30 years with the Knee injury and Osteoarthritis Outcome Score (KOOS).

This narrative review describes the development and use of patient-reported outcomes over 30 years, focusing on the Knee injury and Osteoarthritis Outcome Score (KOOS). KOOS is a five-subscale patient-reported instrument intended for use from the time of knee injury to the development of osteoarthritis. Numerous studies have confirmed that the psychometric properties of the KOOS and its short-form KOOS-12 are acceptable. More recent research has focused on the use and interpretation of KOOS scores in clinical trials using thresholds, such as minimal important differences, patient-acceptable symptom states, and treatment failure. As an indication of KOOS's popularity, the total 3854 PubMed results for KOOS have increased exponentially since the first KOOS paper was published 25 years ago and now seem to have plateaued at around 650 annually. The selected articles are not based on a systematic search, but on the author's own publications, reading, and literature search that grew organically from that.

Defining the Minimal Important Change and Meaningful Change Value of the Disease Activity Index for Psoriatic Arthritis: A Chinese Longitudinal Study.

OBJECTIVE: To determine the minimal important change (MIC) and meaningful change value (MCV) of the Disease Activity Index for Psoriatic Arthritis (DAPSA) and the effect size (ES) of DAPSA. METHODS: This was a retrospective cohort study, recruiting 106 patients who agreed to participate in the research from the Department of Dermatology, Xiangya Hospital, between November 1, 2019, and April 1, 2023. An anchor-based method using linear regression analyses was used to determine the MICs and MCVs of the DAPSA. The anchor question assessed whether the patient's well-being had changed since their previous visit, employing a 5-point Likert scale that ranged from "much improved" to "much deteriorated." RESULTS: The overall MIC value was 8.4 (95% CI 0.01-16.75). The MIC improvement was 9.5 (95% CI 0.89-18.14) and MIC deterioration was 1.1 (95% CI -9.81 to 12.05). The overall MCV was 10.5 (95% CI 4.34-16.72). MCV improvement was 11.4 (95% CI 5.95-16.95) and MCV deterioration was 1.1 (95% CI -9.81 to 12.05). The ES was 0.6. CONCLUSION: A change in DAPSA of 8.4 is indicative of an MIC, offering physicians an additional means to contextualize the patient's perception of disease activity during treatment, and a change in DAPSA of 10.5 is likely to be regarded as MCV. These values can enhance the utility of DAPSA in psoriatic arthritis clinical trials.

Establishing how much improvement in lung function and distance walked is clinically important for adult patients with Pompe disease.

BACKGROUND AND PURPOSE: Pompe disease is a rare, inheritable, progressive metabolic myopathy. This study aimed to estimate the minimal clinically important difference (MCID) for an improvement in forced vital capacity in the upright seated position (FVCup) and the 6-min walk test (6MWT) after a year of treatment with enzyme replacement therapy. METHODS: Data were obtained from two prospective follow-up studies. Between-group and within-group MCIDs were estimated using anchor-based methods. Additionally, a distribution-based method was used to generate supportive evidence. As anchors, self-reported change in health and in physical functioning, shortness of breath and a categorization of the Short-Form 36 Physical Component Summary score were used. Anchor appropriateness was assessed using Spearman correlations (absolute values ≥0.29) and a sufficient number of observations in each category. RESULTS: In all, 102 patients had at least one FVCup or 6MWT measurement during enzyme replacement therapy. Based on the anchors assessed as appropriate, the between-group MCID for an improvement in FVCup ranged from 2.47% to 4.83% points. For the 6MWT, it ranged from 0.35% to 7.47% points which is equivalent to a distance of 2.18-46.61 m and 1.97-42.13 m for, respectively, a man and a woman of age 50, height 1.75 m and weight 80 kg. The results of the distribution-based method were within these ranges when applied to change in the outcome values. CONCLUSION: The MCIDs for FVCup and 6MWT derived in this study can be used to interpret differences between and within groups of patients with Pompe disease in clinical trials and cohort studies.

The gap between statistical and clinical significance: time to pay attention to clinical relevance in patient-reported outcome measures of insomnia.

