Minimal important difference and patient acceptable symptom state for common outcome instruments in patients with a closed humeral shaft fracture Analysis of the FISH randomised clinical trial data

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Sumrein, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1490870/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background: Two common ways of assessing the clinical relevance of treatment outcomes are the minimal important difference (MID) and the patient acceptable symptom state (PASS). The former represents the smallest change in the given outcome that makes people feel better, while the latter is the symptom level at which patients feel well. Methods: We recruited 124 humeral shaft fracture patients to a randomised controlled trial comparing surgery to nonsurgical care. Outcome instruments included the Disabilities of Arm, Shoulder, and Hand score (DASH), the Constant-Murley score, and two numerical rating scales (NRS) for pain (at rest and on activities). A reduction in DASH and pain score, and increase in the Constant-Murley score represents improvement. We used four methods (receiver operating characteristic [ROC] curve, the mean difference of change, the mean change, and predictive modelling methods) to determine the MID, and two methods (the ROC and 75th percentile) for the PASS. As an anchor for the analyses, we assessed patients’ satisfaction regarding the injured arm using a 7-item Likert-scale. Results: The change in the anchor question was strongly correlated with the change in DASH, moderately correlated with the change of the Constant-Murley score and pain on activities, and poorly correlated with the change in pain at rest (Spearman’s rho 0.51, ‑0.40, 0.36, and 0.15, respectively). Depending on the method, the MID estimates for DASH ranged from -6.7 to -11.2, pain on activities from -0.5 to -1.3, and the Constant-Murley score from 6.3 to 13.5. The ROC method provided reliable estimates for DASH (-6.7 points, Area Under Curve [AUC] 0.77), the Constant-Murley Score (7.6 points, AUC 0.71), and pain on activities (‑0.5 points, AUC 0.68). The PASS estimates were 14 and 10 for DASH, 2.5 and 2 for pain on activities, and 68 and 74 for the Constant-Murley score with the ROC and 75th percentile methods, respectively. Conclusion: Our study provides credible estimates for the MID and PASS values of DASH, pain on activities and the Constant-Murley score, but not for pain at rest. The suggested cut-offs can be used in future studies and for assessing treatment success in humeral shaft fracture patients. Trial registration: ClinicalTrials.gov NCT01719887, first registration 01/11/2012 Clinimetrics minimal important difference MID MCID patient accepted symptom state PASS responsiveness Outcome measures DASH Constant-Murley score Pain Humeral shaft fracture Background Medical interventions should be aimed at improving health and well-being of patients. Accordingly, patients’ values and preferences lie at the heart of evaluating the effects of treatments. Due to their subjective nature, values and preferences need to be assessed using patient-reported outcome measures (PROMs). Two of the most common PROMs for evaluating treatment outcome in patients with humeral shaft fractures are the Disabilities of the Arm, Shoulder, and Hand (DASH) score and Constant-Murley score.( 1 – 3 ) Patients are also usually queried about the pain they experience. But what is the minimal benefit that justifies use of a medical intervention? Over the past decades, we have witnessed increasing calls to replace statistical significance with ‘clinical relevance’ – our treatments should generate benefits that patients consider meaningful. To inform the magnitude of such effects on different outcome instruments, two important concepts have been developed: the minimal important difference (MID)( 4 ) and the patient acceptable symptom state (PASS)( 5 ). The MID is "the smallest difference in score in the domain of interest which patients perceive as beneficial and which would mandate, in the absence of troublesome side effects and excessive cost, a change in the patient's management”.( 4 ) PASS is the symptom level above which patients consider themselves well, providing a tool for determining treatment success.( 5 ) The main difference between MID and PASS is that the MID defines the smallest change in the given outcome that makes people feel better, and PASS defines the level at which the patient feels well. To our knowledge, there are no previous studies reporting PASS, and only one study reporting MID estimates for two outcome measures (DASH and Constant-Murley score) in patients with humeral shaft fractures.( 1 ) Therefore, we report the MID and PASS analyses of four outcome instruments commonly used to assess treatment outcomes after humeral shaft fractures using data from the Finnish Shaft of the Humerus (FISH) trial.( 3 ) Methods Design, setting, and participants The FISH trial was a randomised clinical trial comparing the effectiveness of surgical treatment with open reduction and plate fixation and non-surgical treatment with functional bracing for closed humeral shaft fractures. The execution of the FISH trial has been described in detail previously.( 3 , 6 , 7 ) The trial was carried out at the Helsinki and Tampere University hospitals in Finland between 2012 and 2018, and conducted in accordance with the Declaration of Helsinki. Participants provided written informed consent upon recruitment. We included adult patients (18 years and older) with a closed, unilateral, and displaced humeral shaft fracture. Patients were excluded if they had a previous injury or a condition affecting the function of the injured upper limb, pathological fracture, other concomitant injury affecting the same upper limb, other fracture, cognitive disabilities affecting the patient compliance, or polytrauma. Characteristics of participants 6 weeks after the fracture are presented in Table 1 . For the analyses of the current study, we included data from all 82 randomised participants and 42 participants who declined to be randomised (opted to choose their preferred treatment) but gave consent for prospective follow-up using the same outcomes as for the FISH trial. Accordingly, the study sample for the analyses consisted of 124 participants. Outcomes The four outcomes analysed were the DASH score, the Constant-Murley score, and the numerical rating scale (NRS) for pain of the upper extremity, both at rest and on activities. DASH is a validated and responsive questionnaire of self-rated upper extremity disability and symptoms with a score ranging from 0 to 100 (higher is worse).( 8 ) The Constant-Murley score is a functional assessment score of the shoulder consisting of patients’ subjective estimate of pain and functionality in daily activities, and objective measures of range of movement and upper extremity strength. The Constant-Murley score ranges from 0 to 100 (higher is better).( 9 ) The NRS for pain has been widely used to evaluate clinical pain intensity.( 10 ) Participants are asked to rate their average pain at rest and on activities of daily living during the last seven days on a 11-point NRS ranging from 0 to 10 (higher is worse). As the anchor for determining both the MID and the PASS, we used the following subjective global rating question: “How satisfied are you with the overall condition of your injured upper limb and its effect on your daily life?” (for methodological details, see below). The answer options for this anchor question were from 1 to 7 in this order: “Very satisfied”, “Satisfied”, “Somewhat satisfied”, “Not satisfied nor dissatisfied”, “Somewhat dissatisfied”, “Dissatisfied”, and “Very dissatisfied”. All outcomes were collected at 6 weeks, 3, 6, 12, and 24 months after the injury. Data handling and analyses Minimal important difference (MID) MIDs for improvement by of each of the four outcome measures were determined using four methods: three anchor-based methods and a predictive modelling method. For the three anchor-based methods, we calculated change in each outcome for each previous follow-up point by deducting the earlier score from the later score, thus a negative change in DASH and pain NRS represents improvement and conversely, a negative change in the Constant-Murley score indicates worsening. For the receiver operating characteristic (ROC) method , we dichotomised the anchor question between better than the previous follow-up point (e.g., from ‘somewhat dissatisfied’ to ‘not satisfied nor dissatisfied’) and not better than the previous follow-up point. The change in the outcome score was calculated always from the previous follow-up time point to the next follow-up point (i.e., change between each follow-up). The optimal discrimination values for the outcome scores (between better and not better in subjective global rating) were determined by ROC analysis using the closest point to top left corner method to maximise specificity and sensitivity.( 11 ) Nonparametric bootstrapping with 1000 replications were used to calculate the 95% confidence interval for ROC MID values.( 12 ) To measure discrimination ability of the obtained cut-off, we calculated the area under the ROC curve (AUC) with 95% CIs by DeLong’s method by bootstrapping 2000 samples.