Validation and Clinical Application of the Chinese Version of Rapid Response Scale

Validation and Clinical Application of the Chinese Version of Rapid Response Scale | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Validation and Clinical Application of the Chinese Version of Rapid Response Scale Ziying Chen, Junhao Shen, Yifang Chen, Zerui You, Xiaoyu Chen, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6789022/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Oct, 2025 Read the published version in BMC Psychiatry → Version 1 posted 12 You are reading this latest preprint version Abstract Background Current assessment tools inadequately capture rapid symptom changes. This study aims to translate, validate and explore clinical application of the Chinese version of the McIntyre and Rosenblat Rapid Response Scale (CMRRRS) for rapid onset antidepression treatment. Methods The McIntyre and Rosenblat Rapid Response Scale (MARRRS) was translated and culturally adapted. 71 MDD patients undergoing esketamine treatment were assessed utilizing CMRRRS and other validated scales. Reliability, validity, and sensitivity were evaluated through Cronbach’s alpha, correlation with established scales, exploratory factor analysis and mixed-effects modeling. Latent Profile Analysis and Kernel Density Estimation curves were utilized for classification. Minimum Clinically Important Difference was determined to explore minimum change that related to treatment response evaluation. Results The CMRRRS showed high reliability and robust validity. Factor analysis result explaining over 60% of variance. Latent Profile Analysis revealed three classes with distinct thresholds determined by Kernel Density Estimation. 5 points was optimal for detecting minimum clinically meaningful changes. Conclusion The CMRRRS is a reliable, valid, and sensitive tool for tracking rapid symptom changes in MDD patients treated with esketamine. It supports real-time symptom monitoring and personalized treatment adjustments. Further studies are recommended to explore its broader applicability. Rapid Response Scale Scale Validation Clinical Application Measurement-Based Care Rapid-Acting Antidepressants Esketamine Figures Figure 1 Figure 2 Figure 3 Background Major Depressive Disorder (MDD) is one of the most common and disabling mental illnesses and is considered one of the leading causes of disability worldwide, according to the World Health Organization ( 1 ). Traditional antidepressants, such as selective serotonin reuptake inhibitors (SSRIs), are currently the mainstream treatment for MDD. However, their onset of action is typically delayed. Given that the impact of MDD is becoming a significant contributor to global healthcare and socioeconomic burdens, the anticipations for MDD treatment are evolving ( 2 )). There has been an increasing demand for treatment that can respond rapidly in order to alleviate depressive symptoms effectively and reduce suicidal impulses. In response to this demand, rapid response depressive disorder treatments, such as ketamine ( 3 ), psilocybin ( 4 ), transcranial magnetic stimulation (TMS) are being developed, within which ketamine is showing promising outcomes (5; 6). Measurement-Based Care (MBC) is a patient-centered, dynamic treatment model that uses sensitive assessment tools to monitor symptom changes in real-time, optimize treatment strategies, and enhance therapeutic outcomes. The goal of MBC is to support clinical decision-making with quantitative data, enabling clinicians to adjust interventions promptly and achieve personalized treatment optimization. Studies have shown that MBC significantly improves patient adherence, therapeutic efficacy, and satisfaction compared to traditional care methods (7; 8). However, there is a lack of suitable assessment tools for the rapid-acting antidepressant effects during the treatment. Commonly used tools, for example, the Montgomery-Asberg Depression Rating Scale (MADRS)(9; 10) and 17 Items Hamilton Depression Rating Scale (HAMD-17) (11; 12) have been validated as reliable tools for describing depressive symptoms comprehensively. However, these tools were designed for traditional antidepressants that typically require 4 to 8 weeks to show significant efficacy. Fast-acting treatments often show significant efficacy within hours to days after administration, therefore, existing tools may fail to fully capture the main symptom changes in patients undergoing such treatments ( 13 ). The limitations of assessment tools can hinder accurate evaluation of treatment outcomes, impeding clinicians' ability to optimize strategies based on real-time symptom changes. Therefore, developing specialized assessment tools tailored for fast-acting antidepressant therapies is crucial for the broader adoption of the MBC model and the optimization of depression treatment. Besides, existing scales require time-consuming evaluations performed by professional clinical workers, which are imposing a burden on clinical settings, especially during rapid-acting antidepressant treatments which anticipate constant evaluation for monitoring and guiding. Quick self-assessment tools can reduce healthcare professionals' workload while effectively detecting short-term changes in depressive symptoms( 14 ). Many studies determined to improve those classic rating scale and questionnaire by changing it to self-report in order to relieve clinical burden and by shaping them into different form so that they are able to describe emotional features more accurately (15; 16). Among those research, the McIntyre team developed the McIntyre And Rosenblat Rapid Response Scale (MARRRS) based on the Questionnaire 16 Self-Rating Scale (Q16-SRS), which adhere to MBC standards, improving the scale's ability to detect changes in depressive symptoms over shorter period ( 17 ). It shows promise by aiding in better capturing patient symptoms’ character for treatment guidance and enabling faster, more precise evaluations of patients' depressive symptom changes in research to support further technological development and taking off burdens causing by redundant evaluation for clinical workers. While some studies have been conducted on the MARRRS development, neither have they been translated into different languages nor have they been empirically tested on diverse populations. The research on the clinical application and significance of MARRRS also remains limited. There have been an increasing numbers of studies and treatment projects of fast acting antidepressant treatment conducted in China, the demand for a suitable tool is becoming urgent. This article aims to translate MARRRS, which originally developed by Roger S. McIntyre’s team ( 17 ), into its Chinese version and further explore the reliability and validity of the Chinese version of MARRRS (CMRRRS) during esketamine treatment, while examining its sensitivity to grasp rapid shifts in patients’ depressive symptoms. In addition, we also intend to fill the gap in the practical application of CMRRRS in clinical settings, including symptom severity grading and the determination of the minimum clinically important difference (MCID), and exploring its value in predicting patient treatment response. Methods Recruitment Participants were recruited from The Affiliated Brain Hospital, Guangzhou Medical University, where they completed the entire treatment process and clinical evaluations. Translation of the MARRRS The permission of the developer of the MARRRS was obtained via email. Subsequently, the English version of MARRRS ( 17 ) was translated into Chinese and adapted to Chinese cultural and social differences by 2 independent Chinese psychiatrist specialists. After, 2 Chinese researcher who have a sophisticated command of English translated the CMRRRS back to English, performed a linguistic validation process and reported potential linguistic and understanding difficulties. Finally, after a consensus meeting, 2 experienced clinicians proofread the CMRRRS, and confirmed its adaptation to Chinese culture and did not reveal any grammatical mistakes. Whatever discrepancies in terms of different meanings of the items occurred, the translation was revised. After this final approval, the CMRRRS was accomplished. Data resource and screening criteria Our studies were designed to observe real world patients’ responses to esketamine treatment. This treatment aimed to provide options for individuals who suffered from treatment resistant depression of have high suicidal tendencies. This treatment belongs to medication beyond the instruction manual. Therefore, all enrolled adult patients are diagnosed of MDD supported by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria. In addition, they would either have previously completed at least two full courses of antidepressant therapy and were identified as having a poor response to these treatments, or display moderate to severe levels of depression or moderate to high suicidal tendencies, as indicated by Hamilton Depression Rating Scale score of ≥ 17 or Beck Scale for Suicide Ideation Part I (SSI-I) score of ≥ 2 during the initial screening. Patients with any serious or unstable physical illnesses identified through examination and those with alcohol or substance abuse problems were excluded from participating this treatment. Individuals taking psychiatric medication had to continue using the same medications consistently throughout the ketamine treatment process. Prior to joining the treatment program, all participants gave written informed consent to participate in the study. Additional detailed information on the project can be referenced in our previous research ( 18 ). Treatment Process At baseline, patient personal data such as age, gender, height and weight were gathered. Following an overnight fast, patients received esketamine intravenously through a syringe pump at a dosage of 0.25 mg/kg, with the infusion lasting over 40 minutes. Treatment responders, defined as patients with a ≥ 50% reduction in the MADRS total score from baseline or a MADRS total score ≤ 10, could complete the program after the third treatment and were advised to continue maintenance therapy. Non-responders, defined as those with a ≤ 20% reduction in MADRS total score from baseline, could terminate treatment after the fourth or fifth infusion. All patients were eligible to receive up to six infusions, administered every other day on Days 1, 3, 5, 8, 10, and 12, depending on their response to treatment. Vital signs (blood pressure, pulse, and oxygen saturation) were monitored throughout the infusion and post-infusion to ensure a return to pre-infusion levels. Assessment Within two hours after each infusion, patients were asked to completed self-assessments using the CMRRRS and SSI-I to monitor symptom changes. Additionally, trained professionals conducted comprehensive evaluations within 24 hours after the third infusion (infusion 3-24h) and again within 24 hours after completing or terminating the treatment (post-treatment). These evaluations included the MADRS, Hamilton Anxiety Rating Scale (HAMA), and the Snaith-Hamilton Pleasure Scale (SHAPS). Patients were also required to repeat self-assessments using the CMRRRS and SSI-I during these evaluations. Analysis IBM Statistical Package for the Social Sciences (SPSS) version 26.0 was employed to perform reliability and validation analyses. Cronbach’s alpha was calculated to determine the consistency of the CMRRRS. Then, a Spearman’s correlation was also conducted between MADRS and the CMRRRS total scores from pre-treatment, infusion 3, and post-treatment to assess validation in the CMRRRS. The change in score from pre-treatment, infusion 3, and post-treatment was also correlated between the MADRS and the CMRRRS utilizing Spearman’s correlation. An exploratory factor analysis of the 14 CMRRRS items was performed. Items were subjected to principal component analysis (PCA) with varimax rotation. An eigenvalue threshold of 1.0 or higher was applied to determine number of extracted factors. In addition, we utilized a linear mixed effects model to test the ability of the CMRRRS to detect main symptomatic fluctuations over short period using data across first 3 infusions due to treatment may end at infusion 4 to 6. Age, sex, BMI and baseline depression severity