.866-0.978, P = .008) was associated with reduced stroke severity, while ischemic stroke mortality score (OR = 37.477, 95% CI, 9.636-145.756, P < .001) was associated with increased stroke severity.

Conclusion

Increased ischemic stroke mortality risk score was associated with increased severity in both male and female AIS patients with hypertriglyceridemia. Our findings provide information about sex differences in specific risk factors that can be managed to improve the care of male and female ischemic stroke patients with hypertriglyceridemia.

Motor-cognitive intervention concepts can improve gait in chronic stroke, but their effect on cognitive functions is unclear: A systematic review with meta-analyses

Motor-cognitive intervention concepts are promising to counteract residual gait and cognitive impairments in chronic stroke. There is, however, considerable variation in motor-cognitive intervention types, which may lead to different effects. This systematic review strived to summarize and compare the effects of different motor-cognitive intervention concepts on gait and cognitive functions in chronic stroke. The systematic search identified twenty-nine articles, which were allocated to three types of motor-cognitive training concepts; SEQUENTIAL, SIMULTANEOUS-ADDITIONAL, and SIMULTANEOUS-INCORPORATED. Random-effects meta-analyses revealed that motor-cognitive interventions may be better than non-combined training approaches for improving gait function in chronic stroke (e.g. gait speed: g = 0.43, 95 % CI [0.22, 0.64], p < 0.0001). SIMULTANEOUS-INCORPORATED motor-cognitive training seems the most promising concept. As very few articles measured both, spatiotemporal gait parameters and cognitive outcomes, future studies are warranted to investigate the effects of motor-cognitive intervention concepts on gait control and cognitive functions in chronic stroke.

Comparison of multistate Markov modeling with contemporary outcomes in a reanalysis of the NINDS tissue plasminogen activator for acute ischemic stroke treatment trial

Historically, ordinal measures of functional outcome have been dichotomized for the primary analysis in acute stroke therapy trials. A number of alternative methods to analyze the ordinal scales have been proposed, with an emphasis on maintaining the ordinal structure as much as possible. In addition, despite the availability of longitudinal outcome data in many trials, the primary analysis consists of a single endpoint. Inclusion of information about the course of disease progression allows for a more complete understanding of the treatment effect. Multistate Markov modeling, which allows for the full ordinal scale to be analyzed longitudinally, is compared with previously suggested analytic techniques for the ordinal modified Rankin Scale (dichotomous-logistic regression; continuous-linear regression; ordinal- shift analysis, proportional odds model, partial proportional odds model, adjacent categories logit model; sliding dichotomy; utility weights; repeated measures). In addition, a multistate Markov model utilizing an estimate of the unobservable baseline outcome derived from principal component analysis is compared Each of the methods is used to re-analyze the National Institute of Neurological Diseases and Stroke tissue plasminogen activator study which showed a consistently significant effect of tissue plasminogen activator using a global test of four dichotomized outcomes in the analysis of the primary outcome at 90 days post-stroke in the primary analysis. All methods detected a statistically significant treatment effect except the multistate Markov model without predicted baseline (p = 0.053). This provides support for the use of the estimated baseline in the multistate Markov model since the treatment effect is able to be detected with its inclusion. Multistate Markov modeling allows for a more refined examination of treatment effect and describes the movement between modified Rankin Scale states over time which may provide more clinical insight into the treatment effect. Multistate Markov models are feasible and desirable in describing treatment effect in acute stroke therapy trials.

Improving precision by adjusting for prognostic baseline variables in randomized trials with binary outcomes, without regression model assumptions

In randomized clinical trials with baseline variables that are prognostic for the primary outcome, there is potential to improve precision and reduce sample size by appropriately adjusting for these variables. A major challenge is that there are multiple statistical methods to adjust for baseline variables, but little guidance on which is best to use in a given context. The choice of method can have important consequences. For example, one commonly used method leads to uninterpretable estimates if there is any treatment effect heterogeneity, which would jeopardize the validity of trial conclusions. We give practical guidance on how to avoid this problem, while retaining the advantages of covariate adjustment. This can be achieved by using simple (but less well-known) standardization methods from the recent statistics literature. We discuss these methods and give software in R and Stata implementing them. A data example from a recent stroke trial is used to illustrate these methods.

Assessing type I error and power of multistate Markov models for panel data-A simulation study

Ordinal outcomes collected at multiple follow-up visits are common in clinical trials. Sometimes, one visit is chosen for the primary analysis and the scale is dichotomized amounting to loss of information. Multistate Markov models describe how a process moves between states over time. Here, simulation studies are performed to investigate the type I error and power characteristics of multistate Markov models for panel data with limited non-adjacent state transitions. The results suggest that the multistate Markov models preserve the type I error and adequate power is achieved with modest sample sizes for panel data with limited non-adjacent state transitions.