BACKGROUND: Appropriately defining and using the minimal important change (MIC) and the minimal clinically important difference (MCID) are crucial for determining whether the results are clinically significant. The aim of this study is to survey the status of randomized controlled trials (RCTs) for insomnia interventions to assess the inclusion and interpretation of MIC/MCID values. METHODS: We conducted a cross-sectional study to survey the status of RCTs for insomnia interventions to assess the inclusion and appropriate interpretation of MIC/MCID values. A literature search was conducted by searching the main sleep medicine journals indexed in PubMed, the Excerpta Medica Database (EMBASE), and the Cochrane Central Register of Controlled Trials (CENTRAL) to identify a broad range of search terms. We included RCTs with no restriction on the intervention. The included studies used the Insomnia Severity Index (ISI) or the Pittsburgh Sleep Quality Index (PSQI) questionnaire as the outcome measures. RESULTS: 81 eligible studies were identified, and more than one-third of the included studies used MIC/MCID (n = 31, 38.3%). Among them, 21 studies with ISI as the outcome used MIC defined as a relative decrease ranging from 3 to 8 points. The most frequently used MIC value was a 6-point decrease (n = 7), followed by 8-point (n = 6) and 7-point decrease (n = 4), a 4 to 5-points decrease (n = 3), and a 30% reduction from baseline; 6 studies used MCID values, ranging from 2.8 to 4 points. The most frequently used MCID value was a 4-point decrease in the ISI (n = 4). 4 studies with PSQI as the outcome used a 3-point change as the MIC (n = 2) and a 2.5 to 2.7-point difference as MCID (n = 2). 4 non-inferiority design studies considered interval estimation when drawing clinically significant conclusions in their MCID usage. CONCLUSIONS: The lack of consistent MIC/MCID interpretation and usage in outcome measures for insomnia highlights the urgent need for further efforts to address this issue and improve reporting practices.

Effect of present state bias on minimal important change estimates: a simulation study.

PURPOSE: The minimal important change (MIC) in a patient-reported outcome measure is often estimated using patient-reported transition ratings as anchor. However, transition ratings are often more heavily weighted by the follow-up state than by the baseline state, a phenomenon known as "present state bias" (PSB). It is unknown if and how PSB affects the estimation of MICs using various methods. METHODS: We simulated 3240 samples in which the true MIC was simulated as the mean of individual MICs, and PSB was created by basing transition ratings on a "weighted change", differentially weighting baseline and follow-up states. In each sample we estimated MICs based on the following methods: mean change (MC), receiver operating characteristic (ROC) analysis, predictive modeling (PM), adjusted predictive modeling (APM), longitudinal item response theory (LIRT), and longitudinal confirmatory factor analysis (LCFA). The latter two MICs were estimated with and without constraints on the transition item slope parameters (LIRT) or factor loadings (LCFA). RESULTS: PSB did not affect MIC estimates based on MC, ROC, and PM but these methods were biased by other factors. PSB caused imprecision in the MIC estimates based on APM, LIRT and LCFA with constraints, if the degree of PSB was substantial. However, the unconstrained LIRT- and LCFA-based MICs recovered the true MIC without bias and with high precision, independent of the degree of PSB. CONCLUSION: We recommend the unconstrained LIRT- and LCFA-based MIC methods to estimate anchor-based MICs, irrespective of the degree of PSB. The APM-method is a feasible alternative if PSB is limited.

Estimating anchor-based minimal important change using longitudinal confirmatory factor analysis.

PURPOSE: The minimal important change (MIC) is defined as the smallest within-individual change in a patient-reported outcome measure (PROM) that patients on average perceive as important. We describe a method to estimate this value based on longitudinal confirmatory factor analysis (LCFA). The method is evaluated and compared with a recently published method based on longitudinal item response theory (LIRT) in simulated and real data. We also examined the effect of sample size on bias and precision of the estimate. METHODS: We simulated 108 samples with various characteristics in which the true MIC was simulated as the mean of individual MICs, and estimated MICs based on LCFA and LIRT. Additionally, both MICs were estimated in existing PROMIS Pain Behavior data from 909 patients. In another set of 3888 simulated samples with sample sizes of 125, 250, 500, and 1000, we estimated LCFA-based MICs. RESULTS: The MIC was equally well recovered with the LCFA-method as using the LIRT-method, but the LCFA analyses were more than 50 times faster. In the Pain Behavior data (with higher scores indicating more pain behavior), an LCFA-based MIC for improvement was estimated to be 2.85 points (on a simple sum scale ranging 14-42), whereas the LIRT-based MIC was estimated to be 2.60. The sample size simulations showed that smaller sample sizes decreased the precision of the LCFA-based MIC and increased the risk of model non-convergence. CONCLUSION: The MIC can accurately be estimated using LCFA, but sample sizes need to be preferably greater than 125.