measure by MADRS were controlled for in the model. A compound symmetry covariance matrix was utilized, and the data was fit using Restricted Maximum Likelihood with the α-value set at 0.05. Then, we utilized Latent Profile Analysis (LPA) to identify latent categorical structures within the data and classifies individuals into distinct latent groups based on their performance across multiple variables. The analysis was conducted using the LPA tool in Mplus, with the maximum number of categories set to five. Model fit was evaluated using the Bayesian Information Criterion (BIC), Akaike Information Criterion (AIC), Lo-Mendell-Rubin Likelihood Ratio Test (LMRT) and Bootstrapped Likelihood Ratio Test (BLRT) to determine the optimal number of categories. To validate the significance of differences among the classes identified by LPA, the Kruskal-Wallis test was performed using SPSS 26.0 after. Specific analysis metrics included scores on the CMRRRS, SSI-I, MADRS, HAMA, and SHAPS. To further explore the distribution characteristics of each latent group, Kernel Density Estimation (KDE) was employed. Using the "density" function in R software, kernel density curves were plotted, and the intersection points between curves were calculated to analyze the overlap and distinctions among the different latent groups. After, MCID is used to assess the clinical significance of treatment intervention effects. In this study, MCID was calculated using both anchor-based and distribution-based methods in SPSS 26.0. CMRRRS score change was used as a variable, and the mean score change for each group was calculated and used as the estimated MCID value according to anchor-based methods. MCID was also defined based on 0.5 times the standard deviation of CMRRRS score change according to distribution-based methods. The MCID results obtained from the anchor-based and distribution-based methods were compared. Finally, cross-tabulation was used to assess the relationship between whether the CMRRRS change larger or smaller than MCID during first infusion and the treatment response outcomes. Results Demographics To validate the CMRRRS, a sample of 71 patients completed the CMRRRS and MADRS assessments between November 2021 and April 2024. Among them, 9 patients had comorbid spectrum disorders, including 3 with generalized anxiety disorder, 4 with obsessive-compulsive disorder, 1 with attention-deficit/hyperactivity disorder, and 1 with other condition. Patient demographics and the outcomes of all scales are described in Table 1 . Table 1 Demographics Characteristic Age M = 30.7 (SD = 11.2) Sex N = 37 (52.1% male) BMI M = 23.8 (SD = 4.3) Primary diagnosis (MDD) N = 71 (100%) Comorbidity N = 9 (12.7%) MARRRS score Baseline M = 28.6 (SD = 5.8) Post-infusion 1 M = 24.9 (SD = 7.5) Post-infusion 2 M = 21.5 (SD = 7.5) Post-infusion 3 M = 20.0 (SD = 8.5) Post-infusion 3-24h M = 23.2 (SD = 9.5) Post-infusion 4 M = 23.0 (SD = 9.6) Post-infusion 5 M = 22.1 (SD = 8.5) Post-infusion 6 M = 20.1 (SD = 8.4) Post-treatment M = 17.6 (SD = 9.4) SSI-I score Baseline M = 11.1 (SD = 3.0) Post-infusion 1 M = 9.1 (SD = 3.1) Post-infusion 2 M = 8.7 (SD = 3.2) Post-infusion 3 M = 8.5 (SD = 3.2) Post-infusion 3-24h M = 9.2 (SD = 3.3) Post-infusion 4 M = 9.3 (SD = 3.5) Post-infusion 5 M = 9.0 (SD = 3.9) Post-infusion 6 M = 9.1 (SD = 4.0) Post-treatment M = 7.9 (SD = 3.1) MADRS score Baseline M = 30.3 (SD = 5.9) Post-infusion 3-24h M = 23.2 (SD = 7.5) Post-treatment M = 18.5 (SD = 8.7) HAMA score Baseline M = 22.2 (SD = 9.6) Post-infusion 3-24h M = 18.5 (SD = 7.9) Post-treatment M = 15.3 (SD = 9.6) SHAPS score Baseline M = 36.1 (SD = 9.3) Post-treatment M = 19.8.1 (SD = 9.2) M = mean, N = case numbers, SD = standard deviation. Consistency and Validity The CMRRRS exhibited robust internal consistency across infusions, as determined by Cronbach’s alpha (all: α = 0.93, baseline and post-treatment: α = 0.94, across all infusions: α = 0.93). (Details about Cronbach’s alpha values for different subsets see Supplementary Table S1 ). Correlational analysis indicated that there was convergent validity between the MADRS and the CMRRRS total scores (r = 0.77, p < 0.001) (Fig. 1 . A.). Moreover, the change in MADRS total score was highly correlated with change in the CMRRRS scores (r = 0.67, p < 0.001) (Fig. 1 . B.). Factor analysis The Kaiser-Meyer-Olkin measure of sampling adequacy (0.9) indicated that the sample was adequate for factor-analytical modelling. Bartlett’s test of sphericity was highly significant (p < 0.001). Results from the PCA of the CMRRRS are reported. Two factors were identified from data of baseline and after treatment: vigor and distress (details about 2 factors loading see supplementary Table S2). The vigor factor included ten items: feeling happy, concentration, general interest, motivation, pleasure and enjoyment, energy, social isolation, signs of positive emotion, feelings of restlessness, and worry. The distress factor consisted of four items: feeling sad, self-perception, suicidal thoughts, feelings of hopefulness and hopelessness. Both factors combined accounted for 63.0% of the total variance. Three factors were identified from data across infusions: psychic anxiety, vigor, and distress (details about 3 factors loading see supplementary Table S3). The vigor factor included six items: feeling happy, general interest, motivation, pleasure and enjoyment, social isolation and signs of positive emotion. The psychic anxiety factor consisted of four items: concentration, energy, feelings of restlessness, worry. The distress factor consisted of the same four items. All factors combined accounted for 68.2% of the total variance. Sample size The data from 71 participants, who completed a total of 368 responses to a 14-item scale at different time points during treatment, are sufficient to support the validation of the scale's reliability, validity, and sensitivity. According to the recommended guidelines for scale validation, which suggest a sample size of 5–10 times the number of items on the scale, the sample size in this study is adequate. Cronbach’s α ranged from 0.93 to 0.94, indicating excellent internal consistency. Additionally, factor analysis, including Kaiser-Meyer-Olkin measure and Bartlett’s test of sphericity, showed good fit indices, further confirming the scale’s structural validity. Therefore, the results of this study indicate that the sample size meets the necessary requirements for reliability and validity analysis, providing sufficient statistical power to support the conclusions. Sensitivity There was a main effect of the first 3 infusions on the CMRRRS total score: F (1, 42.0) = 730.775, p < 0.001, indicating sensitivity to change over time in response to ketamine infusions (details about the results of linear mixed effects model see Supplementary Table S4). Corrected pairwise comparison indicated a significant reduction from baseline to post-infusions 1 (p = 0.003), 2 (p < 0.001), 3 (p < 0.001). There was a significant reduction between post-infusion 1 and post-infusions 2 (p = 0.001) and 3 (p < 0.001). Classification LPA classification performance of models was evaluated based on model fit indices, including BIC, AIC, adjusted BIC (aBIC), LMRT and BLRT (Table 2 ). The results showed that as the number of classes increased, the AIC, BIC, and adjusted BIC (aBIC) values all decreased, while the differences between 2 and 3 classes are the largest. 2 class and 3 class models showed statistically significant p-values for LMRT. All models showed statistically significant p-values (p < 0.001) for BLRT tests. Additionally, all models had an entropy value larger than 0.8, which indicating strong classification accuracy. Considering these findings, the 3-class model was determined to be the optimal solution. For clarity, we name these classes as group 1, group 2 and group 3. Table 2 Latent Profile Analysis Class AIC BIC aBIC LMRT P value BLRT P value Entropy 2 11142.998 11311.279 11174.854 p < 0.001 *** p < 0.001 *** 0.948 3 10748.124 10975.107 10791.092 p = 0.028 * p < 0.001 *** 0.893 4 10530.113 10815.799 10584.194 p = 0.150 p < 0.001 *** 0.885 5 10330.093 10674.481 10395.287 P = 0.093 p < 0.001 *** 0.910 * P < 0.05, *** P < 0.001 indicates statistical significance. Category validation and group characters To validate the rationality of the LPA-based classification and identify the characteristics of the three LPA classifications, Kruskal-Wallis tests were conducted on the scores of the MARRRS, MADRS, SSI-I, HAMA, and SHAPS scales. The results of Kruskal-Wallis tests for various scales demonstrate significant differences among 3 classes. The MRRS scale yielded an H(K) value of 314.252 with a p-value < 0.001, while the MADRS and SSI-I scales had H(K) values of 80.788 and 113.001, respectively, both with p-values < 0.001. Similarly, the HAMA scale showed an H(K) value of 32.459 with a p-value < 0.001, and the SHAPS scale had an H(K) value of 12.016 with a p-value of 0.002. The scores from the five psychological assessment scales reveals clear hierarchical differences among classes (Fig. 2 ). Threshold positions Kernel Density Estimation (KDE) was employed to further explore the distribution of total scores within each class. The intersection point between group 1 and group 2 is approximately 16.9, and the intersection point between group 2 and group 3 is approximately 27.5 (Fig. 3 ). Minimum clinically important difference MCID values were calculated and estimated using both distribution-based and anchor-based methods. The distribution-based method, relying on the standard deviation of the change scores (7.28), determined the MCID to be 3.64. The anchor-based method calculated the mean differences between groups with pairwise comparisons. The mean difference between Group 1 and Group 2 was 9.00, with a 95% confidence interval (CI) of 7.58 to 10.42. Between Group 1 and Group 3, the mean difference was 13.26 (95% CI: 11.78 to 14.73), while the mean difference between Group 2 and Group 3 was 4.26 (95% CI: 2.96 to 5.56). Smaller MCID candidate values were considered to capture slight but clinically meaningful improvements. Therefore, this study combined the results of the distribution-based and anchor-based methods and selected 4, 5, and 6 as candidate MCID values. Based on whether the change from baseline after the first treatment exceeded the MCID value, and then compared it to post-treatment response outcomes utilizing cross-tabulation, chi-square tests, and consistency analysis (Kappa measurement) to evaluate the classification performance of different MCID values, details can be seen at Table 3 . Table 3 Performance of different MCID values in classifying response status MCID a b c d Chi-square P value Kappa 4 60.7% 39.3% 68% 52.4% 1.169 0.280 0.158 5 82.1% 17.9% 74.2% 33.3% 7.086 0.008 ** 0.389 6 82.1% 17.9% 69.7% 38.5% 3.820 0.051 0.280 a: Non-responders correctly classified (%), b: Non-responders misclassified (%), c: Responders misclassified (%), d: Responders correctly classified (%) ** P < 0.01 indicates statistical significance. Kappa values: 0.4 = moderate agreement. Discussion This study is the first to systematically explore the validation and clinical application of the CMRRRS scale based on the rapid antidepressant effects of esketamine. The reliability of the CMRRRS remained consistently high before and after treatment, as well as during the treatment process. We are confident in the preliminary translation, proofreading and back translation work of the CMRRRS, thus affirming its sound content validity (19; 20). Additionally, the MADRS had been utilized to assess depressive symptoms worldwide, affirming its accuracy and efficacy, therefore correlation analyses results comparing the MADRS and the CMRRRS suggest that the CMRRRS effectively reflects the level of depressive symptoms in patients, demonstrating confirming criterion validity. As for structural validity, individuals undergoing treatment can be classified into two dimensions or three dimensions depending on the treatment progression. There are slight discrepancies in the results from factor analysis between before and after treatment data and the separate analysis of infusions data, considering unique drug related anxiety symptoms emerging from the ketamine treatment. Considering several studies have identified