Identifying demographic, social, and clinical predictors of interleukin inhibitor biologic therapy effects using the real-world clinical data

Identifying demographic, social, and clinical predictors of interleukin inhibitor biologic therapy effects using the real-world clinical data | 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 Article Identifying demographic, social, and clinical predictors of interleukin inhibitor biologic therapy effects using the real-world clinical data Shasha Han, Peng Wu, Zhihui Yang, Ruoyu Li, Hang Li, Xiao-Hua Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2274250/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 Appropriate and effective use of biological agents is important to improve the benefits of psoriasis patients. We examined how the effects of interleukin (IL) inhibitors vary across patients' demographic, social, and clinical characteristics in treating psoriasis, and whether IL inhibitors are effective for managing mild-to-moderate psoriasis. Methods Data were collected from a large national registry in China from Sep 2020 to Sep 2021. Clinical benefits, measured by 75% (and 90%) or more improvement from baseline Psoriasis Area and Severity Index (PASI 75 and PASI 90), were contrasted using the propensity-score-based causal inference methodology between the IL inhibitors and the conventional therapies. Candidates that can differentiate the benefits with P-values less than 0.05 were identified as predictors. Results Baseline PASI, DLQI, and employment demonstrated stronger predictability in achieving the benefits of IL inhibitors. For weeks 5-46, baseline PASI predicted an increasing relative benefit of biologics as the value increased from 0 to 5, followed by a V-shaped benefit as the value further increased. Baseline PASI scores at 5.4 and 1.0 predicted the maximal and minimal benefits on achieving PASI 75, with an increase in probabilities of 0.36 (95CI 0.28 to 0.44) and 0.03 (-0.14 to 0.20), respectively. Higher DLQI predicted the maximal benefit (0.55, 0.26 to 0.83) of achieving PASI 75 and lower DLQI predicted the minimal benefit of 0.13 (0.04 to 0.23). Part-time job predicted the maximal benefit of 0.28 (0.21 to 0.36) and full-time job predicted the minimal benefit of 0.15 (0.10 to 0.21). These findings were consistent in achieving PASI 75 and PASI 90. Conclusions This article fills the gap in treating mild psoriasis with IL inhibitor biologics. Patients with mild psoriasis, i.e. with PASI below 5 or BSA scores below 5, had statistically significant benefits from treatment with IL inhibitors. The studying provides evidence from real-world data on patients’ heterogeneous responses to IL inhibitor biologics. Identified clinical and social predictors can be used for treatment differentiation in clinical practice. Health sciences/Biomarkers/Predictive markers Health sciences/Medical research/Epidemiology Health sciences/Diseases/Immunological disorders Declarations Declarations Conflicts of interest The authors declare no competing interests. Funding: National Science and Technology Major Project of the Ministry of Science and Technology of China (No.2021YFF0901400), National Natural Science Fund of China (No. 81773546), and the National Key R&D Program of China (2021YFF1201100). Author contributions X.-H.Z., and H.L. supervised the work. S.H., P.W, and Z.Y. designed the study. Z.Y. collected data. S.H. and P.W. performed analysis. S.H., P.W, Z.Y., X.-H.Z., and H.L. interpreted the findings. S.H. P.W, and Z.Y. wrote the manuscript. R.L.,X.-H.Z., and H.L. commented on and revised the manuscript. All authors approved the final manuscript as submitted. Acknowledgments This study was supported by the grants from National Science and Technology Major Project of the Ministry of Science and Technology of China (No.2021YFF0901400), National Natural Science Fund of China (No. 81773546), and the National Key R&D Program of China (2021YFF1201100). The funder of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the manuscript. References Griffiths C, Lancet JB-T, 2007 U. Pathogenesis and clinical features of psoriasis. New England journal of medicine . 2007;370:263–271. Griffiths CEM, Armstrong AW, Gudjonsson JE, Barker JNWN. Psoriasis. The Lancet. 2021;397(10281):1301–1315. doi: 10.1016/S0140-6736(20)32549-6 Kurd SK, Gelfand JM. The prevalence of previously diagnosed and undiagnosed psoriasis in US adults: Results from NHANES 2003–2004. 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Journal of Vascular Surgery. 2002;36(1):194–195. doi: 10.1067/MVA.2002.125015 R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing . Published online 2020. https://www.r-project.org/ National Psoriasis Foundation. About psoriasis. National Psoriasis Foundation. Accessed November 11, 2021. https://www.psoriasis.org/about-psoriasis Warren RB, Gooderham M, Burge R, et al. Comparison of cumulative clinical benefits of biologics for the treatment of psoriasis over 16 weeks: Results from a network meta-analysis. Journal of the American Academy of Dermatology. 2020;82(5):1138–1149. doi: 10.1016/J.JAAD.2019.12.038 Leonardi C, See K, Gallo G, et al. Psoriasis Severity Assessment Combining Physician and Patient Reported Outcomes: The Optimal Psoriasis Assessment Tool. Dermatology and Therapy. 2021;11(4):1249–1263. doi: 10.1007/S13555-021-00544-6/FIGURES/7 Warren RB, Marsden A, Tomenson B, et al. Identifying demographic, social and clinical predictors of biologic therapy effectiveness in psoriasis: a multicentre longitudinal cohort study. British Journal of Dermatology. 2019;180(5):1069–1076. doi: 10.1111/BJD.16776 Sarkar R, Chugh S, Bansal S. General measures and quality of life issues in psoriasis. Indian dermatology online journal. 2016;7(6):481. doi: 10.4103/2229-5178.193908 Schmitt JM, Ford DE. Work limitations and productivity loss are associated with health-related quality of life but not with clinical severity in patients with psoriasis. Dermatology (Basel, Switzerland). 2006;213(2):102–110. doi: 10.1159/000093848 National Institute for Health and Clinical Excellence. Ustekinumab for the treatment of adults with moderate to severe psoriasis NICE Technology Appraisal Guidance [TA180]. Published 2009. https://www.nice.org.uk/guidance/ta180 Hägg D, Sundström A, Eriksson M, Schmitt-Egenolf M. Decision for biological treatment in real life is more strongly associated with the Psoriasis Area and Severity Index (PASI) than with the Dermatology Life Quality Index (DLQI). Journal of the European Academy of Dermatology and Venereology. 2015;29(3):452–456. doi: 10.1111/JDV.12576 Foster SA, Zhu B, Guo J, et al. Patient Characteristics, Health Care Resource Utilization, and Costs Associated with Treatment-Regimen Failure with Biologics in the Treatment of Psoriasis. Journal of managed care & specialty pharmacy. 2016;22(4):396–405. doi: 10.18553/JMCP.2016.22.4.396 Ger T-Y, Huang Y-H, Hui RC-Y, Tsai T-F, Chiu H-Y. Effectiveness and safety of secukinumab for psoriasis in real-world practice: analysis of subgroups stratified by prior biologic failure or reimbursement. Therapeutic advances in chronic disease. 2019;10:2040622319843756. doi: 10.1177/2040622319843756 Unsectioned Figure Details A Fig. 1 Estimated conditional treatment effects of IL inhibitor biologics versus the conventional therapies in weeks 5–46, on candidate predictors with continuous values . Relative benefits measured by PASI 75 for candidates ( A ) baseline PASI, ( B ) DLQI, ( C ) BSA, ( D ) Age, and ( E ) BMI. Relative benefits measured by PASI 90 for candidates ( F ) baseline PASI, ( G ) DLQI, ( H ) BSA, ( I ) Age, and ( J ) BMI. Shaded areas refer to the 95 confidence interval. The dotted line denoted the zero line. A Fig. 2 Estimated conditional treatment effects of IL inhibitor biologics versus the conventional therapies in weeks 1–4, on candidate predictors with continuous values . Relative benefits measured by PASI 75 for candidates ( A ) baseline PASI, ( B ) DLQI, ( C ) BSA, ( D ) Age, and ( E ) BMI. Relative benefits measured by PASI 90 for candidates ( F ) baseline PASI, ( G ) DLQI, ( H ) BSA, ( I ) Age, and ( J ) BMI. Shaded areas refer to the 95 confidence interval. The dotted line denoted the zero line. Unsectioned Paragraphs Background Appropriate and effective use of biological agents is important to improve the benefits of psoriasis patients. We examined how the effects of interleukin (IL) inhibitors vary across patients' demographic, social, and clinical characteristics in treating psoriasis, and whether IL inhibitors are effective for managing mild-to-moderate psoriasis. Methods Data were collected from a large national registry in China from Sep 2020 to Sep 2021. Clinical benefits, measured by 75% (and 90%) or more improvement from baseline Psoriasis Area and Severity Index (PASI 75 and PASI 90), were contrasted using the propensity-score-based causal inference methodology between the IL inhibitors and the conventional therapies. Candidates that can differentiate the benefits with P -values less than 0.05 were identified as predictors. Results Baseline PASI, DLQI, and employment demonstrated stronger predictability in achieving the benefits of IL inhibitors. For weeks 5−46, baseline PASI predicted an increasing relative benefit of biologics as the value increased from 0 to 5, followed by a V-shaped benefit as the value further increased. Baseline PASI scores at 5.4 and 1.0 predicted the maximal and minimal benefits on achieving PASI 75, with an increase in probabilities of 0.36 (95CI 0.28 to 0.44) and 0.03 (-0.14 to 0.20), respectively. Higher DLQI predicted the maximal benefit (0.55, 0.26 to 0.83) of achieving PASI 75 and lower DLQI predicted the minimal benefit of 0.13 (0.04 to 0.23). Part-time job predicted the maximal benefit of 0.28 (0.21 to 0.36) and full-time job predicted the minimal benefit of 0.15 (0.10 to 0.21). These findings were consistent in achieving PASI 75 and PASI 90. Conclusions This article fills the gap in treating mild psoriasis with IL inhibitor biologics. Patients with mild psoriasis, i.e. with PASI below 5 or BSA scores below 5, had statistically significant benefits from treatment with IL inhibitors. The studying provides evidence from real-world data on patients’ heterogeneous responses to IL inhibitor biologics. Identified clinical and social predictors can be used for treatment differentiation in clinical practice. Xiao-Hua Zhou ( [email protected] ) and Hang Li ( [email protected] ). Abstract (323 words) Word counts: 3200 Background Psoriasis is a chronic immune-medicated inflammatory disease that can lead to serious impairment in patients’ quality of life and a substantial burden on society. 1 , 2 Although recognized as an important predictor for comorbidity, the disease was often underdiagnosed or undertreated, with the problem more severe for mild-to-moderate psoriasis. 3 , 4 Immunological and genetic studies have identified the proinflammatory cytokines (tumour necrosis factor-alpha (TNFα), interleukin-17 (IL-17) and interleukin-23 (IL-23)) as key drivers of psoriasis pathogenesis. 5 Immune targeting of these cytokines by biological therapies has been validated in clinical trials primarily for severe psoriasis. 6 , 7 New generation of biologics IL-17 inhibitors (secukinumab, ixekizumab, and brodalumab) and IL-23 inhibitors (guselkumab, risankizumab, and tildrakizumab) entered the market successively. In China, Secukinumab has been added into the 2020 national drug reimbursement list (NDRL) for improving the care of psoriasis; clinical use of biologics has substantially increased since the initiation of the 2020 NRDL, Mar 1 2021. Although biologic therapies have been a mainstay in the treatment of moderate-to-severe disease, the effectiveness profile for mild-to-moderate psoriasis is yet to be established. Three studies were identified on studying the relationships of bioinformatic metric with the treatment responses, such as a gene signature score derived from skin mRNA, 8 serum cytokines, 9 and lymphocyte phenotypes, 10 one on determining the optimal therapeutic range for adalimumab, a TNFα inhibitor, 11 and none of them included mild-to-moderate psoriasis. Also, evidence of the first-line biologic treatment of severe psoriasis in clinical trials is limited, with little information on biologic treatment of mild-to-moderate psoriasis. 6 , 7 As such, biological agents are indicated only for the treatment of mild-to-severe plaque psoriasis or arthropathic psoriasis among patients who do not respond, are intolerant or contraindicated for systemic therapies in Chinese psoriasis guidelines. 