'A Meaningful Difference, but Not Ultimately the Difference I Would Want': A Mixed-Methods Approach to Explore and Benchmark Clinically Meaningful Changes in Aphasia Recovery.

INTRODUCTION: Outcome measurement instruments (OMIs) are used to gauge the effects of treatment. In post-stroke aphasia rehabilitation, benchmarks for meaningful change are needed to support the interpretation of patient outcomes. This study is part of a research programme to establish minimal important change (MIC) values (the smallest change above which patients perceive themselves as importantly changed) for core OMIs. As a first step in this process, the views of people with aphasia and clinicians were explored, and consensus was sought on a threshold for clinically meaningful change. METHODS: Sequential mixed-methods design was employed. Participants included people with post-stroke aphasia and speech pathologists. People with aphasia were purposively sampled based on time post-stroke, age and gender, whereas speech pathologists were sampled according to their work setting (hospital or community). Each participant attended a focus group followed by a consensus workshop with a survey component. Within the focus groups, experiences and methods for measuring meaningful change during aphasia recovery were explored. Qualitative data were transcribed and analysed using reflexive thematic analysis. In the consensus workshop, participants voted on thresholds for meaningful change in core outcome constructs of language, communication, emotional well-being and quality of life, using a six-point rating scale (much worse, slightly worse, no change, slightly improved, much improved and completely recovered). Consensus was defined a priori as 70% agreement. Voting results were reported using descriptive statistics. RESULTS: Five people with aphasia (n = 4, > 6 months after stroke; n = 5, < 65 years; n = 3, males) and eight speech pathologists (n = 4, hospital setting; n = 4, community setting) participated in one of four focus groups (duration: 92-112 min). Four themes were identified describing meaningful change as follows: (1) different for every single person; (2) small continuous improvements; (3) measured by progress towards personally relevant goals; and (4) influenced by personal factors. 'Slightly improved' was agreed as the threshold of MIC on the anchor-rating scale (75%-92%) within 6 months of stroke, whereas after 6 months there was a trend towards supporting 'much improved' (36%-66%). CONCLUSION: Our mixed-methods research with people with aphasia and speech pathologists provides novel evidence to inform the definition of MIC in aphasia rehabilitation. Future research will aim to establish MIC values for core OMIs. PATIENT OR PUBLIC CONTRIBUTION: This work is the result of engagement between people with lived experience of post-stroke aphasia, including people with aphasia, family members, clinicians and researchers. Engagement across the research cycle was sought to ensure that the research tasks were acceptable and easily understood by participants and that the outcomes of the study were relevant to the aphasia community. This engagement included the co-development of a plain English summary of the results. Advisors were remunerated in accordance with Health Consumers Queensland guidelines. Interview guides for clinicians were piloted by speech pathologists working in aphasia rehabilitation.

Responsiveness and minimal important change of specific and generic patient-reported outcome measures for back patients: the Norwegian Neck and Back Register.

PURPOSE: To evaluate responsiveness and minimal important change (MIC) of Oswestry Disability Index (ODI), pain during activity on a numeric rating scale (NRSa) and health related quality of life (EQ-5D) based on data from the Norwegian neck and back registry (NNRR). METHODS: A total of 1617 patients who responded to NNRR follow-up after both 6 and 12 months were included in this study. Responsiveness was calculated using standardized response mean and area under the receiver operating characteristic (ROC) curve. We calculated MIC with both an anchor-based and distribution-based method. RESULTS: The condition specific ODI had best responsiveness, the more generic NRSa and EQ-5D had lower responsiveness. We found that the MIC for ODI varied from 3.0 to 9.5, from 0.4 to 2.5 for NRSa while the EQ5D varied from 0.05 to 0.12 depending on the method for calculation. CONCLUSION: In a register based back pain population, the condition specific ODI was more responsive to change than the more generic tools NRSa and EQ5D. The variations in responsiveness and MIC estimates also indicate that they should be regarded as indicative, rather than fixed estimates.