different possible subscales and symptom clusters appeared in patients with MDD depending on the severity, chronicity, treatment resistance of the sample, time of assessment relative to treatment course (21; 22), it is understandable that our CMRRRS’s symptom clusters are different from McIntyre’s research. Whether it is two or three dimensions, the combined factors explain over 60% of the questionnaire's variability. Furthermore, the CMRRRS shows sensitivity to changes in depressive symptoms according to its linear mixed effects model results. The results demonstrate that the CMRRRS scale exhibits significant sensitivity and practicality in ketamine treatment. It can reflect symptom improvements within hours after treatment, providing a reliable tool for real-time monitoring of therapeutic effects. This dynamic assessment capability aligns with the principles of MBC, offering critical guidance for treatment decisions and enabling clinicians to adjust interventions based on real-time patient symptoms. This study also analyzed the distribution characteristics and classification thresholds of CMRRRS scores. The LPA results showed that as the number of classes increased, the AIC, BIC, and adjusted BIC (aBIC) values all decreased, indicating a progressive improvement in model fit. However, when the number of classes exceeded three, the p-value of the LMRT rose above 0.05, suggesting that adding additional classes did not significantly enhance model fit. Furthermore, the largest changes in model fit indices occurred between the 2-class and 3-class models, and the classification accuracy (E index) of the three-class model was 0.893, indicating high classification quality. Considering these findings, the 3-class model was determined to be the optimal solution. Thus, the optimal number of latent classes was determined to be three ( 23 ). The result from Kruskal-Wallis test revealed clear hierarchical differences among the 3 groups with the result that statistically significant differences across all scales. In addition, the varying degrees of depression, anxiety, suicidal ideation, and anhedonia across different patient groups helped us understand the main character of the classification. Based on the performance across the five scales, the three groups can be named as follows mild, moderate and severe group to reflect the gradient changes in the severity of psychological symptoms. The KDE curves for the three groups each presented relatively distinct peaks, with clear intersection points between groups. This indicates that the classification results obtained through LPA were reasonable, with significant differentiation in symptom severity among the groups. Additionally, the x-coordinate values of the curve intersection points represented the threshold positions between the score distributions of different groups. The intersection points x-coordinate values suggest that a score of 17 and 28 could serve as the threshold. They can be utilized to categorize the severity levels of symptoms and facilitate more precise symptom assessment and treatment decision-making, providing an essential foundation for the development of personalized treatment strategies. MCID is an essential metric for determining the clinical significance of treatment interventions( 24 ). Based on the results of distribution-based and anchor-based methods, 4, 5, and 6 were selected as candidate MCID values. The applicability of MCID was validated by assessing the concordance between classification results and patient response outcomes. When the MCID was set to 5, the classification demonstrated optimal sensitivity and specificity (Kappa = 0.389), supporting the selection of 5 as the MCID for the CMRRRS scale. It could effectively capture the slight but clinically significant symptom improvements with strong statistical significance (P = 0.008). By optimizing the MCID, this study further refined the clinical application standards of the CMRRRS scale in fast-acting antidepressant therapies, enabling it to more effectively capture thresholds for meaningful symptom improvement. This optimization is crucial for the preliminary evaluation of the efficacy of fast-acting antidepressant therapies and provides a scientific basis for subsequent treatment decisions. Moreover, by matching MCID with patient response outcomes, its application establishes a foundation for the standardized evaluation of treatment results at the beginning of treatment. For instance, the MCID can be used to identify patients with insufficient treatment response, guiding dose adjustments or the selection of adjunct therapies. The findings above further validate the scientific basis of the CMRRRS scale for symptom identification and classification, providing data support for the standardization of assessment tools. They give clinicians a new perspective for determining the patient's condition more accurately by determining the severity level of a patient’s symptoms. They also offer a foundation for optimizing personalized treatment, enabling the formulation of more targeted treatment strategies ( 25 ). The strength of this study lies in its design, which better reflects patient responses to ketamine treatment in the real world. Additionally, the treatment followed standardized procedures, and all post-treatment assessments were conducted by experienced clinicians, ensuring the authenticity and effectiveness of both the treatment process and the evaluations. The results also support data previously collected by the McIntyre team from various treatment centers in Canada, highlighting the scale's consistency across different populations. Although this study provides preliminary evidence for the application of the CMRRRS scale, several limitations remain. First, the results were obtained from a single arm open label study without a control arm. This may introduce expectation bias both to assessments from the clinician and patients( 26 ). Second, sample is relatively small and primarily based on data from a single center, which may limit the generalizability of the results, particularly in subgroup analyses and assessments of the effects of varying patient characteristics. Lastly, the study's timeframe was relatively short, focusing mainly on the evaluation of rapid treatment effects, and did not include long-term follow-up data. Whether the short-term improvements from fast-acting therapies are sustainable and whether the MCID remains applicable during long-term treatment and maintenance phases require further validation. Future research should expand the sample size and adopt a multicenter study design to include more patients with diverse demographic, cultural, and clinical characteristics. Future research could also explore other rapidly acting antidepressant therapies, such as mirtazapine and electroconvulsive therapy, to validate the generalizability of the CMRRRS scale. Such research would provide comprehensive guidance for the full-cycle management of fast-acting antidepressants and support the broader application of the CMRRRS scale across different treatment stages. Conclusions In summary, the CMRRRS is among the first scales validated in Chinese patients and demonstrates strong reliability in reflecting changes in depressive symptoms during rapid antidepressant treatment procedures. It provides a reliable basis for real-time efficacy assessment and individualized treatment optimization. This study also proposed classification criteria based on LPA and KDE and determined 5 as the most appropriate MCID using a combination of distribution-based and anchor-based methods, offering substantial advancements to treatment guidance and the creation of novel rapid antidepressant therapies.Future research should focus on expanding the sample size, conducting multicenter studies, exploring other fast acting antidepression treatments, and incorporating long-term follow-up to further validate its applicability, thereby promoting the clinical use of rapid-acting antidepressant therapies and the development of novel antidepressant drugs. Abbreviations aBIC Adjusted Bayesian Information Criterion AIC Akaike Information Criterion BIC Bayesian Information Criterion BLRT Bootstrapped Likelihood Ratio Test CI Confidence interval CMRRRS Chinese version of the McIntyre and Rosenblat Rapid Response Scale DSM-5 Diagnostic and Statistical Manual of Mental Disorders HAMA Hamilton Anxiety Rating Scale HAMD-17 17 Items Hamilton Depression Rating Scale KDE Kernel Density Estimation LMRT Lo-Mendell-Rubin Likelihood Ratio Test LPA Latent Profile Analysis MADRS Montgomery-Asberg Depression Rating Scale MARRRS McIntyre and Rosenblat Rapid Response Scale MBC Measurement-Based Care MCID Minimum clinically important difference MDD Major Depressive Disorder PCA Principal component analysis Q16-SRS Questionnaire 16 Self-Rating Scale SHAPS Snaith-Hamilton Pleasure Scale SPSS Statistical Package for the Social Sciences SSI-I Beck Scale for Suicide Ideation Part I Declarations Ethics approval and consent to participate Participants were recruited from The Affiliated Brain Hospital, Guangzhou Medical University, where they completed the entire treatment process and clinical evaluations. This study was approved by the Clinical Research Ethics Committee of The Affiliated Brain Hospital, Guangzhou Medical University (2021073). Prior to joining the treatment program, all participants gave written informed consent to participate in the study. Consent for publication Not applicable. Availability of data The data that support the findings of this study are available from the corresponding author upon reasonable request. Competing interests The authors declare that there is no competing interest. Funding This work was supported by the National Natural Science Foundation of China (grant number 82471546), Guangzhou Health Science and Technology Project (grant number 20231A010038), Innovative Clinical Technique of Guangzhou (2024-2026), Guangzhou Research-oriented Hospital. Authors' contributions ZC: Conceptualization, Methodology, Validation, Data Curation, Formal analysis, Writing - Original Draft, Visualization. JS: Investigation, Data Curation, Validation. YC, ZY, XC, ZF, XL: Investigation. CW: Methodology. YN: Conceptualization, Supervision. YZ: Conceptualization, Methodology, Validation, Writing - Review & Editing, Project administration. Acknowledgments All the team members and coauthors for their contributions to this manuscript are sincerely appreciated. References Njenga C, Ramanuj PP, de Magalhães FJC, Pincus HA. New and emerging treatments for major depressive disorder. BMJ. 2024;386:e073823. Hirschfeld RMA, Montgomery SA, Aguglia E, Amore M, Delgado PL, Gastpar M, et al. Partial response and nonresponse to antidepressant therapy: current approaches and treatment options. J Clin Psychiatry. 2002;63(9):826–37. Di Vincenzo JD, Lipsitz O, Rodrigues NB, Lee Y, Gill H, Kratiuk K, et al. Ketamine monotherapy versus adjunctive ketamine in adults with treatment-resistant depression: Results from the Canadian Rapid Treatment Centre of Excellence. J Psychiatr Res. 2021;143:209–14. Aaronson ST, van der Vaart A, Miller T, LaPratt J, Swartz K, Shoultz A, et al. Single-Dose Synthetic Psilocybin With Psychotherapy for Treatment-Resistant Bipolar Type II Major Depressive Episodes: A Nonrandomized Open-Label Trial. JAMA Psychiatry. 2024;81(6):555–62. Lipsitz O, Di Vincenzo JD, Rodrigues NB, Cha DS, Lee Y, Greenberg D, et al. Safety, Tolerability, and Real-World Effectiveness of Intravenous Ketamine in Older Adults With Treatment-Resistant Depression: A Case Series. Am J Geriatr Psychiatry Off J Am Assoc Geriatr Psychiatry. 2021;29(9):899–913. Zolghadriha A, Anjomshoaa A, Jamshidi MR, Taherkhani F. Rapid and sustained antidepressant effects of intravenous ketamine in treatment-resistant major depressive disorder and suicidal ideation: a randomized clinical trial. BMC Psychiatry. 2024;24(1):341. Holley D, Brooks A, Hartz M, Rao S, Zaubler T. mHealth-Augmented Care for Reducing Depression Symptom Severity Among Patients With Chronic Pain: Exploratory, Retrospective Cohort Study. JMIR MHealth UHealth. 