12 , 13 With the inclusion of biologics in the 2020 NDRL, patient preference for biologics could increase sharply. Appropriate and effective use of biologics for psoriasis has drawn the attention of many dermatologists. Although efforts have been made to reduce discrepancies between treatment guidelines and clinical practice, 14 as individual responses to biologics in patients with psoriasis could vary considerably, controversy still exists in the clinical practice. 15 – 17 There is an important need to address the heterogeneity in the clinical practice of biologic treatment and to guide clinicians by providing evidence-based advice. We aim to identify the predictors that can predict the appropriate therapies for individual patients with psoriasis, including mild conditions. Data were collected from a large national registry, covering 237 tertiary hospitals in China. The doubly robust causal inference methods were used to estimate the heterogenous treatment efficacies of biologics versus the conventional therapies, conditioning on prespecified predictor candidates. Candidates’ capacity to differentiate relatively clinical benefits of IL inhibitors was assessed, and those demonstrating strong predictability of future responses were selected as predictors. Methods Evidence before this study We systematically searched 12 electronic and regional databaes (Medline, EmBASe, Web of Science, SciELO, Korean Journal DataBASes, Russian Science Citation, Index, WPRIM, SaudiMedLit, Informit, IndMed, and HERDIN) from their respective inception dates to October 2021. We used the search terms (“psoria*”) AND (“biologic*”) AND (“DLQI” OR “PASI”) AND (“heterogene*” OR “precision med*”). No language restrictions were applied. We identified 4 papers published in peer-review journals and none of them included mild-to-moderate psoriasis. Three of them studied on the relationships of bioinformatic metric with the treatment responses, such as a gene signature score derived from skin mRNA, 14 serum cytokines, 15 and lymphocyte phenotypes, 16 one on determining the optimal therapeutic range for adalimumab, a TNFα inhibitor. 17 We also screened the references of all included studies and published review articles to identify studies on the efficacies of inhibitor biologics. In the two widely cited clinical trials 6 , 7 , inhibitor biologics were assigned to individuals having a score of 12 or higher on the psoriasis-area-and-severity index score (PASI \(\ge 12\) ) and involvement of 10% or more of the body surface area (BSA \(\ge 10\) ), and had been inadequately controlled by topical treatments, phototherapy, and/or previous systemic therapy. Until now, there has been scarce information to understand the efficacies of biologics on mild-to-moderate psoriasis or the heterogeneity of the causal effects across clinical characteristics and social conditions. Study design Patients were collected from a national registry in China, Psoriasis Center Data Platform (PCDP) which was initiated in Sep 2020 and covered 237 tertiary hospitals in about 100 cities in mainland of China. PCDP was led by the National Clinical Research Center for Skin and Immune Diseases to facilitate the tracking of patients with psoriasis. All patients signed the informed consent at the registry enrollment. The eligibility criteria for the present study were patients who enrolled from Sep 2020 to Sep 2021 and had been diagnosed with plaque psoriasis at or before the enrollment and had at least one follow-up visit. In addition, patients were treated with IL-inhibitor biologics (mainly the IL-17 inhibitor, Secukinumab) or conventional therapies (topic drugs, systemic medicines or phototherapy) at the enrollment. Patients who had used medications at any time before entering the registry were excluded. The final sample comprised 5663 patients. 1696 patients were treated with IL-inhibitor biologics and about 92.6% of biologics therapies used a dose of 300mg injection; the remaining were treated with conventional therapies. Primary endpoints were 75% (90%) or more improvement from baseline Psoriasis Area and Severity Index score (PASI 75 and PASI 90) in the follow-up visits. Patients’ demographics and clinical characteristics were collected at the registry enrollment. The demographics include sex, age, Body Mass Index (BMI), smoking history and comorbidity conditions, insurance status, education status, marital status, and the location of visiting hospitals (including seven districts in China, Dongbei District, Huabei District, Huadong District, Huanan District, Huazhong District, Xibei District and Xinan District). Clinical characteristics include self-reported Dermatology Quality of Life Index (DQLI), and baseline disease severity estimated by PASI and Body Surface Area (BSA) score. A description of these variables is given in Table 1. Statistical analysis Prespecified candidate predictors included the disease severity measures (baseline PASI, baseline BSA score, nail involvement), demographics (age, sex, BMI, smoke, education, and comorbidity), social conditions (marital status, employment, and insurance condition), as well as patients’ self-reported baseline DLQI. Because the purpose is to identify predictors for the use of biologics in the induction period and the maintenance period, we assessed the candidate predictors in the endpoints weeks 1–4 and weeks 5–46 separately. We estimated the heterogenous treatment effects of IL inhibitors versus the conventional therapies conditioning on the candidate predictors. We employed the propensity-score-based doubly robust estimation method to adjust for confounders. Unlike the propensity-score-based weighting method, the method can provide unbiased estimates if either the propensity score model or the outcome models are specified correctly. 18 , 19 The propensity score and the outcome were estimated using the logistic regressions, with all baseline covariates included. Since the physicians tended to use biologics more often after the initiation of 2020 NDRL (Mar 1 2021) —i.e., the treatment assignment mechanism changed after Mar 1 2021, we estimated the propensity scores for patients who entered the registry before and after Mar 1 2021 separately. Age outside of 9–100 or BMI outside of 10–50 were considered recording errors, and 135 individuals were excluded from the analysis as a consequence. Also, since there were only a few samples available at extremely high values of baseline PASI, we grouped the baseline PASI above 45 into the PASI 45. Similarly, we grouped BSA above 65, age above 75, and BMI above 40. For each candidate predictor, we selected the 95% Confidence Intervals (95 CIs) corresponding to the maximal and minimal estimated conditional effects. Statistical differences between the two means, the maximum and minimum of conditional effects, were assessed by examining the overlap of the two 95CIs using the method in the literature. 20 Candidates with the two means differing with \(P\le 0.05\) at both PASI 75 and PASI 90 were qualified as predictors. For candidates that were selected as predictors for both weeks 1– 4 and weeks 5–46, the clinical benefits at the same values of predictors were visually compared. All analyses were performed in R 4.0.2. 21 This study was approved by the Biomedical Ethics Committee of The Peking University First Hospital in Beijing, China, approval number 2020 − 255. All research was performed in accordance with relevant regulations, and informed consent was obtained from all participants. Results Predictors in weeks 5 – 46 Baseline PASI, DLQI and employment were identified to be strong predictors of achieving PASI 75 and PASI 90, when assessing the benefits of IL-inhibitor therapy versus conventional therapies in the maintenance period. The relative advantage of biologics over the conventional therapies increased as baseline PASI increased from 0 to 5; and when the score exceeded 5, it presented a V-shape, with a trough around the PASI value of 20.0 (Fig. 1A and 1F). Baseline PASI scores at 5.4 and 1.0 predicted the maximal and minimal benefits on achieving PASI 75, with an increase in probabilities of 0.36 (95CI 0.28 to 0.44) and 0.03 (-0.14 to 0.20) respectively(Table 2). The minimal benefits on achieving PASI 90 (mean 0.10, 95CI 0.02 to 0.19) were obtained at the trough of the V-shape (PASI 21.0) and the maximal benefits (0.39, 0.09 to 0.69) were obtained at the highest PASI value, PASI 45. DLQI and employment presented a much simpler pattern. Patients with higher DLQI or part-time jobs indicate higher benefits of biologics (Fig. 1 and eFigure 1). For DLQI, the predicted maximal and minimal benefits on achieving PASI 75 were 0.55 (0.26 to 0.83) and 0.13 (0.04 to 0.23), and on achieving PASI 90 were 0.46 (0.13 to 0.78) and 0.16 (0.07 to 0.25) (Table 2). Part-time job predicted a maximal benefit of 0.28 (0.21 to 0.36) and full-time job predicted a minimal benefit of 0.15 (0.10 to 0.21) on achieving PASI 75. The maximal benefits and minimal benefits on achieving PASI 90 were similar, with effects of 0.29 (0.21 to 0.37) and 0.13 (0.08 to 0.19), respectively. BSA score only indicated moderate predictability (Fig. 1C and 1H). The statistical differences between the maximal benefits and the minimal benefits showed P -values of 0.05 at PASI 75 and 0.06 at PASI 90 (Table 2). Predictors in weeks 1 – 4 PASI and BSA demonstrated good capacities to differentiate the benefits of IL-inhibitor therapy and conventional therapies in the induction period. Both scores showed a W-shaped benefit pattern as the values increased from 0 to the highest values (Fig. 2). The maximal benefits (mean 0.38) were all achieved at the minimum of the scores 1.0, with 95 confidence intervals varying from 0.20 to 0.58; the minimal benefits were obtained at the relatively higher scores, with benefits of IL-inhibitor therapy being no statistically significant than that of conventional therapies. The rest variables were not capable of predicting statistically significant large differences between the maximal and minimal benefits of IL-inhibitor therapy (eFigure 2 and Table 2). Analyses across weeks 1 – 4 and weeks 5–46 PASI was identified as the predictor for both weeks 1–4 and 5–46. DLQI was only weakly correlated with baseline PASI, with the Pearson correlations of 0.26 and 0.27 ( P -values < 0.001) in weeks 1–4 and 5–46, respectively, and thus was considered a predictor different from PASI. The Kendall rank correlations between employment and baseline PASI were not statistically significant ( P -values 0.19 in weeks 1–4 and 0.61 in weeks 5–46), demonstrating that employment was an independent predictor. On the other hand, the BSA score was moderately correlated with PASI, with Pearson correlations of 0.76 and 0.77( P -values < 0.001) in weeks 1–4 and 5–46, respectively. We compared the benefits on achieving PASI 75 and 90 in weeks 1–4 and 5–46 across baseline PASI values visually (Figs. 1 and 2). Baseline PASI below 5 indicated relatively higher benefits in weeks 1–4, followed by decreased benefits in weeks 5–46. Baseline PASI above 5 predicted an increasing relative benefit from weeks 1–4 to weeks 5–46. In particular, baseline PASI at 15–20 predicted relatively smaller increased benefits from weeks 1–4 to weeks 5–46 than other PASI values above 5, leading to reversed V-shape benefits between weeks 1–4 and weeks 5–46 for moderate-to-severe psoriasis (PASI above 5). Finally, we noted that in both weeks 1–4 and 5–46, patients with mild psoriasis, indicated by baseline PASI below 5 or BSA score below 5, had substantial benefits from treatment with biologics as compared to the conventional therapy (Figs. 1 and 2). Discussion In this large-scale real-world study reported here, baseline PASI, DLQI and employment demonstrated stronger predictability in achieving the benefits of IL inhibitors independently. For weeks 5—46, baseline PASI predicted an increasing relative benefit of biologics as the value increased from 0 to 5, followed by a V-shaped benefit as the value further increased. Higher DLQI and part-time job indicated higher benefits. For weeks 1—4, baseline PASI and its correlated BSA score predicted W-shaped benefits of biologics as their values varied from the lowest to the highest. The identified predictors and the prediction patterns for PASI 75 and PASI 90 were similar. The benefits of biologics relative to the conventional therapies in weeks 5—46 were all significantly larger than zero, but the lowest in weeks 1—4 could be negligible. Finally, we found that patients with mild psoriasis, i.e. with PASI below 5 or BSA scores below 5, had statistically significant benefits from treatment with IL inhibitors. Conventionally, biologics were recommended in clinical guidelines mainly for severe psoriasis with BSA above 10, and as the second-line medications only. 13 , 22 These guidelines are mostly based on the clinical evidence where biologics were assigned to patients with severe psoriasis who had not responded to conventional therapies. 