( 13 ) For the mean difference of the change method , we calculated the difference in outcomes between participants who had improved one point in the subjective global rating from those who had not improved from the previous follow-up. For the mean change method , we calculated the mean change with 95% confidence intervals (CIs) for the population whose response to the anchor question (subjective global rating) was one point higher than in the previous follow-up point. For the predictive modelling method , we used logistic regression analysis to calculate MIDs as described by Terluin et al.( 14 ) To assess the correlation of anchor and target outcome measures, we calculated Spearman’s rho for the change of the anchor and 1) the change in each of the outcomes, 2) prescores, and 3) postscores.( 15 ) The 95% CIs were defined by bootstrapping 1000 samples. Patient acceptable symptom state (PASS) For PASS estimates, we used the ROC method and the 75th percentile method . For the ROC method , we dichotomised the participants based on their responses to the subjective global rating anchor question: those responding “Very satisfied” and “Satisfied” on a 7-item Likert scale were deemed to have reached to a patient acceptable symptom state (PASS) while those responding anything between “Somewhat satisfied” to “Very dissatisfied” were deemed not reaching the PASS. Determination of the optimal cut off and 95% CIs was carried out in the same way as for the MID. For the 75th percentile method , we calculated the PASS as the 25th percentile score for the Constant-Murley score, and the 75th percentile score for the DASH score and for the pain-NRS (at rest and on activities) in participants who responded either “Very satisfied” or “Satisfied” on the subjective global rating question. Primary and secondary analyses For the primary analysis, we performed the MID and PASS analyses by combining all the different time points into one analysis to obtain a sufficient number of anchor–outcome pairs. We also determined the MID values separately for every follow-up point as a secondary analysis (Tables S1 and S2 of the supplementary appendix). Results In the FISH trial, 82 of 140 eligible patients were randomised to surgical (n = 38) or functional bracing (n = 44) groups. Of 58 who declined randomisation, 42 consented to follow-up (declined cohort), providing us with data from a total of 124 participants (Table 1 ). In the declined cohort, nine participants chose surgery and 33 chose functional bracing. Missing data varied from 6 to 14 items at the different follow-up time points.( 3 ) Table 1 Participant characteristics at 6 weeks post-injury Characteristics Values Total number of participants (surgery/bracing) 124 (47/77) Completed follow-up (%) 113 (91%) Sex, n, Female (%) 54 (44%) Age, years, mean (SD) 47 (17) Fracture side, n, dominant (%) 60 (48%) Smoker, n (%) 31 (25%) DASH score * , mean (SD) 46 (19) Pain at rest † , mean (SD) 1.8 (1.9) Pain on activities † , mean (SD) 4.9 (2.6) Constant-Murley score ‡ , mean (SD) 35 (20) * The Disabilities of the Arm, Shoulder, and Hand (DASH) score from 0 to 100 (0 = best) † Pain scores are 11-point Numerical Rating Scales with score from 0 to 10 (0 = best) ‡ Constant-Murley score from 0 to 100 (100 = best) MID estimates Depending on the method used, the MID estimates ranged from − 6.7 to -11.2 for DASH, from 6.3 to 13.5 for the Constant-Murley score, from − 0.2 to -0.5 for pain-NRS at rest, and from − 0.5 to -1.3 for pain-NRS on activities. The MID estimates for DASH and the Constant-Murley score proved acceptable discrimination, while the corresponding estimates for pain-NRS on activities discriminated poorly and pain-NRS at rest failed to discriminate states at all (Tables 2 and 3 ). The ROC curves and the MID estimates at all follow-up time points are shown in Fig. S1 and Tables S1 and S2 of the supplementary appendix. Table 2 MID * estimates from the ROC † analyses Outcome MID (95% CI) Sensitivity Specificity AUC # (95% CI) N ** DASH ‡ -6.7 (-7.9 to -5.4) 0.71 0.74 0.77 (0.73 to 0.82) 420 Pain at rest § -0.5 (-0.5 to -0.5) 0.73 0.38 0.56 (0.51 to 0.61) 429 Pain on activities § -0.5 (-0.5 to -0.5) 0.62 0.69 0.68 (0.63 to 0.73) 427 Constant-Murley score ¶ 7.6 (7.4 to 13.2) 0.61 0.71 0.71 (0.66 to 0.76) 416 * Minimal important difference † Receiving operating characteristics, graphs shown in Fig. S1 of the supplementary appendix ‡ The Disabilities of the Arm, Shoulder, and Hand (DASH) score from 0 to 100 (0 = optimal outcome) § Pain scores are 11-point Numerical Rating Scales with score from 0 to 10 (0 = optimal outcome) ¶ Constant-Murley score from 0 to 100 (100 = optimal outcome) # Area under the curve ** N = count of anchor – outcome pairs used in the analysis Table 3 MID * values calculated by mean difference of change, mean change and predictive methods. Method Mean difference of change Mean change Predictive Outcome MID (95% CI) MID (95% CI) MID (95% CI) N1 ¶ N2 ** DASH † -6.8 (-9.2 to -4.3) -11.2 (-13.3 to -9.1) -9.4 (-10.5 to -8.3) 105 248 Pain rest ‡ -0.2 (-0.5 to 0.1) -0.4 (-0.7 to -0.2) -0.4 (-0.5 to -0.3) 108 251 Pain active ‡ -0.9 (-1.4 to -0.5) -1.3 (-1.6 to -0.9) -1.0 (-1.2 to -0.8) 108 249 Constant-Murley score § 6.3 (3.2 to 9.4) 13.5 (10.9 to 16.2) 12.1 (10.8 to 13.4) 104 244 * MID = Minimal important difference. Values are MIDs with 95% CIs. † The Disabilities of the Arm, Shoulder, and Hand (DASH) score from 0 to 100 (0 = optimal outcome) ‡ Pain scores are 11-point Numerical Rating Scales with score from 0 to 10 (0 = optimal outcome) § Constant-Murley score from 0 to 100 (100 = optimal outcome) ¶ N1 = number of patients whose condition was one point better than at the previous follow-up using the 7-point Likert-scale. ** N2 = number of patients whose condition was not better (same or worse) than at the previous follow-up using the 7-point Likert-scale. Correlations A change in the anchor question showed a good correlation with a change in the DASH score (0.51; 95% CI, 0.44 to 0.59). The change in the Constant-Murley score (-0.40; 95% CI, -0.50 to -0.31) was moderately correlated to the anchor. The correlation to pain NRS on activities (0.36; 95% CI, 0.26 to 0.47) was moderate, and poor for pain NRS at rest (0.15; 95% CI, 0.06 to 0.25). Correlations between the postscore of the outcomes and the change of the anchor ranged between − 0.01 and 0.06. Correlation between the prescore of the outcomes and the change in the anchor was negative for the DASH score, pain NRS at rest, and pain NRS on activities. The correlation was positive for the Constant-Murley score (Table 4 ). Table 4 Correlations between the change in the anchor question and outcomes Correlation between DASH Pain at rest Pain on activities Constant-Murley score Postscore 0.04 (-0.05 to 0.14) 0.04 (-0.06 to 0.14) 0.06 (-0.04 to 0.15) -0.01 (-0.10 to 0.09) Change of the outcome 0.51*** (0.44 to 0.59) 0.15** (0.06 to 0.25) 0.36*** (0.26 to 0.47) -0.40*** (-0.50 to -0.31) Prescore -0.27*** (-0.36 to -0.18) -0.09 (-0.19 to 0.01) -0.23*** (-0.32 to -0.14) 0.20*** (0.11 to 0.30) Values are Spearman’s rho with 95% CIs. * p < 0.05. ** p < 0.01. *** p < 0.001. DASH = Disabilities of the Arm, Shoulder, and Hand score PASS estimates PASS values showed excellent discrimination in the DASH and Constant-Murley scores. PASS values discriminated well for pain NRS on activities while discriminating moderately for pain NRS at rest. PASS values defined by the 75th percentile method were closer to the best possible score of the outcomes than the estimates obtained from the ROC method (Table 5 ). Table 5 . PASS estimates from 75th percentile method and ROC analysis Outcome 75th percentile method ROC method PASS PASS Sensitivity Specificity AUC (95% CI) DASH * 10 14 0.87 0.87 0.94 (0.92 to 0.95) Pain at rest † 0.0 0.5 0.79 0.69 0.76 (0.73 to 0.80) Pain on activities † 2.0 2.5 0.87 0.78 0.89 (0.86 to 0.91) Constant-Murley score ‡ 74 68 0.85 0.83 0.90 (0.88 to 0.93) * Disabilities of the Arm, Shoulder, and Hand (DASH) score from 0 to 100 (0 = optimal outcome) † Pain scores are 11-point Numerical Rating Scales with score from 0 to 10 (0 = optimal outcome) ‡ Constant-Murley score from 0 to 100 (100 = optimal outcome) PASS = Patient acceptable symptom state ROC = Receiver operating characteristic Discussion In this study, we calculated the MID and PASS estimates for four outcomes in adult patients with closed humeral shaft fractures. We used four methods to calculate the MID and two methods to calculate PASS. Our MID estimates varied depending on the method used. The change in DASH score had a good correlation, and the change of Constant-Murley score and pain on activities had moderate correlations with the change in anchor question. Taken together, these results indicated credible MID estimates. The ROC method for cut-off values of the MID of both DASH (-6.7 points) and Constant-Murley (7.6 points) scores had an acceptable discrimination. Pain on activities (-0.5 points) discriminated poorly and pain at rest (-0.5 points) was not able to discriminate at all with the ROC method. The established PASS values with the ROC method for DASH (14 points) and Constant-Murley score (68 points) had excellent discrimination. The discrimination was good to moderate with the pain on activities (2.5 points) and pain at rest (0.5 points). The 75th percentile method yielded more stringent limits for PASS in all the outcomes (DASH, 10 points; the Constant-Murley score, 74 points; pain at rest, 0 points; pain on activities, 2 points). We suggest that differences smaller than the smallest point estimates of the MIDs from this study are unlikely to be clinically meaningful. Conversely, differences above the upper limits are very likely to be clinically important to patients. Depending on the potential benefits and inherent risks of treatment methods, researchers may choose either the lower or upper limit of the suggested MID when interpreting the clinical relevance of treatment effects. For PASS, the upper point estimate depicts the cut-off above which the patients are very likely to be satisfied with the treatment outcome and conversely, the lower point estimate reflects the level below which the patients are unlikely to be satisfied. We identified one previous prospective comparative study on the MID of two different outcomes in patients with humeral shaft fractures reporting the MID of 6.7 points for DASH and 6.1 points for the Constant-Murley score.