Establishing a Generalizable Early Improvement Cutoff in Psoriasis Area and Severity Index for Outcome Prediction Across Systemic Treatments

Establishing a Generalizable Early Improvement Cutoff in Psoriasis Area and Severity Index for Outcome Prediction Across Systemic Treatments | 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 Establishing a Generalizable Early Improvement Cutoff in Psoriasis Area and Severity Index for Outcome Prediction Across Systemic Treatments Siqi Li, Yuxiong Jiang, Min Dai, Qianyu Chen, Dawei Huang, Yu Wang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8873727/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Early identification of treatment response is clinically important in psoriasis management, as it may facilitate timely treatment modification and reduce exposure to ineffective therapies. Although early improvement in the Psoriasis Area and Severity Index (PASI) has been associated with long term outcomes for individual therapies, a broadly applicable early PASI threshold across different treatment modalities in real-world practice remains unclear. Objectives To evaluate the predictive value of early PASI improvement for subsequent PASI90 achievement across multiple psoriasis treatment modalities and to identify a clinically meaningful early PASI response threshold applicable in routine clinical practice. Methods This prospective, multicenter real-world cohort study included adult patients with moderate-to-severe plaque psoriasis receiving methotrexate, phototherapy, IL-17 inhibitors, or IL-23 inhibitors. Receiver operating characteristic (ROC) curve analyses were used to evaluate the predictive performance of week-4 PASI improvement for achieving PASI90 at 3 months and 6 months. The optimal early response threshold was identified using the Youden Index. Multivariable logistic regression analyses were conducted to assess the independent association between early PASI response and subsequent PASI90 achievement. Results A total of 2,023 patients were included in the analysis. The optimal week-4 PASI improvement threshold for predicting PASI90 at 3 months was 62% for IL-17 inhibitors, 62% for IL-23 inhibitors, 59% for methotrexate and 61% for phototherapy. Multivariable logistic regression analyses further demonstrated that a PASI improvement of approximately 60% at week 4 was independently associated with a higher likelihood of achieving PASI90 at 3 months. Conclusions Achieving PASI60 at week 4 serves as a consistent and broadly applicable early indicator of subsequent PASI90 response across multiple psoriasis treatment modalities. Incorporating this threshold into early treatment assessment may help inform individualized treatment decisions and optimize outcomes in real-world management of moderate-to-severe psoriasis. Psoriasis Early treatment response Psoriasis Area and Severity Index PASI90 Outcome prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Psoriasis is a chronic immune-mediated disease that requires long-term treatment with substantial variability in therapeutic response between individuals 1 , 2 . Early identification of treatment response plays a critical clinical role by reducing exposure to ineffective therapies and facilitating prompt modification of treatment adjustment 3 . Multiple studies have indicated that early improvements in psoriasis area and severity index (PASI) under traditional therapies 4 , 5 or individual biologics 6 , 7 are associated with improved long-term clinical outcomes in psoriasis patients. However, most of the available evidence is restricted to single drugs or selected trial populations and there is still no widely accepted early PASI threshold applicable across different treatment modalities in routine clinical settings. In clinical trials evaluating biologic therapies for psoriasis, PASI 90 is widely regarded as an important treatment target as patients who achieve PASI 90 experience substantially greater improvements in health-related quality of life (HRQoL) and lower risk of comorbidities compared with those achieving PASI75 8,9 . Therefore, using PASI90 as the long-term outcome allows a more accurate indicator in real-world practice. Meanwhile, identifying an early and broadly applicable PASI threshold that can reliably predict the achievement of PASI 90 has important clinical implications. Week 4 has been suggested as an appropriate early assessment point, as prior evidence shows PASI improvements at this stage demonstrate initial therapeutic responsiveness and provide clinical guidance for timely treatment decisions 10 – 12 . Consequently, based on a real-world cohort including methotrexate, phototherapy, IL-17 inhibitors, and IL-23 inhibitors, this study systematically evaluates week 4 PASI response thresholds as predictors of achieving PASI90. Such a tool could facilitate more individualized treatment decisions across systemic therapies, phototherapy, and biologic agents, enabling dermatologists to optimize therapy selection, reduce ineffective treatment exposure, and ultimately improve patient outcomes. 2. Methods Study Design and Setting This study analyzed data collected adult patients with moderate-to-severe plaque psoriasis enrolled in the Shanghai Psoriasis Effectiveness Evaluation CoHort (SPEECH). SPEECH is a real-world, prospective, multicenter observational registry designed to evaluate psoriasis treatment patterns and effectiveness of systemic therapies in routine clinical practice. A total of 2,470 patients were enrolled from November 2022 to June 2023 across seven dermatology centers in Shanghai, China. This cohort has been registered at www.chictr.org.cn with registration number ChiCTR2000036186. Ethical approval for the use of clinical data was granted by the Ethics Committee of the Shanghai Skin Disease Hospital (approval No. 2020-36). All participants provided informed consent for study participation and use of their clinical data. Adult patients (≥ 18 years) with dermatologist-confirmed moderate-to-severe plaque psoriasis were eligible if they provided written informed consent and completed scheduled clinical assessments. Key exclusion criteria were: individuals presenting with uncontrolled malignancies, active infections, concomitant autoimmune disorders, or conditions potentially affecting treatment evaluation or protocol adherence; current pregnancy or breastfeeding and the absence of baseline data and follow-up information. Patients received systemic modalities, phototherapy or biologic agents according to clinical judgment and individualized management plans. Clinical evaluations were performed at baseline and during follow-up at 1 month, 3 months and 6 months. All patients provided written informed consent for the research use of their clinical data. Treatment and Clinical Data Collection After exclusion of patients with incomplete PASI assessments or loss to follow-up, patients were categorized into four treatment-based subcohort according to the initial systemic therapy received: methotrexate, phototherapy, IL-17 inhibitors (ixekizumab or secukinumab), and IL-23 inhibitors (guselkumab or ustekinumab). Patients received methotrexate, phototherapy, or biologic therapies according to standard clinical practice and approved dosing recommendations. Clinical data were collected using a standardized protocol to minimize bias. The following baseline demographic and disease-related data were collected: Age (years), Sex, Bodyweight (kg), Height (cm), Lifestyles factors (smoking and alcohol use, educational level), Duration of psoriasis (year), Age at onset of psoriasis (year), Early-onset psoriasis, Family history, Allergy history, History of comorbidities (hypertension, hyperlipidemia, diabetes mellitus, obesity), Prior treatment history (systemic non-biologic treatments, phototherapy, biologic agents), Special areas involvement (joints, nails, scalp, palmoplantar area and genital area), Baseline PASI score, Baseline body surface area (BSA) score and Baseline dermatology quality of life index (DLQI) score. All clinical evaluations were performed by trained dermatologists following standardized assessment procedures. Statistical Analysis Descriptive statistics Baseline demographic and clinical characteristics were summarized using appropriate descriptive statistics. Continuous variables were summarized using mean with standard deviation or median with interquartile range, depending on data distribution. Categorical variables were described as frequencies and percentages. As the primary objective of this study was to evaluate early PASI thresholds for predicting long-term outcome rather than to perform between-group comparisons, no hypothesis-driven statistical testing was applied to baseline variables. ROC curve analysis Week 4 PASI improvement and the achievement of PASI 90 were selected as the early predictor and long-term outcome, respectively, based on evidence supporting their value in assessing therapeutic response. Receiver operating characteristic (ROC) analysis was conducted to evaluate the discriminatory ability of week 4 PASI improvement in predicting the achievement of PASI 90 at 3 months and 6 months. Separate ROC curves were generated for each endpoint, and the corresponding areas under the curve (AUCs) with 95% confidence intervals were calculated as measures of predictive performance. The two AUCs were subsequently compared to determine whether early PASI response provides stronger predictive value for longer-term treatment outcomes. Youden Index analysis For the ROC curve with the larger AUC, sensitivity and specificity were subsequently calculated at each percentage of week 4 PASI improvement. The Youden Index (YI) was calculated to identify the threshold of PASI percentage improvement with the highest predictive value, which is defined as YI(a) = sensitivity(a) + specificity(a) − 1. Early PASI response–stratified analysis To facilitate clinical interpretation of early treatment response, patients were stratified into two subgroups according to whether they achieved the week-4 PASI improvement threshold identified by the Youden Index. For each treatment category (IL-17 inhibitors, IL-23 inhibitors, methotrexate, and phototherapy), the proportions of patients achieving PASI90 at 3 months were calculated within each treatment-specific cohort, including the overall population as well as the two week-4 PASI-defined subgroups. Response rates were expressed as percentages and graphically presented using bar charts to illustrate the association between early clinical response and subsequent treatment outcomes. Logistic regression Multivariable logistic regression analyses were performed to evaluate the association between early treatment response and subsequent achievement of PASI90 at 3 months. The dependent variable was achievement of PASI90 at 3 months (yes/no), and the primary independent variable was the achievement of week-4 PASI improvement threshold identified by the Youden Index. Separate hierarchical models were fitted within each treatment-specific cohort. Model 1 included no covariate adjustment. Model 2 was adjusted for demographic and lifestyle factors, including age, sex, BMI, educational level, smoking status, and alcohol consumption. Model 3 was further adjusted for disease-related characteristics, including age at disease onset, hypertension, hyperlipidaemia, arthritis, previous use of biological agents, baseline DLQI score, and baseline PASI score. Results were reported as odds ratios (ORs) with 95% confidence intervals (CIs), whereas a two-sided P value < 0.05 was considered statistically significant. Software All statistical analyses were conducted using R software (version 3.6.1) with the packages pROC , ggplot2 , dplyr , and stats . A two-sided P value < 0.05 was considered statistically significant. 