The minimal important change is not a universal fixed value across diagnoses when using the FAOS and FAAM in patients undergoing elective foot and ankle surgery.

PURPOSE: This study aimed to calculate region and diagnosis-specific minimal important changes (MICs) of the Foot and Ankle Outcome Score (FAOS) and the Foot and Ankle Ability Measure (FAAM) in patients requiring foot and ankle surgery and to assess their variability across different foot and ankle diagnoses. METHODS: The study used routinely collected data from patients undergoing elective foot and ankle surgery. Patients had been invited to complete the FAOS and FAAM preoperatively and at 3-6 months after surgery, along with two anchor questions encompassing change in pain and daily function. Patients were categorised according to region of pathology and subsequent diagnoses. MICs were calculated using predictive modelling (MICPRED) and receiver operating characteristic curve (MICROC) method and evaluated according to strict credibility criteria. RESULTS: Substantial variability of the MICs between forefoot and ankle/hindfoot region was observed, as well as among specific foot and ankle diagnoses, with MICPRED and MICROC values ranging from 7.8 to 25.5 points and 9.4 to 27.8, respectively. Despite differences between MICROC and MICPRED estimates, both calculation methods exhibited largely consistent patterns of variation across subgroups, with forefoot conditions systematically showing smaller MICs than ankle/hindfoot conditions. Most MICs demonstrated high credibility; however, the majority of the MICs for the FAOS symptoms subscale and forefoot conditions exhibited insufficient or low credibility. CONCLUSION: The MICs of the FAOS and FAAM vary across foot and ankle diagnoses in patients undergoing elective foot and ankle surgery and should not be used as a universal fixed value, but recognised as contextual parameters. This can help clinicians and researchers in more accurate interpretation of the FAOS and FAAM change scores. LEVEL OF EVIDENCE: Level IV.

How strong should my anchor be for estimating group and individual level meaningful change? A simulation study assessing anchor correlation strength and the impact of sample size, distribution of change scores and methodology on establishing a true meaningful change threshold.

PURPOSE: Treatment benefit as assessed using clinical outcome assessments (COAs), is a key endpoint in many clinical trials at both the individual and group level. Anchor-based methods can aid interpretation of COA change scores beyond statistical significance, and help derive a meaningful change threshold (MCT). However, evidence-based guidance on the selection of appropriately related anchors is lacking. METHODS: A simulation was conducted which varied sample size, change score variability and anchor correlation strength to assess the impact of these variables on recovering the simulated MCT for interpreting individual and group-level results. To assess MCTs derived at the individual-level (i.e. responder definitions; RDs), Receiver Operating Characteristic (ROC) curves and Predictive Modelling (PM) analyses were conducted. To assess MCTs for interpreting change at the group-level, the mean change method was conducted. RESULTS: Sample sizes, change score variability and magnitude of anchor correlation affected accuracy of the estimated MCT. For individual-level RDs, ROC curves were less accurate than PM methods at recovering the true MCT. For both methods, smaller samples led to higher variability in the returned MCT, but higher variability still using ROC. Anchors with weaker correlations with COA change scores had increased variability in the estimated MCT. An anchor correlation of around 0.50-0.60 identified a true MCT cut-point under certain conditions using ROC. However, anchor correlations as low as 0.30 were appropriate when using PM under certain conditions. For interpreting group-level results, the MCT derived using the mean change method was consistently underestimated regardless of the anchor correlation. CONCLUSION: Sample size and change score variability influence the necessary anchor correlation strength when recovering individual-level RDs. Often, this needs to be higher than the commonly accepted threshold of 0.30. Stronger correlations than 0.30 are required when using the mean change method. Results can assist researchers selecting and assessing the quality of anchors.

Establishing thresholds for meaningful within-individual change using longitudinal item response theory.