( 1 ) We could not identify a previous study reporting PASS estimates for patients with humeral shaft fractures. Our estimates for the MID for pain at rest and pain on activities are smaller than in degenerative shoulder conditions.( 16 , 17 ) However, due to poor correlation especially on the pain at rest, our results should be interpreted with caution. We decided to use a prospective anchor question for our analyses (i.e., patients reported their current symptom state using the subjective global rating as opposed to comparing it to baseline status), which is the method used often in the MID analyses for degenerative conditions. In a trauma setting, it is not possible to obtain reliable baseline data prior the injury. Our approach may be less susceptible to recall bias as the participants did not have to remember their symptoms state several months ago—a task that people tend to fail in.( 18 ) Both the MID and PASS are valuable tools both in medical research and clinical practice. The MID provides a tool for future trial sample size calculations. However, when contemplating different treatment methods during shared decision-making in clinical settings, the concept of PASS (i.e., the proportion of patients reaching acceptable symptom state) may be more understandable for patients.( 19 ) Strengths and weaknesses A strength of our study is high internal validity as we used prospective homogenous data from a randomised clinical trial performed by experienced research personnel with little missing data. We also used the most common outcome instruments to assess the outcome of treatment in patients with upper extremity injuries and the methods for obtaining several MID and PASS estimates. In addition, our determination to analyse the MID and PASS was published in the protocol article, prior to any access to trial data.( 7 ) An obvious limitation of our study is that the results are obtained from a randomised clinical trial with stringent inclusion and exclusion criteria (i.e., adult patients with closed, unilateral humeral shaft fracture without severe comorbidities or compliance problems). Thus, our results may not be directly applicable to all patients with this injury. Conclusions We provide credible estimates for the MID and PASS for adult patients with humeral shaft fractures including several of the most used methods and outcomes. Depending on the application, the upper or lower limit of the established MIDs and PASS values should be chosen. The MID might be more useful especially for scientific purposes (i.e., sample size calculation), whereas the PASS concept is—in addition to scientific applications—more understandable to patients, and accordingly, we advocate its use as a more appropriate measure for gauging treatment success in patients with a humeral shaft fracture. Abbreviations AUC: Area under curve; CI: Confidence interval; DASH: the Disabilities of arm, Shoulder, and Hand score; FISH: Finnish Shaft of the Humerus trial; MID: Minimal important difference; NRS: Numerical rating scale; PASS: Patient acceptable symptom state; PROM: Patient-reported outcome measure; ROC: Receiver operating characteristic Declarations Ethics approval and consent to participate The study protocol was approved by the Institutional Review Board of the Helsinki and Uusimaa Hospital District (118/13/03/02/2012; May 14, 2012) and informed consent was obtained from all participants prior to inclusion in the study. The trial was conducted in accordance with the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials FISH trial data are not publicly available owing to data privacy issues, but access to the anonymised dataset can be obtained from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by the state funding for university-level health research in Finland. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Authors' contributions TI, TL, TJ, MP, CA, TK, ST, and LR conceived and designed the study, TI, TL, BS, MP, and LR collected the data, JJ and TK executed data analyses, TI, JJ, TL, TJ, CA, TK, ST, and LR drafted and revised the article. 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Tubach F, Dougados M, Falissard B, Baron G, Logeart I, Ravaud P. Feeling good rather than feeling better matters more to patients. Arthritis Rheum. 2006;55(4):526–30. Additional Declarations No competing interests reported. Supplementary Files Supplement.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 12 Sep, 2022 Reviews received at journal 01 Sep, 2022 Reviewers agreed at journal 04 Jul, 2022 Reviews received at journal 11 Jun, 2022 Reviewers agreed at journal 20 May, 2022 Reviewers agreed at journal 17 May, 2022 Reviewers invited by journal 17 May, 2022 Editor assigned by journal 12 May, 2022 Editor invited by journal 29 Mar, 2022 Submission checks completed at journal 29 Mar, 2022 First submitted to journal 25 Mar, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1490870","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":94493619,"identity":"208ebe50-979d-4b14-bd52-60a794c4f8d2","order_by":0,"name":"Thomas Ibounig","email":"","orcid":"","institution":"Finnish Centre for Evidence-Based Orthopaedics (FICEBO), Department of Orthopaedics and Traumatology, University of Helsinki and Helsinki University Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Ibounig","suffix":""},{"id":94493620,"identity":"35ee76a7-d407-4bcd-ac14-50ecfe291851","order_by":1,"name":"Joona Juurakko","email":"","orcid":"","institution":"Finnish Centre for Evidence-Based Orthopaedics (FICEBO), Department of Hand Surgery, Central Finland Central Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Joona","middleName":"","lastName":"Juurakko","suffix":""},{"id":94493621,"identity":"3705c863-b4b1-443a-87b1-4a665b29f2b4","order_by":2,"name":"Tuomas Lähdeoja","email":"","orcid":"","institution":"Finnish Centre for Evidence-Based Orthopaedics (FICEBO), Department of Orthopaedics and Traumatology, University of Helsinki and Helsinki University Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tuomas","middleName":"","lastName":"Lähdeoja","suffix":""},{"id":94493622,"identity":"c834d8fc-de7b-4a21-be67-a43065270d49","order_by":3,"name":"Bakir O. Sumrein","email":"","orcid":"","institution":"Department of Orthopaedics and Traumatology, University of Tampere and Tampere University Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bakir","middleName":"O.","lastName":"Sumrein","suffix":""},{"id":94493623,"identity":"020b2544-e9fa-4b31-825a-6f8a6b73e102","order_by":4,"name":"Teppo LN Järvinen","email":"","orcid":"","institution":"Finnish Centre for Evidence-Based Orthopaedics (FICEBO), Department of Orthopaedics and Traumatology, University of Helsinki and Helsinki University Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Teppo","middleName":"LN","lastName":"Järvinen","suffix":""},{"id":94493624,"identity":"8fb13eb2-ded9-4364-8c8a-a7a8bea26310","order_by":5,"name":"Mika Paavola","email":"","orcid":"","institution":"Finnish Centre for Evidence-Based Orthopaedics (FICEBO), Department of Orthopaedics and Traumatology, University of Helsinki and Helsinki University Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mika","middleName":"","lastName":"Paavola","suffix":""},{"id":94493625,"identity":"1da1e79d-39af-4a7f-9060-485f63972c66","order_by":6,"name":"Clare L. Ardern","email":"","orcid":"","institution":"Department of Family Practice, University of British Columbia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Clare","middleName":"L.","lastName":"Ardern","suffix":""},{"id":94493626,"identity":"86efb836-36cc-4d3d-8087-5e0ab4d58704","order_by":7,"name":"Teemu Karjalainen","email":"","orcid":"","institution":"Finnish Centre for Evidence-Based Orthopaedics (FICEBO), Department of Hand Surgery, Central Finland Central Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Teemu","middleName":"","lastName":"Karjalainen","suffix":""},{"id":94493627,"identity":"2da1a150-5a6c-46ed-a99d-cb6164e6c2be","order_by":8,"name":"Simo Taimela","email":"","orcid":"","institution":"Finnish Centre for Evidence-Based Orthopaedics (FICEBO), Department of Orthopaedics and Traumatology, University of Helsinki and Helsinki University Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Simo","middleName":"","lastName":"Taimela","suffix":""},{"id":94493628,"identity":"4cc344e9-782c-4670-9871-45990957d61e","order_by":9,"name":"Lasse