3. Result 3.1 Baseline characteristics A total of 2,470 patients with plaque psoriasis were initially recruited from the SPEECH cohort (Fig. 1 ). After exclusion of 237 patients with missing baseline data and 210 patients with insufficient follow-up, 2,023 patients were included in the final analysis (Table 1 ). Table 1 Baseline patients and clinical characteristics of the treatment with IL-17 inhibitors, IL-23 inhibitors, methotrexate, and phototherapy. Characteristics IL17 IL23 Methotrexate Phototherapy (n = 636) (n = 312) (n = 588) (N = 487) Mean age, y 47 ± 15 50 ± 16 52 ± 15 49 ± 16 Female, n (%) 157 (24.7%) 68 (21.9%) 153 (26.1%) 170 (34.9%) Duration of psoriasis, y 15 ± 11 14 ± 13 15 ± 13 13 ± 12 Weight, y 74 ± 16 74 ± 14 71 ± 13 71 ± 14 Height, y 171 ± 8 171 ± 8 169 ± 8 169 ± 8 BMI, kg/m 2 25.3 ± 4.4 25.3 ± 4.1 24.8 ± 3.8 24.6 ± 3.9 Smoking, n (%) Never 378 (67.0%) 159 (57.4%) 304 (56.8%) 282 (61.4%) Former 38 (6.7%) 24 (8.7%) 74 (13.8%) 41 (8.9%) Current 148 (26.2%) 94 (33.9%) 157 (29.3%) 136 (29.6%) Alcohol, n (%) Never 497 (84.4%) 215 (76.8%) 409 (76.0%) 341 (75.6%) Former 36 (6.1%) 23 (8.2%) 64 (11.9%) 46 (10.2%) Current 56 (9.5%) 42 (15.0%) 65 (12.1%) 64 (14.2%) Education level, n (%) High school graduate or less 214 (35.7%) 124 (41.5%) 339 (61.4%) 235 (50.1%) Some college 160 (26.7%) 72 (24.1%) 105 (19.0%) 100 (21.3%) College graduate or higher 226 (37.7%) 103 (34.4%) 108 (19.6%) 134 (28.6%) History of allergy, n (%) 66 (10.5%) 43 (14.0%) 63 (10.9%) 68 (14.0%) Family history of psoriasis, n (%) 149 (23.8%) 61 (20.0%) 131 (22.8%) 104 (21.4%) Age of onset, y 31 ± 15 35 ± 17 38 ± 16 35 ± 17 Early onset of psoriasis, n (%) 415 (71.7%) 173 (63.6%) 332 (58.2%) 288 (63.3%) Prior treatment Phototherapy, n (%) 294 (46.9%) 136 (44.7%) 203 (35.4%) 196 (40.5%) Acitretin, n (%) 232 (37.0%) 101 (33.3%) 162 (28.1%) 87 (17.9%) Methotrexate, n (%) 115 (18.3%) 51 (16.8%) 73 (12.7%) 20 (4.1%) Biological agents, n (%) 85 (13.6%) 60 (19.7%) 23 (4.0%) 8 (1.7%) Obesity, n (%) 127 (20.3%) 63 (20.6%) 105 (18.1%) 74 (15.4%) Hypertension, n (%) 146 (23.0%) 100 (32.1%) 160 (27.2%) 119 (24.4%) Hyperlipidemia, n (%) 64 (10.1%) 33 (10.6%) 76 (12.9%) 43 (8.8%) Non-alcoholic fatty liver disease, n (%) 66 (10.4%) 37 (11.9%) 74 (12.6%) 66 (13.6%) Arthritis, n (%) 93 (14.6%) 42 (13.5%) 115 (19.6%) 31 (6.4%) Baseline PASI score 16 ± 9 15 ± 7 14 ± 8 10 ± 5 Baseline BSA score 24 ± 18 21 ± 16 20 ± 16 14 ± 10 Baseline DLQI score 11 ± 7 10 ± 6 9 ± 6 8 ± 6 Abbreviations: BMI, body mass index; PASI, Psoriasis Area and Severity Index; BSA, body surface area; DLQI, Dermatology Life Quality Index. Baseline demographic and clinical characteristics are summarized in Table 1 . Patients were predominantly male, with a mean age ranging from 47 to 52 years and a mean disease duration of approximately 13–15 years. At treatment initiation, patients receiving IL-17 and IL-23 inhibitors generally presented with a higher baseline disease burden, as reflected by PASI scores. Obesity and metabolic comorbidities were frequently observed across all treatment categories. Patients receiving biologic therapies commonly had prior exposure to systemic treatment, including phototherapy, methotrexate, or other biologic agents. 3.2 Predictive performance of 4w PASI improvement for PASI90 response ROC curve analyses were performed to evaluate the predictive performance of week-4 PASI improvement for achieving a PASI90 response at 3 months and 6 months (Fig. 2 ). In patients treated with IL-17 inhibitors, the AUCs for predicting PASI90 using week-4 PASI improvement were 0.752 at 3 months and 0.646 at 6 months. In contrast, among patients receiving IL-23 inhibitors, the corresponding AUCs were 0.679 at 3 months and 0.667 at 6 months. For patients treated with methotrexate, the AUCs were 0.835 at 3 months and 0.733 at 6 months, whereas in the phototherapy group, the AUC was 0.670 at both 3 months and 6 months. Overall, across all four treatment subgroups, week-4 PASI improvement was generally more predictive of PASI90 at 3 months than 6 months. 3.3 Identification of optimal early response thresholds using the Youden Index The optimal cut-off value of week-4 PASI percentage improvement for predicting PASI90 at 3 months was determined using the YI (Fig. 3 ). Specifically, the best cut-off values of PASI improvement was 62% for IL-17 inhibitors, 62% for IL-23 inhibitors, 59% for methotrexate, and 61% for phototherapy. The unimodal distribution of YI was observed at a week-4 PASI improvement of approximately 60%, indicating the threshold with the highest combined sensitivity and specificity. On the basis of the limited variation among these values, a week-4 PASI improvement of approximately 60% was identified as a consistent and clinically relevant early response threshold across treatment modalities. 3.4 Week-4 PASI60 as a strong predictor of subsequent response When patients were stratified according to the week-4 PASI60 cut-off identified by YI analysis, marked differences in 3 months PASI90 response rates were observed across all treatment subgroups (Fig. 4 ). In the IL-17 inhibitor group, patients achieving PASI60 at week 4 exhibited a higher PASI90 response rate at 3 months compared with those not achieving PASI60 (70.1% vs. 28.0%). And in the IL-23 inhibitor group, PASI90 response rates were higher among patients achieving PASI60 at week 4 (47.7% vs. 22.0%). Similarly, PASI90 response increased in patients meeting PASI60 at week 4 in the methotrexate group (47.0% vs. 6.6%) and phototherapy group (29.0% vs. 9.0%). To further evaluate whether achievement of PASI60 at week 4 was independently associated with subsequent PASI90 response, multivariable logistic regression analyses were performed. For patients treated with IL-17i, week-4 PASI60 increased odds of achieving PASI90 at 3 months in the partially adjusted model (OR, 6.21; CI, 3.73–10.55) and fully adjusted model (OR, 7.49; CI, 4.34–13.33) (Table 2 ). Comparable associations were observed in patients treated with IL-23i in both the partially adjusted model (OR, 4.25; CI, 1.99–9.37) and fully adjusted model (OR, 5.64; CI, 2.45–13.80). Among patients receiving methotrexate, similar associations were noted, with week-4 PASI60 increased odds of achieving PASI90 at 3 months in the partially adjusted model (OR, 10.14; CI, 5.24–20.30) and fully adjusted model (OR, 11.41; CI, 5.48–24.96), as well as among those receiving phototherapy in the partially adjusted model (OR, 5.97; CI, 2.26–16.33) and fully adjusted models (OR, 5.77; CI, 2.04–16.80). Collectively, achievement of PASI60 at week 4 was consistently and independently associated with higher odds of PASI90 at 3 months after adjustment for potential confounders. Table 2 Association between achievement of PASI60 at week 4 and PASI90 at 3 months. Treatment Achieving 4w PASI60 Model 1 Model 2 Model 3 Achieving 3months PASI90 Not achieving 3months PASI90 OR 95% CI p-value OR 95% CI p-value OR 95% CI p-value IL-17 inhibitors 70.09%(246/351) 29.91%(105/351) 5.96 4.23, 8.47 < 0.001 6.21 3.73, 10.55 < 0.001 7.49 4.34, 13.33 < 0.001 IL-23 inhibitors 47.66%(51/107) 52.34%(56/107) 3.24 1.96, 5.38 < 0.001 4.25 1.99, 9.37 < 0.001 5.64 2.45, 13.80 < 0.001 Methotrexate 47.01%(63/134) 52.99%(71/134) 12.54 7.66, 20.96 < 0.001 10.14 5.24, 20.30 < 0.001 11.41 5.48, 24.96 < 0.001 Phototherapy 29.00%(29/100) 71.00%(71/100) 4.11 2.35, 7.15 < 0.001 5.97 2.26, 16.33 < 0.001 5.77 2.04, 16.80 < 0.001 Abbreviations: CI = Confidence Interval, OR = Odds Ratio Model 1 : no covariates were adjusted Model 2 : adjusted for Age, Gender, BMI, Education level, Smoking, and Alcohol Model 3 : adjusted for Age, Gender, BMI, Education level, Smoking, and Alcohol, Age of onset, Hypertension, Hyperlipidemia, Arthritis, Previous use of biological agents, Baseline DLQI score, and Baseline PASI score 4. Discussion Previous studies have consistently demonstrated that early clinical improvement may serve as a prognostic indicator of subsequent treatment response in psoriasis 10 , 13 . In a phase II ixekizumab study, Zhu et al. showed that PASI 40 to PASI 50 at week 4–6 was strongly associated with PASI75 and PASI90 achievement at 3 months, highlighting the clinical relevance of early response assessment 14 . Consistent findings have been reported for conventional systemic therapy, as failure to achieve PASI25 at week 4 predicted a low likelihood of attaining PASI75 at week 16 in patients treated with methotrexate 15 . Importantly, this prognostic signal extends beyond pharmacologic treatments, attainment of PASI30 at week 3 during narrowband UVB phototherapy also shown to predict PASI90 at the end of treatment 16 . Collectively, these studies support the prognostic value of early PASI improvement but also underscore a key limitation: the absence of a standardized, broadly applicable early PASI threshold that can be consistently applied across different systemic treatments in routine clinical practice. Against this background, we sought to systematically establish optimal early PASI thresholds for biologic agents, conventional systemic therapy, and phototherapy within a single prospective real-world cohort. Notably, we identified a consistent and clinically meaningful threshold of approximately 