6 , 7 , 23 As such, despite the higher effectiveness in treating severe psoriasis, the advancement in biologics treatment of mild-to-moderate psoriasis is limited. Our results relied on real-world clinical data and were capable of providing the full picture of the heterogenous clinical benefits of biologics from mild to severe psoriasis. The findings showed that IL inhibitor had more substantial improvements than the conventional therapies for mild-to-moderate patients in both the induction and maintenance periods, i.e, weeks 1–4 and weeks 5–46. For severe psoriasis, the results confirmed the clinical findings 6 , 7 , 23 but presented more nuances on the heterogenous responses as baseline PASI or DLQI varied. Of note, our analysis showed that baseline BSA score had strong predictability of benefits of biologics for weeks 1–4 and also moderate predictability for weeks 5–46. Although the BSA score appeared less accurate than PASI in measuring disease severity, it was more widely used in real-world practice due to the simple and time-saving measuring process. 24 Therefore, baseline BSA score may be used as an easily implementable predictive biomarker in real-world practice when PASI is not available. Unemployment has been associated with poor PASI 75 and PASI 90 responses in a multicentre registry on biologics therapies in the United Kingdom and the Republic of Ireland 25 . Our findings added to the literature by showing that the impact of job conditions on treatment outcome is larger for biologics therapies than conventional therapies. DLQI, a self-reported measurement that may capture many personalized unmeasured factors 26 , has been shown to be strongly associated with the social cost of the psoriasis disease than the disease severity measurements. 27 In the United Kingdom, DLQI has been recommended for determining the eligibility of receiving biologics for moderate-to-severe psoriasis patients. 28 However, according to the real-world data in Sweden, it appeared that the index has not been associated with the decision to initiate biologics treatment. 29 Our findings suggested DLQI as an important independent predictive biomarker for biologics treatment, calling for more patient-centric therapy solutions in clinical practice. However, both DLQI and part-time job were identified as predictive biomarkers in weeks 5–46 only. The reason could be due to the lower treatment adherence in patients with lower DLQI or full-time jobs, the impact of which could be less evident in a short period. Relevant data on treatment duration and compliance is needed to clarify this. PASI were identified as predictive biomarkers in both weeks 1–4 and weeks 5–46. An interesting finding in comparing the relative benefits predicted by PASI in the two periods was the smaller increased benefits from weeks 1–4 to weeks 5–46 for baseline PASI 15—20. It is not precisely clear why patients with PASI around 15—20 had quicker clinical responses in the first few weeks of the treatment but relatively lower responses in the following weeks until one year. The inference methods that we used were able to control the baseline covariates. As expected, we didn’t find any significant differences between the baseline covariates between the patients with PASI 15— 20 and those with other values of PASI (above 5) in both weeks 1–4 and weeks 5–46. Also, although IL-23 inhibitor biologics were shown to be more delayed in improving psoriasis than IL-17 inhibitor biologics 23 , this is likely to be the dominant reason in our case. In the data, more than 96% of biologics used were IL-17 inhibitor, specifically, Secukinumab. Possibly, underlying characteristics that are not captured by our data may contribute to the phenomena and further investigation with more rich data is needed. Understanding the appropriate first-line therapies is critical for targeting higher patient satisfaction and better treatment outcomes, since patients with treatment-region failure tended to experience longer disease duration and higher chances of developing multiple comorbidities 30 , 31 . Knowledge of predictive biomarkers based on the heterogenous efficacies from real-world data can help to pinpoint the first-line therapies that are more suitable for an individual patient. The analyses used doubly robust causal inference methods to estimate the conditional treatment effects of IL inhibitors versus the conventional therapies, conditioning on clinically meaningful factors. The inference methods can control all measured baseline covariates and thus produce unbiased and stable estimates, provided that all the confounding between the psoriasis treatments and PASI responses are captured by the baseline covariates. This study has some limitations. The causal inference methods that we used assume that all the confounding has been captured by the measured baseline covariates. The estimated effects could be biased if there were some important unmeasured confounding that our data failed to capture. However, given the large number of covariates we have included, we believe that the important unmeasured covariates, if exist, are likely to be associated with some of our measured covariates, and thus, by controlling the measured covariates, we are capable of reducing the biases due to the unmeasured covariates as well. The IL inhibitor therapies included in the study were mainly 300mg injections, and the study was short-term with ending points of less than one year. Long-term data with varying doses of injections would allow for a fully understanding of the biologics treatment heterogeneity. Conclusions Our real-world data analysis provides evidence on the biologics used for mild-to-moderate psoriasis and data-driven recommendations for moderate-to-severe psoriasis. Heterogenous efficacy comparisons reported here revealed that baseline PASI, DLQI, and employment were good and independent predictors for psoriasis biologics on achieving PASI 75 and 90. Understanding patient heterogeneity in response to psoriasis biologics in the real world bears broad implications towards personalized treatment for psoriasis. The identified predictors could contribute to assisting clinicians in differentiating among treatment choices for their patients, and may help policymakers develop guidelines that target at higher patient satisfaction and better treatment outcomes. Unsectioned Tables Table 1 Description of the data at the baseline. Number (Proportion) / Median (IQR) (n = 6,220) Demographic characteristics Sex Female (%) 2,169 (34.87) Male (%) 4,051 (65.13) Age 39.5 (30.0–54.0) BMI 24.01 (21.6–26.4) Smoking Ex-smokers (%) 374 (6.01) Current smokers (%) 1,566 (25.18) Non-smokers (%) 4,280 (68.81) Comorbidity conditions No-comorbidity (%) 4,909 (78.92) Have comorbidity (%) 801 (12.88) Not clear (%) 510 (8.20) Hospital locations Dongbei district (%) 1,199 (19.28) Huabei District (%) 1,204 (19.36) Huadong District (%) 1,021 (16.41) Huanan District (%) 746 (11.99) Huazhong District (%) 1,652 (26.56) Xibei District (%) 190 (3.05) Xinan District (%) 208 (3.34) Social conditions Insurance Free or commercial medical care (%) 483 (7.77) General government funded medical care (%) 5,737 (92.23) Education College and higher (%) 1,849 (29.73) High school and lower (%) 4,371 (70.27) Marital status Married (%) 4,575 (73.55) Unmarried (%) 1,645 (26.45) Employment Full-time (%) 3,683 (59.21) Part-time (%) 2,537 (40.79) Disease severity BSA 14.1 (5.0–30.0) PASI 9.0 (3.6–16.8) Quality of life DLQI 7 (3–12) Table 2 Maximum and minimum of relative benefits and their statistical differences for each candidate predictor. PASI 75 PASI 90 P-values below 0.05 at PASI 75, 90 Maximum Minimum P-value Maximum Minimum P-value Mean 95%CI Mean 95%CI Mean 95%CI Mean 95%CI Week 5–46 PASI 0.36 (0.28, 0.44) 0.03 (-0.14, 0.20) < 0.001 0.39 (0.09, 0.69) 0.10 (0.02, 0.19) 0.04 Yes DLQI 0.55 (0.26, 0.83) 0.13 (0.04, 0.23) 0.01 0.46 (0.13, 0.78) 0.16 (0.07, 0.25) 0.04 Yes BSA 0.29 (0.16, 0.41) 0.13 (-0.01, 0.27) 0.05 0.28 (0.11, 0.48) 0.12 (0.02, 0.22) 0.06 No Age 0.34 (0.02, 0.66) 0.09 (-0.16, 0.34) 0.12 0.34 (0.01, 0.66) -0.01 (-0.28, 0.26) 0.06 No BMI 0.51 (-0.43, 1.00) 0.18 (0.12, 0.24) 0.24 0.56 (-0.29, 1.00) 0.05 (-0.13, 0.23) 0.12 No Employment 0.28 (0.21, 0.36) 0.15 (0.10, 0.20) 0.01 0.29 (0.21, 0.37) 0.13 (0.08, 0.19) < 0.001 Yes Marital status 0.26 (0.17, 0.35) 0.18 (0.13, 0.23) 0.07 0.26 (0.17, 0.36) 0.17 (0.12, 0.22) 0.05 No Nail involvement 0.36 (0.18, 0.53) 0.19 (0.14, 0.24) 0.04 0.32 (0.13, 0.52) 0.18 (0.14, 0.23) 0.09 No Education 0.20 (0.15, 0.25) 0.20 (0.11, 0.28) 0.45 0.21 (0.13, 0.30) 0.19 (0.13, 0.24) 0.28 No Insurance 0.26 (0.10, 0.42) 0.20 (0.15, 0.24) 0.23 0.28 (0.11, 0.45) 0.19 (0.14, 0.23) 0.15 No Sex 0.21 (0.14, 0.29) 0.19 (0.14, 0.25) 0.37 0.23 (0.16, 0.31) 0.17 (0.12, 0.23) 0.10 No Smoke 0.29 (0.03, 0.56) 0.19 (0.07, 0.32) 0.25 0.19 (0.12, 0.27) 0.19 (-0.10, 0.47) 0.48 No Comorbidity 0.30 (0.17, 0.44) 0.15 (-0.03, 0.33) 0.09 0.26 (0.07, 0.46) 0.11 (-0.07, 0.29) 0.13 No Week 1–4 PASI 0.38 (0.20, 0.56) 0.00 (-0.13, 0.12) < 0.001 0.37 (0.18, 0.56) -0.06 (-0.17, 0.05) < 0.001 Yes DLQI 0.14 (0.06, 0.21) 0.04 (-0.06, 0.13) 0.06 0.14 (0.07, 0.20) -0.08 (-0.29, 0.13) 0.03 No BSA 0.29 (0.15, 0.44) -0.08 (-0.21, 0.05) < 0.001 0.29 (0.15, 0.43) -0.14 (-0.24, -0.04) < 0.001 Yes Age 0.15 (0.07, 0.23) 0.05 (-0.06, 0.15) 0.07 0.10 (0.03, 0.17) -0.05 (-0.26, 0.15) 0.08 No BMI 0.11 (0.01, 0.21) 0.00 (-0.21, 0.21) 0.17 0.11 (0.04, 0.18) -0.09 (-0.64, 0.45) 0.24 No Employment 0.12 (0.07, 0.17) 0.08 (0.01, 0.15) 0.21 0.08 (0.01, 0.14) 0.07 (0.03, 0.11) 0.44 No Marital status 0.16 (0.07, 0.25) 0.08 (0.03, 0.13) 0.06 0.10 (0.02, 0.17) 0.06 (0.02, 0.11) 0.23 No Nail involvement 0.18 (0.04, 0.32) 0.09 (0.05, 0.14) 0.12 0.07 (0.04, 0.11) 0.05 (-0.05, 0.15) 0.34 No Education 0.10 (0.03, 0.17) 0.10 (0.05, 0.15) 0.50 0.09 (0.04, 0.15) 0.06 (0.02, 0.11) 0.20 No Insurance 0.11 (0.06, 0.15) 0.01 (-0.15, 0.17) 0.12 0.08 (0.05, 0.12) -0.08 (-0.17, 0.00) < 0.001 No Sex 0.12 (0.07, 0.17) 0.07 (-0.01, 0.14) 0.12 0.08 (0.04, 0.12) 0.06 (-0.00, 0.13) 0.36 No Smoke 0.12 (0.04, 0.19) -0.04 (-0.24, 0.16) 0.08 0.09 (0.02, 0.15) -0.09 (-0.25, 0.06) 0.02 No Comorbidity 0.15 (-0.11, 0.41) 0.02 (-0.14, 0.18) 0.19 0.09 (0.04, 0.15) -0.03 (-0.16, 0.10) 0.05 No Additional Declarations No competing interests reported. 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18:07:40","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":315695,"visible":true,"origin":"","legend":"","description":"","filename":"supplidentifyingpredictorofilsr.docx","url":"https://assets-eu.researchsquare.com/files/rs-2274250/v1/8a5a4489cc11e264c78cabee.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identifying demographic, social, and clinical predictors of interleukin inhibitor biologic therapy effects using the real-world clinical data","fulltext":[{"header":"Declarations","content":"\u003ch2\u003eDeclarations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eConflicts of interest\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNational Science and Technology Major Project of the Ministry of Science and Technology of China (No.2021YFF0901400), National Natural Science Fund of China (No. 81773546), and the National Key R\u0026amp;D Program of China (2021YFF1201100).\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eX.-H.Z., and H.L. supervised the work. S.H., P.W, and Z.Y. designed the study. Z.Y. collected data. S.H. and P.W. performed analysis. S.H., P.W, Z.Y., X.-H.Z., and H.L. interpreted the findings. S.H. P.W, and Z.Y. wrote the manuscript. R.L.,X.-H.Z., and H.L. commented on and revised the manuscript. All authors approved the final manuscript as submitted.