2025;13:e52764. Schmidt UH, Claudino A, Fernández-Aranda F, Giel KE, Griffiths J, Hay PJ, et al. The current clinical approach to feeding and eating disorders aimed to increase personalization of management. World Psychiatry Off J World Psychiatr Assoc WPA. 2025;24(1):4–31. Montgomery SA, Asberg M. A new depression scale designed to be sensitive to change. Br J Psychiatry J Ment Sci. 1979;134:382–9. Santen G, Danhof M, Della Pasqua O. Sensitivity of the Montgomery Asberg Depression Rating Scale to response and its consequences for the assessment of efficacy. J Psychiatr Res. 2009;43(12):1049–56. Faries D, Herrera J, Rayamajhi J, DeBrota D, Demitrack M, Potter WZ. The responsiveness of the Hamilton Depression Rating Scale. J Psychiatr Res. 2000;34(1):3–10. Ruhé HG, Dekker JJ, Peen J, Holman R, Jonghe FD. Clinical use of the Hamilton Depression Rating Scale: is increased efficiency possible? A post hoc comparison of Hamilton Depression Rating Scale, Maier and Bech subscales, Clinical Global Impression, and Symptom Checklist-90 scores. Compr Psychiatry. 2005;46(6):417–27. Khan A, Khan SR, Shankles EB, Polissar NL. Relative sensitivity of the Montgomery-Asberg Depression Rating Scale, the Hamilton Depression rating scale and the Clinical Global Impressions rating scale in antidepressant clinical trials. Int Clin Psychopharmacol. 2002;17(6):281–5. Barber J, Resnick SG. Can Measurement-Based Care Reduce Burnout in Mental Health Clinicians? Adm Policy Ment Health. 2024. Trivedi MH, Rush AJ, Ibrahim HM, Carmody TJ, Biggs MM, Suppes T, et al. The Inventory of Depressive Symptomatology, Clinician Rating (IDS-C) and Self-Report (IDS-SR), and the Quick Inventory of Depressive Symptomatology, Clinician Rating (QIDS-C) and Self-Report (QIDS-SR) in public sector patients with mood disorders: a psychometric evaluation. Psychol Med. 2004;34(1):73–82. Yavorsky C, Ballard E, Opler M, Sedway J, Targum SD, Lenderking W. Recommendations for selection and adaptation of rating scales for clinical studies of rapid-acting antidepressants. Front Psychiatry. 2023;14:1135828. McIntyre RS, Rodrigues NB, Lipsitz O, Lee Y, Cha DS, Gill H, et al. Validation of the McIntyre And Rosenblat Rapid Response Scale (MARRRS) in Adults with Treatment-Resistant Depression Receiving Intravenous Ketamine Treatment. J Affect Disord. 2021;288:210–6. Zhou Y. Cognitive Function Mediates the Anti-suicide Effect of Repeated Intravenous Ketamine in Adult Patients With Suicidal Ideation. Front Psychiatry. 2022;13. Brune CS, Toporowski G, Rölfing JD, Gosheger G, Fresen J, Frommer A, et al. German Translation and Cross-Cultural Adaptation of the Limb Deformity-Scoliosis Research Society (LD-SRS) Questionnaire. Healthc Basel Switz. 2022;10(7):1299. Wang XM, Ma HY, Zhong J, Huang XJ, Yang CJ, Sheng DF, et al. A Chinese adaptation of six items, self-report Hamilton Depression Scale: Factor structure and psychometric properties. Asian J Psychiatry. 2022;73:103104. Borentain S, Gogate J, Williamson D, Carmody T, Trivedi M, Jamieson C, et al. Montgomery-Åsberg Depression Rating Scale factors in treatment-resistant depression at onset of treatment: Derivation, replication, and change over time during treatment with esketamine. Int J Methods Psychiatr Res. 2022;31(4):e1927. Rajewska-Rager A, Dmitrzak-Weglarz M, Lepczynska N, Kapelski P, Pawlak J, Szczepankiewicz A, et al. Dimensions of the Hamilton Depression Rating Scale Correlate with Impulsivity and Personality Traits among Youth Patients with Depression. J Clin Med. 2023;12(5):1744. Guo R. What Is the Optimal Cut-Off Point of the 10-Item Center for Epidemiologic Studies Depression Scale for Screening Depression Among Chinese Individuals Aged 45 and Over? An Exploration Using Latent Profile Analysis. Front Psychiatry. 2022;13. Falissard B, Sapin C, Loze JY, Landsberg W, Hansen K. Defining the minimal clinically important difference (MCID) of the Heinrichs–carpenter quality of life scale (QLS). Int J Methods Psychiatr Res. 2015. von Glischinski M, von Brachel R, Thiele C, Hirschfeld G. Not sad enough for a depression trial? A systematic review of depression measures and cut points in clinical trial registrations. J Affect Disord. 2021;292:36–44. Capodilupo G, Blattner R, Must A, Navarro SG, Opler M. A qualitative investigation of the Montgomery-Åsberg depression rating scale: discrepancies in rater perceptions and data trends in remote assessments of rapid-acting antidepressants in treatment resistant depression. Front Psychiatry. 2024;15:1289630. Additional Declarations No competing interests reported. Supplementary Files CMRRRSSupplementaryMaterial.docx Supplmentary Materials Supplmentary Table S1: Cronbach’s alpha values for different subsets of the CMRRRS. Supplmentary Table S2: the factor loadings of the CMRRRS items from baseline and after treatment. Supplmentary Table S3: the factor loadings of the CMRRRS items across different infusions. Supplmentary Table S4: the linear mixed effects model results of the CMRRRS total score across first 3 infusions. Cite Share Download PDF Status: Published Journal Publication published 08 Oct, 2025 Read the published version in BMC Psychiatry → Version 1 posted Editorial decision: Revision requested 22 Jul, 2025 Reviews received at journal 22 Jul, 2025 Reviews received at journal 28 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviews received at journal 25 Jun, 2025 Reviewers agreed at journal 20 Jun, 2025 Reviewers agreed at journal 11 Jun, 2025 Reviewers invited by journal 10 Jun, 2025 Editor assigned by journal 10 Jun, 2025 Editor invited by journal 09 Jun, 2025 Submission checks completed at journal 07 Jun, 2025 First submitted to journal 07 Jun, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6789022","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469825556,"identity":"44d64204-997f-47f4-b1f4-eed09740e9ae","order_by":0,"name":"Ziying Chen","email":"","orcid":"","institution":"The Affiliated Brain Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ziying","middleName":"","lastName":"Chen","suffix":""},{"id":469825557,"identity":"375d3aea-81e1-46b0-8abc-c4f1c9c86908","order_by":1,"name":"Junhao Shen","email":"","orcid":"","institution":"The Affiliated Brain Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junhao","middleName":"","lastName":"Shen","suffix":""},{"id":469825558,"identity":"cc4bba5a-8edc-432f-a1df-e047cdc0d9ff","order_by":2,"name":"Yifang Chen","email":"","orcid":"","institution":"The Affiliated Brain Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yifang","middleName":"","lastName":"Chen","suffix":""},{"id":469825560,"identity":"4d6ee537-45fc-4596-b972-3b56fe99e814","order_by":3,"name":"Zerui You","email":"","orcid":"","institution":"The Affiliated Brain Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zerui","middleName":"","lastName":"You","suffix":""},{"id":469825562,"identity":"6fe864e7-9b8e-40d4-93a3-248588c4c215","order_by":4,"name":"Xiaoyu Chen","email":"","orcid":"","institution":"The Affiliated Brain Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Chen","suffix":""},{"id":469825563,"identity":"daa4878c-6ec4-45f2-a421-75c1da56c715","order_by":5,"name":"Fan Zhang","email":"","orcid":"","institution":"The Affiliated Brain Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Zhang","suffix":""},{"id":469825564,"identity":"07d3ee6f-e1ae-43c4-bce8-9e833aed1c14","order_by":6,"name":"Xiaofeng Lan","email":"","orcid":"","institution":"The Affiliated Brain Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Lan","suffix":""},{"id":469825565,"identity":"5c0eb1f9-f8a9-4d10-a1b8-593b51df77bd","order_by":7,"name":"Chenyu Wang","email":"","orcid":"","institution":"The Affiliated Brain Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chenyu","middleName":"","lastName":"Wang","suffix":""},{"id":469825567,"identity":"f7f98b81-fbf8-4c3f-b591-6b9d0ca4dc1d","order_by":8,"name":"Yuping Ning","email":"","orcid":"","institution":"The Affiliated Brain Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuping","middleName":"","lastName":"Ning","suffix":""},{"id":469825570,"identity":"d8edbf25-03ad-4e72-bb58-9b77f37efeb2","order_by":9,"name":"Yanling Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYDACCRBRACTZGxgPJBCvxQBI8hxgIEkLiJHAcIAoHfKzm49J8xhYJG64+fjBgYdth/MY2A8f3YBPC+OcY2lALRKJG26nGRxIbDtczMCTlnYDnxZmiRwzkBZjg9sJYC2JDRI8Zni1sMG13Dz+gTgtPFAtcgY3eIi0RUIiLdlyDlCL5JmcggMJ59IT2wj5RX5G8sEbbyrqePiOH9/48EeZdWI/++FjeLWAABMPkFA4ACQY2YC+I6QcBBh/gKxrADH/EKN+FIyCUTAKRhoAAGrvS5xOIgJ4AAAAAElFTkSuQmCC","orcid":"","institution":"The Affiliated Brain Hospital of Guangzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yanling","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-05-31 06:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6789022/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6789022/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12888-025-07413-y","type":"published","date":"2025-10-08T15:57:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84531373,"identity":"9533fc2e-fa1c-4143-be1d-193c977fc6fa","added_by":"auto","created_at":"2025-06-13 06:09:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108441,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation test results. A. Spearman’s correlation between MADRS and MRRRS total scores. B. Spearman’s correlation between the change in MADRS and MRRRS total scores.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6789022/v1/ae9b5e2fc658ddf34dd7272c.png"},{"id":84531374,"identity":"70d5ddc3-b843-4c14-b349-db50972a2b79","added_by":"auto","created_at":"2025-06-13 06:09:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":91454,"visible":true,"origin":"","legend":"\u003cp\u003eKruskal-Wallis tests results. A-E. CMRRRS, SSI-I, MADRS, HAMA, SHAPS scores vary by LPA classes. * P \u0026lt; 0.05, ** P \u0026lt; 0.01, *** P \u0026lt; 0.001 indicates statistical significance.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6789022/v1/c70b4e2d14894dd97b233b00.png"},{"id":84531375,"identity":"56d128ac-cc41-4a1a-ab2e-51d62525acfb","added_by":"auto","created_at":"2025-06-13 06:09:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":37980,"visible":true,"origin":"","legend":"\u003cp\u003eKernel Density Estimation curves.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6789022/v1/045382db666ad2ad7a96b870.png"},{"id":93419749,"identity":"1d8b719f-0d75-4e49-8ea7-2d06a660508e","added_by":"auto","created_at":"2025-10-13 16:07:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":793401,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6789022/v1/01f7a601-ca79-4d17-a1b7-7f0c677899f4.pdf"},{"id":84531377,"identity":"613e71c2-6fa9-4704-b5d8-0f8a315763f4","added_by":"auto","created_at":"2025-06-13 06:09:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24188,"visible":true,"origin":"","legend":"\u003cp\u003eSupplmentary Materials\u003c/p\u003e\n\u003cp\u003eSupplmentary Table S1: Cronbach’s alpha values for different subsets of the CMRRRS.\u003c/p\u003e\n\u003cp\u003eSupplmentary Table S2: the factor loadings of the CMRRRS items from baseline and after treatment.\u003c/p\u003e\n\u003cp\u003eSupplmentary Table S3: the factor loadings of the CMRRRS items across different infusions.\u003c/p\u003e\n\u003cp\u003eSupplmentary Table S4: the linear mixed effects model results of the CMRRRS total score across first 3 infusions.\u003c/p\u003e","description":"","filename":"CMRRRSSupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6789022/v1/8b62dc758912e061e4e012f5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Validation and Clinical Application of the Chinese Version of Rapid Response Scale","fulltext":[{"header":"Background","content":"\u003cp\u003eMajor Depressive Disorder (MDD) is one of the most common and disabling mental illnesses and is considered one of the leading causes of disability worldwide, according to the World Health Organization (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Traditional antidepressants, such as selective serotonin reuptake inhibitors (SSRIs), are currently the mainstream treatment for MDD. However, their onset of action is typically delayed. Given that the impact of MDD is becoming a significant contributor to global healthcare and socioeconomic burdens, the anticipations for MDD treatment are evolving (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)). There has been an increasing demand for treatment that can respond rapidly in order to alleviate depressive symptoms effectively and reduce suicidal impulses. In response to this demand, rapid response depressive disorder treatments, such as ketamine (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), psilocybin (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), transcranial magnetic stimulation (TMS) are being developed, within which ketamine is showing promising outcomes (5; 6).