60% PASI improvement at week 4 that was applicable across treatment categories, supporting the robustness and generalizability of early response assessment in routine practice. Several factors may explain why PASI60 at week 4 emerged as an optimal and generalizable predictor. Compared with lower thresholds such as PASI25 or PASI30, PASI60 likely reflects a level of early disease control that exceeds transient improvement and more reliably captures true biological responsiveness to therapy. At the same time, it remains attainable within a short treatment window across both rapidly acting biologics and more gradually effective conventional therapies. From a clinical perspective, PASI60 at week 4 therefore represents a pragmatic compromise between sensitivity and specificity: it facilitates early identification of patients unlikely to achieve high-level clearance and reduces premature treatment switching among individuals with slower but meaningful response profiles. The strong predictive performance of early clinical improvement may reflect pharmacodynamic responsiveness to treatment and underlying disease responsiveness to immune modulation 17 . These differences in predictive performance across treatment modalities are likely attributable to distinct response kinetics rather than fundamental differences in prognostic relevance 18 . Therapies characterized by a rapid onset of action, such as IL-17 inhibitors, tended to show clearer early discrimination between responders and non-responders 19 , 20 , whereas treatments with more gradual response patterns also demonstrated consistent predictive performance over longer follow-up periods 21 , 22 . Importantly, these findings indicate that early clinical improvement represents a broadly applicable marker of treatment responsiveness, rather than a drug-specific effect. From a clinical perspective, early identification of patients unlikely to achieve subsequent PASI90 achievement is crucial for optimizing therapeutic strategies. PASI90 has increasingly been recognized as a meaningful long-term treatment target, which has been associated with greater improvements in health-related quality of life and reduced disease burden 23 , 24 . Our results indicate that failure to achieve PASI60 at week 4 is associated with a substantially reduced probability of achieving PASI90 by 3 months, irrespective of treatment modality. Incorporating week-4 PASI60 into early treatment assessment may therefore facilitate timely treatment modification and reduce prolonged exposure to ineffective therapies in real-world psoriasis management. The present study is strengthened by several key features. First, the prospective, multicenter and large sample enhance the robustness and external validity of the findings. Second, the inclusion of both conventional systemic therapies and biologic agents allowed direct comparison of early response patterns across treatment modalities within the same real-world cohort. Third, comprehensive adjustment for demographic, lifestyle, and disease-related variables supports the independence of the association between early PASI response and subsequent PASI90 achievement. Finally, the use of PASI90 as the primary endpoint provides a stringent and clinically relevant measure of treatment response. Several limitations of this study should be acknowledged. As an observational analysis, residual confounding and indication bias cannot be fully excluded despite multivariable adjustment. Treatment allocation was determined by clinical judgment rather than randomization, and PASI assessments were performed at predefined follow-up time points. Future studies should explore whether integrating early clinical response with additional biomarkers or patient-level factors can further refine individualized prediction of treatment outcomes. In addition, validation of the week-4 PASI60 threshold in other real-world cohorts will be important to confirm its broader generalizability. 5. Conclusions In summary, this real-world study demonstrates that achieving PASI60 at week 4 represents a robust and broadly applicable early indicator of subsequent PASI90 response across multiple psoriasis treatment modalities. Early assessment using this threshold may provide a practical and clinically actionable tool to support individualized treatment decisions and optimize long-term outcomes in patients with moderate-to-severe psoriasis. Abbreviations BMI body mass index BSA body surface area CI confidence interval DLQI Dermatology Life Quality Index HRQoL health-related quality of life IL interleukin IQR interquartile range NB UVB narrow-band ultraviolet B OR odds ratio PASI Psoriasis Area and Severity Index ROC receiver operating characteristic SD standard deviation SPEECH Shanghai Psoriasis Effectiveness Evaluation CoHort TNF-α tumor necrosis factor-alpha YI Youden Index. Declarations Ethics approval and consent to participate The study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval for the use of clinical data was granted by the Ethics Committee of the Shanghai Skin Disease Hospital (approval No. 2020-36). All participants provided informed consent for study participation and use of their clinical data. Consent for publication Not applicable. Competing interests The authors declare no conflicts of interest. Funding This work was sponsored by grants from National Natural Science Foundation of China (No. 81872522, 82073429, 82203913), Innovation Program of Shanghai Municipal Education Commission (No. 2019-01-07-00-07-E00046), Clinical Research Plan of SHDC (No. SHDC2020CR1014B), Program of Shanghai Academic Research Leader (No. 20XD1403300) and the Fundamental Research Funds for the Central Universities (No. 22120220602). Authors' contributions Siqi Li: Writing – original draft, Conceptualization, Investigation, Visualization. Yuxiong Jiang: Writing – original draft, Visualization. Min Dai: Writing – original draft. Qianyu Chen: Writing – original draft, Visualization. Dawei Huang: Writing – Visualization, Investigation. Yu Wang : Writing – Visualization. Yifan Hu: Writing – Visualization. Yuanwenke Zhang: Writing – Investigation. Suyang Lin: Writing – Investigation. Xilin Zhang: Writing –review & editing, Resources, Methodology. Qian Yu: Writing – review & editing, Resources, Methodology, and Funding acquisition. Yuling Shi: Writing – review & editing, Conceptualization, and Funding acquisition. Final approval of the version to be published: all authors. Acknowledgements We sincerely thank the participants of the SPEECH cohort, as well as the clinicians, research staff, and all individuals who provided support, for their valuable contributions to the completion of this study. Availability of data and material All data and material supporting the findings of this study are contained within the article. Additional information is available from the corresponding author upon reasonable request. References Griffiths CEM, Armstrong AW, Gudjonsson JE, Barker. J Psoriasis Lancet. 2021;397(10281):1301–15. Guo J, Zhang H, Lin W, Lu L, Su J, Chen X. Signaling pathways and targeted therapies for psoriasis. Signal Transduct Target Ther. 2023;8(1):437. Strober B. Biologic therapy for psoriasis: early response implies future success. Br J Dermatol. 2013;169(6):1178–9. Saurat JH, Langley RG, Reich K, Unnebrink K, Sasso EH, Kampman W. Relationship between methotrexate dosing and clinical response in patients with moderate to severe psoriasis: subanalysis of the CHAMPION study. Br J Dermatol. 2011;165(2):399–406. Shmarov F, Smith GR, Weatherhead SC, Reynolds NJ, Zuliani P. Individualised computational modelling of immune mediated disease onset, flare and clearance in psoriasis. PLoS Comp Biol. 2022;18(9):e1010267. Augustin M, Gallo G, See K, McKean-Matthews M, Burge R, Gooderham M, et al. Early Response is Associated With Stable Long-Term Response in Psoriasis Patients Receiving Ixekizumab or Ustekinumab. J Drugs Dermatol. 2022;21(2):122–6. Loft N, Egeberg A, Rasmussen MK, Bryld LE, Nissen CV, Dam TN, et al. Response to Biologics During the First Six Months of Therapy in Biologic-naïve Patients with Psoriasis Predicts Risk of Disease Flares: A Danish Nationwide Study. Acta Derm Venereol. 2021;101(1):adv00357. Torii H, Sato N, Yoshinari T, Nakagawa H. Dramatic impact of a Psoriasis Area and Severity Index 90 response on the quality of life in patients with psoriasis: an analysis of Japanese clinical trials of infliximab. J Dermatol. 2012;39(3):253–9. Puig L. PASI90 response: the new standard in therapeutic efficacy for psoriasis. J Eur Acad Dermatol Venereol. 2015;29(4):645–8. Jiang Y, Huang D, Chen Q, Yu Y, Hu Y, Wang Y, et al. A novel online calculator based on clinical features and hematological parameters to predict total skin clearance in patients with moderate to severe psoriasis. J Transl Med. 2024;22(1):121. Erol SN, Kartal SP. Can Week 4 PASI Response Serve as an Early Predictor of Apremilast Efficacy in Psoriasis? A Single-Centre Preliminary Study. Acta Derm Venereol. 2025;105:adv44624. Feldman SR, Merola JF, Pariser DM, Zhang J, Zhao Y, Mendelsohn AM, et al. Clinical implications and predictive values of early PASI responses to tildrakizumab in patients with moderate-to-severe plaque psoriasis. J Dermatolog Treat. 2022;33(3):1670–5. Akdogan N, Mutlu ÖK, Karabulut E, Yalici-Armagan B, Gulseren D, Dogan Gunaydın S. Early response is a strong predictor of the long-term response in psoriasis patients receiving risankizumab or guselkumab in the real-world: a retrospective analysis. Expert Rev Clin Pharmacol. 2025;18(6):417–23. Zhu B, Edson-Heredia E, Cameron GS, Shen W, Erickson J, Shrom D, et al. Early clinical response as a predictor of subsequent response to ixekizumab treatment: results from a phase II study of patients with moderate-to-severe plaque psoriasis. Br J Dermatol. 2013;169(6):1337–41. Gordon KB, Betts KA, Sundaram M, Signorovitch JE, Li J, Xie M, et al. Poor early response to methotrexate portends inadequate long-term outcomes in patients with moderate-to-severe psoriasis: Evidence from 2 phase 3 clinical trials. J Am Acad Dermatol. 2017;77(6):1030–7. Watson N, Wilson N, Shmarov F, Zuliani P, Reynolds NJ, Weatherhead SC. The use of psoriasis biomarkers, including trajectory of clinical response, to predict clearance and remission duration to UVB phototherapy. J Eur Acad Dermatol Venereol. 2021;35(11):2250–8. Nast A, Smith C, Spuls PI, Avila Valle G, Bata-Csörgö Z, Boonen H, et al. EuroGuiDerm Guideline on the systemic treatment of Psoriasis vulgaris - Part 1: treatment and monitoring recommendations. J Eur Acad Dermatol Venereol. 