German S3‐Guideline on the treatment of Psoriasis vulgaris, adapted from EuroGuiDerm – Part 1: Treatment goals and treatment recommendations

Abstract

For the chapters 1 (Notes on use/Disclaimer), 3 (Funding), 4 (Scope and purpose of this guideline), 5 (Population and health questions covered by the guideline) and 6 (Targeted users of this guideline) see the long version of the guideline. All documents are available in an up-to-date version on the following website: https://debm.charite.de/ Although it has its drawbacks, the most established parameter to measure the severity of skin symptoms in psoriasis is the Psoriasis Area and Severity Index (PASI), which was first introduced in 1978 as an outcome measure in a retinoid trial [1]. A physician global assessment (PGA) score to evaluate disease severity can be beneficial for the everyday clinician in order to assess rapidly the severity of psoriasis. It is important to note that different PGAs exist and that they may differ in the way they are defined and scales that are used. An estimate of the percentage of the effected body surface (BSA) is also being used as a means to measure disease severity [2]. Health related quality of life (HRQoL) is an important aspect of psoriasis, not only in defining disease severity but also as an outcome measure in clinical trials. The Dermatology Life Quality Index (DLQI) is the most commonly used score for assessing the impact of psoriasis on HRQoL. It consists of a questionnaire with ten questions related to symptoms and feelings, daily activities, leisure, work and school, personal relationships, and bother with psoriasis treatment [3]. The construct validity of the DLQI has been challenged, as items answered as being "not relevant" to a specific patient may not be accompanied by an adequate adjustment in the final result of the patient's DLQI [4]. In addition to the options described above, further parameters of measuring disease severity can be useful. Defining disease severity in psoriasis is complex, and a multitude of clinical aspects and aspects related to HRQoL need to be taken into consideration. The existing scores have various limitations, and patients have pointed out repeatedly that none of the existing scores successfully comprise a patient's full complexity. "Severity has become defined technically and bureaucratically, in terms of scores derived from instruments like say, PASI, DLQI and Skindex-25. These simply fail to capture the seriousness of psoriasis as experienced by those who have the disease." (Mara Maccarone, Ray Jobling, Patient perspective, patient representatives EDF Guidelines 2015.) Currently, the disease definition most commonly used for psoriasis was strongly influenced by the definition used in clinical trials and was thoroughly discussed and formally agreed upon in a European consensus project in 2011. The fundamental goal of any therapy is to achieve complete clearance of symptoms – that is, the absence of cutaneous symptoms of psoriasis. This goal is not realistically achievable in all patients at this time, however. The successful establishment of treatment goals requires that a minimum target be defined which must be achieved by therapy. If the "lowest hurdle" is not reached within a given amount of time, the therapy must be modified. Various forms of adjustment include increasing the dosage, initiation of combination therapy, or transitioning to another drug or procedure. At the end of the induction period, a PASI 75 response is the minimal target, which should be controlled for at regular intervals during the further course of treatment. In light of even higher achievable treatment goals for the majority of patients, such as a PASI 90 response when using the new antibody therapies, which allow a higher quality of life with higher response rates, there is an ongoing discussion about higher treatment goals or an absolute PASI ≤ 3 or DLQI ≤ 2. Strong Consensus In the presence of criteria such as distinct involvement of visible areas, involvement of major parts of the scalp, genitals, palms of hands or feet, onycholysis or onychodystrophy of at least two fingernails, pruritus leading to scratching, presence of recalcitrant plaques, we recommend to follow up treatment goals individually determined for this specific manifestation (using appropriate scores e.g., NAPSI) and to modify the therapy if necessary. For treatments with a fast onset of action, treatment goals should be controlled for at the end of the induction therapy (10–12 weeks); for treatments with a slower onset of action, this should be done after 16 to 24 weeks. These time frames may not always include the time point of maximal therapeutic effect. During maintenance therapy, control of treatment goal should be done at the same intervals as the general monitoring, usually every eight to twelve weeks. Psoriasis can have a severe impact on an individual's health related quality of life. Although psoriasis is a chronic skin disease, rapid clearance has been identified as a crucial outcome for patients [6]. However, the time until the onset of action of different treatments for psoriasis has been found to vary between the different treatment