Minimal Clinically Important Changes of Patient-reported Outcome Measures for Acute Postsurgical Pain

PROMPT NIT-1 was registered in ClinicalTrials.gov (No. NCT03834922 ) on February 8, 2019. It was conducted in accordance with the latest version of the Declaration of Helsinki and approved by the ethics committee of Jena University Hospital, Jena, Germany (No. 2019-1298-Bef; February 6, 2019) as well as local ethics committees at all participating hospitals. The PROMPT NIT-1 study was a prospective, multicenter observational cohort study collecting PROMs and clinical data from patients before and after surgery (fig. 1 A). One aim of the study was to assess acute postsurgical pain outcomes in patients receiving routine care after four different surgical procedures (total knee replacement, breast surgery, sternotomy, and surgery related to endometriosis) by investigating the characteristics of PROMs, including pain intensity (with the subdomains [1] pain intensity at rest, [2] worst, [3] average, [4] during physiotherapy, and [5] during activity) as well as physical function (with the subdomains [1] physical function at rest, [2] during physio, and [3] during activity). These PROMs were completed by patients on the first and third postsurgical days (day 1, day 3), together with anchors (PGIC and patient’s specific impression of change [PSIC]) on day 3. A detailed description of the study design can be found in the publication about sensitivity to change of the PROMs 16 and in the Supplemental Digital Content ( https://links.lww.com/ALN/E273 ). Patient cohort and methods for calculation of MCID. ( A ) The Patient-reported Outcomes in Multimodal Pain Treatment, Non-interventional Trial 1 (PROMPT NIT-1) was a prospective, multicenter observational cohort study collecting PROMs and clinical data from patients before and after four different surgeries (total knee replacement, breast surgery, sternotomy, and surgery related to endometriosis) from 2,661 patients. Based on this, the sensitivity of the PROMs to changes in pain intensity (with the subdomains (1) pain intensity at rest and (2) pain intensity during activity) was investigated. PROMs were assessed based on different anchors (global [PGIC] and specific [PSIC] impression of change by patients). We utilized the data from day 1 and day 3 for the MCID calculation. ( B ) The 30% SD method for determining the MCID involves calculating 30% of the SD of the baseline scores for each scale. This percentage of the SD is used as a criterion to define the threshold for clinically meaningful change in PROMs. ( C ) The standard error of the measurement (SEM) method involves calculating the SEM for each scale. The SEM provides an estimate of the amount of measurement error in the scores, and it is used as a criterion to define MCID in PROMs. ( D ) The global anchor method uses the PGIC to identify individuals who reported a minimal change. The subsequent step involves the computation of the average score variation among these patients, establishing a benchmark to evaluate clinically significant changes in PROMs. ( E ) PSIC is used for each scale in the specific anchor method. This method identifies patients who report minimal change on each scale and computes the average change in their scores to establish the threshold for a clinically significant change. ( F ) The overall MCID estimates were calculated as the mean of these four individual criteria ( B to E ) and are presented alongside the 95% CI for each PROM. MCID, minimal clinically important difference; PGIC, patient’s global impression of change; PROM, patient-reported outcome measure; PSIC, patient’s specific impression of change; SD, standard deviation. The PROMPT NIT-1 study enrolled patients undergoing total knee arthroplasty, breast surgery, sternotomy, or endometriosis-related surgery. Apart from breast and endometriosis-related surgery, both sexes were included. There was no upper age limit. The general inclusion criteria for all surgeries were that the patient was of consenting age (older than 18 yr), was undergoing elective surgery with a planned hospital stay after surgery, was available to be approached before surgery, and consented to participate. Patients were excluded if they were unable to give consent, e.g. , due to cognitive impairment, or if the questionnaire was not available in a language in which the patient was fluent (please refer for details to the Supplemental Digital Content, https://links.lww.com/ALN/E273 ). For assessing the domain pain intensity, five 11-point numeric rating scales were used. One of these scales assessed pain intensity at rest, two pain intensity during activity (one by using a procedure-specific activity, the other by asking about pain intensity during physiotherapy), and two assessed average and worst pain intensity during the previous 24 h. Anchors used for all scales were “no pain” and “worst pain imaginable.” Second, physical function in relation to acute postsurgical pain was assessed by using single questions related to rating interference with specific physical activities due to pain (0 to 10 during general activities, during surgery-specific activities, during physiotherapy) with the anchors “did not interfere” and “completely interfered.” These PROMs were either taken from or adapted from the Pain-Out questionnaire, which was developed and validated 10 yr ago for benchmarking postoperative pain. 17 All PROMs used defined introductory statements and descriptions 17 (for details, see Vollert et al . 