2020;34(11):2461–98. Papp KA, Vender R, Purdy K, Lovegrove F, Oroz I, Grewal P, et al. Time to Onset of Action for Biologics and Targeted Treatments in Psoriasis: Systematic Targeted Literature Review and Network Meta-Analysis. Dermatol Ther (Heidelb). 2025;15(9):2615–29. Blauvelt A, Papp K, Gottlieb A, Jarell A, Reich K, Maari C, et al. A head-to-head comparison of ixekizumab vs. guselkumab in patients with moderate-to-severe plaque psoriasis: 12-week efficacy, safety and speed of response from a randomized, double-blinded trial. Br J Dermatol. 2020;182(6):1348–58. Blauvelt A, Sofen H, Papp K, Gooderham M, Tyring S, Zhao Y, et al. Tildrakizumab efficacy and impact on quality of life up to 52 weeks in patients with moderate-to-severe psoriasis: a pooled analysis of two randomized controlled trials. J Eur Acad Dermatol Venereol. 2019;33(12):2305–12. Saurat JH, Stingl G, Dubertret L, Papp K, Langley RG, Ortonne JP, et al. Efficacy and safety results from the randomized controlled comparative study of adalimumab vs. methotrexate vs. placebo in patients with psoriasis (CHAMPION). Br J Dermatol. 2008;158(3):558–66. Elmets CA, Lim HW, Stoff B, Connor C, Cordoro KM, Lebwohl M, et al. Joint American Academy of Dermatology-National Psoriasis Foundation guidelines of care for the management and treatment of psoriasis with phototherapy. J Am Acad Dermatol. 2019;81(3):775–804. Gisondi P, Talamonti M, Chiricozzi A, Piaserico S, Amerio P, Balato A, et al. Treat-to-Target Approach for the Management of Patients with Moderate-to-Severe Plaque Psoriasis: Consensus Recommendations. Dermatol Ther (Heidelb). 2021;11(1):235–52. Augustin M, Gottlieb AB, Lebwohl M, Pinter A, Warren RB, Puig L, et al. Complete Skin Clearance is Associated with the Greatest Benefits to Health-Related Quality of Life and Perceived Symptoms for Patients with Psoriasis. Dermatol Ther (Heidelb). 2024;14(10):2841–57. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8873727","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600432827,"identity":"641c02e6-6b7f-4e32-8524-2e8c07d59c90","order_by":0,"name":"Siqi Li","email":"","orcid":"","institution":"Skin Diseases Hospital of Tongji University: Shanghai Skin Diseases Hospital","correspondingAuthor":false,"prefix":"","firstName":"Siqi","middleName":"","lastName":"Li","suffix":""},{"id":600432828,"identity":"198edcf9-7908-4fbe-aaf6-a39874a7f3ee","order_by":1,"name":"Yuxiong Jiang","email":"","orcid":"","institution":"Skin Diseases Hospital of Tongji University: Shanghai Skin Diseases Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuxiong","middleName":"","lastName":"Jiang","suffix":""},{"id":600432829,"identity":"6d4da3d1-6959-44ea-93ac-f7d37cbf6e25","order_by":2,"name":"Min Dai","email":"","orcid":"","institution":"Skin Diseases Hospital of Tongji University: Shanghai Skin Diseases Hospital","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Dai","suffix":""},{"id":600432830,"identity":"51d14b9b-e856-410c-aba8-58907a25ff90","order_by":3,"name":"Qianyu Chen","email":"","orcid":"","institution":"Skin Diseases Hospital of Tongji University: Shanghai Skin Diseases Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qianyu","middleName":"","lastName":"Chen","suffix":""},{"id":600432831,"identity":"957cd68f-e164-43dd-9e1a-c303b9f23bfe","order_by":4,"name":"Dawei Huang","email":"","orcid":"","institution":"Skin Diseases Hospital of Tongji University: Shanghai Skin Diseases Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dawei","middleName":"","lastName":"Huang","suffix":""},{"id":600432832,"identity":"ffba53be-b4b1-4807-bb5d-78e7eedb4f90","order_by":5,"name":"Yu Wang","email":"","orcid":"","institution":"Skin Diseases Hospital of Tongji University: Shanghai Skin Diseases Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Wang","suffix":""},{"id":600432833,"identity":"0282774c-ab63-477b-96be-0f14e12a744e","order_by":6,"name":"Yifan Hu","email":"","orcid":"","institution":"Skin Diseases Hospital of Tongji University: Shanghai Skin Diseases Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"Hu","suffix":""},{"id":600432834,"identity":"a7e145e1-ebc8-4fd0-9501-248327fc3c6c","order_by":7,"name":"Yuanwenke Zhang","email":"","orcid":"","institution":"Skin Diseases Hospital of Tongji University: Shanghai Skin Diseases Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuanwenke","middleName":"","lastName":"Zhang","suffix":""},{"id":600432835,"identity":"7647f6a3-74a4-4171-8e20-edd2839d9718","order_by":8,"name":"Suyang Lin","email":"","orcid":"","institution":"Skin Diseases Hospital of Tongji University: Shanghai Skin Diseases Hospital","correspondingAuthor":false,"prefix":"","firstName":"Suyang","middleName":"","lastName":"Lin","suffix":""},{"id":600432836,"identity":"57c6f719-baad-4b9a-aeec-49dfe29a2c43","order_by":9,"name":"Xilin Zhang","email":"","orcid":"","institution":"Skin Diseases Hospital of Tongji University: Shanghai Skin Diseases Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xilin","middleName":"","lastName":"Zhang","suffix":""},{"id":600432837,"identity":"71aeb0d1-bfc2-43f8-9dee-b6354f50f141","order_by":10,"name":"Qian Yu","email":"","orcid":"","institution":"Skin Diseases Hospital of Tongji University: Shanghai Skin Diseases Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Yu","suffix":""},{"id":600432838,"identity":"1d22078b-8fba-4ce7-8323-1b5115552545","order_by":11,"name":"Yuling Shi","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-1273-7881","institution":"Skin Diseases Hospital of Tongji University: Shanghai Skin Diseases Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yuling","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2026-02-13 16:04:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8873727/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8873727/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104779924,"identity":"a7d64c0d-fcbf-4d8d-aad7-c8140c2a24e2","added_by":"auto","created_at":"2026-03-17 07:48:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2319600,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flowchart of patients inclusion\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"FIGURE1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8873727/v1/1a9f17d67dff506c3e621fb4.jpg"},{"id":104311524,"identity":"835d1b07-4094-4b2e-8e03-797520b51b6a","added_by":"auto","created_at":"2026-03-10 10:59:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":735021,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic (ROC) curves for week-4 PASI improvement predicting PASI90 at 3 months and 6 month.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic (ROC) curves for the predictive performance of week-4 Psoriasis\u003c/p\u003e\n\u003cp\u003eArea and Severity Index (PASI) percentage improvement for achievement of PASI90 at 3 months and 6 months in patients treated with (A) IL-17 inhibitors, (B) IL-23 inhibitors, (C) methotrexate, and (D) phototherapy. Corresponding areas under the curve (AUCs) are presented for each subgroup and time point.\u003c/p\u003e","description":"","filename":"FIGURE2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8873727/v1/56a0edd1eebd6af44f27a81b.jpg"},{"id":104311526,"identity":"ea65958c-028f-440c-8a3f-c249f03d8fd6","added_by":"auto","created_at":"2026-03-10 10:59:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1032283,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eYouden Index, sensitivity and specificity plotted against week-4 PASI improvement.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYouden Index, sensitivity and specificity plotted against each percentage of week-4 Psoriasis Area and Severity Index (PASI) improvement for patients treated with (A) IL-17 inhibitors, (B) IL-23 inhibitors, (C) methotrexate, and (D) phototherapy. Peak Youden Index indicates the optimal early PASI improvement threshold for predicting PASI90 at 3 months in each treatment group.\u003c/p\u003e","description":"","filename":"FIGURE3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8873727/v1/125382395f7a718e382af12b.jpg"},{"id":104405324,"identity":"38c0e26d-7fce-4756-82b8-86d6abd1718d","added_by":"auto","created_at":"2026-03-11 12:22:35","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":834947,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePASI90 response at 3 months between early responders (patients achieving PASI60 at week 4) and early nonresponders across patients treated with IL-17 inhibitors, IL-23 inhibitors, methotrexate, and phototherapy.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"FIGURE4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8873727/v1/dcdd5b47e197a58412a0d1cb.jpg"},{"id":106727572,"identity":"cfbf1977-9100-49fd-8770-f89bf326f698","added_by":"auto","created_at":"2026-04-12 18:39:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6228365,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8873727/v1/e96ae593-a2e4-4055-a65a-4930d5d05c48.pdf"}],"financialInterests":"","formattedTitle":"Establishing a Generalizable Early Improvement Cutoff in Psoriasis Area and Severity Index for Outcome Prediction Across Systemic Treatments","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePsoriasis is a chronic immune-mediated disease that requires long-term treatment with substantial variability in therapeutic response between individuals\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Early identification of treatment response plays a critical clinical role by reducing exposure to ineffective therapies and facilitating prompt modification of treatment adjustment\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Multiple studies have indicated that early improvements in psoriasis area and severity index (PASI) under traditional therapies\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003eor individual biologics\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003eare associated with improved long-term clinical outcomes in psoriasis patients. However, most of the available evidence is restricted to single drugs or selected trial populations and there is still no widely accepted early PASI threshold applicable across different treatment modalities in routine clinical settings.