PURPOSE: Thresholds for meaningful within-individual change (MWIC) are useful for interpreting patient-reported outcome measures (PROM). Transition ratings (TR) have been recommended as anchors to establish MWIC. Traditional statistical methods for analyzing MWIC such as mean change analysis, receiver operating characteristic (ROC) analysis, and predictive modeling ignore problems of floor/ceiling effects and measurement error in the PROM scores and the TR item. We present a novel approach to MWIC estimation for multi-item scales using longitudinal item response theory (LIRT). METHODS: A Graded Response LIRT model for baseline and follow-up PROM data was expanded to include a TR item measuring latent change. The LIRT threshold parameter for the TR established the MWIC threshold on the latent metric, from which the observed PROM score MWIC threshold was estimated. We compared the LIRT approach and traditional methods using an example data set with baseline and three follow-up assessments differing by magnitude of score improvement, variance of score improvement, and baseline-follow-up score correlation. RESULTS: The LIRT model provided good fit to the data. LIRT estimates of observed PROM MWIC varied between 3 and 4 points score improvement. In contrast, results from traditional methods varied from 2 to 10 points-strongly associated with proportion of self-rated improvement. Best agreement between methods was seen when approximately 50% rated their health as improved. CONCLUSION: Results from traditional analyses of anchor-based MWIC are impacted by study conditions. LIRT constitutes a promising and more robust analytic approach to identifying thresholds for MWIC.

To evaluate responsiveness and minimal important change (MIC) for the Persian versions of FABQ, TSK, and PCS.

INTRODUCTION: Fear avoidance beliefs questionnaire (FABQ), Tampa scale of kinesiophobia (TSK), and pain catastrophizing scale (PCS) are tools widely used to measure fear-avoidance beliefs, fear of movement, and pain-related catastrophic thinking in people with chronic spinal disorders. PURPOSE: To evaluate responsiveness and minimal important change (MIC) for the Persian version of FABQ, TSK, and PCS. METHOD: One hundred people with chronic non-specific neck pain participated in an intervention program including routine physiotherapy plus pain neuroscience education. They fulfilled FABQ, TSK, and PCS questionnaires at baseline and 4-week follow-up. The 7-point global rating of change (GRC) as the external anchor was also completed in follow-up by patients. Responsiveness was evaluated using receiver operating characteristic (ROC) curve analysis and correlation analysis. According to GRC, patients were classified into two groups (improved vs. unimproved). The best cutoff or MIC was estimated via the ROC curve. RESULTS: Acceptable responsiveness obtained for FABQ, TSK, and PCS with the area under the curve ranging from 0.84 to 0.94 and spearman coefficient > 0.6. The MIC values reflecting improvement were 9.5, 10.5, and 12.5 points, respectively, for FABQ, TSK, and PCS. CONCLUSIONS: The results of this study demonstrated that the Persian version of FABQ, TSK, and PCS have sufficient responsiveness and good ability to measure meaningful clinical changes in people with patient CNNP. The MIC scores of the FABQ, TSK, and PCS can help clinicians and researchers to detect changes significant to the patient following a rehabilitation program.

Obese Patients Achieve Good Improvements in Patient-Reported Outcome Measures After Medial Unicompartmental Knee Arthroplasty Despite a Lower Preoperative Score.

BACKGROUND: In this study, we examined the association between obesity and patient-reported outcome measures after medial unicompartmental knee arthroplasty (MUKA), assessed through score changes, Patient Acceptable Symptom State (PASS), and minimal important change (MIC). Second, the association between obesity and early readmissions was examined. METHODS: A total of 450 MUKAs (mean body mass index [BMI] 30.3, range, 19.6-53.1), performed from February 2016 to December 2020, were grouped using BMI: <30, 30-34.9, and >34.9. Oxford Knee Score (OKS), Forgotten Joint Score (FJS), and Activity and Participation Questionnaire (APQ) were assessed preoperatively and at 3, 12, and 24 months, postoperatively. The 12-month PASS and MIC were also assessed, defining PASS as OKS = 30, MIC-OKS as change in OKS = 8, and MIC-FJS as change in FJS = 14. RESULTS: No significant differences in OKS change were found between BMI groups. After 12 months, patients who had a BMI of 30-34.9 had lower change in FJS (estimate -8.1, 95% CI -14.9 to -1.4) and were less likely to reach PASS (odds ratio 0.4, 95% CI 0.2-0.7) as well as MIC-FJS (odds ratio 0.5, 95% CI 0.2-0.9). Both obese groups had lower change in APQ after 12 months. Differences in 90-day readmission rates were nonsignificant between groups. CONCLUSION: Our findings of no differences in OKS improvement between BMI groups and achieving MIC for BMI > 34.9 suggest good improvements in obese patients despite lower preoperative scores, supporting contemporary indications for MUKA. Lower APQ development and achievement of 12-month PASS may be used when addressing expectations of recovery.