\u003c/p\u003e \u003cp\u003eMeasurement-Based Care (MBC) is a patient-centered, dynamic treatment model that uses sensitive assessment tools to monitor symptom changes in real-time, optimize treatment strategies, and enhance therapeutic outcomes. The goal of MBC is to support clinical decision-making with quantitative data, enabling clinicians to adjust interventions promptly and achieve personalized treatment optimization. Studies have shown that MBC significantly improves patient adherence, therapeutic efficacy, and satisfaction compared to traditional care methods (7; 8).\u003c/p\u003e \u003cp\u003eHowever, there is a lack of suitable assessment tools for the rapid-acting antidepressant effects during the treatment. Commonly used tools, for example, the Montgomery-Asberg Depression Rating Scale (MADRS)(9; 10) and 17 Items Hamilton Depression Rating Scale (HAMD-17) (11; 12) have been validated as reliable tools for describing depressive symptoms comprehensively. However, these tools were designed for traditional antidepressants that typically require 4 to 8 weeks to show significant efficacy. Fast-acting treatments often show significant efficacy within hours to days after administration, therefore, existing tools may fail to fully capture the main symptom changes in patients undergoing such treatments (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The limitations of assessment tools can hinder accurate evaluation of treatment outcomes, impeding clinicians' ability to optimize strategies based on real-time symptom changes. Therefore, developing specialized assessment tools tailored for fast-acting antidepressant therapies is crucial for the broader adoption of the MBC model and the optimization of depression treatment.\u003c/p\u003e \u003cp\u003eBesides, existing scales require time-consuming evaluations performed by professional clinical workers, which are imposing a burden on clinical settings, especially during rapid-acting antidepressant treatments which anticipate constant evaluation for monitoring and guiding. Quick self-assessment tools can reduce healthcare professionals' workload while effectively detecting short-term changes in depressive symptoms(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany studies determined to improve those classic rating scale and questionnaire by changing it to self-report in order to relieve clinical burden and by shaping them into different form so that they are able to describe emotional features more accurately (15; 16). Among those research, the McIntyre team developed the McIntyre And Rosenblat Rapid Response Scale (MARRRS) based on the Questionnaire 16 Self-Rating Scale (Q16-SRS), which adhere to MBC standards, improving the scale's ability to detect changes in depressive symptoms over shorter period (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). It shows promise by aiding in better capturing patient symptoms’ character for treatment guidance and enabling faster, more precise evaluations of patients' depressive symptom changes in research to support further technological development and taking off burdens causing by redundant evaluation for clinical workers.\u003c/p\u003e \u003cp\u003eWhile some studies have been conducted on the MARRRS development, neither have they been translated into different languages nor have they been empirically tested on diverse populations. The research on the clinical application and significance of MARRRS also remains limited. There have been an increasing numbers of studies and treatment projects of fast acting antidepressant treatment conducted in China, the demand for a suitable tool is becoming urgent. This article aims to translate MARRRS, which originally developed by Roger S. McIntyre’s team (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), into its Chinese version and further explore the reliability and validity of the Chinese version of MARRRS (CMRRRS) during esketamine treatment, while examining its sensitivity to grasp rapid shifts in patients’ depressive symptoms. In addition, we also intend to fill the gap in the practical application of CMRRRS in clinical settings, including symptom severity grading and the determination of the minimum clinically important difference (MCID), and exploring its value in predicting patient treatment response.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eRecruitment\u003c/p\u003e\u003cp\u003eParticipants were recruited from The Affiliated Brain Hospital, Guangzhou Medical University, where they completed the entire treatment process and clinical evaluations.\u003c/p\u003e\u003cp\u003eTranslation of the MARRRS\u003c/p\u003e\u003cp\u003eThe permission of the developer of the MARRRS was obtained via email. Subsequently, the English version of MARRRS (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) was translated into Chinese and adapted to Chinese cultural and social differences by 2 independent Chinese psychiatrist specialists. After, 2 Chinese researcher who have a sophisticated command of English translated the CMRRRS back to English, performed a linguistic validation process and reported potential linguistic and understanding difficulties. Finally, after a consensus meeting, 2 experienced clinicians proofread the CMRRRS, and confirmed its adaptation to Chinese culture and did not reveal any grammatical mistakes. Whatever discrepancies in terms of different meanings of the items occurred, the translation was revised. After this final approval, the CMRRRS was accomplished.\u003c/p\u003e\u003cp\u003eData resource and screening criteria\u003c/p\u003e\u003cp\u003eOur studies were designed to observe real world patients’ responses to esketamine treatment. This treatment aimed to provide options for individuals who suffered from treatment resistant depression of have high suicidal tendencies. This treatment belongs to medication beyond the instruction manual. Therefore, all enrolled adult patients are diagnosed of MDD supported by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria. In addition, they would either have previously completed at least two full courses of antidepressant therapy and were identified as having a poor response to these treatments, or display moderate to severe levels of depression or moderate to high suicidal tendencies, as indicated by Hamilton Depression Rating Scale score of ≥ 17 or Beck Scale for Suicide Ideation Part I (SSI-I) score of ≥ 2 during the initial screening. Patients with any serious or unstable physical illnesses identified through examination and those with alcohol or substance abuse problems were excluded from participating this treatment. Individuals taking psychiatric medication had to continue using the same medications consistently throughout the ketamine treatment process. Prior to joining the treatment program, all participants gave written informed consent to participate in the study. Additional detailed information on the project can be referenced in our previous research (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTreatment Process\u003c/p\u003e\u003cp\u003eAt baseline, patient personal data such as age, gender, height and weight were gathered. Following an overnight fast, patients received esketamine intravenously through a syringe pump at a dosage of 0.25 mg/kg, with the infusion lasting over 40 minutes. Treatment responders, defined as patients with a ≥ 50% reduction in the MADRS total score from baseline or a MADRS total score ≤ 10, could complete the program after the third treatment and were advised to continue maintenance therapy. Non-responders, defined as those with a ≤ 20% reduction in MADRS total score from baseline, could terminate treatment after the fourth or fifth infusion. All patients were eligible to receive up to six infusions, administered every other day on Days 1, 3, 5, 8, 10, and 12, depending on their response to treatment. Vital signs (blood pressure, pulse, and oxygen saturation) were monitored throughout the infusion and post-infusion to ensure a return to pre-infusion levels.\u003c/p\u003e\u003cp\u003eAssessment\u003c/p\u003e\u003cp\u003eWithin two hours after each infusion, patients were asked to completed self-assessments using the CMRRRS and SSI-I to monitor symptom changes. Additionally, trained professionals conducted comprehensive evaluations within 24 hours after the third infusion (infusion 3-24h) and again within 24 hours after completing or terminating the treatment (post-treatment). These evaluations included the MADRS, Hamilton Anxiety Rating Scale (HAMA), and the Snaith-Hamilton Pleasure Scale (SHAPS). Patients were also required to repeat self-assessments using the CMRRRS and SSI-I during these evaluations.\u003c/p\u003e\u003cp\u003eAnalysis\u003c/p\u003e\u003cp\u003eIBM Statistical Package for the Social Sciences (SPSS) version 26.0 was employed to perform reliability and validation analyses. Cronbach’s alpha was calculated to determine the consistency of the CMRRRS. Then, a Spearman’s correlation was also conducted between MADRS and the CMRRRS total scores from pre-treatment, infusion 3, and post-treatment to assess validation in the CMRRRS. The change in score from pre-treatment, infusion 3, and post-treatment was also correlated between the MADRS and the CMRRRS utilizing Spearman’s correlation. An exploratory factor analysis of the 14 CMRRRS items was performed. Items were subjected to principal component analysis (PCA) with varimax rotation. An eigenvalue threshold of 1.0 or higher was applied to determine number of extracted factors. In addition, we utilized a linear mixed effects model to test the ability of the CMRRRS to detect main symptomatic fluctuations over short period using data across first 3 infusions due to treatment may end at infusion 4 to 6. Age, sex, BMI and baseline depression severity measure by MADRS were controlled for in the model. A compound symmetry covariance matrix was utilized, and the data was fit using Restricted Maximum Likelihood with the α-value set at 0.05.\u003c/p\u003e\u003cp\u003eThen, we utilized Latent Profile Analysis (LPA) to identify latent categorical structures within the data and classifies individuals into distinct latent groups based on their performance across multiple variables. The analysis was conducted using the LPA tool in Mplus, with the maximum number of categories set to five. Model fit was evaluated using the Bayesian Information Criterion (BIC), Akaike Information Criterion (AIC), Lo-Mendell-Rubin Likelihood Ratio Test (LMRT) and Bootstrapped Likelihood Ratio Test (BLRT) to determine the optimal number of categories.\u003c/p\u003e\u003cp\u003eTo validate the significance of differences among the classes identified by LPA, the Kruskal-Wallis test was performed using SPSS 26.0 after. Specific analysis metrics included scores on the CMRRRS, SSI-I, MADRS, HAMA, and SHAPS. To further explore the distribution characteristics of each latent group, Kernel Density Estimation (KDE) was employed. Using the \"density\" function in R software, kernel density curves were plotted, and the intersection points between curves were calculated to analyze the overlap and distinctions among the different latent groups.\u003c/p\u003e\u003cp\u003eAfter, MCID is used to assess the clinical significance of treatment intervention effects. In this study, MCID was calculated using both anchor-based and distribution-based methods in SPSS 26.0. CMRRRS score change was used as a variable, and the mean score change for each group was calculated and used as the estimated MCID value according to anchor-based methods. MCID was also defined based on 0.5 times the standard deviation of CMRRRS score change according to distribution-based methods. The MCID results obtained from the anchor-based and distribution-based methods were compared. Finally, cross-tabulation was used to assess the relationship between whether the CMRRRS change larger or smaller than MCID during first infusion and the treatment response outcomes.