\u003c/p\u003e \u003cp\u003eIn clinical trials evaluating biologic therapies for psoriasis, PASI 90 is widely regarded as an important treatment target as patients who achieve PASI 90 experience substantially greater improvements in health-related quality of life (HRQoL) and lower risk of comorbidities compared with those achieving PASI75\u003csup\u003e8,9\u003c/sup\u003e. Therefore, using PASI90 as the long-term outcome allows a more accurate indicator in real-world practice. Meanwhile, identifying an early and broadly applicable PASI threshold that can reliably predict the achievement of PASI 90 has important clinical implications. Week 4 has been suggested as an appropriate early assessment point, as prior evidence shows PASI improvements at this stage demonstrate initial therapeutic responsiveness and provide clinical guidance for timely treatment decisions\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConsequently, based on a real-world cohort including methotrexate, phototherapy, IL-17 inhibitors, and IL-23 inhibitors, this study systematically evaluates week 4 PASI response thresholds as predictors of achieving PASI90. Such a tool could facilitate more individualized treatment decisions across systemic therapies, phototherapy, and biologic agents, enabling dermatologists to optimize therapy selection, reduce ineffective treatment exposure, and ultimately improve patient outcomes.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e \u003cb\u003eStudy Design and Setting\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study analyzed data collected adult patients with moderate-to-severe plaque psoriasis enrolled in the Shanghai Psoriasis Effectiveness Evaluation CoHort (SPEECH). SPEECH is a real-world, prospective, multicenter observational registry designed to evaluate psoriasis treatment patterns and effectiveness of systemic therapies in routine clinical practice. A total of 2,470 patients were enrolled from November 2022 to June 2023 across seven dermatology centers in Shanghai, China. This cohort has been registered at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.chictr.org.cn\" target=\"_blank\"\u003ewww.chictr.org.cn\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.chictr.org.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e with registration number ChiCTR2000036186. Ethical approval for the use of clinical data was granted by the Ethics Committee of the Shanghai Skin Disease Hospital (approval No. 2020-36). All participants provided informed consent for study participation and use of their clinical data.\u003c/p\u003e \u003cp\u003eAdult patients (\u0026ge;\u0026thinsp;18 years) with dermatologist-confirmed moderate-to-severe plaque psoriasis were eligible if they provided written informed consent and completed scheduled clinical assessments.\u003c/p\u003e \u003cp\u003eKey exclusion criteria were: individuals presenting with uncontrolled malignancies, active infections, concomitant autoimmune disorders, or conditions potentially affecting treatment evaluation or protocol adherence; current pregnancy or breastfeeding and the absence of baseline data and follow-up information.\u003c/p\u003e \u003cp\u003ePatients received systemic modalities, phototherapy or biologic agents according to clinical judgment and individualized management plans. Clinical evaluations were performed at baseline and during follow-up at 1 month, 3 months and 6 months. All patients provided written informed consent for the research use of their clinical data.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTreatment and Clinical Data Collection\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter exclusion of patients with incomplete PASI assessments or loss to follow-up, patients were categorized into four treatment-based subcohort according to the initial systemic therapy received: methotrexate, phototherapy, IL-17 inhibitors (ixekizumab or secukinumab), and IL-23 inhibitors (guselkumab or ustekinumab).\u003c/p\u003e \u003cp\u003ePatients received methotrexate, phototherapy, or biologic therapies according to standard clinical practice and approved dosing recommendations. Clinical data were collected using a standardized protocol to minimize bias. The following baseline demographic and disease-related data were collected: Age (years), Sex, Bodyweight (kg), Height (cm), Lifestyles factors (smoking and alcohol use, educational level), Duration of psoriasis (year), Age at onset of psoriasis (year), Early-onset psoriasis, Family history, Allergy history, History of comorbidities (hypertension, hyperlipidemia, diabetes mellitus, obesity), Prior treatment history (systemic non-biologic treatments, phototherapy, biologic agents), Special areas involvement (joints, nails, scalp, palmoplantar area and genital area), Baseline PASI score, Baseline body surface area (BSA) score and Baseline dermatology quality of life index (DLQI) score. All clinical evaluations were performed by trained dermatologists following standardized assessment procedures.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDescriptive statistics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBaseline demographic and clinical characteristics were summarized using appropriate descriptive statistics. Continuous variables were summarized using mean with standard deviation or median with interquartile range, depending on data distribution. Categorical variables were described as frequencies and percentages. As the primary objective of this study was to evaluate early PASI thresholds for predicting long-term outcome rather than to perform between-group comparisons, no hypothesis-driven statistical testing was applied to baseline variables.\u003c/p\u003e \u003cp\u003e \u003cb\u003eROC curve analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWeek 4 PASI improvement and the achievement of PASI 90 were selected as the early predictor and long-term outcome, respectively, based on evidence supporting their value in assessing therapeutic response. Receiver operating characteristic (ROC) analysis was conducted to evaluate the discriminatory ability of week 4 PASI improvement in predicting the achievement of PASI 90 at 3 months and 6 months. Separate ROC curves were generated for each endpoint, and the corresponding areas under the curve (AUCs) with 95% confidence intervals were calculated as measures of predictive performance. The two AUCs were subsequently compared to determine whether early PASI response provides stronger predictive value for longer-term treatment outcomes.\u003c/p\u003e \u003cp\u003e \u003cb\u003eYouden Index analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor the ROC curve with the larger AUC, sensitivity and specificity were subsequently calculated at each percentage of week 4 PASI improvement. The Youden Index (YI) was calculated to identify the threshold of PASI percentage improvement with the highest predictive value, which is defined as YI(a) = sensitivity(a) + specificity(a)\u0026thinsp;\u0026minus;\u0026thinsp;1.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEarly PASI response\u0026ndash;stratified analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo facilitate clinical interpretation of early treatment response, patients were stratified into two subgroups according to whether they achieved the week-4 PASI improvement threshold identified by the Youden Index. For each treatment category (IL-17 inhibitors, IL-23 inhibitors, methotrexate, and phototherapy), the proportions of patients achieving PASI90 at 3 months were calculated within each treatment-specific cohort, including the overall population as well as the two week-4 PASI-defined subgroups. Response rates were expressed as percentages and graphically presented using bar charts to illustrate the association between early clinical response and subsequent treatment outcomes.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLogistic regression\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMultivariable logistic regression analyses were performed to evaluate the association between early treatment response and subsequent achievement of PASI90 at 3 months. The dependent variable was achievement of PASI90 at 3 months (yes/no), and the primary independent variable was the achievement of week-4 PASI improvement threshold identified by the Youden Index. Separate hierarchical models were fitted within each treatment-specific cohort. Model 1 included no covariate adjustment. Model 2 was adjusted for demographic and lifestyle factors, including age, sex, BMI, educational level, smoking status, and alcohol consumption. Model 3 was further adjusted for disease-related characteristics, including age at disease onset, hypertension, hyperlipidaemia, arthritis, previous use of biological agents, baseline DLQI score, and baseline PASI score. Results were reported as odds ratios (ORs) with 95% confidence intervals (CIs), whereas a two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSoftware\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted using R software (version 3.6.1) with the packages \u003cem\u003epROC\u003c/em\u003e, \u003cem\u003eggplot2\u003c/em\u003e, \u003cem\u003edplyr\u003c/em\u003e, and \u003cem\u003estats\u003c/em\u003e. A two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 2,470 patients with plaque psoriasis were initially recruited from the SPEECH cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After exclusion of 237 patients with missing baseline data and 210 patients with insufficient follow-up, 2,023 patients were included in the final analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\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\u003eBaseline patients and clinical characteristics of the treatment with IL-17 inhibitors, IL-23 inhibitors, methotrexate, and phototherapy.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIL17\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIL23\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMethotrexate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePhototherapy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;636)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;312)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;588)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;487)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean age, y\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157 (24.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153 (26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e170 (34.