Defining the minimal important change (MIC) and meaningful change value (MCV) for the Psoriatic Arthritis Disease Activity Score (PASDAS) in a routine practice cohort of patients with psoriatic arthritis.

OBJECTIVE: The aim of the study was to determine the Minimal Important Change (MIC) values and Meaningful Change (MCV) values for the Psoriatic Arthritis Disease Activity Score (PASDAS) and the Standard Error of Measurement (s.e.m.) of the PASDAS. METHODS: The routine practice data for 544 patients with PsA was available for analysis. An anchor-based method using linear regression analyses was used to determine the MICs and MCVs for the PASDAS. With this anchor-based method, we compared changes in the PASDAS score with an external reference (anchor). The anchor question inquired whether the patient's well-being had changed since their previous visit. It consisted of a 7-point Likert scale (range: very much improved to very much deteriorated). Interperiod correlation matrix analysis was performed to determine the s.e.m. RESULTS: The overall MIC and MCV for the PASDAS were 0.67 (95% CI: 0.55, 0.79) and 1.34 (95% CI: 1.21, 1.46), respectively. Results for improvement and deterioration were 0.65 (95% CI: 0.46, 0.83) and 0.71 (95% CI: 0.49, 0.93) for the MIC, respectively, and 1.29 (95% CI: 1.11, 1.48) and 1.42 (95% CI: 1.19, 1.64) for the MCV, respectively. The s.e.m. was determined at 0.81. CONCLUSION: The MIC for the PASDAS is a tool for physicians treating patients with PsA enabling them to give context to the patient's perspective of disease activity, while the MCV might aid the use of the PASDAS in PsA clinical trials.

Methodological approach for determining the Minimal Important Difference and Minimal Important Change scores for the European Organisation for Research and Treatment of Cancer Head and Neck Cancer Module (EORTC QLQ-HN43) exemplified by the Swallowing scale.

PURPOSE: The aim of this study was to explore what methods should be used to determine the minimal important difference (MID) and minimal important change (MIC) in scores for the European Organisation for Research and Treatment of Cancer Head and Neck Cancer Module, the EORTC QLQ-HN43. METHODS: In an international multi-centre study, patients with head and neck cancer completed the EORTC QLQ-HN43 before the onset of treatment (t1), three months after baseline (t2), and six months after baseline (t3). The methods explored for determining the MID were: (1) group comparisons based on performance status; (2) 0.5 and 0.3 standard deviation and standard error of the mean. The methods examined for the MIC were patients' subjective change ratings and receiver-operating characteristics (ROC) curves, predictive modelling, standard deviation, and standard error of the mean. The EORTC QLQ-HN43 Swallowing scale was used to investigate these methods. RESULTS: From 28 hospitals in 18 countries, 503 patients participated. Correlations with the performance status were |r|< 0.4 in 17 out of 19 scales; hence, performance status was regarded as an unsuitable anchor. The ROC approach yielded an implausible MIC and was also discarded. The remaining approaches worked well and delivered MID values ranging from 10 to 14; the MIC for deterioration ranged from 8 to 16 and the MIC for improvement from - 3 to - 14. CONCLUSIONS: For determining MIDs of the remaining scales of the EORTC QLQ-HN43, we will omit comparisons of groups based on the Karnofsky Performance Score. Other external anchors are needed instead. Distribution-based methods worked well and will be applied as a starting strategy for analyses. For the calculation of MICs, subjective change ratings, predictive modelling, and standard-deviation based approaches are suitable methods whereas ROC analyses seem to be inappropriate.

Adaptation of the Patient Benefit Assessment Scale for Hospitalised Older Patients: development, reliability and validity of the P-BAS picture version.