16 ). For anchor-based MCID analysis, a scale of perceived change since day 1 in general (PGIC) and scales of perceived change regarding pain intensity and physical function (PSIC) were used. All scales of perceived change since day 1 regarding pain intensity, physical function, and the PGIC since day 1 were assessed on day 3, using a 7-point Likert scale with the introduction “Since the first day after surgery until now, how would you describe the change in […]?” The options available for the response were “very much improved,” “much improved,” “minimally improved,” “no change,” “minimally worse,” “much worse,” and “very much worse.” The PROMs assessing pain intensity and physical function after surgery and the PGIC and PSIC scales (including the standardized description) were translated into eight different languages (French, German, Italian, Spanish, Portuguese, Finnish, Serbian, and Swedish). Translations were performed according to a defined forward–backward procedure 18 with double-checks based on the World Health Organization (Geneva, Switzerland) “Process of Translation and Adaptation of Instruments.” 19 While there is currently no consensus on the ideal method for determining the MCID, 12 it is often recommended to use a triage of several methods, including both scale-based and anchor-based metrics. 20 Here, we follow an approach similar to the approach of Myles et al . 21 for pain intensity and determine the MCID of each of the scales as the average of four criteria, two distribution-based and two anchor-based methods: Thirty percent SD. We determined the SD (of each scale at baseline and used 30% of the SD as criterion 1 22 (fig. 1 B). Standard error of the measurement. We determined the standard error of the measurement of each scale 23 (fig. 1 C). PGIC. We used the PGIC, reported on a 7-point Likert scale with the question “Since the start of the study, my overall status is…” The options provided were “very much improved,” “much improved,” “minimally improved,” “no change,” “minimally worse,” “much worse,” and “very much worse.” Patients who reported “minimally improved” or “minimally worse” were considered to experience an MCID, and the absolute changes in the respective 11-point numeric rating scales were calculated for these patients between days 1 and 3 24 (fig. 1 D). PSIC. In analog to the PGIC, we used specific change items on the same 7-point Likert scale with an introduction specific to each PROM. For example, pain at rest used the introduction, “Since the first day after surgery until now, how would you describe the change (if any) in your pain intensity at rest while lying in bed?” As for the PGIC, we used this specific impression of change (PSIC, a change item directly related to each scale) to identify those who reported either “minimally improved” or “minimally worse” and calculated the mean absolute change for these patients between days 16 (fig. 1 E). We present descriptive demographic and surgery data as means or medians (as specified), along with SD, interquartile range, or range, as appropriate and as specified. The standard error of the measurement was calculated using the formula S E M = S D 1 − I C C with ICC the intraclass correlation coefficient between days 1 and 3 for those patients who did not report changes between days. The overall MCID was calculated as the mean of the four individual criteria and is presented alongside its 95% CI. We performed three sensitivity analyses. In addition to calculating the PGIC and PSIC criteria for all patients, we calculated them separately for (1) patients who reported minimal improvement versus minimal worsening, (2) patients undergoing each of the four surgery types, and (3) patients with no or mild (0 to 3), moderate (4 to 6), and severe (7 to 10) baseline pain at rest (day 1). If the point estimate of these was within the 95% CI of the estimate from the whole sample, we did not consider these subgroups to be significantly different, and therefore they were not relevant for the specific item. If the 95% CI of these did not include the total sample estimate in more than one scale, we calculated and presented a specific MCID estimate using the subgroup-specific PGIC and PSIC approximations. Since none of the analyses presented here are null hypothesis–testing, we do not report or interpret P values.