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDuration of psoriasis, y\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeight, y\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeight, y\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e169\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e169\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI, kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking, n (%)\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e378 (67.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159 (57.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e304 (56.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e282 (61.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (8.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (33.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e136 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol, n (%)\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e497 (84.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e215 (76.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e409 (76.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e341 (75.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 (11.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (10.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (15.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 (14.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level, n (%)\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (41.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e339 (61.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e235 (50.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (24.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100 (21.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e226 (37.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (34.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e134 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistory of allergy, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (14.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68 (14.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily history of psoriasis, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149 (23.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131 (22.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge of onset, y\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEarly onset of psoriasis, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e415 (71.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e173 (63.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e332 (58.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e288 (63.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrior treatment\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhototherapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e294 (46.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (44.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e203 (35.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e196 (40.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcitretin, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232 (37.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162 (28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethotrexate, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (16.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiological agents, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eObesity, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127 (20.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (20.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105 (18.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146 (23.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (32.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160 (27.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHyperlipidemia, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-alcoholic fatty liver disease, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (11.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (12.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArthritis, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline PASI score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline BSA score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline DLQI score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: BMI, body mass index; PASI, Psoriasis Area and Severity Index; BSA, body surface area; DLQI, Dermatology Life Quality Index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBaseline demographic and clinical characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients were predominantly male, with a mean age ranging from 47 to 52 years and a mean disease duration of approximately 13\u0026ndash;15 years. At treatment initiation, patients receiving IL-17 and IL-23 inhibitors generally presented with a higher baseline disease burden, as reflected by PASI scores.\u003c/p\u003e \u003cp\u003eObesity and metabolic comorbidities were frequently observed across all treatment categories. Patients receiving biologic therapies commonly had prior exposure to systemic treatment, including phototherapy, methotrexate, or other biologic agents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Predictive performance of 4w PASI improvement for PASI90 response\u003c/h2\u003e \u003cp\u003eROC curve analyses were performed to evaluate the predictive performance of week-4 PASI improvement for achieving a PASI90 response at 3 months and 6 months (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn patients treated with IL-17 inhibitors, the AUCs for predicting PASI90 using week-4 PASI improvement were 0.752 at 3 months and 0.646 at 6 months. In contrast, among patients receiving IL-23 inhibitors, the corresponding AUCs were 0.679 at 3 months and 0.667 at 6 months. For patients treated with methotrexate, the AUCs were 0.835 at 3 months and 0.733 at 6 months, whereas in the phototherapy group, the AUC was 0.670 at both 3 months and 6 months.\u003c/p\u003e \u003cp\u003eOverall, across all four treatment subgroups, week-4 PASI improvement was generally more predictive of PASI90 at 3 months than 6 months.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Identification of optimal early response thresholds using the Youden Index\u003c/h2\u003e \u003cp\u003eThe optimal cut-off value of week-4 PASI percentage improvement for predicting PASI90 at 3 months was determined using the YI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Specifically, the best cut-off values of PASI improvement was 62% for IL-17 inhibitors, 62% for IL-23 inhibitors, 59% for methotrexate, and 61% for phototherapy. The unimodal distribution of YI was observed at a week-4 PASI improvement of approximately 60%, indicating the threshold with the highest combined sensitivity and specificity. On the basis of the limited variation among these values, a week-4 PASI improvement of approximately 60% was identified as a consistent and clinically relevant early response threshold across treatment modalities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Week-4 PASI60 as a strong predictor of subsequent response\u003c/h2\u003e \u003cp\u003eWhen patients were stratified according to the week-4 PASI60 cut-off identified by YI analysis, marked differences in 3 months PASI90 response rates were observed across all treatment subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the IL-17 inhibitor group, patients achieving PASI60 at week 4 exhibited a higher PASI90 response rate at 3 months compared with those not achieving PASI60 (70.1% vs. 28.0%). And in the IL-23 inhibitor group, PASI90 response rates were higher among patients achieving PASI60 at week 4 (47.7% vs. 22.0%). Similarly, PASI90 response increased in patients meeting PASI60 at week 4 in the methotrexate group (47.0% vs. 6.6%) and phototherapy group (29.0% vs. 9.0%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further evaluate whether achievement of PASI60 at week 4 was independently associated with subsequent PASI90 response, multivariable logistic regression analyses were performed. For patients treated with IL-17i, week-4 PASI60 increased odds of achieving PASI90 at 3 months in the partially adjusted model (OR, 6.21; CI, 3.73\u0026ndash;10.55) and fully adjusted model (OR, 7.49; CI, 4.34\u0026ndash;13.33) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Comparable associations were observed in patients treated with IL-23i in both the partially adjusted model (OR, 4.25; CI, 1.99\u0026ndash;9.37) and fully adjusted model (OR, 5.64; CI, 2.45\u0026ndash;13.80). Among patients receiving methotrexate, similar associations were noted, with week-4 PASI60 increased odds of achieving PASI90 at 3 months in the partially adjusted model (OR, 10.14; CI, 5.24\u0026ndash;20.30) and fully adjusted model (OR, 11.41; CI, 5.48\u0026ndash;24.96), as well as among those receiving phototherapy in the partially adjusted model (OR, 5.97; CI, 2.26\u0026ndash;16.33) and fully adjusted models (OR, 5.77; CI, 2.04\u0026ndash;16.80). Collectively, achievement of PASI60 at week 4 was consistently and independently associated with higher odds of PASI90 at 3 months after adjustment for potential confounders.\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\u003eAssociation between achievement of PASI60 at week 4 and PASI90 at 3 months.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAchieving 4w PASI60\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAchieving 3months PASI90\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot achieving 3months PASI90\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-17 inhibitors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.09%(246/351)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.91%(105/351)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.23, 8.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.73, 10.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.34, 13.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-23 inhibitors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.66%(51/107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.34%(56/107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.96, 5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.99, 9.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.45, 13.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMethotrexate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.01%(63/134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.99%(71/134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.66, 20.