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis study was supported by the grants from National Science and Technology Major Project of the Ministry of Science and Technology of China (No.2021YFF0901400), National Natural Science Fund of China (No. 81773546), and the National Key R\u0026amp;D Program of China (2021YFF1201100). The funder of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGriffiths C, Lancet JB-T, 2007 U. 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Journal of Vascular Surgery. 2002;36(1):194\u0026ndash;195. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1067/MVA.2002.125015\u003c/span\u003e\u003cspan address=\"10.1067/MVA.2002.125015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. R: A language and environment for statistical computing. \u003cem\u003eR Foundation for Statistical Computing\u003c/em\u003e. Published online 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Psoriasis Foundation. About psoriasis. National Psoriasis Foundation. Accessed November 11, 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.psoriasis.org/about-psoriasis\u003c/span\u003e\u003cspan address=\"https://www.psoriasis.org/about-psoriasis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWarren RB, Gooderham M, Burge R, et al. Comparison of cumulative clinical benefits of biologics for the treatment of psoriasis over 16 weeks: Results from a network meta-analysis. Journal of the American Academy of Dermatology. 2020;82(5):1138\u0026ndash;1149. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/J.JAAD.2019.12.038\u003c/span\u003e\u003cspan address=\"10.1016/J.JAAD.2019.12.038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeonardi C, See K, Gallo G, et al. Psoriasis Severity Assessment Combining Physician and Patient Reported Outcomes: The Optimal Psoriasis Assessment Tool. Dermatology and Therapy. 2021;11(4):1249\u0026ndash;1263. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/S13555-021-00544-6/FIGURES/7\u003c/span\u003e\u003cspan address=\"10.1007/S13555-021-00544-6/FIGURES/7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWarren RB, Marsden A, Tomenson B, et al. Identifying demographic, social and clinical predictors of biologic therapy effectiveness in psoriasis: a multicentre longitudinal cohort study. British Journal of Dermatology. 2019;180(5):1069\u0026ndash;1076. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/BJD.16776\u003c/span\u003e\u003cspan address=\"10.1111/BJD.16776\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarkar R, Chugh S, Bansal S. General measures and quality of life issues in psoriasis. Indian dermatology online journal. 2016;7(6):481. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/2229-5178.193908\u003c/span\u003e\u003cspan address=\"10.4103/2229-5178.193908\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmitt JM, Ford DE. Work limitations and productivity loss are associated with health-related quality of life but not with clinical severity in patients with psoriasis. Dermatology (Basel, Switzerland). 2006;213(2):102\u0026ndash;110. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1159/000093848\u003c/span\u003e\u003cspan address=\"10.1159/000093848\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Institute for Health and Clinical Excellence. Ustekinumab for the treatment of adults with moderate to severe psoriasis NICE Technology Appraisal Guidance [TA180]. Published 2009. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nice.org.uk/guidance/ta180\u003c/span\u003e\u003cspan address=\"https://www.nice.org.uk/guidance/ta180\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH\u0026auml;gg D, Sundstr\u0026ouml;m A, Eriksson M, Schmitt-Egenolf M. Decision for biological treatment in real life is more strongly associated with the Psoriasis Area and Severity Index (PASI) than with the Dermatology Life Quality Index (DLQI). Journal of the European Academy of Dermatology and Venereology. 2015;29(3):452\u0026ndash;456. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/JDV.12576\u003c/span\u003e\u003cspan address=\"10.1111/JDV.12576\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFoster SA, Zhu B, Guo J, et al. Patient Characteristics, Health Care Resource Utilization, and Costs Associated with Treatment-Regimen Failure with Biologics in the Treatment of Psoriasis. Journal of managed care \u0026amp; specialty pharmacy. 2016;22(4):396\u0026ndash;405. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18553/JMCP.2016.22.4.396\u003c/span\u003e\u003cspan address=\"10.18553/JMCP.2016.22.4.396\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGer T-Y, Huang Y-H, Hui RC-Y, Tsai T-F, Chiu H-Y. Effectiveness and safety of secukinumab for psoriasis in real-world practice: analysis of subgroups stratified by prior biologic failure or reimbursement. Therapeutic advances in chronic disease. 2019;10:2040622319843756. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/2040622319843756\u003c/span\u003e\u003cspan address=\"10.1177/2040622319843756\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Unsectioned Figure Details","content":"\u003cdiv category=\"Standard\" float=\"Yes\" id=\"Fig1\" class=\"Figure\"\u003e\u003cdiv category=\"Completeness\" id=\"12\" ruleid=\"MissingFigureImage_01\" status=\"Neutral\" values=\"Fig. 1\" class=\"btn-xs-small Annotation tooltipped\" data-position=\"top\" data-tooltip=\"\"\u003eA\u003c/div\u003e \u003cdiv language=\"En\" class=\"Caption\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eFig. 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eEstimated conditional treatment effects of IL inhibitor biologics versus the conventional therapies in weeks 5\u0026ndash;46, on candidate predictors with continuous values\u003c/span\u003e. Relative benefits measured by PASI 75 for candidates (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eA\u003c/span\u003e) baseline PASI, (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eB\u003c/span\u003e) DLQI, (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eC\u003c/span\u003e) BSA, (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eD\u003c/span\u003e) Age, and (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eE\u003c/span\u003e) BMI. Relative benefits measured by PASI 90 for candidates (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eF\u003c/span\u003e) baseline PASI, (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eG\u003c/span\u003e) DLQI, (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eH\u003c/span\u003e) BSA, (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eI\u003c/span\u003e) Age, and (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eJ\u003c/span\u003e) BMI. Shaded areas refer to the 95 confidence interval. The dotted line denoted the zero line.\u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e\u003cbr/\u003e\u003cdiv category=\"Standard\" float=\"Yes\" id=\"Fig2\" class=\"Figure\"\u003e\u003cdiv category=\"Completeness\" id=\"14\" ruleid=\"MissingFigureImage_01\" status=\"Neutral\" values=\"Fig. 2\" class=\"btn-xs-small Annotation tooltipped\" data-position=\"top\" data-tooltip=\"\"\u003eA\u003c/div\u003e \u003cdiv language=\"En\" class=\"Caption\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eFig. 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eEstimated conditional treatment effects of IL inhibitor biologics versus the conventional therapies in weeks 1\u0026ndash;4, on candidate predictors with continuous values\u003c/span\u003e. Relative benefits measured by PASI 75 for candidates (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eA\u003c/span\u003e) baseline PASI, (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eB\u003c/span\u003e) DLQI, (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eC\u003c/span\u003e) BSA, (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eD\u003c/span\u003e) Age, and (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eE\u003c/span\u003e) BMI. Relative benefits measured by PASI 90 for candidates (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eF\u003c/span\u003e) baseline PASI, (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eG\u003c/span\u003e) DLQI, (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eH\u003c/span\u003e) BSA, (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eI\u003c/span\u003e) Age, and (\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eJ\u003c/span\u003e) BMI. Shaded areas refer to the 95 confidence interval. The dotted line denoted the zero line.\u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e\u003cbr/\u003e"},{"header":"Unsectioned Paragraphs","content":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e Appropriate and effective use of biological agents is important to improve the benefits of psoriasis patients. We examined how the effects of interleukin (IL) inhibitors vary across patients' demographic, social, and clinical characteristics in treating psoriasis, and whether IL inhibitors are effective for managing mild-to-moderate psoriasis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e Data were collected from a large national registry in China from Sep 2020 to Sep 2021. Clinical benefits, measured by 75% (and 90%) or more improvement from baseline Psoriasis Area and Severity Index (PASI 75 and PASI 90), were contrasted using the propensity-score-based causal inference methodology between the IL inhibitors and the conventional therapies. Candidates that can differentiate the benefits with \u003cem\u003eP\u003c/em\u003e-values less than 0.05 were identified as predictors.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e Baseline PASI, DLQI, and employment demonstrated stronger predictability in achieving the benefits of IL inhibitors. For weeks 5\u0026minus;46, baseline PASI predicted an increasing relative benefit of biologics as the value increased from 0 to 5, followed by a V-shaped benefit as the value further increased. Baseline PASI scores at 5.4 and 1.0 predicted the maximal and minimal benefits on achieving PASI 75, with an increase in probabilities of 0.36 (95CI 0.28 to 0.44) and 0.03 (-0.14 to 0.20), respectively. Higher DLQI predicted the maximal benefit (0.55, 0.26 to 0.83) of achieving PASI 75 and lower DLQI predicted the minimal benefit of 0.13 (0.04 to 0.23). Part-time job predicted the maximal benefit of 0.28 (0.21 to 0.36) and full-time job predicted the minimal benefit of 0.15 (0.10 to 0.21). These findings were consistent in achieving PASI 75 and PASI 90.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e This article fills the gap in treating mild psoriasis with IL inhibitor biologics. Patients with mild psoriasis, i.e. with PASI below 5 or BSA scores below 5, had statistically significant benefits from treatment with IL inhibitors. The studying provides evidence from real-world data on patients\u0026rsquo; heterogeneous responses to IL inhibitor biologics. Identified clinical and social predictors can be used for treatment differentiation in clinical practice.\u003c/p\u003e\u003cp\u003eXiao-Hua Zhou ([email protected]) and Hang Li ([email protected]).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAbstract (323 words)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eWord counts: 3200\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePsoriasis is a chronic immune-medicated inflammatory disease that can lead to serious impairment in patients\u0026rsquo; quality of life and a substantial burden on society.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Although recognized as an important predictor for comorbidity, the disease was often underdiagnosed or undertreated, with the problem more severe for mild-to-moderate psoriasis.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Immunological and genetic studies have identified the proinflammatory cytokines (tumour necrosis factor-alpha (TNFα), interleukin-17 (IL-17) and interleukin-23 (IL-23)) as key drivers of psoriasis pathogenesis.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Immune targeting of these cytokines by biological therapies has been validated in clinical trials primarily for severe psoriasis.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e New generation of biologics IL-17 inhibitors (secukinumab, ixekizumab, and brodalumab) and IL-23 inhibitors (guselkumab, risankizumab, and tildrakizumab) entered the market successively. In China, Secukinumab has been added into the 2020 national drug reimbursement list (NDRL) for improving the care of psoriasis; clinical use of biologics has substantially increased since the initiation of the 2020 NRDL, Mar 1 2021.