We included data from 2,661 patients from our Innovative Medicines Initative-PainCare PROMPT NIT-1 cohort 16 (fig. 1 A; table 1 ). Of these, 972 (37%) underwent sternotomy, 695 (26%) surgery related to endometriosis (laparoscopy, complex surgery, hysterectomy), 510 (19%) total knee arthroplasty, and 484 (18%) breast surgery (breast conservation or mastectomy). The mean age of all participants was 54 yr (range, 18 to 91). Overall, 65% of participants were female, which was biased by breast cancer and endometriosis-related surgery. Of participants, 44% reported persistent pain before surgery (total knee arthroplasty, 91%; breast, 15%; endometriosis, 68%; sternotomy, 16%), and 6% opioid use (knee, 15%; breast, 1%; endometriosis, 8%; sternotomy, 2%). Of these patients, n = 792 (30%) reported minimal improvement in the global impression, and 134 (5%) reported minimal worsening. All numbers for the four surgeries and the PSICs can be found in table 1 . Basic Demographic Characteristics of the Cohort The overall cohort is identical to the cohort of Vollert et al. 16 For the anchor-based approaches, we focused on patients who reported minimal improvement or minimal worsening in the respective global (patient’s global impression of change) or specific (patient’s specific impression of change) questions. HADS, Hospital Anxiety and Depression Scale; PCS, Pain Catastrophizing Scale. Descriptive results for the PROMs related to pain intensity and physical function on day 1 and day 3 and for the PGIC and PSICs have been reported previously. 16 Pain intensity, along with its subdomains, showed MCID estimates ranging from 1.2 (pain at rest and average pain) to 1.6 (pain during physiotherapy; fig. 2 A; table 2 ). For physical function and its subdomains, the estimates ranged from 1.5 (in bed) to 1.6 (during movement; fig. 2 B; table 3 ). It should be noted that the sample size for estimating physiotherapy items was smaller ( e.g. , n = 379 for pain during physiotherapy and n = 580 for physical function during physiotherapy, compared to n = 910 to n = 926 for other items for the anchor-based criteria) because it was only assessed if physiotherapy was performed. Mostly, the CI of the MCID estimates overlapped between scales. MCID Estimates for Pain Intensity (with the Subdomains) MCID, minimal clinically important difference; SEM, standard error of the measurement. MCID Estimates for Physical Functions (with the Subdomains) MCID, minimal clinically important difference; SEM, standard error of the measurement. Minimal clinically important difference (MCID) estimates and sensitivity analyses. ( A and B ) MCID estimates and sensitivity analysis for pain intensity ( A ) and physical function ( B ) with their respective subdomains. Overall: mean (range); all other categories: mean (95% CI). ( C ) All patients showed an improvement in pain at rest, while a smaller percentage experienced worsening. Patients with severe or moderate initial pain at rest showed a greater improvement than patients with mild pain, where only a small percentage improved and the majority showed no change. The sensitivity analyses revealed no difference in PGIC- or PSIC-based MCID estimates between patients who reported minimal improvement versus patients who reported minimal worsening (tables 4 and 5 ). Sensitivity Analysis of MCID Estimates by Reported Change for Pain Intensity (with the Subdomains) Mean (95% CI). MCID, minimal clinically important difference. Sensitivity Analysis of MCID Estimates by Reported Change for Physical Functions (with the Subdomains) Mean (95% CI). MCID, minimal clinically important difference. However, severe, moderate, or mild baseline pain affected the MCID estimates in both domains and the proportion of patients experiencing clinically relevant change (fig. 2 C). The minimal relevant change perceived by patients was lower for those starting out with mild baseline pain compared to moderate or severe baseline pain (fig. 2 , A and B; tables 4 and 5 ). Overall, the data show that 67.4% (n = 1,794) demonstrated an at least minimal clinically relevant improvement in pain at rest using the overall MCID estimate, whereas a smaller percentage (8.8%; n = 235) experienced a worsening (fig. 2 C). Patients who initially experienced severe (80.4%) or moderate (65.7%) pain at rest were more likely to show improvement than patients with mild baseline pain. Here, only 14.4% reported improvement, while 77.4% reported no change (fig. 2 C).