BACKGROUND: The Patient Benefit Assessment Scale for Hospitalised Older Patients (P-BAS HOP) is a tool developed to both identify the priorities of the individual patient and to measure the outcomes relevant to him/her, resulting in a Patient Benefit Index (PBI), indicating how much benefit the patient had experienced from the hospitalisation. The reliability and the validity of the P-BAS HOP appeared to be not yet satisfactory and therefore the aims of this study were to adapt the P-BAS HOP and transform it into a picture version, resulting in the P-BAS-P, and to evaluate its feasibility, reliability, validity, responsiveness and interpretability. METHODS: Process of instrument development and evaluation performed among hospitalised older patients including pilot tests using Three-Step Test-Interviews (TSTI), test-retest reliability on baseline and follow-up, comparing the PBI with Intraclass Correlation Coefficient (ICC), and hypothesis testing to evaluate the construct validity. Responsiveness of individual P-BAS-P scores and the PBI with two different weighing schemes were evaluated using anchor questions. Interpretability of the PBI was evaluated with the visual anchor-based minimal important change (MIC) distribution method and computation of smallest detectable change (SDC) based on ICC. RESULTS: Fourteen hospitalised older patients participated in TSTIs at baseline and 13 at follow-up after discharge. After several adaptations, the P-BAS-P appeared feasible with good interviewer's instructions. The pictures were considered relevant and helpful by the participants. Reliability was tested with 41 participants at baseline and 50 at follow-up. ICC between PBI1 and PBI2 of baseline test and retest was 0.76, respectively 0.73. At follow-up 0.86, respectively 0.85. For the construct validity, tested in 169 participants, hypotheses regarding importance of goals were confirmed. Regarding status of goals, only the follow-up status was confirmed, baseline and change were not. The responsiveness of the individual scores and PBI were weak, resulting in poor interpretability with many misclassifications. The SDC was larger than the MIC. CONCLUSIONS: The P-BAS-P appeared to be a feasible instrument, but there were methodological barriers for the evaluation of the reliability, validity, and responsiveness. We therefore recommend further research into the P-BAS-P.

Minimal important difference and patient acceptable symptom state for pain, Constant-Murley score and Simple Shoulder Test in patients with subacromial pain syndrome.

BACKGROUND: The results of clinical trials should be assessed for both statistical significance and importance of observed effects to patients. Minimal important difference (MID) is a threshold denoting a difference that is important to patients. Patient acceptable symptom state (PASS) is a threshold above which patients feel well. OBJECTIVE: To determine MID and PASS for common outcome instruments in patients with subacromial pain syndrome (SAPS). METHODS: We used data from the FIMPACT trial, a randomised controlled trial of treatment for SAPS that included 193 patients. The outcomes were shoulder pain at rest and on arm activity, both measured with the 0-100 mm visual analogue scale (VAS), the Constant-Murley score (CS), and the Simple Shoulder Test (SST). The transition question was a five-point global rating of change. We used three anchor-based methods to determine the MID for improvement: the receiver operating characteristic (ROC) curve, the mean difference of change and the mean change methods. For the PASS, we used the ROC and 75th percentile methods and calculated estimates using two different anchor question thresholds. RESULTS: Different MID methods yielded different estimates. The ROC method yielded the smallest estimates for MID: 20 mm for shoulder pain on arm activity, 10 points for CS and 1.5 points for SST, with good to excellent discrimination (areas under curve (AUCs) from 0.86 to 0.94). We could not establish a reliable MID for pain at rest. The PASS estimates were consistent between methods. The ROC method PASS thresholds using a conservative anchor question threshold were 2 mm for pain at rest, 9 mm for pain on activity, 80 points for CS and 11 points for SST, with AUCs from 0.74 to 0.83. CONCLUSION: We recommend the smallest estimate from different methods as the MID, because it is very unlikely that changes smaller than the smallest MID estimate are important to patients: 20 mm for pain VAS on arm activity, 10 points for CS and 1.5 points for SST. We recommend PASS estimates of 9 mm for pain on arm activity, 80 points for CS, and 11 points for SST. TRIAL REGISTRATION: ClinicalTrials.gov NCT00428870 (first registered January 29, 2007).

Responsiveness and interpretability of commonly used outcome assessments of mobility capacity in older hospital patients with cognitive spectrum disorders.