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDemographics\u003c/p\u003e \u003cp\u003eTo validate the CMRRRS, a sample of 71 patients completed the CMRRRS and MADRS assessments between November 2021 and April 2024. Among them, 9 patients had comorbid spectrum disorders, including 3 with generalized anxiety disorder, 4 with obsessive-compulsive disorder, 1 with attention-deficit/hyperactivity disorder, and 1 with other condition. Patient demographics and the outcomes of all scales are described in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;30.7 (SD\u0026thinsp;=\u0026thinsp;11.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;37 (52.1% male)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;23.8 (SD\u0026thinsp;=\u0026thinsp;4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary diagnosis (MDD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;71 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;9 (12.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMARRRS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;28.6 (SD\u0026thinsp;=\u0026thinsp;5.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-infusion 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;24.9 (SD\u0026thinsp;=\u0026thinsp;7.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-infusion 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;21.5 (SD\u0026thinsp;=\u0026thinsp;7.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-infusion 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;20.0 (SD\u0026thinsp;=\u0026thinsp;8.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-infusion 3-24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;23.2 (SD\u0026thinsp;=\u0026thinsp;9.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-infusion 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;23.0 (SD\u0026thinsp;=\u0026thinsp;9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-infusion 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;22.1 (SD\u0026thinsp;=\u0026thinsp;8.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-infusion 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;20.1 (SD\u0026thinsp;=\u0026thinsp;8.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;17.6 (SD\u0026thinsp;=\u0026thinsp;9.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSI-I score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;11.1 (SD\u0026thinsp;=\u0026thinsp;3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-infusion 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;9.1 (SD\u0026thinsp;=\u0026thinsp;3.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-infusion 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;8.7 (SD\u0026thinsp;=\u0026thinsp;3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-infusion 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;8.5 (SD\u0026thinsp;=\u0026thinsp;3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-infusion 3-24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;9.2 (SD\u0026thinsp;=\u0026thinsp;3.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-infusion 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;9.3 (SD\u0026thinsp;=\u0026thinsp;3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-infusion 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;9.0 (SD\u0026thinsp;=\u0026thinsp;3.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-infusion 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;9.1 (SD\u0026thinsp;=\u0026thinsp;4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;7.9 (SD\u0026thinsp;=\u0026thinsp;3.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMADRS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;30.3 (SD\u0026thinsp;=\u0026thinsp;5.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-infusion 3-24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;23.2 (SD\u0026thinsp;=\u0026thinsp;7.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;18.5 (SD\u0026thinsp;=\u0026thinsp;8.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAMA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;22.2 (SD\u0026thinsp;=\u0026thinsp;9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-infusion 3-24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;18.5 (SD\u0026thinsp;=\u0026thinsp;7.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;15.3 (SD\u0026thinsp;=\u0026thinsp;9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHAPS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;36.1 (SD\u0026thinsp;=\u0026thinsp;9.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;19.8.1 (SD\u0026thinsp;=\u0026thinsp;9.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;mean, N\u0026thinsp;=\u0026thinsp;case numbers, SD\u0026thinsp;=\u0026thinsp;standard deviation.\u003c/p\u003e \u003cp\u003eConsistency and Validity\u003c/p\u003e \u003cp\u003eThe CMRRRS exhibited robust internal consistency across infusions, as determined by Cronbach\u0026rsquo;s alpha (all: α\u0026thinsp;=\u0026thinsp;0.93, baseline and post-treatment: α\u0026thinsp;=\u0026thinsp;0.94, across all infusions: α\u0026thinsp;=\u0026thinsp;0.93). (Details about Cronbach\u0026rsquo;s alpha values for different subsets see Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Correlational analysis indicated that there was convergent validity between the MADRS and the CMRRRS total scores (r\u0026thinsp;=\u0026thinsp;0.77, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A.). Moreover, the change in MADRS total score was highly correlated with change in the CMRRRS scores (r\u0026thinsp;=\u0026thinsp;0.67, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. B.).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFactor analysis\u003c/p\u003e \u003cp\u003eThe Kaiser-Meyer-Olkin measure of sampling adequacy (0.9) indicated that the sample was adequate for factor-analytical modelling. Bartlett\u0026rsquo;s test of sphericity was highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Results from the PCA of the CMRRRS are reported. Two factors were identified from data of baseline and after treatment: vigor and distress (details about 2 factors loading see supplementary Table S2). The vigor factor included ten items: feeling happy, concentration, general interest, motivation, pleasure and enjoyment, energy, social isolation, signs of positive emotion, feelings of restlessness, and worry. The distress factor consisted of four items: feeling sad, self-perception, suicidal thoughts, feelings of hopefulness and hopelessness. Both factors combined accounted for 63.0% of the total variance. Three factors were identified from data across infusions: psychic anxiety, vigor, and distress (details about 3 factors loading see supplementary Table S3). The vigor factor included six items: feeling happy, general interest, motivation, pleasure and enjoyment, social isolation and signs of positive emotion. The psychic anxiety factor consisted of four items: concentration, energy, feelings of restlessness, worry. The distress factor consisted of the same four items. All factors combined accounted for 68.2% of the total variance.\u003c/p\u003e \u003cp\u003eSample size\u003c/p\u003e \u003cp\u003eThe data from 71 participants, who completed a total of 368 responses to a 14-item scale at different time points during treatment, are sufficient to support the validation of the scale's reliability, validity, and sensitivity. According to the recommended guidelines for scale validation, which suggest a sample size of 5\u0026ndash;10 times the number of items on the scale, the sample size in this study is adequate. Cronbach\u0026rsquo;s α ranged from 0.93 to 0.94, indicating excellent internal consistency. Additionally, factor analysis, including Kaiser-Meyer-Olkin measure and Bartlett\u0026rsquo;s test of sphericity, showed good fit indices, further confirming the scale\u0026rsquo;s structural validity. Therefore, the results of this study indicate that the sample size meets the necessary requirements for reliability and validity analysis, providing sufficient statistical power to support the conclusions.\u003c/p\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003cp\u003eThere was a main effect of the first 3 infusions on the CMRRRS total score: F (1, 42.0)\u0026thinsp;=\u0026thinsp;730.775, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, indicating sensitivity to change over time in response to ketamine infusions (details about the results of linear mixed effects model see Supplementary Table S4). Corrected pairwise comparison indicated a significant reduction from baseline to post-infusions 1 (p\u0026thinsp;=\u0026thinsp;0.003), 2 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 3 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There was a significant reduction between post-infusion 1 and post-infusions 2 (p\u0026thinsp;=\u0026thinsp;0.001) and 3 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eClassification\u003c/p\u003e \u003cp\u003eLPA classification performance of models was evaluated based on model fit indices, including BIC, AIC, adjusted BIC (aBIC), LMRT and BLRT (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results showed that as the number of classes increased, the AIC, BIC, and adjusted BIC (aBIC) values all decreased, while the differences between 2 and 3 classes are the largest. 2 class and 3 class models showed statistically significant p-values for LMRT. All models showed statistically significant p-values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for BLRT tests. Additionally, all models had an entropy value larger than 0.8, which indicating strong classification accuracy. Considering these findings, the 3-class model was determined to be the optimal solution. For clarity, we name these classes as group 1, group 2 and group 3.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLatent Profile Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLMRT\u003c/p\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBLRT\u003c/p\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEntropy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11142.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11311.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11174.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10748.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10975.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10791.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.028 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10530.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10815.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10584.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10330.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10674.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10395.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 indicates statistical significance.\u003c/p\u003e \u003cp\u003eCategory validation and group characters\u003c/p\u003e \u003cp\u003eTo validate the rationality of the LPA-based classification and identify the characteristics of the three LPA classifications, Kruskal-Wallis tests were conducted on the scores of the MARRRS, MADRS, SSI-I, HAMA, and SHAPS scales. The results of Kruskal-Wallis tests for various scales demonstrate significant differences among 3 classes. The MRRS scale yielded an H(K) value of 314.252 with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001, while the MADRS and SSI-I scales had H(K) values of 80.788 and 113.001, respectively, both with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Similarly, the HAMA scale showed an H(K) value of 32.459 with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and the SHAPS scale had an H(K) value of 12.016 with a p-value of 0.002. The scores from the five psychological assessment scales reveals clear hierarchical differences among classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThreshold positions\u003c/p\u003e \u003cp\u003eKernel Density Estimation (KDE) was employed to further explore the distribution of total scores within each class. The intersection point between group 1 and group 2 is approximately 16.9, and the intersection point between group 2 and group 3 is approximately 27.