In this study, we analyzed a large cohort of patients undergoing surgery to determine the MCID on a numerical rating or Likert 0 to 10 scale for eight PROMs related to pain intensity and pain-related physical function in the acute postoperative period. By including four different types of surgery and recruiting from 18 hospitals in 10 countries across Europe, we cast a wide net, allowing for robust interpretation. The overall large number of patients allowed us not only to compare between the eight outcomes but also to perform sensitivity analyses focusing on worsening versus improvement and high versus low baseline pain, to show whether MCIDs should be considered generalizable or may need to be defined for specific settings. For all eight PROMs, we found values between 1.2 and 1.6 using the triage method. Those for pain intensity are well within the range presented by a systematic literature review, which showed a wide range of previous estimates (0.8 to 4.0), 10 whereas ours provide narrower estimates. In one of the largest previous studies (n = 224 patients), also including a range of surgeries and using multicentric design, with a similar triage estimate approach but focusing only on pain at rest (and therefore rather low baseline values), an MCID of 1.0 was estimated, which is comparable to our 1.2 for pain intensity at rest, with overlapping CI. 21 However, for pain intensity ratings that start higher, the threshold of 1.0 postulated by Myles et al. 21 and even our threshold of 1.2 do not fit. Here, we found a significant difference between patients reporting mild and severe baseline pain. Patients starting with mild pain require a smaller change before perceiving an improvement, whereas those starting with severe baseline pain require a significantly larger change before perceiving an improvement. To illustrate, for pain at rest, a change that is perceived as minimally important for severe pain (rated at 2.1) is more than double the change perceived for mild baseline pain (rated at 1.0). Such a difference in pain intensity is clinically relevant and should be addressed appropriately. Implementing this knowledge is therefore a prerequisite for deciding on the effectiveness of an intervention—for example, in clinical trials, guidelines and recommendations—by considering baseline pain levels when using a MCID to assess the efficacy and effectiveness of an intervention. To date, we are not aware of any studies investigating MCIDs for PROMs related to physical function after surgery. As for interference with physical function, we found very similar MCIDs for all three PROMs, namely 1.5 for physical function in bed and during movement and 1.6 for physical function during physiotherapy. Generally, the CIs of the estimates were highly overlapping, with point estimates in almost all cases within the CI of other point estimates, indicating that the true value of all the MCIDs presented here may well be the same. On the basis of these results, we therefore question the previously postulated hypothesis that the MCID should be specific for the specific PROM and the patient population. 25 , 26 The more likely cause of the different MCID estimates seems to us to be an appreciation of baseline levels, as indicated in our sensitivity analysis for varying baseline pain levels. While these MCID estimates are primarily meant to provide a general indication about what change at an individual level is usually considered meaningful, they can be useful in the planning of clinical trials in more than one way. Trials powered for binary outcomes related to change, such as responder rates, can use MCID estimates to define what is considered as a responder (instead of, for example, a 30% pain relief threshold). Trials comparing mean differences (such as postsurgical pain level in the active versus placebo group) or mean change differences (such as difference in pain reduction between active and placebo group) should still consider MCID estimates to be relevant in their study planning, although they should not be seen as the only relevant criterion. 