Rämö","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACPgST+RiI5GcjpAVJAVsaiJRsI0ELjxlYSwNBLezHH3/4wHBPXre959tjnj8MEnwEtfDkmEnOYCg23Hbm7HZj3jYGCSL8ksPGzMOQwLjtRu42ad4GhjrCWvifP/78hyHBftuNnGfSIIcR1iKRYCDNwJCQCNTCJs3DRpSWN2aSPQYJydvOHDOTnNsmQVgLP3/64w8/KhJstx1vfibx5o+NhHwDIT1gYABnSRClfhSMglEwCkYBAQAAedw0IwDx1/sAAAAASUVORK5CYII=","orcid":"","institution":"Finnish Centre for Evidence-Based Orthopaedics (FICEBO), Department of Orthopaedics and Traumatology, University of Helsinki and Helsinki University Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Lasse","middleName":"","lastName":"Rämö","suffix":""}],"badges":[],"createdAt":"2022-03-26 02:29:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1490870/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1490870/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":19821494,"identity":"10e0b787-4156-4c20-988e-7d79d6d8e99f","added_by":"auto","created_at":"2022-03-31 14:45:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":502670,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1490870/v1/9897aa03-ba7b-490d-85af-0274f0906ca5.pdf"},{"id":19821492,"identity":"6eea8865-33f2-4567-aa3e-6ead93ac0013","added_by":"auto","created_at":"2022-03-31 14:45:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":165108,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1490870/v1/63b87f094178467df00d7c70.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Minimal important difference and patient acceptable symptom state for common outcome instruments in patients with a closed humeral shaft fracture Analysis of the FISH randomised clinical trial data","fulltext":[{"header":"Background","content":"\u003cp\u003eMedical interventions should be aimed at improving health and well-being of patients. Accordingly, patients\u0026rsquo; values and preferences lie at the heart of evaluating the effects of treatments. Due to their subjective nature, values and preferences need to be assessed using patient-reported outcome measures (PROMs). Two of the most common PROMs for evaluating treatment outcome in patients with humeral shaft fractures are the Disabilities of the Arm, Shoulder, and Hand (DASH) score and Constant-Murley score.(\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Patients are also usually queried about the pain they experience.\u003c/p\u003e \u003cp\u003eBut what is the minimal benefit that justifies use of a medical intervention? Over the past decades, we have witnessed increasing calls to replace statistical significance with \u0026lsquo;clinical relevance\u0026rsquo; \u0026ndash; our treatments should generate benefits that patients consider meaningful. To inform the magnitude of such effects on different outcome instruments, two important concepts have been developed: the minimal important difference (MID)(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) and the patient acceptable symptom state (PASS)(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe MID is \"the smallest difference in score in the domain of interest which patients perceive as beneficial and which would mandate, in the absence of troublesome side effects and excessive cost, a change in the patient's management\u0026rdquo;.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) PASS is the symptom level above which patients consider themselves well, providing a tool for determining treatment success.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) The main difference between MID and PASS is that the MID defines the smallest change in the given outcome that makes people feel better, and PASS defines the level at which the patient feels well.\u003c/p\u003e \u003cp\u003eTo our knowledge, there are no previous studies reporting PASS, and only one study reporting MID estimates for two outcome measures (DASH and Constant-Murley score) in patients with humeral shaft fractures.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Therefore, we report the MID and PASS analyses of four outcome instruments commonly used to assess treatment outcomes after humeral shaft fractures using data from the Finnish Shaft of the Humerus (FISH) trial.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign, setting, and participants\u003c/h2\u003e \u003cp\u003eThe FISH trial was a randomised clinical trial comparing the effectiveness of surgical treatment with open reduction and plate fixation and non-surgical treatment with functional bracing for closed humeral shaft fractures. The execution of the FISH trial has been described in detail previously.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) The trial was carried out at the Helsinki and Tampere University hospitals in Finland between 2012 and 2018, and conducted in accordance with the Declaration of Helsinki. Participants provided written informed consent upon recruitment.\u003c/p\u003e \u003cp\u003eWe included adult patients (18 years and older) with a closed, unilateral, and displaced humeral shaft fracture. Patients were excluded if they had a previous injury or a condition affecting the function of the injured upper limb, pathological fracture, other concomitant injury affecting the same upper limb, other fracture, cognitive disabilities affecting the patient compliance, or polytrauma. Characteristics of participants 6 weeks after the fracture are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFor the analyses of the current study, we included data from all 82 randomised participants and 42 participants who declined to be randomised (opted to choose their preferred treatment) but gave consent for prospective follow-up using the same outcomes as for the FISH trial. Accordingly, the study sample for the analyses consisted of 124 participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eThe four outcomes analysed were the DASH score, the Constant-Murley score, and the numerical rating scale (NRS) for pain of the upper extremity, both at rest and on activities. DASH is a validated and responsive questionnaire of self-rated upper extremity disability and symptoms with a score ranging from 0 to 100 (higher is worse).(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) The Constant-Murley score is a functional assessment score of the shoulder consisting of patients\u0026rsquo; subjective estimate of pain and functionality in daily activities, and objective measures of range of movement and upper extremity strength. The Constant-Murley score ranges from 0 to 100 (higher is better).(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) The NRS for pain has been widely used to evaluate clinical pain intensity.(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) Participants are asked to rate their average pain at rest and on activities of daily living during the last seven days on a 11-point NRS ranging from 0 to 10 (higher is worse).\u003c/p\u003e \u003cp\u003eAs the anchor for determining both the MID and the PASS, we used the following subjective global rating question: \u0026ldquo;How satisfied are you with the overall condition of your injured upper limb and its effect on your daily life?\u0026rdquo; (for methodological details, see below). The answer options for this anchor question were from 1 to 7 in this order: \u0026ldquo;Very satisfied\u0026rdquo;, \u0026ldquo;Satisfied\u0026rdquo;, \u0026ldquo;Somewhat satisfied\u0026rdquo;, \u0026ldquo;Not satisfied nor dissatisfied\u0026rdquo;, \u0026ldquo;Somewhat dissatisfied\u0026rdquo;, \u0026ldquo;Dissatisfied\u0026rdquo;, and \u0026ldquo;Very dissatisfied\u0026rdquo;. All outcomes were collected at 6 weeks, 3, 6, 12, and 24 months after the injury.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData handling and analyses\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eMinimal important difference (MID)\u003c/h2\u003e \u003cp\u003eMIDs for improvement by of each of the four outcome measures were determined using four methods: three anchor-based methods and a predictive modelling method.\u003c/p\u003e \u003cp\u003eFor the three anchor-based methods, we calculated change in each outcome for each previous follow-up point by deducting the earlier score from the later score, thus a negative change in DASH and pain NRS represents improvement and conversely, a negative change in the Constant-Murley score indicates worsening.\u003c/p\u003e \u003cp\u003eFor the \u003cem\u003ereceiver operating characteristic (ROC) method\u003c/em\u003e, we dichotomised the anchor question between better than the previous follow-up point (e.g., from \u0026lsquo;somewhat dissatisfied\u0026rsquo; to \u0026lsquo;not satisfied nor dissatisfied\u0026rsquo;) and not better than the previous follow-up point. The change in the outcome score was calculated always from the previous follow-up time point to the next follow-up point (i.e., change between each follow-up). The optimal discrimination values for the outcome scores (between better and not better in subjective global rating) were determined by ROC analysis using the \u003cem\u003eclosest point to top left corner method\u003c/em\u003e to maximise specificity and sensitivity.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) Nonparametric bootstrapping with 1000 replications were used to calculate the 95% confidence interval for ROC MID values.