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMinimum clinically important difference\u003c/p\u003e \u003cp\u003eMCID values were calculated and estimated using both distribution-based and anchor-based methods. The distribution-based method, relying on the standard deviation of the change scores (7.28), determined the MCID to be 3.64. The anchor-based method calculated the mean differences between groups with pairwise comparisons. The mean difference between Group 1 and Group 2 was 9.00, with a 95% confidence interval (CI) of 7.58 to 10.42. Between Group 1 and Group 3, the mean difference was 13.26 (95% CI: 11.78 to 14.73), while the mean difference between Group 2 and Group 3 was 4.26 (95% CI: 2.96 to 5.56).\u003c/p\u003e \u003cp\u003eSmaller MCID candidate values were considered to capture slight but clinically meaningful improvements. Therefore, this study combined the results of the distribution-based and anchor-based methods and selected 4, 5, and 6 as candidate MCID values. Based on whether the change from baseline after the first treatment exceeded the MCID value, and then compared it to post-treatment response outcomes utilizing cross-tabulation, chi-square tests, and consistency analysis (Kappa measurement) to evaluate the classification performance of different MCID values, details can be seen at Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance of different MCID values in classifying response status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKappa\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003ea: Non-responders correctly classified (%), b: Non-responders misclassified (%), c: Responders misclassified (%), d: Responders correctly classified (%)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e** P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 indicates statistical significance.\u003c/p\u003e \u003cp\u003eKappa values: \u0026lt;0.2\u0026thinsp;=\u0026thinsp;poor agreement, 0.2\u0026ndash;0.4\u0026thinsp;=\u0026thinsp;fair agreement, \u0026gt;\u0026thinsp;0.4\u0026thinsp;=\u0026thinsp;moderate agreement.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to systematically explore the validation and clinical application of the CMRRRS scale based on the rapid antidepressant effects of esketamine. The reliability of the CMRRRS remained consistently high before and after treatment, as well as during the treatment process. We are confident in the preliminary translation, proofreading and back translation work of the CMRRRS, thus affirming its sound content validity (19; 20). Additionally, the MADRS had been utilized to assess depressive symptoms worldwide, affirming its accuracy and efficacy, therefore correlation analyses results comparing the MADRS and the CMRRRS suggest that the CMRRRS effectively reflects the level of depressive symptoms in patients, demonstrating confirming criterion validity.\u003c/p\u003e \u003cp\u003eAs for structural validity, individuals undergoing treatment can be classified into two dimensions or three dimensions depending on the treatment progression. There are slight discrepancies in the results from factor analysis between before and after treatment data and the separate analysis of infusions data, considering unique drug related anxiety symptoms emerging from the ketamine treatment. Considering several studies have identified different possible subscales and symptom clusters appeared in patients with MDD depending on the severity, chronicity, treatment resistance of the sample, time of assessment relative to treatment course (21; 22), it is understandable that our CMRRRS\u0026rsquo;s symptom clusters are different from McIntyre\u0026rsquo;s research. Whether it is two or three dimensions, the combined factors explain over 60% of the questionnaire's variability.\u003c/p\u003e \u003cp\u003eFurthermore, the CMRRRS shows sensitivity to changes in depressive symptoms according to its linear mixed effects model results. The results demonstrate that the CMRRRS scale exhibits significant sensitivity and practicality in ketamine treatment. It can reflect symptom improvements within hours after treatment, providing a reliable tool for real-time monitoring of therapeutic effects. This dynamic assessment capability aligns with the principles of MBC, offering critical guidance for treatment decisions and enabling clinicians to adjust interventions based on real-time patient symptoms.\u003c/p\u003e \u003cp\u003eThis study also analyzed the distribution characteristics and classification thresholds of CMRRRS scores. The LPA results showed that as the number of classes increased, the AIC, BIC, and adjusted BIC (aBIC) values all decreased, indicating a progressive improvement in model fit. However, when the number of classes exceeded three, the p-value of the LMRT rose above 0.05, suggesting that adding additional classes did not significantly enhance model fit. Furthermore, the largest changes in model fit indices occurred between the 2-class and 3-class models, and the classification accuracy (E index) of the three-class model was 0.893, indicating high classification quality. Considering these findings, the 3-class model was determined to be the optimal solution. Thus, the optimal number of latent classes was determined to be three (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe result from Kruskal-Wallis test revealed clear hierarchical differences among the 3 groups with the result that statistically significant differences across all scales. In addition, the varying degrees of depression, anxiety, suicidal ideation, and anhedonia across different patient groups helped us understand the main character of the classification. Based on the performance across the five scales, the three groups can be named as follows mild, moderate and severe group to reflect the gradient changes in the severity of psychological symptoms.\u003c/p\u003e \u003cp\u003eThe KDE curves for the three groups each presented relatively distinct peaks, with clear intersection points between groups. This indicates that the classification results obtained through LPA were reasonable, with significant differentiation in symptom severity among the groups. Additionally, the x-coordinate values of the curve intersection points represented the threshold positions between the score distributions of different groups. The intersection points x-coordinate values suggest that a score of 17 and 28 could serve as the threshold. They can be utilized to categorize the severity levels of symptoms and facilitate more precise symptom assessment and treatment decision-making, providing an essential foundation for the development of personalized treatment strategies.\u003c/p\u003e \u003cp\u003eMCID is an essential metric for determining the clinical significance of treatment interventions(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Based on the results of distribution-based and anchor-based methods, 4, 5, and 6 were selected as candidate MCID values. The applicability of MCID was validated by assessing the concordance between classification results and patient response outcomes. When the MCID was set to 5, the classification demonstrated optimal sensitivity and specificity (Kappa\u0026thinsp;=\u0026thinsp;0.389), supporting the selection of 5 as the MCID for the CMRRRS scale. It could effectively capture the slight but clinically significant symptom improvements with strong statistical significance (P\u0026thinsp;=\u0026thinsp;0.008).\u003c/p\u003e \u003cp\u003eBy optimizing the MCID, this study further refined the clinical application standards of the CMRRRS scale in fast-acting antidepressant therapies, enabling it to more effectively capture thresholds for meaningful symptom improvement. This optimization is crucial for the preliminary evaluation of the efficacy of fast-acting antidepressant therapies and provides a scientific basis for subsequent treatment decisions. Moreover, by matching MCID with patient response outcomes, its application establishes a foundation for the standardized evaluation of treatment results at the beginning of treatment. For instance, the MCID can be used to identify patients with insufficient treatment response, guiding dose adjustments or the selection of adjunct therapies.\u003c/p\u003e \u003cp\u003eThe findings above further validate the scientific basis of the CMRRRS scale for symptom identification and classification, providing data support for the standardization of assessment tools. They give clinicians a new perspective for determining the patient's condition more accurately by determining the severity level of a patient\u0026rsquo;s symptoms. They also offer a foundation for optimizing personalized treatment, enabling the formulation of more targeted treatment strategies (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe strength of this study lies in its design, which better reflects patient responses to ketamine treatment in the real world. Additionally, the treatment followed standardized procedures, and all post-treatment assessments were conducted by experienced clinicians, ensuring the authenticity and effectiveness of both the treatment process and the evaluations. The results also support data previously collected by the McIntyre team from various treatment centers in Canada, highlighting the scale's consistency across different populations.\u003c/p\u003e \u003cp\u003eAlthough this study provides preliminary evidence for the application of the CMRRRS scale, several limitations remain. First, the results were obtained from a single arm open label study without a control arm. This may introduce expectation bias both to assessments from the clinician and patients(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Second, sample is relatively small and primarily based on data from a single center, which may limit the generalizability of the results, particularly in subgroup analyses and assessments of the effects of varying patient characteristics. Lastly, the study's timeframe was relatively short, focusing mainly on the evaluation of rapid treatment effects, and did not include long-term follow-up data. Whether the short-term improvements from fast-acting therapies are sustainable and whether the MCID remains applicable during long-term treatment and maintenance phases require further validation.\u003c/p\u003e \u003cp\u003eFuture research should expand the sample size and adopt a multicenter study design to include more patients with diverse demographic, cultural, and clinical characteristics. Future research could also explore other rapidly acting antidepressant therapies, such as mirtazapine and electroconvulsive therapy, to validate the generalizability of the CMRRRS scale. Such research would provide comprehensive guidance for the full-cycle management of fast-acting antidepressants and support the broader application of the CMRRRS scale across different treatment stages.