27 – 29 In the statistical planning of a trial, it should be taken into account what a meaningful change for a relevant proportion of the patients will be, to avoid planning of excessive large trials at high cost and risk for participants with little expected gain. However, as sample size planning should be conservative, considering a change slightly below MCID estimates will increase study sensitivity and is therefore overall recommendable. Finally, when considering trial results or meta-analytic evidence, a complex triage of factors needs to be taken into account, such as certainty of evidence, relevance to other outcome domains, cost, feasibility, availability of alternative treatments, and risk–benefit ratio along with difference over comparator. 15 , 30 These aspects mean that MCID estimates should never be the sole criterion to judge the efficacy or effectiveness of an intervention, but should certainly be considered relevant in its assessment. 30 As with all PROMs, the data collected may be subject to recall bias, questionnaire fatigue, or the influence of external factors. Pain was measured using the numeric rating scale, and although most pain scales correlate well with each other, this needs to be seen as a limitation when extrapolating to visual analog scale–based pain assessment. In general, pain scales may not accurately represent the patient’s complex individual experience of pain. Although we found differences in MCID estimates depending on baseline pain, it should be noted that with current knowledge, we cannot determine whether these will be validated in external cohorts, and whether they are surrogate measures of underlying true variables that we have not assessed. As our MCID estimates were assessed by using a time window of 2 days (from day 1 to day 3 after surgery), we cannot exclude that MCIDs for other time windows might differ. There is no universally agreed method to determine MCID in the literature. Here, we use a triage method to overcome the criticisms that have been made of single MCID estimates in the past— i.e. , that anchor-based methods suffer from recall bias, and that distribution-based methods do not use patient impressions at all. Nevertheless, the four estimates we incorporated into our triage are not exclusive, and any triage estimate will vary depending on the included estimates. As there is no current accepted standard for determining MCIDs, it is important to recognize the level of uncertainty provided by any work. Hence, we use the term “MCID estimates” rather than “MCIDs.” This can be seen in the sensitivity analysis, demonstrating that what constitutes an important change may differ for patients based on their individual situation. It can also be seen in the four estimates we use, as these differ, with especially the 30% SD estimate being significantly lower than the other three estimates. As this is a secondary analysis, the cohort was not prospectively planned to provide perfect MCID estimates. This may lead to bias in the sample, such as the included surgeries, the participating centers, and the patient self-selection. Reproduction in external cohorts to show robustness of our estimates is therefore key. Here, we present MCID estimates for a range of PROMs related to pain intensity and—for the first time—pain interference with physical function after surgery. The MCIDs of eight different PROMs were assessed using a variety of multiple methods on an extensive data set. The evaluation covered four different surgical procedures that were prospectively assessed in 18 hospitals in 10 European countries. While we found slight differences between PROMs, these were within overlapping CIs. Most intriguing, however, were indications of baseline levels factoring into patients’ perception of change with higher baseline pain reaching a higher MCID. This suggests that when assessing the effect of a treatment, the MCID applied should take baseline levels into account. The authors would like to thank all clinical investigators in the additional centers who participated in PROMPT NIT-1: Prof. Benno Rehberg, MD, Division of Anesthesiology, Department of Anesthesiology, Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland; Carles Fiquet, MD and Prof. Philippe Neyret, MD, Orthopedic Unit, Infirmerie Protestante de Lyon, Lyon, France; Prof. Caterina Aurilio, MD, Prof. Maria Caterina Pace, MD, and