(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) To measure discrimination ability of the obtained cut-off, we calculated the area under the ROC curve (AUC) with 95% CIs by DeLong\u0026rsquo;s method by bootstrapping 2000 samples.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFor the \u003cem\u003emean difference of the change method\u003c/em\u003e, we calculated the difference in outcomes between participants who had improved one point in the subjective global rating from those who had not improved from the previous follow-up.\u003c/p\u003e \u003cp\u003eFor the \u003cem\u003emean change method\u003c/em\u003e, we calculated the mean change with 95% confidence intervals (CIs) for the population whose response to the anchor question (subjective global rating) was one point higher than in the previous follow-up point.\u003c/p\u003e \u003cp\u003eFor the \u003cem\u003epredictive modelling method\u003c/em\u003e, we used logistic regression analysis to calculate MIDs as described by Terluin et al.(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eTo assess the correlation of anchor and target outcome measures, we calculated Spearman\u0026rsquo;s rho for the change of the anchor and 1) the change in each of the outcomes, 2) prescores, and 3) postscores.(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) The 95% CIs were defined by bootstrapping 1000 samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003ePatient acceptable symptom state (PASS)\u003c/h2\u003e \u003cp\u003eFor PASS estimates, we used the \u003cem\u003eROC method\u003c/em\u003e and the \u003cem\u003e75th percentile method\u003c/em\u003e. For the \u003cem\u003eROC method\u003c/em\u003e, we dichotomised the participants based on their responses to the subjective global rating anchor question: those responding \u0026ldquo;Very satisfied\u0026rdquo; and \u0026ldquo;Satisfied\u0026rdquo; on a 7-item Likert scale were deemed to have reached to a patient acceptable symptom state (PASS) while those responding anything between \u0026ldquo;Somewhat satisfied\u0026rdquo; to \u0026ldquo;Very dissatisfied\u0026rdquo; were deemed not reaching the PASS. Determination of the optimal cut off and 95% CIs was carried out in the same way as for the MID.\u003c/p\u003e \u003cp\u003eFor the \u003cem\u003e75th percentile method\u003c/em\u003e, we calculated the PASS as the 25th percentile score for the Constant-Murley score, and the 75th percentile score for the DASH score and for the pain-NRS (at rest and on activities) in participants who responded either \u0026ldquo;Very satisfied\u0026rdquo; or \u0026ldquo;Satisfied\u0026rdquo; on the subjective global rating question.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003ePrimary and secondary analyses\u003c/h2\u003e \u003cp\u003eFor the primary analysis, we performed the MID and PASS analyses by combining all the different time points into one analysis to obtain a sufficient number of anchor\u0026ndash;outcome pairs. We also determined the MID values separately for every follow-up point as a secondary analysis (Tables S1 and S2 of the supplementary appendix).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn the FISH trial, 82 of 140 eligible patients were randomised to surgical (n\u0026thinsp;=\u0026thinsp;38) or functional bracing (n\u0026thinsp;=\u0026thinsp;44) groups. Of 58 who declined randomisation, 42 consented to follow-up (declined cohort), providing us with data from a total of 124 participants (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In the declined cohort, nine participants chose surgery and 33 chose functional bracing. Missing data varied from 6 to 14 items at the different follow-up time points.(\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipant characteristics at 6 weeks post-injury\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValues\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal number of participants (surgery/bracing)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124 (47/77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompleted follow-up (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113 (91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex, n, Female (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, years, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFracture side, n, dominant (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoker, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDASH score\u003csup\u003e*\u003c/sup\u003e, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePain at rest\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePain on activities\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant-Murley score\u003csup\u003e\u003cem\u003e\u0026Dagger;\u003c/em\u003e\u003c/sup\u003e, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003eThe Disabilities of the Arm, Shoulder, and Hand (DASH) score from 0 to 100 (0\u0026thinsp;=\u0026thinsp;best)\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e \u003cem\u003ePain scores are 11-point Numerical Rating Scales with score from 0 to 10 (0\u0026thinsp;=\u0026thinsp;best)\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\u003csup\u003e\u003cem\u003e\u0026Dagger;\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eConstant-Murley score from 0 to 100 (100\u0026thinsp;=\u0026thinsp;best)\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003eMID estimates\u003c/h2\u003e\n \u003cp\u003eDepending on the method used, the MID estimates ranged from \u0026minus;\u0026thinsp;6.7 to -11.2 for DASH, from 6.3 to 13.5 for the Constant-Murley score, from \u0026minus;\u0026thinsp;0.2 to -0.5 for pain-NRS at rest, and from \u0026minus;\u0026thinsp;0.5 to -1.3 for pain-NRS on activities. The MID estimates for DASH and the Constant-Murley score proved acceptable discrimination, while the corresponding estimates for pain-NRS on activities discriminated poorly and pain-NRS at rest failed to discriminate states at all (Tables \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The ROC curves and the MID estimates at all follow-up time points are shown in Fig. S1 and Tables S1 and S2 of the supplementary appendix.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eMID\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e \u003cstrong\u003eestimates from the ROC\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003e \u003cstrong\u003eanalyses\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMID (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003csup\u003e#\u003c/sup\u003e (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDASH\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.7 (-7.9 to -5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77 (0.73 to 0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e420\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePain at rest\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5 (-0.5 to -0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56 (0.51 to 0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePain on activities\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5 (-0.5 to -0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68 (0.63 to 0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant-Murley score\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.6 (7.4 to 13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71 (0.66 to 0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eMinimal important difference\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eReceiving operating characteristics, graphs shown in Fig. S1 of the supplementary appendix\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cem\u003e\u0026Dagger;\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eThe Disabilities of the Arm, Shoulder, and Hand (DASH) score from 0 to 100 (0\u0026thinsp;=\u0026thinsp;optimal outcome)\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cem\u003e\u0026sect;\u003c/em\u003e\u003c/sup\u003e \u003cem\u003ePain scores are 11-point Numerical Rating Scales with score from 0 to 10 (0\u0026thinsp;=\u0026thinsp;optimal outcome)\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cem\u003e\u0026para;\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eConstant-Murley score from 0 to 100 (100\u0026thinsp;=\u0026thinsp;optimal outcome)\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cem\u003e# Area under the curve\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cem\u003e** N\u0026thinsp;=\u0026thinsp;count of anchor \u0026ndash; outcome pairs used in the analysis\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003e\u003cbr\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eMID\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e \u003cstrong\u003evalues calculated by mean difference of change, mean change and predictive methods.