\u003c/p\u003e\u003cp\u003eAlthough biologic therapies have been a mainstay in the treatment of moderate-to-severe disease, the effectiveness profile for mild-to-moderate psoriasis is yet to be established. Three studies were identified on studying the relationships of bioinformatic metric with the treatment responses, such as a gene signature score derived from skin mRNA,\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e serum cytokines, \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and lymphocyte phenotypes,\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e one on determining the optimal therapeutic range for adalimumab, a TNFα inhibitor,\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and none of them included mild-to-moderate psoriasis. Also, evidence of the first-line biologic treatment of severe psoriasis in clinical trials is limited, with little information on biologic treatment of mild-to-moderate psoriasis.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e As such, biological agents are indicated only for the treatment of mild-to-severe plaque psoriasis or arthropathic psoriasis among patients who do not respond, are intolerant or contraindicated for systemic therapies in Chinese psoriasis guidelines. \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e With the inclusion of biologics in the 2020 NDRL, patient preference for biologics could increase sharply. Appropriate and effective use of biologics for psoriasis has drawn the attention of many dermatologists. Although efforts have been made to reduce discrepancies between treatment guidelines and clinical practice,\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e as individual responses to biologics in patients with psoriasis could vary considerably, controversy still exists in the clinical practice.\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThere is an important need to address the heterogeneity in the clinical practice of biologic treatment and to guide clinicians by providing evidence-based advice. We aim to identify the predictors that can predict the appropriate therapies for individual patients with psoriasis, including mild conditions. Data were collected from a large national registry, covering 237 tertiary hospitals in China. The doubly robust causal inference methods were used to estimate the heterogenous treatment efficacies of biologics versus the conventional therapies, conditioning on prespecified predictor candidates. Candidates\u0026rsquo; capacity to differentiate relatively clinical benefits of IL inhibitors was assessed, and those demonstrating strong predictability of future responses were selected as predictors.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEvidence before this study\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe systematically searched 12 electronic and regional databaes (Medline, EmBASe, Web of Science, SciELO, Korean Journal DataBASes, Russian Science Citation, Index, WPRIM, SaudiMedLit, Informit, IndMed, and HERDIN) from their respective inception dates to\u003c/p\u003e\u003cp\u003eOctober 2021. We used the search terms (\u0026ldquo;psoria*\u0026rdquo;) AND (\u0026ldquo;biologic*\u0026rdquo;) AND (\u0026ldquo;DLQI\u0026rdquo; OR \u0026ldquo;PASI\u0026rdquo;) AND (\u0026ldquo;heterogene*\u0026rdquo; OR \u0026ldquo;precision med*\u0026rdquo;). No language restrictions were applied. We identified 4 papers published in peer-review journals and none of them included mild-to-moderate psoriasis. Three of them studied on the relationships of bioinformatic metric with the treatment responses, such as a gene signature score derived from skin mRNA,\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e serum cytokines, \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and lymphocyte phenotypes,\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e one on determining the optimal therapeutic range for adalimumab, a TNFα inhibitor.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e We also screened the references of all included studies and published review articles to identify studies on the efficacies of inhibitor biologics. In the two widely cited clinical trials\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, inhibitor biologics were assigned to individuals having a score of 12 or higher on the psoriasis-area-and-severity index score (PASI \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge 12\\)\u003c/span\u003e\u003c/span\u003e) and involvement of 10% or more of the body surface area (BSA \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge 10\\)\u003c/span\u003e\u003c/span\u003e), and had been inadequately controlled by topical treatments, phototherapy, and/or previous systemic therapy. Until now, there has been scarce information to understand the efficacies of biologics on mild-to-moderate psoriasis or the heterogeneity of the causal effects across clinical characteristics and social conditions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy design\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatients were collected from a national registry in China, Psoriasis Center Data Platform (PCDP) which was initiated in Sep 2020 and covered 237 tertiary hospitals in about 100 cities in mainland of China. PCDP was led by the National Clinical Research Center for Skin and Immune Diseases to facilitate the tracking of patients with psoriasis. All patients signed the informed consent at the registry enrollment. The eligibility criteria for the present study were patients who enrolled from Sep 2020 to Sep 2021 and had been diagnosed with plaque psoriasis at or before the enrollment and had at least one follow-up visit. In addition, patients were treated with IL-inhibitor biologics (mainly the IL-17 inhibitor, Secukinumab) or conventional therapies (topic drugs, systemic medicines or phototherapy) at the enrollment. Patients who had used medications at any time before entering the registry were excluded. The final sample comprised 5663 patients. 1696 patients were treated with IL-inhibitor biologics and about 92.6% of biologics therapies used a dose of 300mg injection; the remaining were treated with conventional therapies.\u003c/p\u003e\u003cp\u003ePrimary endpoints were 75% (90%) or more improvement from baseline Psoriasis Area and Severity Index score (PASI 75 and PASI 90) in the follow-up visits. Patients\u0026rsquo; demographics and clinical characteristics were collected at the registry enrollment. The demographics include sex, age, Body Mass Index (BMI), smoking history and comorbidity conditions, insurance status, education status, marital status, and the location of visiting hospitals (including seven districts in China, Dongbei District, Huabei District, Huadong District, Huanan District, Huazhong District, Xibei District and Xinan District). Clinical characteristics include self-reported Dermatology Quality of Life Index (DQLI), and baseline disease severity estimated by PASI and Body Surface Area (BSA) score. A description of these variables is given in Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePrespecified candidate predictors included the disease severity measures (baseline PASI, baseline BSA score, nail involvement), demographics (age, sex, BMI, smoke, education, and comorbidity), social conditions (marital status, employment, and insurance condition), as well as patients\u0026rsquo; self-reported baseline DLQI. Because the purpose is to identify predictors for the use of biologics in the induction period and the maintenance period, we assessed the candidate predictors in the endpoints weeks 1\u0026ndash;4 and weeks 5\u0026ndash;46 separately.\u003c/p\u003e\u003cp\u003eWe estimated the heterogenous treatment effects of IL inhibitors versus the conventional therapies conditioning on the candidate predictors. We employed the propensity-score-based doubly robust estimation method to adjust for confounders. Unlike the propensity-score-based weighting method, the method can provide unbiased estimates if either the propensity score model or the outcome models are specified correctly.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e The propensity score and the outcome were estimated using the logistic regressions, with all baseline covariates included. Since the physicians tended to use biologics more often after the initiation of 2020 NDRL (Mar 1 2021) \u0026mdash;i.e., the treatment assignment mechanism changed after Mar 1 2021, we estimated the propensity scores for patients who entered the registry before and after Mar 1 2021 separately. Age outside of 9\u0026ndash;100 or BMI outside of 10\u0026ndash;50 were considered recording errors, and 135 individuals were excluded from the analysis as a consequence. Also, since there were only a few samples available at extremely high values of baseline PASI, we grouped the baseline PASI above 45 into the PASI 45. Similarly, we grouped BSA above 65, age above 75, and BMI above 40.\u003c/p\u003e\u003cp\u003eFor each candidate predictor, we selected the 95% Confidence Intervals (95 CIs) corresponding to the maximal and minimal estimated conditional effects. Statistical differences between the two means, the maximum and minimum of conditional effects, were assessed by examining the overlap of the two 95CIs using the method in the literature.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Candidates with the two means differing with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(P\\le 0.05\\)\u003c/span\u003e\u003c/span\u003e at both PASI 75 and PASI 90 were qualified as predictors. For candidates that were selected as predictors for both weeks 1\u0026ndash; 4 and weeks 5\u0026ndash;46, the clinical benefits at the same values of predictors were visually compared. All analyses were performed in R 4.0.2. \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e This study was approved by the Biomedical Ethics Committee of The Peking University First Hospital in Beijing, China, approval number 2020\u0026thinsp;\u0026minus;\u0026thinsp;255. All research was performed in accordance with relevant regulations, and informed consent was obtained from all participants.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePredictors in weeks 5\u003c/b\u003e\u0026ndash;\u003cb\u003e46\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBaseline PASI, DLQI and employment were identified to be strong predictors of achieving PASI 75 and PASI 90, when assessing the benefits of IL-inhibitor therapy versus conventional therapies in the maintenance period.\u003c/p\u003e\u003cp\u003eThe relative advantage of biologics over the conventional therapies increased as baseline PASI increased from 0 to 5; and when the score exceeded 5, it presented a V-shape, with a trough around the PASI value of 20.0 (Fig.\u0026nbsp;1A and 1F). Baseline PASI scores at 5.4 and 1.0 predicted the maximal and minimal benefits on achieving PASI 75, with an increase in probabilities of 0.36 (95CI 0.28 to 0.44) and 0.03 (-0.14 to 0.20) respectively(Table\u0026nbsp;2). The minimal benefits on achieving PASI 90 (mean 0.10, 95CI 0.02 to 0.19) were obtained at the trough of the V-shape (PASI 21.0) and the maximal benefits (0.39, 0.09 to 0.69) were obtained at the highest PASI value, PASI 45.\u003c/p\u003e\u003cp\u003eDLQI and employment presented a much simpler pattern. Patients with higher DLQI or part-time jobs indicate higher benefits of biologics (Fig.\u0026nbsp;1 and eFigure 1). For DLQI, the predicted maximal and minimal benefits on achieving PASI 75 were 0.55 (0.26 to 0.83) and 0.13 (0.04 to 0.23), and on achieving PASI 90 were 0.46 (0.13 to 0.78) and 0.16 (0.07 to 0.25) (Table\u0026nbsp;2). Part-time job predicted a maximal benefit of 0.28 (0.21 to 0.36) and full-time job predicted a minimal benefit of 0.15 (0.10 to 0.21) on achieving PASI 75. The maximal benefits and minimal benefits on achieving PASI 90 were similar, with effects of 0.29 (0.21 to 0.37) and 0.13 (0.08 to 0.19), respectively.\u003c/p\u003e\u003cp\u003eBSA score only indicated moderate predictability (Fig.\u0026nbsp;1C and 1H). The statistical differences between the maximal benefits and the minimal benefits showed \u003cem\u003eP\u003c/em\u003e-values of 0.05 at PASI 75 and 0.06 at PASI 90 (Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePredictors in weeks 1\u003c/b\u003e\u0026ndash;\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePASI and BSA demonstrated good capacities to differentiate the benefits of IL-inhibitor therapy and conventional therapies in the induction period. Both scores showed a W-shaped benefit pattern as the values increased from 0 to the highest values (Fig.