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.24, 20.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.48, 24.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhototherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.00%(29/100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.00%(71/100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.35, 7.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.26, 16.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.04, 16.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: CI\u0026thinsp;=\u0026thinsp;Confidence Interval, OR\u0026thinsp;=\u0026thinsp;Odds Ratio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eModel 1 : no covariates were adjusted \u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eModel 2 : adjusted for Age, Gender, BMI, Education level, Smoking, and Alcohol\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eModel 3 : adjusted for Age, Gender, BMI, Education level, Smoking, and Alcohol, Age of onset, Hypertension, Hyperlipidemia, Arthritis, Previous use of biological agents, Baseline DLQI score, and Baseline PASI score\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePrevious studies have consistently demonstrated that early clinical improvement may serve as a prognostic indicator of subsequent treatment response in psoriasis\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In a phase II ixekizumab study, Zhu et al. showed that PASI 40 to PASI 50 at week 4\u0026ndash;6 was strongly associated with PASI75 and PASI90 achievement at 3 months, highlighting the clinical relevance of early response assessment\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Consistent findings have been reported for conventional systemic therapy, as failure to achieve PASI25 at week 4 predicted a low likelihood of attaining PASI75 at week 16 in patients treated with methotrexate\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Importantly, this prognostic signal extends beyond pharmacologic treatments, attainment of PASI30 at week 3 during narrowband UVB phototherapy also shown to predict PASI90 at the end of treatment\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Collectively, these studies support the prognostic value of early PASI improvement but also underscore a key limitation: the absence of a standardized, broadly applicable early PASI threshold that can be consistently applied across different systemic treatments in routine clinical practice.\u003c/p\u003e \u003cp\u003eAgainst this background, we sought to systematically establish optimal early PASI thresholds for biologic agents, conventional systemic therapy, and phototherapy within a single prospective real-world cohort. Notably, we identified a consistent and clinically meaningful threshold of approximately 60% PASI improvement at week 4 that was applicable across treatment categories, supporting the robustness and generalizability of early response assessment in routine practice.\u003c/p\u003e \u003cp\u003eSeveral factors may explain why PASI60 at week 4 emerged as an optimal and generalizable predictor. Compared with lower thresholds such as PASI25 or PASI30, PASI60 likely reflects a level of early disease control that exceeds transient improvement and more reliably captures true biological responsiveness to therapy. At the same time, it remains attainable within a short treatment window across both rapidly acting biologics and more gradually effective conventional therapies. From a clinical perspective, PASI60 at week 4 therefore represents a pragmatic compromise between sensitivity and specificity: it facilitates early identification of patients unlikely to achieve high-level clearance and reduces premature treatment switching among individuals with slower but meaningful response profiles.\u003c/p\u003e \u003cp\u003eThe strong predictive performance of early clinical improvement may reflect pharmacodynamic responsiveness to treatment and underlying disease responsiveness to immune modulation\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThese differences in predictive performance across treatment modalities are likely attributable to distinct response kinetics rather than fundamental differences in prognostic relevance\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Therapies characterized by a rapid onset of action, such as IL-17 inhibitors, tended to show clearer early discrimination between responders and non-responders\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, whereas treatments with more gradual response patterns also demonstrated consistent predictive performance over longer follow-up periods\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Importantly, these findings indicate that early clinical improvement represents a broadly applicable marker of treatment responsiveness, rather than a drug-specific effect.\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, early identification of patients unlikely to achieve subsequent PASI90 achievement is crucial for optimizing therapeutic strategies. PASI90 has increasingly been recognized as a meaningful long-term treatment target, which has been associated with greater improvements in health-related quality of life and reduced disease burden\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Our results indicate that failure to achieve PASI60 at week 4 is associated with a substantially reduced probability of achieving PASI90 by 3 months, irrespective of treatment modality. Incorporating week-4 PASI60 into early treatment assessment may therefore facilitate timely treatment modification and reduce prolonged exposure to ineffective therapies in real-world psoriasis management.\u003c/p\u003e \u003cp\u003eThe present study is strengthened by several key features. First, the prospective, multicenter and large sample enhance the robustness and external validity of the findings. Second, the inclusion of both conventional systemic therapies and biologic agents allowed direct comparison of early response patterns across treatment modalities within the same real-world cohort. Third, comprehensive adjustment for demographic, lifestyle, and disease-related variables supports the independence of the association between early PASI response and subsequent PASI90 achievement. Finally, the use of PASI90 as the primary endpoint provides a stringent and clinically relevant measure of treatment response.\u003c/p\u003e \u003cp\u003eSeveral limitations of this study should be acknowledged. As an observational analysis, residual confounding and indication bias cannot be fully excluded despite multivariable adjustment. Treatment allocation was determined by clinical judgment rather than randomization, and PASI assessments were performed at predefined follow-up time points. Future studies should explore whether integrating early clinical response with additional biomarkers or patient-level factors can further refine individualized prediction of treatment outcomes. In addition, validation of the week-4 PASI60 threshold in other real-world cohorts will be important to confirm its broader generalizability.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn summary, this real-world study demonstrates that achieving PASI60 at week 4 represents a robust and broadly applicable early indicator of subsequent PASI90 response across multiple psoriasis treatment modalities. Early assessment using this threshold may provide a practical and clinically actionable tool to support individualized treatment decisions and optimize long-term outcomes in patients with moderate-to-severe psoriasis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody surface area\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDLQI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDermatology Life Quality Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHRQoL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehealth-related quality of life\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterleukin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNB UVB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enarrow-band ultraviolet B\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePASI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePsoriasis Area and Severity Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPEECH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eShanghai Psoriasis Effectiveness Evaluation CoHort\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNF-α\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor necrosis factor-alpha\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eYI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eYouden Index.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThe study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval for the use of clinical data was granted by the Ethics Committee of the Shanghai Skin Disease Hospital (approval No. 2020-36). All participants provided informed consent for study participation and use of their clinical data.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was sponsored by grants from National Natural Science Foundation of China (No. 81872522, 82073429, 82203913), Innovation Program of Shanghai Municipal Education Commission (No. 2019-01-07-00-07-E00046), Clinical Research Plan of SHDC (No. SHDC2020CR1014B), Program of Shanghai Academic Research Leader (No. 20XD1403300) and the Fundamental Research Funds for the Central Universities (No. 22120220602).\u003c/p\u003e\u003ch2\u003eAuthors' contributions\u003c/h2\u003e \u003cp\u003eSiqi Li: Writing \u0026ndash; original draft, Conceptualization, Investigation, Visualization. Yuxiong Jiang: Writing \u0026ndash; original draft, Visualization. Min Dai: Writing \u0026ndash; original draft. Qianyu Chen: Writing \u0026ndash; original draft, Visualization. Dawei Huang: Writing \u0026ndash; Visualization, Investigation. Yu Wang : Writing \u0026ndash; Visualization. Yifan Hu: Writing \u0026ndash; Visualization. Yuanwenke Zhang: Writing \u0026ndash; Investigation. Suyang Lin: Writing \u0026ndash; Investigation. Xilin Zhang: Writing \u0026ndash;review \u0026amp; editing, Resources, Methodology. Qian Yu: Writing \u0026ndash; review \u0026amp; editing, Resources, Methodology, and Funding acquisition. Yuling Shi: Writing \u0026ndash; review \u0026amp; editing, Conceptualization, and Funding acquisition. Final approval of the version to be published: all authors.