\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean difference of change\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean change\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictive\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMID (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMID (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMID (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN1\u003c/strong\u003e\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN2\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e**\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDASH\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.8 (-9.2 to -4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.2 (-13.3 to -9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.4 (-10.5 to -8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePain rest\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2 (-0.5 to 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4 (-0.7 to -0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4 (-0.5 to -0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePain active\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.9 (-1.4 to -0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.3 (-1.6 to -0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.0 (-1.2 to -0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant-Murley score \u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.3 (3.2 to 9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.5 (10.9 to 16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.1 (10.8 to 13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eMID\u0026thinsp;=\u0026thinsp;Minimal important difference. Values are MIDs with 95% CIs.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eThe Disabilities of the Arm, Shoulder, and Hand (DASH) score from 0 to 100 (0\u0026thinsp;=\u0026thinsp;optimal outcome)\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cem\u003e\u0026Dagger;\u003c/em\u003e\u003c/sup\u003e \u003cem\u003ePain scores are 11-point Numerical Rating Scales with score from 0 to 10 (0\u0026thinsp;=\u0026thinsp;optimal outcome)\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cem\u003e\u0026sect;\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eConstant-Murley score from 0 to 100 (100\u0026thinsp;=\u0026thinsp;optimal outcome)\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cem\u003e\u0026para;\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eN1\u0026thinsp;=\u0026thinsp;number of patients whose condition was one point better than at the previous follow-up using the 7-point Likert-scale.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cem\u003e** N2\u0026thinsp;=\u0026thinsp;number of patients whose condition was not better (same or worse) than at the previous follow-up using the 7-point Likert-scale.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec11\"\u003e\n \u003ch2\u003eCorrelations\u003c/h2\u003e\n \u003cp\u003eA change in the anchor question showed a good correlation with a change in the DASH score (0.51; 95% CI, 0.44 to 0.59). The change in the Constant-Murley score (-0.40; 95% CI, -0.50 to -0.31) was moderately correlated to the anchor. The correlation to pain NRS on activities (0.36; 95% CI, 0.26 to 0.47) was moderate, and poor for pain NRS at rest (0.15; 95% CI, 0.06 to 0.25). Correlations between the postscore of the outcomes and the change of the anchor ranged between \u0026minus;\u0026thinsp;0.01 and 0.06. Correlation between the prescore of the outcomes and the change in the anchor was negative for the DASH score, pain NRS at rest, and pain NRS on activities. The correlation was positive for the Constant-Murley score (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelations between the change in the anchor question and outcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCorrelation between\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDASH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePain at rest\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePain on activities\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConstant-Murley score\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePostscore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003cp\u003e(-0.05 to 0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003cp\u003e(-0.06 to 0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003cp\u003e(-0.04 to 0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003cp\u003e(-0.10 to 0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChange of the outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51***\u003c/p\u003e\n \u003cp\u003e(0.44 to 0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15**\u003c/p\u003e\n \u003cp\u003e(0.06 to 0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36***\u003c/p\u003e\n \u003cp\u003e(0.26 to 0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.40***\u003c/p\u003e\n \u003cp\u003e(-0.50 to -0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrescore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.27***\u003c/p\u003e\n \u003cp\u003e(-0.36 to -0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003cp\u003e(-0.19 to 0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.23***\u003c/p\u003e\n \u003cp\u003e(-0.32 to -0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20***\u003c/p\u003e\n \u003cp\u003e(0.11 to 0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eValues are Spearman\u0026rsquo;s rho with 95% CIs.\u003c/em\u003e \u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/em\u003e \u003csup\u003e\u003cem\u003e**\u003c/em\u003e\u003c/sup\u003e \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/em\u003e \u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec12\"\u003e\n \u003ch2\u003eDASH\u0026thinsp;=\u0026thinsp;Disabilities of the Arm, Shoulder, and Hand score\u003c/h2\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec13\"\u003e\n \u003ch2\u003ePASS estimates\u003c/h2\u003e\n \u003cp\u003ePASS values showed excellent discrimination in the DASH and Constant-Murley scores. PASS values discriminated well for pain NRS on activities while discriminating moderately for pain NRS at rest. PASS values defined by the 75th percentile method were closer to the best possible score of the outcomes than the estimates obtained from the ROC method (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e.\u003cstrong\u003e\u0026nbsp;PASS estimates from 75th percentile method and ROC analysis\u003c/strong\u003e\u003c/p\u003e\n \u003ctable align=\"left\" border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.7328%;\" width=\"23.728813559322035%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3499%;\" width=\"14.265536723163843%\"\u003e\n \u003cp\u003e\u003cstrong\u003e75th percentile method\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 46.6116%;\" width=\"58.898305084745765%\"\u003e\n \u003cp\u003e\u003cstrong\u003eROC method\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.7328%;\" width=\"23.695345557122707%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3499%;\" width=\"14.245416078984485%\"\u003e\n \u003cp\u003ePASS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1708%;\" width=\"7.898448519040903%\"\u003e\n \u003cp\u003ePASS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3499%;\" width=\"14.245416078984485%\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2397%;\" width=\"14.245416078984485%\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.8512%;\" width=\"22.566995768688294%\"\u003e\n \u003cp\u003eAUC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.7328%;\" width=\"23.695345557122707%\"\u003e\n \u003cp\u003eDASH\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3499%;\" valign=\"top\" width=\"14.245416078984485%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1708%;\" valign=\"top\" width=\"7.898448519040903%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3499%;\" valign=\"top\" width=\"14.245416078984485%\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2397%;\" valign=\"top\" width=\"14.245416078984485%\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.8512%;\" width=\"22.566995768688294%\"\u003e\n \u003cp\u003e0.94 (0.92 to 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.7328%;\" width=\"23.695345557122707%\"\u003e\n \u003cp\u003ePain at rest\u003cem\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3499%;\" valign=\"top\" width=\"14.245416078984485%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1708%;\" valign=\"top\" width=\"7.898448519040903%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3499%;\" valign=\"top\" width=\"14.245416078984485%\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2397%;\" valign=\"top\" width=\"14.245416078984485%\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.8512%;\" width=\"22.566995768688294%\"\u003e\n \u003cp\u003e0.76 (0.73 to 0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.7328%;\" width=\"23.695345557122707%\"\u003e\n \u003cp\u003ePain on activities\u003cem\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3499%;\" valign=\"top\" width=\"14.245416078984485%\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1708%;\" valign=\"top\" width=\"7.898448519040903%\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3499%;\" valign=\"top\" width=\"14.245416078984485%\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2397%;\" valign=\"top\" width=\"14.245416078984485%\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.8512%;\" width=\"22.566995768688294%\"\u003e\n \u003cp\u003e0.89 (0.86 to 0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.7328%;\" width=\"23.695345557122707%\"\u003e\n \u003cp\u003eConstant-Murley