options [6]. A number of systematic reviews summarise the evidence on the speed of onset of action of the different drugs [7-9]. This can be defined, for example, as the time necessary for 25 % or 50 % of patients to achieve a given PASI or ACR (modified American Rheumatology criteria). Data on what is acceptable for a patient as "waiting time" until a treatment becomes effective are sparse, but the proportion of patients dropping out of clinical trials due to a lack of efficacy can been used as a proxy. This shows that the greatest increase in the rate of dropouts due to a lack of efficacy was seen after 10–12 weeks [10]. Sequential combination of slow acting drugs with low response rates carries a risk of long patient "waiting times", until a noticeable, clinically meaningful improvement in their health-related quality of life [11]. For more detailed information, see the Guideline Development Report (online supplement or www.awmf.org). This guideline is an update of the 2017 version of the "S3 Guideline for the treatment of psoriasis vulgaris" [12, 13]. The update took the form of an adaptation of the "EuroGuiDerm Guideline on the systemic treatment of Psoriasis vulgaris" by Nast A. et al., which has been published in its final form at https://doi.org/10.1111/jdv.16915 and https://doi.org/10.1111/jdv.16926 and is also available at the European Dermatology Forum website (https://www.edf.one/home/Guidelines/EuroGuiDerm-psoriasis-vulgaris.html), licensed under CC BY NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/). Some sections of the guideline have been adopted from the previous versions without changes. The sections on climate therapy, psychosocial therapy, topical therapy (one change in the background text), phototherapy, interfaces between different providers and sectors of care from the 2015 version of the guideline were reviewed with regard to relevant changes, and their period of validity was prolonged. These sections are included in the online supplement as an appendix. Standardised wording as suggested by the GRADE Working Group was used for all recommendations in the newly developed sections, as shown in the overview below [14]. Wording of recommendations, adapted from [15-18] Each formally agreed upon recommendation is presented in the guideline in a box as displayed below: the left-most column shows the content of the recommendation using standardised wording; the middle column shows arrows and colours indicating the direction and the strength of the recommendation; and the right-most column indicates the strength of consensus among the author group and the evidence base (consensus-based vs. evidence- and consensus-based. Example of a recommendation from the long version of the guideline with standardised wording and symbols Strong consensus, consensus-based Like the recommendations, the boxes "Instructions for use" and, in the long version of the guideline, the "Recommendations for lab controls" have also been voted on formally by the author group and are based on expert consensus. Consensus procedure/external review/approval by commissioning societies/implementation See long version of the guideline. Updates/validity Continuous updating in alignment with the European guideline as a living guideline is intended and will be undertaken in accordance with the AWMF-Guidance Manual and Rules for Guideline Development (Version 2.0) at least once per year. The contact person regarding updates is Prof. Dr. med. Alexander Nast ([email protected]). How to read and understand a network meta-analysis By Emilie Sbidian, MD PhD & Laurence Le Cleach, MD PhD A network meta-analysis (NMA) provides estimates of effect size for all pairwise comparisons of interventions that are connected within a network, including those that have never been directly compared in randomised controlled trials (RCTs). The latter being referred to as indirect comparisons. Prerequisites for a methodologically sound network meta-analysis See long version of the guideline. How to interpret the results of an NMA First, network plots (Figure 1) provide useful information: Each circle is a different intervention and its size is proportional to the number of included participants; each line represents a direct comparison and its size is proportional to the number of trials assessing this comparison. Then, forest plots (Figure 2) show all the relative effects from the network meta-analyses against placebo with their 95 % confidence intervals. For each outcome, a so-called cumulative ranking curve can be plotted for each intervention. This curve indicates, for each possible rank, the cumulative probability of the intervention occupying that rank. The surface under the cumulative ranking (SUCRA) curve is a numeric presentation of the overall ranking for each intervention with regard to the outcome, and is expressed as a percentage between 0 % (when it is certain that an intervention is the worst with regard to this outcome) and 100 % (when it is certain an intervention is the best with regard to this outcome). However, the ranking does not consider the magnitude of differences in effects between treatments, among other factors. For example, intervention 