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe sincerely thank the participants of the SPEECH cohort, as well as the clinicians, research staff, and all individuals who provided support, for their valuable contributions to the completion of this study.\u003c/p\u003e\u003ch2\u003eAvailability of data and material\u003c/h2\u003e \u003cp\u003eAll data and material supporting the findings of this study are contained within the article. Additional information is available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGriffiths CEM, Armstrong AW, Gudjonsson JE, Barker. J Psoriasis Lancet. 2021;397(10281):1301\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo J, Zhang H, Lin W, Lu L, Su J, Chen X. Signaling pathways and targeted therapies for psoriasis. Signal Transduct Target Ther. 2023;8(1):437.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrober B. Biologic therapy for psoriasis: early response implies future success. Br J Dermatol. 2013;169(6):1178\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaurat JH, Langley RG, Reich K, Unnebrink K, Sasso EH, Kampman W. Relationship between methotrexate dosing and clinical response in patients with moderate to severe psoriasis: subanalysis of the CHAMPION study. Br J Dermatol. 2011;165(2):399\u0026ndash;406.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShmarov F, Smith GR, Weatherhead SC, Reynolds NJ, Zuliani P. Individualised computational modelling of immune mediated disease onset, flare and clearance in psoriasis. PLoS Comp Biol. 2022;18(9):e1010267.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAugustin M, Gallo G, See K, McKean-Matthews M, Burge R, Gooderham M, et al. Early Response is Associated With Stable Long-Term Response in Psoriasis Patients Receiving Ixekizumab or Ustekinumab. J Drugs Dermatol. 2022;21(2):122\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoft N, Egeberg A, Rasmussen MK, Bryld LE, Nissen CV, Dam TN, et al. Response to Biologics During the First Six Months of Therapy in Biologic-na\u0026iuml;ve Patients with Psoriasis Predicts Risk of Disease Flares: A Danish Nationwide Study. Acta Derm Venereol. 2021;101(1):adv00357.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorii H, Sato N, Yoshinari T, Nakagawa H. Dramatic impact of a Psoriasis Area and Severity Index 90 response on the quality of life in patients with psoriasis: an analysis of Japanese clinical trials of infliximab. J Dermatol. 2012;39(3):253\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePuig L. PASI90 response: the new standard in therapeutic efficacy for psoriasis. J Eur Acad Dermatol Venereol. 2015;29(4):645\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang Y, Huang D, Chen Q, Yu Y, Hu Y, Wang Y, et al. A novel online calculator based on clinical features and hematological parameters to predict total skin clearance in patients with moderate to severe psoriasis. J Transl Med. 2024;22(1):121.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErol SN, Kartal SP. Can Week 4 PASI Response Serve as an Early Predictor of Apremilast Efficacy in Psoriasis? A Single-Centre Preliminary Study. Acta Derm Venereol. 2025;105:adv44624.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeldman SR, Merola JF, Pariser DM, Zhang J, Zhao Y, Mendelsohn AM, et al. Clinical implications and predictive values of early PASI responses to tildrakizumab in patients with moderate-to-severe plaque psoriasis. J Dermatolog Treat. 2022;33(3):1670\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkdogan N, Mutlu \u0026Ouml;K, Karabulut E, Yalici-Armagan B, Gulseren D, Dogan Gunaydın S. Early response is a strong predictor of the long-term response in psoriasis patients receiving risankizumab or guselkumab in the real-world: a retrospective analysis. Expert Rev Clin Pharmacol. 2025;18(6):417\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu B, Edson-Heredia E, Cameron GS, Shen W, Erickson J, Shrom D, et al. Early clinical response as a predictor of subsequent response to ixekizumab treatment: results from a phase II study of patients with moderate-to-severe plaque psoriasis. Br J Dermatol. 2013;169(6):1337\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGordon KB, Betts KA, Sundaram M, Signorovitch JE, Li J, Xie M, et al. Poor early response to methotrexate portends inadequate long-term outcomes in patients with moderate-to-severe psoriasis: Evidence from 2 phase 3 clinical trials. J Am Acad Dermatol. 2017;77(6):1030\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatson N, Wilson N, Shmarov F, Zuliani P, Reynolds NJ, Weatherhead SC. The use of psoriasis biomarkers, including trajectory of clinical response, to predict clearance and remission duration to UVB phototherapy. J Eur Acad Dermatol Venereol. 2021;35(11):2250\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNast A, Smith C, Spuls PI, Avila Valle G, Bata-Cs\u0026ouml;rg\u0026ouml; Z, Boonen H, et al. EuroGuiDerm Guideline on the systemic treatment of Psoriasis vulgaris - Part 1: treatment and monitoring recommendations. J Eur Acad Dermatol Venereol. 2020;34(11):2461\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapp KA, Vender R, Purdy K, Lovegrove F, Oroz I, Grewal P, et al. Time to Onset of Action for Biologics and Targeted Treatments in Psoriasis: Systematic Targeted Literature Review and Network Meta-Analysis. Dermatol Ther (Heidelb). 2025;15(9):2615\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlauvelt A, Papp K, Gottlieb A, Jarell A, Reich K, Maari C, et al. A head-to-head comparison of ixekizumab vs. guselkumab in patients with moderate-to-severe plaque psoriasis: 12-week efficacy, safety and speed of response from a randomized, double-blinded trial. Br J Dermatol. 2020;182(6):1348\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlauvelt A, Sofen H, Papp K, Gooderham M, Tyring S, Zhao Y, et al. Tildrakizumab efficacy and impact on quality of life up to 52 weeks in patients with moderate-to-severe psoriasis: a pooled analysis of two randomized controlled trials. J Eur Acad Dermatol Venereol. 2019;33(12):2305\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaurat JH, Stingl G, Dubertret L, Papp K, Langley RG, Ortonne JP, et al. Efficacy and safety results from the randomized controlled comparative study of adalimumab vs. methotrexate vs. placebo in patients with psoriasis (CHAMPION). Br J Dermatol. 2008;158(3):558\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElmets CA, Lim HW, Stoff B, Connor C, Cordoro KM, Lebwohl M, et al. Joint American Academy of Dermatology-National Psoriasis Foundation guidelines of care for the management and treatment of psoriasis with phototherapy. J Am Acad Dermatol. 2019;81(3):775\u0026ndash;804.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGisondi P, Talamonti M, Chiricozzi A, Piaserico S, Amerio P, Balato A, et al. Treat-to-Target Approach for the Management of Patients with Moderate-to-Severe Plaque Psoriasis: Consensus Recommendations. Dermatol Ther (Heidelb). 2021;11(1):235\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAugustin M, Gottlieb AB, Lebwohl M, Pinter A, Warren RB, Puig L, et al. Complete Skin Clearance is Associated with the Greatest Benefits to Health-Related Quality of Life and Perceived Symptoms for Patients with Psoriasis. Dermatol Ther (Heidelb). 2024;14(10):2841\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Psoriasis, Early treatment response, Psoriasis Area and Severity Index, PASI90, Outcome prediction","lastPublishedDoi":"10.21203/rs.3.rs-8873727/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8873727/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEarly identification of treatment response is clinically important in psoriasis management, as it may facilitate timely treatment modification and reduce exposure to ineffective therapies. Although early improvement in the Psoriasis Area and Severity Index (PASI) has been associated with long term outcomes for individual therapies, a broadly applicable early PASI threshold across different treatment modalities in real-world practice remains unclear.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo evaluate the predictive value of early PASI improvement for subsequent PASI90 achievement across multiple psoriasis treatment modalities and to identify a clinically meaningful early PASI response threshold applicable in routine clinical practice.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis prospective, multicenter real-world cohort study included adult patients with moderate-to-severe plaque psoriasis receiving methotrexate, phototherapy, IL-17 inhibitors, or IL-23 inhibitors. Receiver operating characteristic (ROC) curve analyses were used to evaluate the predictive performance of week-4 PASI improvement for achieving PASI90 at 3 months and 6 months. The optimal early response threshold was identified using the Youden Index. Multivariable logistic regression analyses were conducted to assess the independent association between early PASI response and subsequent PASI90 achievement.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 2,023 patients were included in the analysis. The optimal week-4 PASI improvement threshold for predicting PASI90 at 3 months was 62% for IL-17 inhibitors, 62% for IL-23 inhibitors, 59% for methotrexate and 61% for phototherapy. Multivariable logistic regression analyses further demonstrated that a PASI improvement of approximately 60% at week 4 was independently associated with a higher likelihood of achieving PASI90 at 3 months.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAchieving PASI60 at week 4 serves as a consistent and broadly applicable early indicator of subsequent PASI90 response across multiple psoriasis treatment modalities. Incorporating this threshold into early treatment assessment may help inform individualized treatment decisions and optimize outcomes in real-world management of moderate-to-severe psoriasis.\u003c/p\u003e","manuscriptTitle":"Establishing a Generalizable Early Improvement Cutoff in Psoriasis Area and Severity Index for Outcome Prediction Across Systemic Treatments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 10:59:53","doi":"10.21203/rs.3.rs-8873727/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e0c1fe1f-6c07-472c-aa4e-c02a7f5b9405","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T22:49:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 10:59:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8873727","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8873727","identity":"rs-8873727","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}