Predictors of a Minimal Clinically Important Difference Following Omalizumab Treatment in Adult Patients With Severe Allergic Asthma

Several factors have been found to be predictors of a good response following omalizumab treatment in patients with severe allergic asthma (SAA). However, it remains unclear whether clinical characteristics can predict a minimal clinically important difference (MCID) following omalizumab treatment in this population. Therefore, the aim of this study was to investigate the features associated with an MCID following omalizumab treatment in adult patients with SAA. Of the 124 participants enrolled in this retrospective, cross-sectional study, 94, 103, 20 and 53 achieved the MCID following treatment with omalizumab and were considered to be responders of exacerbation reduction (no exacerbation during the 1-year follow-up period or ≧50% reduction in exacerbations from baseline), oral corticosteroid (OCS) sparing (no use of OCS to control asthma during the study period or a reduction of the monthly OCS maintenance dose to <50% of baseline), lung function (an increase of ≧230 ml in the forced expiratory volume in 1 s from baseline) and asthma control (an increase of ≧3 points in the asthma control test score from baseline), respectively. Normal weight [<25 vs. ≧30 kg/m2, odds ratio (OR) = 3.86, p = 0.024] was predictive of a responder of reduction in exacerbations following omalizumab treatment while subjects with a blood eosinophil level of <300 cells/μL (<300 vs. ≧300 cells/μL, OR = 5.81, p = 0.001) were more likely to exhibit an MCID in OCS sparing. No factor was found to be a predictor of lung function or asthma control. When choosing treatment for adult patients with SAA, our findings may help to select those who may benefit the most from omalizumab treatment.

No evidence of a minimal clinically important difference for the Beery-Buktenica Developmental Test of Visual-Motor Integration in children with autism spectrum disorder

The Beery Visual-Motor Integration (VMI) battery of tests are some of the most commonly used assessments in pediatric occupational therapy, often used to measure change over time. However, the minimal clinically important difference (MCID) has not been estimated for interpreting change scores. We estimated the MCID for the Beery VMI battery of tests in children with autism spectrum disorder (ASD). Four occupational therapists collected data in a public elementary school on 64 children with ASD. The Beery VMI battery was administered to children with ASD twice, approximately 11 months apart. To estimate MCID values, Beery VMI battery scores were anchored to 15-point Likert questions measuring occupational therapists’ ratings of functional change over three domains: fine motor skills, handwriting, and activities of daily living (ADLs). Using this anchor-based method, we were unable to estimate MCID values for the Beery VMI battery. Children’s Beery VMI battery scores did not change significantly over the course of the school year, and there was only one weak correlation between VMI battery change scores and therapists’ ratings of change. The inability to estimate Beery VMI battery MCID values for children with ASD adds further support for research cautioning the use of the Beery VMI as an outcome measure.

The Minimal Clinically Important Difference: A Review of Clinical Significance

Background: The minimal clinically important difference (MCID) is a term synonymous with orthopaedic clinical research over the past decade. The term represents the smallest change in a patient-reported outcome measure that is of genuine clinical value to patients. It has been derived in a myriad of ways in existing orthopaedic literature. Purpose: To describe the various modalities for deriving the MCID. Study Design: Narrative review; Level of evidence, 4. Methods: The definitions of common MCID determinations were first identified. These were then evaluated by their clinical and statistical merits and limitations. Results: There are 3 primary ways for determining the MCID: anchor-based analysis, distribution-based analysis, and sensitivity- and specificity-based analysis. Each has unique strengths and weaknesses with respect to its ability to evaluate the patient’s clinical status change from baseline to posttreatment. Anchor-based analyses are inherently tied to clinical status yet lack standardization. Distribution-based analyses are the opposite, with strong foundations in statistics, yet they fail to adequately address the clinical status change. Sensitivity and specificity analyses offer a compromise of the other methodologies but still rely on a somewhat arbitrarily defined global transition question. Conclusion: This current concepts review demonstrates the need for (1) better standardization in the establishment of MCIDs for orthopaedic patient-reported outcome measures and (2) better study design—namely, until a universally accepted MCID derivation exists, studies attempting to derive the MCID should utilize the anchor-based within-cohort design based on Food and Drug Administration recommendations. Ideally, large studies reporting the MCID as an outcome will also derive the value for their populations. It is important to consider that there may be reasonable replacements for current derivations of the MCID. As such, future research should consider an alternative threshold score with a more universal method of derivation.

Estimating the minimal clinically important difference for the Physical Component Summary of the Short Form 36 for patients with stroke

Objective To determine the Physical Component Summary (PCS) score's minimal clinically important difference (MCID) on the Short Form 36 (SF-36) for people with stroke. Methods We conducted secondary analysis of data from a large randomized controlled trial (N = 400) in the post-hospital discharge phase of stroke rehabilitation with outcome measurement 6 and 12 months following stroke. Three methods were used for estimating the MCID: two anchor and one distribution. Method 1 compared SF-36 PCS scores at 12 months for responses to the SF-36’s Perceived Health Change (PHC) question. Method 2 compared the change in PCS score between 6 and 12 months for responses to the PHC question. Method 3 used Cohen’s method to estimate the MCID from the PCS score distribution. Results Method 1: the mean PCS score increased by 3.0 units (95% confidence interval [CI] 2.2–3.9) for each unit change in the PHC question. Method 2: the mean change in PCS score increased by 2.1 units (95% CI 1.4–2.8) for each unit change in the PHC question. Method 3: the MCID was estimated to be 1.8 units. Conclusions Our estimate of the MCID for the PCS in patients with stroke was 1.8 to 3.0 units.