Prof. Pasquale Sansone, MD, Department of Women, Child and General and Specialized Surgery, University of Campania Luigi Vanvitelli, Naples, Italy; Prof. Dragana Unic-Stojanovic, M.D., Ph.D., Institute for Cardiovascular Diseases Dedinje, Medical Faculty, University of Belgrade, Belgrade, Serbia; Germano Cardoso, MD, Patricia Redondo, MD, Sofia Dias, MD, Sofia Travisco, MD, Maria Amelia Gomes, MD, Rita Canotilho, MD, Cátia Ribeiro, MD, and Margarida Correia, MD, Surgery, Instituto Português de Oncologia Porto, Porto, Portugal; Julien Cabaton, MD, Orthopedic Anesthesiology, Center Paul Santy–Hôpital Privé Jean Mermoz, Lyon, France; Maria A. Perez-Herrero, MD, FEA Anesthesiology and Resuscitation, Hospital Clínico Universitario de Valladolid, Saint, Spain; Prof. Rainer Sabatowski, MD, Comprehensive Pain Center, Medical Faculty and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany; Svetlana Dinić, PhD, Mina Đaković, MD, Milena Jović, MD, Nemanja Dimić, MD, and Lazar Bralušić, MD, Department of Resuscitation and Intensive Care, Institute for Orthopedic Diseases “Banjica,” Belgrade, Serbia; Theresa Wodehouse, PhD, and Kristin Ullrich, MD, Barts Health National Health Service Trust, St. Bartholomew’s Hospital, London, United Kingdom; Prof. Thomas Volk, MD, Department of Anesthesiology, Intensive Care and Pain Therapy, Saarland University Medical center, Homburg, Germany; and Prof. Ulrike Stamer, MD, Department of Anesthesiology and Pain Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland. The authors thank the Center for Clinical Studies, Jena University Hospital, Jena, Germany. The manuscript was written and edited by the authors. Artificial intelligence–powered applications, namely ProWritingAid (ProWritingAid Inc.) — London, United Kingdom; ChatGPT (OpenAI) — San Francisco, California, were employed for linguistic processing objectives, including grammar correction and word selection enhancement. The options provided by these tools were critically assessed and edited by the authors to ensure accuracy and alignment with the manuscript’s intended tone and content. This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 777500. This joint undertaking receives support from the European Union’s Horizon 2020 research and innovation program and theEuropean Federation of Pharmaceutical Industries and Associations (EFPIA). The statements and opinions presented here reflect the authors’ views, and neither Innovative Medicines Initiative nor the European Union, EFPIA, or any associated partners handle any use that may be made of the information contained therein. Dr. Vollert holds an investigator-initiated grant from Viatris (Canonsburg, Pennsylvania), and conducts contract research for AstraZeneca (Germany), and reports a relationship with Merz Therapeutics (Germany), and Grünenthal (Germany). Dr. Kalso has received fees for advisory board or lecture activities from Orion Pharma (Finland). Dr. Meissner received payments for advisory boards and talks outside the submitted work from Merck (Germany), Sanofi (Neuilly-sur-Seine, France), MSD (Planegg, Germany), Tafalgie (Marseille, France), Kyowa (Düsseldorf, Germany), Mundipharma (Cambridge, United Kingdom), and Grünenthal (Aachen, Germany). His institution received research support from Federal Joint Committee (G-BA; Berlin, Germany), the Innovative Medicines Initiative 2 (Brüssels, Belgium), Medtronic (Neuhausen, Switzerland), Pfizer (New York, New York), Mundipharma (Singapore), and Grünenthal. Dr. Kemp is supported by a National Institute for Health and Care Research Clinical Lectureship. Dr. Pogatzki-Zahn received financial support from Grünenthal, for research activities, and advisory and lecture fees from Grünenthal, MSD/Merck, and Medtronic. In addition, she receives scientific support from the German Research Foundation (DFG), the Federal Ministry of Research, Technology and Space (BMFTR), the Federal Joint Committee (G-BA), and the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 777500. This joint undertaking receives support from the European Union’s Horizon 2020 research and innovation program and EFPIA. All money went to the institutions (University Münster/University Hospital Münster) for which Dr. Pogatzki-Zahn works.