score\u003cem\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3499%;\" valign=\"top\" width=\"14.245416078984485%\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.1708%;\" valign=\"top\" width=\"7.898448519040903%\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3499%;\" valign=\"top\" width=\"14.245416078984485%\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2397%;\" valign=\"top\" width=\"14.245416078984485%\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.8512%;\" valign=\"top\" width=\"22.566995768688294%\"\u003e\n \u003cp\u003e0.90 (0.88 to 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cem\u003e\u003csup\u003e*\u003c/sup\u003e Disabilities of the Arm, Shoulder, and Hand (DASH) score from 0 to 100 (0 = optimal outcome)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u003csup\u003e\u0026dagger;\u0026nbsp;\u003c/sup\u003e\u003c/em\u003e\u003cem\u003ePain scores are 11-point Numerical Rating Scales with score from 0 to 10 (0 = optimal outcome)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u003csup\u003e\u0026Dagger;\u0026nbsp;\u003c/sup\u003eConstant-Murley score from 0 to 100 (100 = optimal outcome)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePASS = Patient acceptable symptom state\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eROC = Receiver operating characteristic\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we calculated the MID and PASS estimates for four outcomes in adult patients with closed humeral shaft fractures. We used four methods to calculate the MID and two methods to calculate PASS.\u003c/p\u003e \u003cp\u003eOur MID estimates varied depending on the method used. The change in DASH score had a good correlation, and the change of Constant-Murley score and pain on activities had moderate correlations with the change in anchor question. Taken together, these results indicated credible MID estimates. The ROC method for cut-off values of the MID of both DASH (-6.7 points) and Constant-Murley (7.6 points) scores had an acceptable discrimination. Pain on activities (-0.5 points) discriminated poorly and pain at rest (-0.5 points) was not able to discriminate at all with the ROC method.\u003c/p\u003e \u003cp\u003eThe established PASS values with the ROC method for DASH (14 points) and Constant-Murley score (68 points) had excellent discrimination. The discrimination was good to moderate with the pain on activities (2.5 points) and pain at rest (0.5 points). The 75th percentile method yielded more stringent limits for PASS in all the outcomes (DASH, 10 points; the Constant-Murley score, 74 points; pain at rest, 0 points; pain on activities, 2 points).\u003c/p\u003e \u003cp\u003eWe suggest that differences smaller than the smallest point estimates of the MIDs from this study are unlikely to be clinically meaningful. Conversely, differences above the upper limits are very likely to be clinically important to patients. Depending on the potential benefits and inherent risks of treatment methods, researchers may choose either the lower or upper limit of the suggested MID when interpreting the clinical relevance of treatment effects. For PASS, the upper point estimate depicts the cut-off above which the patients are very likely to be satisfied with the treatment outcome and conversely, the lower point estimate reflects the level below which the patients are unlikely to be satisfied.\u003c/p\u003e \u003cp\u003eWe identified one previous prospective comparative study on the MID of two different outcomes in patients with humeral shaft fractures reporting the MID of 6.7 points for DASH and 6.1 points for the Constant-Murley score.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) We could not identify a previous study reporting PASS estimates for patients with humeral shaft fractures. Our estimates for the MID for pain at rest and pain on activities are smaller than in degenerative shoulder conditions.(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) However, due to poor correlation especially on the pain at rest, our results should be interpreted with caution.\u003c/p\u003e \u003cp\u003eWe decided to use a prospective anchor question for our analyses (i.e., patients reported their current symptom state using the subjective global rating as opposed to comparing it to baseline status), which is the method used often in the MID analyses for degenerative conditions. In a trauma setting, it is not possible to obtain reliable baseline data prior the injury. Our approach may be less susceptible to recall bias as the participants did not have to remember their symptoms state several months ago\u0026mdash;a task that people tend to fail in.(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eBoth the MID and PASS are valuable tools both in medical research and clinical practice. The MID provides a tool for future trial sample size calculations. However, when contemplating different treatment methods during shared decision-making in clinical settings, the concept of PASS (i.e., the proportion of patients reaching acceptable symptom state) may be more understandable for patients.(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and weaknesses\u003c/h2\u003e \u003cp\u003eA strength of our study is high internal validity as we used prospective homogenous data from a randomised clinical trial performed by experienced research personnel with little missing data. We also used the most common outcome instruments to assess the outcome of treatment in patients with upper extremity injuries and the methods for obtaining several MID and PASS estimates. In addition, our determination to analyse the MID and PASS was published in the protocol article, prior to any access to trial data.(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eAn obvious limitation of our study is that the results are obtained from a randomised clinical trial with stringent inclusion and exclusion criteria (i.e., adult patients with closed, unilateral humeral shaft fracture without severe comorbidities or compliance problems). Thus, our results may not be directly applicable to all patients with this injury.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe provide credible estimates for the MID and PASS for adult patients with humeral shaft fractures including several of the most used methods and outcomes. Depending on the application, the upper or lower limit of the established MIDs and PASS values should be chosen. The MID might be more useful especially for scientific purposes (i.e., sample size calculation), whereas the PASS concept is\u0026mdash;in addition to scientific applications\u0026mdash;more understandable to patients, and accordingly, we advocate its use as a more appropriate measure for gauging treatment success in patients with a humeral shaft fracture.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC: Area under curve; CI: Confidence interval; DASH: the Disabilities of arm, Shoulder, and Hand score; FISH: Finnish Shaft of the Humerus trial; MID: Minimal important difference; NRS: Numerical rating scale; PASS: Patient acceptable symptom state; PROM: Patient-reported outcome measure; ROC: Receiver operating characteristic \u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Institutional Review Board of the Helsinki and Uusimaa Hospital District (118/13/03/02/2012; May 14, 2012) and informed consent was obtained from all participants prior to inclusion in the study. The trial was conducted in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFISH trial data are not publicly available owing to data privacy issues, but access to the anonymised dataset can be obtained from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the state funding for university-level health research in Finland. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTI, TL, TJ, MP, CA, TK, ST, and LR conceived and designed the study, TI, TL, BS, MP, and LR collected the data, JJ and TK executed data analyses, TI, JJ, TL, TJ, CA, TK, ST, and LR drafted and revised the article. All authors contributed to final data interpretation and contributed to and approved the final draft of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the patients who participated in\u0026nbsp;the FISH\u0026nbsp;trial as well as the physical therapists and all other collaborators at the hospital sites and in the community who were involved in conducting the trial.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eMahabier KC, Den Hartog D, Theyskens N, Verhofstad MHJ, Van Lieshout EMM, Investigators HT. Reliability, validity, responsiveness, and minimal important change of the Disabilities of the Arm, Shoulder and Hand and Constant-Murley scores in patients with a humeral shaft fracture. J Shoulder Elbow Surg. 2017;26(1):e1\u0026ndash;e12.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMatsunaga FT, Tamaoki MJ, Matsumoto MH, Netto NA, Faloppa F, Belloti JC. Minimally Invasive Osteosynthesis with a Bridge Plate Versus a Functional Brace for Humeral Shaft Fractures: A Randomized Controlled Trial. J Bone Joint Surg Am. 2017;99(7):583\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eR\u0026auml;m\u0026ouml; L, Sumrein BO, Lepola V, L\u0026auml;hdeoja T, Ranstam J, Paavola M, et al. Effect of Surgery vs Functional Bracing on Functional Outcome Among Patients With Closed Displaced Humeral Shaft Fractures: The FISH Randomized Clinical Trial. JAMA. 2020;323(18):1792\u0026ndash;801.