\u0026nbsp;2). The maximal benefits (mean 0.38) were all achieved at the minimum of the scores 1.0, with 95 confidence intervals varying from 0.20 to 0.58; the minimal benefits were obtained at the relatively higher scores, with benefits of IL-inhibitor therapy being no statistically significant than that of conventional therapies. The rest variables were not capable of predicting statistically significant large differences between the maximal and minimal benefits of IL-inhibitor therapy (eFigure 2 and Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalyses across weeks 1\u003c/b\u003e\u0026ndash;\u003cb\u003e4 and weeks 5\u0026ndash;46\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePASI was identified as the predictor for both weeks 1\u0026ndash;4 and 5\u0026ndash;46. DLQI was only weakly correlated with baseline PASI, with the Pearson correlations of 0.26 and 0.27 (\u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in weeks 1\u0026ndash;4 and 5\u0026ndash;46, respectively, and thus was considered a predictor different from PASI. The Kendall rank correlations between employment and baseline PASI were not statistically significant (\u003cem\u003eP\u003c/em\u003e-values 0.19 in weeks 1\u0026ndash;4 and 0.61 in weeks 5\u0026ndash;46), demonstrating that employment was an independent predictor. On the other hand, the BSA score was moderately correlated with PASI, with Pearson correlations of 0.76 and 0.77( \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in weeks 1\u0026ndash;4 and 5\u0026ndash;46, respectively.\u003c/p\u003e\u003cp\u003eWe compared the benefits on achieving PASI 75 and 90 in weeks 1\u0026ndash;4 and 5\u0026ndash;46 across baseline PASI values visually (Figs.\u0026nbsp;1 and 2). Baseline PASI below 5 indicated relatively higher benefits in weeks 1\u0026ndash;4, followed by decreased benefits in weeks 5\u0026ndash;46. Baseline PASI above 5 predicted an increasing relative benefit from weeks 1\u0026ndash;4 to weeks 5\u0026ndash;46. In particular, baseline PASI at 15\u0026ndash;20 predicted relatively smaller increased benefits from weeks 1\u0026ndash;4 to weeks 5\u0026ndash;46 than other PASI values above 5, leading to reversed V-shape benefits between weeks 1\u0026ndash;4 and weeks 5\u0026ndash;46 for moderate-to-severe psoriasis (PASI above 5).\u003c/p\u003e\u003cp\u003eFinally, we noted that in both weeks 1\u0026ndash;4 and 5\u0026ndash;46, patients with mild psoriasis, indicated by baseline PASI below 5 or BSA score below 5, had substantial benefits from treatment with biologics as compared to the conventional therapy (Figs.\u0026nbsp;1 and 2).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiscussion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this large-scale real-world study reported here, baseline PASI, DLQI and employment demonstrated stronger predictability in achieving the benefits of IL inhibitors independently.\u003c/p\u003e\u003cp\u003eFor weeks 5\u0026mdash;46, baseline PASI predicted an increasing relative benefit of biologics as the value increased from 0 to 5, followed by a V-shaped benefit as the value further increased. Higher DLQI and part-time job indicated higher benefits. For weeks 1\u0026mdash;4, baseline PASI and its correlated BSA score predicted W-shaped benefits of biologics as their values varied from the lowest to the highest. The identified predictors and the prediction patterns for PASI 75 and PASI 90 were similar. The benefits of biologics relative to the conventional therapies in weeks 5\u0026mdash;46 were all significantly larger than zero, but the lowest in weeks 1\u0026mdash;4 could be negligible. Finally, we found that patients with mild psoriasis, i.e. with PASI below 5 or BSA scores below 5, had statistically significant benefits from treatment with IL inhibitors.\u003c/p\u003e\u003cp\u003eConventionally, biologics were recommended in clinical guidelines mainly for severe psoriasis with BSA above 10, and as the second-line medications only.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e These guidelines are mostly based on the clinical evidence where biologics were assigned to patients with severe psoriasis who had not responded to conventional therapies.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e As such, despite the higher effectiveness in treating severe psoriasis, the advancement in biologics treatment of mild-to-moderate psoriasis is limited. Our results relied on real-world clinical data and were capable of providing the full picture of the heterogenous clinical benefits of biologics from mild to severe psoriasis. The findings showed that IL inhibitor had more substantial improvements than the conventional therapies for mild-to-moderate patients in both the induction and maintenance periods, i.e, weeks 1\u0026ndash;4 and weeks 5\u0026ndash;46. For severe psoriasis, the results confirmed the clinical findings \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e but presented more nuances on the heterogenous responses as baseline PASI or DLQI varied.\u003c/p\u003e\u003cp\u003eOf note, our analysis showed that baseline BSA score had strong predictability of benefits of biologics for weeks 1\u0026ndash;4 and also moderate predictability for weeks 5\u0026ndash;46. Although the BSA score appeared less accurate than PASI in measuring disease severity, it was more widely used in real-world practice due to the simple and time-saving measuring process.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Therefore, baseline BSA score may be used as an easily implementable predictive biomarker in real-world practice when PASI is not available.\u003c/p\u003e\u003cp\u003eUnemployment has been associated with poor PASI 75 and PASI 90 responses in a multicentre registry on biologics therapies in the United Kingdom and the Republic of Ireland\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Our findings added to the literature by showing that the impact of job conditions on treatment outcome is larger for biologics therapies than conventional therapies. DLQI, a self-reported measurement that may capture many personalized unmeasured factors\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, has been shown to be strongly associated with the social cost of the psoriasis disease than the disease severity measurements.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e In the United Kingdom, DLQI has been recommended for determining the eligibility of receiving biologics for moderate-to-severe psoriasis patients. \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e However, according to the real-world data in Sweden, it appeared that the index has not been associated with the decision to initiate biologics treatment.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Our findings suggested DLQI as an important independent predictive biomarker for biologics treatment, calling for more patient-centric therapy solutions in clinical practice. However, both DLQI and part-time job were identified as predictive biomarkers in weeks 5\u0026ndash;46 only. The reason could be due to the lower treatment adherence in patients with lower DLQI or full-time jobs, the impact of which could be less evident in a short period. Relevant data on treatment duration and compliance is needed to clarify this.\u003c/p\u003e\u003cp\u003ePASI were identified as predictive biomarkers in both weeks 1\u0026ndash;4 and weeks 5\u0026ndash;46. An interesting finding in comparing the relative benefits predicted by PASI in the two periods was the smaller increased benefits from weeks 1\u0026ndash;4 to weeks 5\u0026ndash;46 for baseline PASI 15\u0026mdash;20. It is not precisely clear why patients with PASI around 15\u0026mdash;20 had quicker clinical responses in the first few weeks of the treatment but relatively lower responses in the following weeks until one year. The inference methods that we used were able to control the baseline covariates. As expected, we didn\u0026rsquo;t find any significant differences between the baseline covariates between the patients with PASI 15\u0026mdash; 20 and those with other values of PASI (above 5) in both weeks 1\u0026ndash;4 and weeks 5\u0026ndash;46. Also, although IL-23 inhibitor biologics were shown to be more delayed in improving psoriasis than IL-17 inhibitor biologics\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, this is likely to be the dominant reason in our case. In the data, more than 96% of biologics used were IL-17 inhibitor, specifically, Secukinumab. Possibly, underlying characteristics that are not captured by our data may contribute to the phenomena and further investigation with more rich data is needed.\u003c/p\u003e\u003cp\u003eUnderstanding the appropriate first-line therapies is critical for targeting higher patient satisfaction and better treatment outcomes, since patients with treatment-region failure tended to experience longer disease duration and higher chances of developing multiple comorbidities \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Knowledge of predictive biomarkers based on the heterogenous efficacies from real-world data can help to pinpoint the first-line therapies that are more suitable for an individual patient. The analyses used doubly robust causal inference methods to estimate the conditional treatment effects of IL inhibitors versus the conventional therapies, conditioning on clinically meaningful factors. The inference methods can control all measured baseline covariates and thus produce unbiased and stable estimates, provided that all the confounding between the psoriasis treatments and PASI responses are captured by the baseline covariates.\u003c/p\u003e\u003cp\u003eThis study has some limitations. The causal inference methods that we used assume that all the confounding has been captured by the measured baseline covariates. The estimated effects could be biased if there were some important unmeasured confounding that our data failed to capture. However, given the large number of covariates we have included, we believe that the important unmeasured covariates, if exist, are likely to be associated with some of our measured covariates, and thus, by controlling the measured covariates, we are capable of reducing the biases due to the unmeasured covariates as well. The IL inhibitor therapies included in the study were mainly 300mg injections, and the study was short-term with ending points of less than one year. Long-term data with varying doses of injections would allow for a fully understanding of the biologics treatment heterogeneity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur real-world data analysis provides evidence on the biologics used for mild-to-moderate psoriasis and data-driven recommendations for moderate-to-severe psoriasis. Heterogenous efficacy comparisons reported here revealed that baseline PASI, DLQI, and employment were good and independent predictors for psoriasis biologics on achieving PASI 75 and 90. Understanding patient heterogeneity in response to psoriasis biologics in the real world bears broad implications towards personalized treatment for psoriasis. The identified predictors could contribute to assisting clinicians in differentiating among treatment choices for their patients, and may help policymakers develop guidelines that target at higher patient satisfaction and better treatment outcomes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Unsectioned Tables","content":" \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 \u003cdiv class=\"SimplePara\"\u003eDescription of the data at the baseline.