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, the CMRRRS is among the first scales validated in Chinese patients and demonstrates strong reliability in reflecting changes in depressive symptoms during rapid antidepressant treatment procedures. It provides a reliable basis for real-time efficacy assessment and individualized treatment optimization. This study also proposed classification criteria based on LPA and KDE and determined 5 as the most appropriate MCID using a combination of distribution-based and anchor-based methods, offering substantial advancements to treatment guidance and the creation of novel rapid antidepressant therapies.Future research should focus on expanding the sample size, conducting multicenter studies, exploring other fast acting antidepression treatments, and incorporating long-term follow-up to further validate its applicability, thereby promoting the clinical use of rapid-acting antidepressant therapies and the development of novel antidepressant drugs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eaBIC\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdjusted\u0026nbsp;Bayesian Information Criterion\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAIC\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAkaike Information Criterion\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBIC\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBayesian Information Criterion\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBLRT\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBootstrapped Likelihood Ratio Test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConfidence interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCMRRRS\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChinese version of the McIntyre and Rosenblat Rapid Response Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDSM-5\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDiagnostic and Statistical Manual of Mental Disorders\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHAMA\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHamilton Anxiety Rating Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHAMD-17\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e17 Items Hamilton Depression Rating Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKDE\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKernel Density Estimation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLMRT\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLo-Mendell-Rubin Likelihood Ratio Test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLPA\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLatent Profile Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMADRS\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMontgomery-Asberg Depression Rating Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMARRRS\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMcIntyre and Rosenblat Rapid Response Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMBC\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMeasurement-Based Care\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMCID\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMinimum clinically important difference\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMDD\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMajor Depressive Disorder\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCA\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrincipal component analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ16-SRS\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQuestionnaire 16 Self-Rating Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSHAPS\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSnaith-Hamilton Pleasure Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSPSS\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSSI-I\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBeck Scale for Suicide Ideation Part I\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eParticipants were recruited from The Affiliated Brain Hospital, Guangzhou Medical University, where they completed the entire treatment process and clinical evaluations. This study was approved by the Clinical Research Ethics Committee of The Affiliated Brain Hospital, Guangzhou Medical University (2021073). Prior to joining the treatment program, all participants gave written informed consent to participate in the study.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no competing interest.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (grant number 82471546), Guangzhou Health Science and Technology Project (grant number 20231A010038), Innovative Clinical Technique of Guangzhou (2024-2026), Guangzhou Research-oriented Hospital.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors' contributions\u003c/p\u003e\n\u003cp\u003eZC: Conceptualization, Methodology, Validation, Data Curation, Formal analysis, Writing - Original Draft, Visualization.\u003c/p\u003e\n\u003cp\u003eJS: Investigation, Data Curation, Validation.\u003c/p\u003e\n\u003cp\u003eYC, ZY, XC, ZF, XL: Investigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCW: Methodology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYN: Conceptualization, Supervision.\u003c/p\u003e\n\u003cp\u003eYZ: Conceptualization, Methodology, Validation, Writing - Review \u0026amp; Editing, Project administration.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eAll the team members and coauthors for their contributions to this manuscript are sincerely appreciated.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNjenga C, Ramanuj PP, de Magalh\u0026atilde;es FJC, Pincus HA. 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BMC Psychiatry. 2024;24(1):341.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolley D, Brooks A, Hartz M, Rao S, Zaubler T. mHealth-Augmented Care for Reducing Depression Symptom Severity Among Patients With Chronic Pain: Exploratory, Retrospective Cohort Study. JMIR MHealth UHealth. 2025;13:e52764.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmidt UH, Claudino A, Fern\u0026aacute;ndez-Aranda F, Giel KE, Griffiths J, Hay PJ, et al. The current clinical approach to feeding and eating disorders aimed to increase personalization of management. World Psychiatry Off J World Psychiatr Assoc WPA. 2025;24(1):4\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontgomery SA, Asberg M. A new depression scale designed to be sensitive to change. Br J Psychiatry J Ment Sci. 1979;134:382\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanten G, Danhof M, Della Pasqua O. 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Psychol Med. 2004;34(1):73\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYavorsky C, Ballard E, Opler M, Sedway J, Targum SD, Lenderking W. Recommendations for selection and adaptation of rating scales for clinical studies of rapid-acting antidepressants. Front Psychiatry. 2023;14:1135828.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcIntyre RS, Rodrigues NB, Lipsitz O, Lee Y, Cha DS, Gill H, et al. Validation of the McIntyre And Rosenblat Rapid Response Scale (MARRRS) in Adults with Treatment-Resistant Depression Receiving Intravenous Ketamine Treatment. J Affect Disord. 2021;288:210\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Y. Cognitive Function Mediates the Anti-suicide Effect of Repeated Intravenous Ketamine in Adult Patients With Suicidal Ideation. Front Psychiatry. 2022;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrune CS, Toporowski G, R\u0026ouml;lfing JD, Gosheger G, Fresen J, Frommer A, et al. German Translation and Cross-Cultural Adaptation of the Limb Deformity-Scoliosis Research Society (LD-SRS) Questionnaire. Healthc Basel Switz. 2022;10(7):1299.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang XM, Ma HY, Zhong J, Huang XJ, Yang CJ, Sheng DF, et al. A Chinese adaptation of six items, self-report Hamilton Depression Scale: Factor structure and psychometric properties. Asian J Psychiatry. 2022;73:103104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorentain S, Gogate J, Williamson D, Carmody T, Trivedi M, Jamieson C, et al. Montgomery-\u0026Aring;sberg Depression Rating Scale factors in treatment-resistant depression at onset of treatment: Derivation, replication, and change over time during treatment with esketamine. Int J Methods Psychiatr Res. 2022;31(4):e1927.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajewska-Rager A, Dmitrzak-Weglarz M, Lepczynska N, Kapelski P, Pawlak J, Szczepankiewicz A, et al. Dimensions of the Hamilton Depression Rating Scale Correlate with Impulsivity and Personality Traits among Youth Patients with Depression. J Clin Med. 2023;12(5):1744.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo R. What Is the Optimal Cut-Off Point of the 10-Item Center for Epidemiologic Studies Depression Scale for Screening Depression Among Chinese Individuals Aged 45 and Over? An Exploration Using Latent Profile Analysis. Front Psychiatry. 2022;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFalissard B, Sapin C, Loze JY, Landsberg W, Hansen K. Defining the minimal clinically important difference (MCID) of the Heinrichs\u0026ndash;carpenter quality of life scale (QLS). Int J Methods Psychiatr Res. 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evon Glischinski M, von Brachel R, Thiele C, Hirschfeld G. Not sad enough for a depression trial? A systematic review of depression measures and cut points in clinical trial registrations. J Affect Disord. 2021;292:36\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCapodilupo G, Blattner R, Must A, Navarro SG, Opler M. A qualitative investigation of the Montgomery-\u0026Aring;sberg depression rating scale: discrepancies in rater perceptions and data trends in remote assessments of rapid-acting antidepressants in treatment resistant depression. Front Psychiatry. 2024;15:1289630.\u003c/span\u003e\u003c/li\u003e\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-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rapid Response Scale, Scale Validation, Clinical Application, Measurement-Based Care, Rapid-Acting Antidepressants, Esketamine","lastPublishedDoi":"10.21203/rs.3.rs-6789022/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6789022/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCurrent assessment tools inadequately capture rapid symptom changes. This study aims to translate, validate and explore clinical application of the Chinese version of the McIntyre and Rosenblat Rapid Response Scale (CMRRRS) for rapid onset antidepression treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe McIntyre and Rosenblat Rapid Response Scale (MARRRS) was translated and culturally adapted. 71 MDD patients undergoing esketamine treatment were assessed utilizing CMRRRS and other validated scales. Reliability, validity, and sensitivity were evaluated through Cronbach’s alpha, correlation with established scales, exploratory factor analysis and mixed-effects modeling. Latent Profile Analysis and Kernel Density Estimation curves were utilized for classification. Minimum Clinically Important Difference was determined to explore minimum change that related to treatment response evaluation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CMRRRS showed high reliability and robust validity. Factor analysis result explaining over 60% of variance. Latent Profile Analysis revealed three classes with distinct thresholds determined by Kernel Density Estimation. 5 points was optimal for detecting minimum clinically meaningful changes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CMRRRS is a reliable, valid, and sensitive tool for tracking rapid symptom changes in MDD patients treated with esketamine. It supports real-time symptom monitoring and personalized treatment adjustments. Further studies are recommended to explore its broader applicability.\u003c/p\u003e","manuscriptTitle":"Validation and Clinical Application of the Chinese Version of Rapid Response Scale","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-13 06:09:28","doi":"10.21203/rs.3.rs-6789022/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-22T15:50:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T13:08:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-29T03:48:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50714410694730475108999464287797628555","date":"2025-06-26T02:32:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-25T08:28:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243794334088666324110300041853358867005","date":"2025-06-20T09:55:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135656928126510681937347036022147800120","date":"2025-06-11T06:54:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-10T19:06:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-10T19:00:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-09T17:26:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-07T14:56:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2025-06-07T14:53:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b802e5c-053a-4907-b1db-a564d8a525e2","owner":[],"postedDate":"June 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:01:53+00:00","versionOfRecord":{"articleIdentity":"rs-6789022","link":"https://doi.org/10.1186/s12888-025-07413-y","journal":{"identity":"bmc-psychiatry","isVorOnly":false,"title":"BMC Psychiatry"},"publishedOn":"2025-10-08 15:57:54","publishedOnDateReadable":"October 8th, 2025"},"versionCreatedAt":"2025-06-13 06:09:28","video":"","vorDoi":"10.1186/s12888-025-07413-y","vorDoiUrl":"https://doi.org/10.1186/s12888-025-07413-y","workflowStages":[]},"version":"v1","identity":"rs-6789022","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6789022","identity":"rs-6789022","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}