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eJaeschke R, Singer J, Guyatt GH. Measurement of health status. Ascertaining the minimal clinically important difference. Control Clin Trials. 1989;10(4):407\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKvien TK, Heiberg T, Hagen KB. Minimal clinically important improvement/difference (MCII/MCID) and patient acceptable symptom state (PASS): what do these concepts mean? Ann Rheum Dis. 2007;66 Suppl 3:40\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eR\u0026auml;m\u0026ouml; L, Paavola M, Sumrein BO, Lepola V, L\u0026auml;hdeoja T, Ranstam J, et al. Outcomes With Surgery vs Functional Bracing for Patients With Closed, Displaced Humeral Shaft Fractures and the Need for Secondary Surgery: A Prespecified Secondary Analysis of the FISH Randomized Clinical Trial. JAMA Surg. 2021;156(6):526\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eR\u0026auml;m\u0026ouml; L, Taimela S, Lepola V, Malmivaara A, L\u0026auml;hdeoja T, Paavola M. Open reduction and internal fixation of humeral shaft fractures versus conservative treatment with a functional brace: a study protocol of a randomised controlled trial embedded in a cohort. BMJ Open. 2017;7(7):e014076.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGummesson C, Atroshi I, Ekdahl C. The disabilities of the arm, shoulder and hand (DASH) outcome questionnaire: longitudinal construct validity and measuring self-rated health change after surgery. BMC Musculoskelet Disord. 2003;4:11.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eConstant CR, Murley AH. A clinical method of functional assessment of the shoulder. Clin Orthop Relat Res. 1987:160\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eJensen MP, Karoly P, Braver S. The measurement of clinical pain intensity: a comparison of six methods. Pain. 1986;27(1):117\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eFroud R, Abel G. Using ROC curves to choose minimally important change thresholds when sensitivity and specificity are valued equally: the forgotten lesson of pythagoras. theoretical considerations and an example application of change in health status. PLoS One. 2014;9(12):e114468.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eTerwee CB, Peipert JD, Chapman R, Lai JS, Terluin B, Cella D, et al. Minimal important change (MIC): a conceptual clarification and systematic review of MIC estimates of PROMIS measures. Qual Life Res. 2021;30(10):2729\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eTerluin B, Eekhout I, Terwee CB, de Vet HC. Minimal important change (MIC) based on a predictive modeling approach was more precise than MIC based on ROC analysis. J Clin Epidemiol. 2015;68(12):1388\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDevji T, Carrasco-Labra A, Guyatt G. Mind the methods of determining minimal important differences: three critical issues to consider. Evid Based Ment Health. 2021;24(2):77\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKanto K, Lahdeoja T, Paavola M, Aronen P, Jarvinen TLN, Jokihaara J, et al. Minimal important difference and patient acceptable symptom state for pain, Constant-Murley score and Simple Shoulder Test in patients with subacromial pain syndrome. BMC Med Res Methodol. 2021;21(1):45.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHao Q, Devji T, Zeraatkar D, Wang Y, Qasim A, Siemieniuk RAC, et al. Minimal important differences for improvement in shoulder condition patient-reported outcomes: a systematic review to inform a BMJ Rapid Recommendation. BMJ Open. 2019;9(2):e028777.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKamper SJ, Ostelo RW, Knol DL, Maher CG, de Vet HC, Hancock MJ. Global Perceived Effect scales provided reliable assessments of health transition in people with musculoskeletal disorders, but ratings are strongly influenced by current status. J Clin Epidemiol. 2010;63(7):760\u0026ndash;6.e1.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eTubach F, Dougados M, Falissard B, Baron G, Logeart I, Ravaud P. Feeling good rather than feeling better matters more to patients. Arthritis Rheum. 2006;55(4):526\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-research-methodology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmrm","sideBox":"Learn more about [BMC Medical Research Methodology](http://bmcmedresmethodol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmrm/default.aspx","title":"BMC Medical Research Methodology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Clinimetrics, minimal important difference, MID, MCID, patient accepted symptom state, PASS, responsiveness, Outcome measures, DASH, Constant-Murley score, Pain, Humeral shaft fracture","lastPublishedDoi":"10.21203/rs.3.rs-1490870/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1490870/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eTwo common ways of assessing the clinical relevance of treatment outcomes are the minimal important difference (MID) and the patient acceptable symptom state (PASS). The former represents the smallest change in the given outcome that makes people feel better, while the latter is the symptom level at which patients feel well.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe recruited 124 humeral shaft fracture patients to a randomised controlled trial comparing surgery to nonsurgical care. Outcome instruments included the Disabilities of Arm, Shoulder, and Hand score (DASH), the Constant-Murley score, and two numerical rating scales (NRS) for pain (at rest and on activities). A reduction in DASH and pain score, and increase in the Constant-Murley score represents improvement. We used four methods (receiver operating characteristic [ROC] curve, the mean difference of change, the mean change, and predictive modelling methods) to determine the MID, and two methods (the ROC and 75th percentile) for the PASS. As an anchor for the analyses, we assessed patients’ satisfaction regarding the injured arm using a 7-item Likert-scale. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe change in the anchor question was strongly correlated with the change in DASH, moderately correlated with the change of the Constant-Murley score and pain on activities, and poorly correlated with the change in pain at rest (Spearman’s rho 0.51, ‑0.40, 0.36, and 0.15, respectively). Depending on the method, the MID estimates for DASH ranged from -6.7 to -11.2, pain on activities from -0.5 to -1.3, and the Constant-Murley score from 6.3 to 13.5. The ROC method provided reliable estimates for DASH (-6.7 points, Area Under Curve [AUC] 0.77), the Constant-Murley Score (7.6 points, AUC 0.71), and pain on activities (‑0.5 points, AUC 0.68). \u003c/p\u003e\u003cp\u003eThe PASS estimates were 14 and 10 for DASH, 2.5 and 2 for pain on activities, and 68 and 74 for the Constant-Murley score with the ROC and 75th percentile methods, respectively. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eOur study provides credible estimates for the MID and PASS values of DASH, pain on activities and the Constant-Murley score, but not for pain at rest. The suggested cut-offs can be used in future studies and for assessing treatment success in humeral shaft fracture patients. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTrial registration: \u003c/strong\u003eClinicalTrials.gov NCT01719887, first registration 01/11/2012\u003c/p\u003e","manuscriptTitle":"Minimal important difference and patient acceptable symptom state for common outcome instruments in patients with a closed humeral shaft fracture Analysis of the FISH randomised clinical trial data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-03-31 14:45:22","doi":"10.21203/rs.3.rs-1490870/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-09-12T07:01:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-09-01T13:26:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156527da-01cd-4cfa-9be2-d80ad683e172","date":"2022-07-04T12:36:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-06-11T23:12:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18acc65c-7107-4fe2-bc6e-957e94d7bfa9","date":"2022-05-20T12:49:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63e50274-fb37-4b40-ae07-a7259f5c53ac","date":"2022-05-17T12:32:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-05-17T12:30:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-05-12T23:14:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-03-29T15:04:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-03-29T13:44:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Research Methodology","date":"2022-03-26T02:20:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-research-methodology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmrm","sideBox":"Learn more about [BMC Medical Research Methodology](http://bmcmedresmethodol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmrm/default.aspx","title":"BMC Medical Research Methodology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b1d1e8fc-90f1-422c-885f-00e9f593d782","owner":[],"postedDate":"March 31st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-10-26T06:44:24+00:00","versionOfRecord":[],"versionCreatedAt":"2022-03-31 14:45:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1490870","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1490870","identity":"rs-1490870","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}