\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNumber (Proportion)\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e/ Median (IQR) (n\u0026thinsp;=\u0026thinsp;6,220)\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eDemographic characteristics\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSex\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eFemale (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2,169 (34.87)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMale (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e4,051 (65.13)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eAge\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e39.5 (30.0\u0026ndash;54.0)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eBMI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e24.01 (21.6\u0026ndash;26.4)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSmoking\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eEx-smokers (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e374 (6.01)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eCurrent smokers (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1,566 (25.18)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eNon-smokers (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e4,280 (68.81)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eComorbidity conditions\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eNo-comorbidity (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e4,909 (78.92)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eHave comorbidity (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e801 (12.88)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eNot clear (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e510 (8.20)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eHospital locations\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eDongbei district (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1,199 (19.28)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eHuabei District (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1,204 (19.36)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eHuadong District (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1,021 (16.41)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eHuanan District (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e746 (11.99)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eHuazhong District (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1,652 (26.56)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eXibei District (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e190 (3.05)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eXinan District (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e208 (3.34)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSocial conditions\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eInsurance\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eFree or commercial medical care (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e483 (7.77)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eGeneral government funded medical care (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e5,737 (92.23)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eEducation\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eCollege and higher (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1,849 (29.73)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eHigh school and lower (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e4,371 (70.27)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMarital status\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMarried (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e4,575 (73.55)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eUnmarried (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1,645 (26.45)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eEmployment\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eFull-time (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3,683 (59.21)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003ePart-time (%)\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2,537 (40.79)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eDisease severity\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eBSA\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e14.1 (5.0\u0026ndash;30.0)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003ePASI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e9.0 (3.6\u0026ndash;16.8)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eQuality of life\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eDLQI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e7 (3\u0026ndash;12)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\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 \u003cdiv class=\"SimplePara\"\u003eMaximum and minimum of relative benefits and their statistical differences for each candidate predictor.\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePASI 75\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003ePASI 90\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\" morerows=\"2\" rowspan=\"3\"\u003e \u003cdiv class=\"SimplePara\"\u003eP-values\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003ebelow 0.05\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003eat PASI 75,\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e90\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMaximum\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMinimum\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eP-value\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMaximum\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMinimum\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eP-value\u003c/span\u003e\u003c/div\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 \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMean\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e95%CI\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMean\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e95%CI\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMean\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e95%CI\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMean\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e95%CI\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eWeek 5\u0026ndash;46\u003c/span\u003e\u003c/div\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\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003ePASI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.36\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.28, 0.44)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.03\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.14, 0.20)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.39\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.09, 0.69)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.02, 0.19)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.04\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eYes\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eDLQI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.55\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.26, 0.83)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.13\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.04, 0.23)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.01\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.46\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.13, 0.78)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.16\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.07, 0.25)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.04\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eYes\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eBSA\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.29\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.16, 0.41)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.13\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.01, 0.27)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.05\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.28\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.11, 0.48)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.12\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.02, 0.22)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.06\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eAge\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.34\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.02, 0.66)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.09\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.16, 0.34)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.12\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.34\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.01, 0.66)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.01\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.28, 0.26)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.06\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eBMI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.51\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.43, 1.00)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.18\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.12, 0.24)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.24\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.56\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.29, 1.00)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.05\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.13, 0.23)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.12\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eEmployment\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.28\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.21, 0.36)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.15\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.10, 0.20)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.01\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.29\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.21, 0.37)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.13\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.08, 0.19)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eYes\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMarital status\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.26\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.17, 0.35)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.18\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.13, 0.23)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.07\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.26\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.17, 0.36)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.17\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.12, 0.22)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.05\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eNail involvement\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.36\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.18, 0.53)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.19\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.14, 0.24)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.04\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.32\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.13, 0.52)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.18\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.14, 0.23)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.09\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eEducation\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.20\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.15, 0.25)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.20\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.11, 0.28)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.45\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.21\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.13, 0.30)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.19\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.13, 0.24)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.28\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eInsurance\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.26\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.10, 0.42)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.20\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.15, 0.24)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.23\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.28\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.11, 0.45)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.19\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.14, 0.23)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.15\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSex\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.21\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.14, 