Patient-reported outcome measures (PROMs) are meant to assess what is meaningful to patients, but there is considerable ambiguity regarding the threshold change in values on a PROM that is relevant to patients. The question of what constitutes a meaningful degree of pain reduction (minimal clinically important difference [MCID]) has been debated. In this multicenter study in 10 European countries, 2,661 patients’ responses on eight pain PROMs (including pain at rest, with movement, and physical function) were used to calculate MCID in pain comparing responses 1 day and 3 days after sternotomy, total knee arthroplasty, breast surgery, or endometriosis surgery. MCIDs were calculated using a combination of two distribution-based (30% of SD, standard error of the measurement) and two anchor-based (change scores between days 1 and 3 in patients reporting “minimal improvement” or “minimal worsening”) methods. MCID was determined to be 1.2 to 1.6 on a 10-point scale across this cohort but varied according to a patient’s starting point. For patients starting with minimal baseline pain, the MCID was lower (1.0) than for patients starting with more severe baseline pain (2.1). Patient-reported outcome measures (PROMs) are used to measure and compare the patients’ perceived health status during or after treatment. When they meet certain quality standards, they are widely accepted as reliable tools for measuring outcomes relevant to the efficacy and effectiveness of interventions in randomized controlled trials and for clinical practice. 1 – 4 However, there is considerable ambiguity regarding the threshold values of the PROMs that represent a meaningful effect of an intervention or a change in parameters that is recognized by and relevant to patients. 5 There are studies showing that statistically significant improvement by a given intervention does not always correspond to a clinically relevant or meaningful benefit. The concept of minimal clinically important difference (MCID) in PROMs acknowledges the importance of identifying the minimum amount of change required to have a meaningful impact for patients. 4 , 6 , 7 MCID is a patient-centered concept and is typically calculated by using consensus, anchor-based, or distribution-based methods. 8 – 11 Anchor-based methods involve correlating changes in pain scores with an external measure that often uses patients’ personal impression, such as a patient’s global impression of change (PGIC), to identify the smallest change perceived as beneficial. As such, they are patient-centered but can be subject to recall bias. Distribution-based methods are derived from statistical properties of the instrument, such as calculating a fraction of the SD of baseline scores to determine the MCID. Distribution-based methods, however, reflect only the properties of the scale, not their relevance to the patient. Therefore, triage approaches, combining anchor- and distribution-based methods, have become standard. These approaches ensure that the identified MCID reflects a change that is both statistically significant and clinically meaningful, guiding the interpretation of the effectiveness of pain interventions in clinical studies. 6 , 12 Determining the MCID of PROMs focuses on patient-centered care, ensuring that score changes reflect meaningful improvements from the patient’s perspective. 13 , 14 This alignment of clinical goals with patient experience ensures that treatments are truly beneficial. In perioperative treatment, MCID helps clinicians make informed decisions about the effectiveness of surgical interventions or treatments by distinguishing between statistical significance and clinical relevance. MCID also provides a standardized way to evaluate and compare the outcomes of different surgical procedures and pain management strategies, which is crucial for clinical trials and research. This standardization ensures that reported benefits are both statistically significant and clinically relevant. 5 Several challenges arise when calculating MCID in pain studies. A major problem is that the MCID must be determined separately for each PROM. This individual evaluation process lacks a universally accepted standard, leading to high variability and inconsistency in MCID values. 9 Among the few studies on acute pain, most focus on traumatic or nonspecific pain, with acute postoperative pain studies being limited, typically based on single types of surgery, assessing fewer patients, including only single PROMs (and only related to the domain pain intensity), and being monocentric. 8 , 10 To overcome these limitations, we aimed to determine the MCIDs for different PROMS related to two acute postsurgical outcome domains that were previously defined as the most important, 15 i.e. , pain intensity and physical function (with their respective subdomains). Data from the multicenter, international Patient-reported Outcomes in Multimodal Pain Treatment, Non-interventional Trial 1 (PROMPT NIT-1) cohort, including 2,661 patients undergoing four different surgical procedures, were analyzed. 16 Using these data, MCIDs were calculated for eight different PROMs using a combination of distribution-based and anchor-based methods, and sensitivity analyses were performed to identify factors affecting the MCIDs.

Supplemental Digital Content, https://links.lww.com/ALN/E273 1. Baseline Process Questionnaires 2. Breast Surgery, Process Case Report Form 3. Endometriosis Surgery, Process Case Report Form 4. Sternotomy, Process Case Report Form 5. Total Knee Arthroplasty, Process Case Report Form 6. Baseline Outcome Questionnaires 7. Breast Surgery, Outcome Case Report Form 8. Endometriosis Surgery, Outcome Case Report Form 9. Sternotomy, Outcome Case Report Form 10. Total Knee Arthroplasty, Outcome Case Report Form 11. Outcome Questionnaires postoperative day (POD)1 and POD3 12. Breast Surgery POD1, Outcome 13. Endometriosis POD1, Outcome 14. Sternotomy POD1, Outcome 15. Total Knee Arthroplasty POD1, Outcome 16. Breast Surgery POD3, Outcome 17. Endometriosis POD3, Outcome 18. Sternotomy POD3, Outcome 19. Total Knee Arthroplasty POD3, Outcome