BACKGROUND: In older hospital patients with cognitive spectrum disorders (CSD), mobility should be monitored frequently with standardised and psychometrically sound measurement instruments. This study aimed to examine the responsiveness, minimal important change (MIC), floor effects and ceiling effects of commonly used outcome assessments of mobility capacity in older patients with dementia, delirium or other cognitive impairment. METHODS: In a cross-sectional study that included acute older hospital patients with CSD (study period: 02/2015-12/2015), the following mobility assessments were applied: de Morton Mobility Index (DEMMI), Hierarchical Assessment of Balance and Mobility (HABAM), Performance Oriented Mobility Assessment, Short Physical Performance Battery, 4-m gait speed test, 5-times chair rise test, 2-min walk test, Timed Up and Go test, Barthel Index mobility subscale, and Functional Ambulation Categories. These assessments were administered shorty after hospital admission (baseline) and repeated prior to discharge (follow-up). Global rating of mobility change scales and a clinical anchor of functional ambulation were used as external criteria to determine the area under the curve (AUC). Construct- and anchor-based approaches determined responsiveness. MIC values for each instrument were established from different anchor- and distribution-based approaches. RESULTS: Of the 63 participants (age range: 69-94 years) completing follow-up assessments with mild (Mini Mental State Examination: 19-24 points; 67%) and moderate (10-18 points; 33%) cognitive impairment, 25% were diagnosed with dementia alone, 13% with delirium alone, 11% with delirium superimposed on dementia and 51% with another cognitive impairment. The follow-up assessment was performed 10.8 ± 2.5 (range: 7-17) days on average after the baseline assessment. The DEMMI was the most responsive mobility assessment (all AUC > 0.7). For the other instruments, the data provided conflicting evidence of responsiveness, or evidence of no responsiveness. MIC values for each instrument varied depending on the method used for calculation. The DEMMI and HABAM were the only instruments without floor or ceiling effects. CONCLUSIONS: Most outcome assessments of mobility capacity seem insufficiently responsive to change in older hospital patients with CSD. The significant floor effects of most instruments further limit the monitoring of mobility alterations over time in this population. The DEMMI was the only instrument that was able to distinguish clinically important changes from measurement error. TRIAL REGISTRATION: German Clinical Trials Register (DRKS00005591). Registered February 2, 2015.

Minimal important change (MIC): a conceptual clarification and systematic review of MIC estimates of PROMIS measures.

We define the minimal important change (MIC) as a threshold for a minimal within-person change over time above which patients perceive themselves importantly changed. There is a lot of confusion about the concept of MIC, particularly about the concepts of minimal important change and minimal detectable change, which questions the validity of published MIC values. The aims of this study were: (1) to clarify the concept of MIC and how to use it; (2) to provide practical guidance for estimating methodologically sound MIC values; and (3) to improve the applicability of PROMIS by summarizing the available evidence on plausible PROMIS MIC values. We discuss the concept of MIC and how to use it and provide practical guidance for estimating MIC values. In addition, we performed a systematic review in PubMed on MIC values of any PROMIS measure from studies using recommended approaches. A total of 50 studies estimated the MIC of a PROMIS measure, of which 19 studies used less appropriate methods. MIC values of the remaining 31 studies ranged from 0.1 to 12.7 T-score points. We recommend to use the predictive modeling method, possibly supplemented with the vignette-based method, in future MIC studies. We consider a MIC value of 2-6 T-score points for PROMIS measures reasonable to assume at this point. For surgical interventions a higher MIC value might be appropriate. We recommend more high-quality studies estimating MIC values for PROMIS.

Assessing baseline dependency of anchor-based minimal important change (MIC): don't stratify on the baseline score!

PURPOSE: The minimal important change (MIC) of a patient-reported outcome measure (PROM) is often suspected to be baseline dependent, typically in the sense that patients who are in a poorer baseline health condition need greater improvement to qualify as minimally important. Testing MIC baseline dependency is commonly performed by creating two or more subgroups, stratified on the baseline PROM score. This study's purpose was to show that this practice produces biased subgroup MIC estimates resulting in spurious MIC baseline dependency, and to develop alternative methods to evaluate MIC baseline dependency. METHODS: Datasets with PROM baseline and follow-up scores and transition ratings were simulated with and without MIC baseline dependency. Mean change MICs, ROC-based MICs, predictive MICs, and adjusted MICs were estimated before and after stratification on the baseline score. Three alternative methods were developed and evaluated. The methods were applied in a real data example for illustration. RESULTS: Baseline stratification resulted in biased subgroup MIC estimates and the false impression of MIC baseline dependency, due to redistribution of measurement error. Two of the alternative methods require a second baseline measurement with the same PROM or another correlated PROM. The third method involves the construction of two parallel tests based on splitting the PROM's item set. Two methods could be applied to the real data. CONCLUSION: MIC baseline dependency should not be tested in subgroups based on stratification on the baseline PROM score. Instead, one or more of the suggested alternative methods should be used.