0.29)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.19\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.14, 0.25)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.37\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.23\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.16, 0.31)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.17\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.12, 0.23)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSmoke\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.29\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.03, 0.56)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.19\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.07, 0.32)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.25\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.19\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.12, 0.27)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.19\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.10, 0.47)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.48\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eComorbidity\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.30\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.17, 0.44)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.15\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.03, 0.33)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.09\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.26\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.07, 0.46)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.11\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.07, 0.29)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.13\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eWeek 1\u0026ndash;4\u003c/span\u003e\u003c/div\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\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003ePASI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.38\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.20, 0.56)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.00\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.13, 0.12)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.37\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.18, 0.56)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.06\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.17, 0.05)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eYes\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eDLQI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.14\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.06, 0.21)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.04\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.06, 0.13)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.06\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.14\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.07, 0.20)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.08\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.29, 0.13)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.03\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eBSA\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.29\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.15, 0.44)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.08\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.21, 0.05)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.29\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.15, 0.43)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.14\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.24, -0.04)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eYes\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eAge\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.15\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.07, 0.23)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.05\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.06, 0.15)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.07\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.03, 0.17)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.05\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.26, 0.15)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.08\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eBMI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.11\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.01, 0.21)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.00\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.21, 0.21)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.17\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.11\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.04, 0.18)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.09\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.64, 0.45)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.24\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eEmployment\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.12\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.07, 0.17)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.08\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.01, 0.15)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.21\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.08\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.01, 0.14)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.07\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.03, 0.11)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.44\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMarital status\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.16\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.07, 0.25)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.08\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.03, 0.13)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.06\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.02, 0.17)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.06\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.02, 0.11)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.23\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eNail involvement\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.18\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.04, 0.32)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.09\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.05, 0.14)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.12\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.07\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.04, 0.11)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.05\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.05, 0.15)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.34\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eEducation\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.03, 0.17)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.05, 0.15)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.50\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.09\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.04, 0.15)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.06\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.02, 0.11)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.20\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eInsurance\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.11\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.06, 0.15)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.01\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.15, 0.17)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.12\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.08\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.05, 0.12)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.08\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.17, 0.00)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSex\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.12\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.07, 0.17)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.07\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.01, 0.14)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.12\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.08\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.04, 0.12)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.06\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.00, 0.13)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.36\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSmoke\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.12\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.04, 0.19)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.04\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e(-0.24, 0.16)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.08\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.09\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e(0.02, 0.15)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\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":"","lastPublishedDoi":"10.21203/rs.3.rs-2274250/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2274250/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Background Appropriate and effective use of biological agents is important to improve the benefits of psoriasis patients. We examined how the effects of interleukin (IL) inhibitors vary across patients' demographic, social, and clinical characteristics in treating psoriasis, and whether IL inhibitors are effective for managing mild-to-moderate psoriasis.\nMethods Data were collected from a large national registry in China from Sep 2020 to Sep 2021. Clinical benefits, measured by 75% (and 90%) or more improvement from baseline Psoriasis Area and Severity Index (PASI 75 and PASI 90), were contrasted using the propensity-score-based causal inference methodology between the IL inhibitors and the conventional therapies. Candidates that can differentiate the benefits with P-values less than 0.05 were identified as predictors.\nResults Baseline PASI, DLQI, and employment demonstrated stronger predictability in achieving the benefits of IL inhibitors. For weeks 5-46, baseline PASI predicted an increasing relative benefit of biologics as the value increased from 0 to 5, followed by a V-shaped benefit as the value further increased. Baseline PASI scores at 5.4 and 1.0 predicted the maximal and minimal benefits on achieving PASI 75, with an increase in probabilities of 0.36 (95CI 0.28 to 0.44) and 0.03 (-0.14 to 0.20), respectively. Higher DLQI predicted the maximal benefit (0.55, 0.26 to 0.83) of achieving PASI 75 and lower DLQI predicted the minimal benefit of 0.13 (0.04 to 0.23). Part-time job predicted the maximal benefit of 0.28 (0.21 to 0.36) and full-time job predicted the minimal benefit of 0.15 (0.10 to 0.21). These findings were consistent in achieving PASI 75 and PASI 90.\nConclusions This article fills the gap in treating mild psoriasis with IL inhibitor biologics. Patients with mild psoriasis, i.e. with PASI below 5 or BSA scores below 5, had statistically significant benefits from treatment with IL inhibitors. The studying provides evidence from real-world data on patients’ heterogeneous responses to IL inhibitor biologics. Identified clinical and social predictors can be used for treatment differentiation in clinical practice.","manuscriptTitle":"Identifying demographic, social, and clinical predictors of interleukin inhibitor biologic therapy effects using the real-world clinical data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-19 18:07:35","doi":"10.21203/rs.3.rs-2274250/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":"4da00699-5426-424a-ac37-571a9fb40a66","owner":[],"postedDate":"November 19th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":17013477,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":17013478,"name":"Health sciences/Medical research/Epidemiology"},{"id":17013479,"name":"Health sciences/Diseases/Immunological disorders"}],"tags":[],"updatedAt":"2023-02-23T04:14:19+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-19 18:07:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2274250","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2274250","identity":"rs-2274250","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}