The Functional IgE-Blocking Factor Induced by Allergen-Specific Immunotherapy Correlates with IgG4 Antibodies and a Decrease of Symptoms in House Dust Mite-Allergic Children

BACKGROUND

At present, there are no validated biomarkers reflecting or predicting the clinical efficacy of allergen-specific immunotherapy (AIT) . We aimed to investigate the correlations between clinical and immunological responses of patients undergoing house dust mite (HDM) AIT.

METHODS

Sixty-nine children diagnosed with HDM allergic rhinitis and/or asthma received standardized Dermatophagoides pteronyssinus (Dp) subcutaneous AIT for 12 months. Twenty HDM-allergic children served as an open control group. Clinical symptom and medication scores were recorded and Dp-specific IgE, IgG4 and IgE-blocking factor were measured before AIT and after 4 and 12 months of AIT.

RESULTS

Symptom scores decreased after 4 months and continued to decrease during 12 months of AIT. No differences in medication scores were observed between AIT and the control group during the study period. Levels of Dp IgG4 increased after 4 months and correlated to symptom scores at 12 months (r = -0.296, p = 0.013) of AIT. The Dp IgE-blocking factor increased after 4 months of AIT, and correlated with symptom scores at 4 months (r = -0.307, p = 0.010) and 12 months (r = -0.288, p = 0.016) of AIT. A strong correlation between Dp IgE-blocking factor and Dp IgG4 during AIT (4 months: r = 0.680; 12 months: r = 0.636, both p < 0.0001) was observed. Patients with IgE-blocking factor ≥0.2 after 4 months of AIT showed lower symptom scores at 12 months of AIT (p = 0.0093).

CONCLUSIONS

Subcutaneous HDM AIT results in a decrease of allergic symptoms among HDM-allergic children. IgE-blocking activity increased after 4 months of AIT and correlated with clinical symptoms. A high IgE-blocking factor at an early stage of AIT is associated with fewer symptoms at a later stage of AIT.

Stroke Severity Is a Crucial Predictor of Outcome: An International Prospective Validation Study

Background

Stroke is among the leading causes of morbidity and mortality worldwide. Without reliable prediction models and outcome measurements, comparison of care systems is impossible. We analyzed prospectively collected data from 4 countries to explore the importance of stroke severity in outcome prediction.

Methods and Results

For 2 months, all acute ischemic stroke patients from the hospitals participating in the Global Comparators Stroke GOAL (Global Outcomes Accelerated Learning) collaboration received a National Institutes of Health Stroke Scale (NIHSS) score on admission and a modified Rankin Scale score at 30 and 90 days. These data were added to the administrative data set, and risk prediction models including age, sex, comorbidity index, and NIHSS were derived for in‐hospital death within 7 days, all in‐hospital death, and death and good outcome at 30 and 90 days. The relative importance of each variable was assessed using the proportion of explained variation. Of 1034 admissions for acute ischemic stroke, 614 had a full set of NIHSS and both modified Rankin Scale values recorded; of these, 507 patients could be linked to administrative data. The marginal proportion of explained variation was 0.7% to 4.0% for comorbidity index, and 11.3 to 25.0 for NIHSS score. The percentage explained by the model varied by outcome (16.6–29.1%) and was highest for good outcome at 30 and 90 days. There was high agreement between 30‐ and 90‐day modified Rankin Scale scores (weighted κ=0.82).

Conclusions

In this prospective pilot study, the baseline NIHSS score was essential for prediction of acute ischemic stroke outcomes, followed by age; whereas traditional comorbidity index contributed little to the overall model. Future studies of stroke outcomes between different care systems will benefit from including a baseline NIHSS score.

The effect of an intervention to break the gender bias habit for faculty at one institution: a cluster randomized, controlled trial

PURPOSE

Despite sincere commitment to egalitarian, meritocratic principles, subtle gender bias persists, constraining women's opportunities for academic advancement. The authors implemented a pair-matched, single-blind, cluster randomized, controlled study of a gender-bias-habit-changing intervention at a large public university.

METHOD

Participants were faculty in 92 departments or divisions at the University of Wisconsin-Madison. Between September 2010 and March 2012, experimental departments were offered a gender-bias-habit-changing intervention as a 2.5-hour workshop. Surveys measured gender bias awareness; motivation, self-efficacy, and outcome expectations to reduce bias; and gender equity action. A timed word categorization task measured implicit gender/leadership bias. Faculty completed a work-life survey before and after all experimental departments received the intervention. Control departments were offered workshops after data were collected.

RESULTS

Linear mixed-effects models showed significantly greater changes post intervention for faculty in experimental versus control departments on several outcome measures, including self-efficacy to engage in gender-equity-promoting behaviors (P = .013). When ≥ 25% of a department's faculty attended the workshop (26 of 46 departments), significant increases in self-reported action to promote gender equity occurred at three months (P = .007). Post intervention, faculty in experimental departments expressed greater perceptions of fit (P = .024), valuing of their research (P = .019), and comfort in raising personal and professional conflicts (P = .025).

CONCLUSIONS

An intervention that facilitates intentional behavioral change can help faculty break the gender bias habit and change department climate in ways that should support the career advancement of women in academic medicine, science, and engineering.