1 could be ranked higher than intervention 2 (i.e. have a better probability of being classified as the best intervention) without there being a significant difference between the two interventions in terms of the relevant efficacy outcome(s). A square matrix called a league table (Figure 3) can help address this problem by presenting the summary estimates, generated in the NMA, of the relative effect and their uncertainty for all possible pairs of interventions. The interventions are reported in rank order of the relative effect for the primary benefit outcome. In the lower triangle, a relative effect larger than 1 favours the intervention to the left. When reading results from an NMA, keep in mind that the level of certainty of evidence is not equal between outcomes and interventions. Network meta-analysis results should be interpreted with caution depending on the level of certainty per outcome, intervention and dosages pooled, keeping in mind gaps in research. Summary of network meta-analysis (taken from Sbidian et al. 2020) "[…] Network meta-analysis at class level showed that all of the interventions (conventional systemic agents, small molecules, and biological treatments) were significantly more effective than placebo in terms of reaching PASI 90. At class level, in terms of reaching PASI 90, the biologic treatments anti-IL17, anti-IL12/23, anti-IL23, and anti-TNF alpha were significantly more effective than the small molecules and the conventional systemic agents. At drug level, in terms of reaching PASI 90, infliximab, all of the anti-IL17 drugs (ixekizumab, secukinumab, bimekizumab and brodalumab) and the anti-IL23 drugs (risankizumab and guselkumab, but not tildrakizumab) were significantly more effective in reaching PASI 90 than ustekinumab and three anti-TNF alpha agents: adalimumab, certolizumab and etanercept. Adalimumab and ustekinumab were significantly more effective in reaching PASI 90 than certolizumab and etanercept. There was no significant difference between tofacitinib or apremilast and between two conventional drugs: ciclosporin and methotrexate. Network meta-analysis also showed that infliximab, ixekizumab, risankizumab, bimekizumab, guselkumab, secukinumab and brodalumab outperformed other drugs when compared to placebo in reaching PASI 90. The clinical effectiveness for these seven drugs was similar: infliximab (versus placebo): risk ratio (RR) 29.52, 95 % confidence interval (CI) 19.94 to 43.70, Surface Under the Cumulative Ranking (SUCRA) = 88.5; moderate-certainty evidence; ixekizumab (versus placebo): RR 28.12, 95 % CI 23.17 to 34.12, SUCRA = 88.3, moderate-certainty evidence; risankizumab (versus placebo): RR 27.67, 95 % CI 22.86 to 33.49, SUCRA = 87.5, high-certainty evidence; bimekizumab (versus placebo): RR 58.64, 95 % CI 3.72 to 923.86, SUCRA = 83.5, low-certainty evidence; guselkumab (versus placebo): RR 25.84, 95 % CI 20.90 to 31.95; SUCRA = 81; moderate-certainty evidence; secukinumab (versus placebo): RR 23.97, 95 % CI 20.03 to 28.70, SUCRA = 75.4; high-certainty evidence; and brodalumab (versus placebo): RR 21.96, 95 % CI 18.17 to 26.53, SUCRA = 68.7; moderate-certainty evidence. Conservative interpretation is warranted for the results for bimekizumab (as well as tyrosine kinase 2 inhibitor, acitretin, ciclosporin, fumaric acid esters, and methotrexate), as these drugs, in the NMA, have been evaluated in few trials. We found no significant difference between any of the interventions and the placebo for the risk of SAEs. Nevertheless, the SAE analyses were based on a very low number of events with low to very low certainty for just under half of the treatment estimates in total, and moderate for the others. Thus, the results have to be viewed with caution and we cannot be sure of the ranking. For other efficacy outcomes (PASI 75 and Physician Global Assessment [PGA] 0/1) the results were very similar to the results for PASI 90. […]" page 2, Sbidian et al. 2020 [19]. Initiating and selecting a systemic treatment (see recommendations box and Figure 4). Strong consensus, consensus-based Strong consensus, evidence- and consensus-based Strong consensus, evidence- and consensus-based In cases of psoriasis where conventional treatments are not expected to lead to a sufficient response*, we suggest initiating a biologic agent that has a "first-line label".** *e.g., particularly severe disease (e.g., PASI ≥ 20) or rapid worsening of disease; severe involvement of the nails, the genital area or the scalp; or a particularly strong impact on quality of life (e.g., DLQI ≥ 15) **"First line to the therapeutic as by the Strong consensus, consensus-based Strong consensus, evidence- and consensus-based All boxes in the chapters systemic therapy and therapy and small molecules "Instructions for use" achieved a strong consensus. due to moderate or severe of have been taken into the patient in a psoriasis assessment of the severity of disease as HRQoL (e.g., using or and clinical should on If patient further may be patient must be and about the risk of the the of effective three after of and the possible of of this should be patient that during treatment and for three after of is not parameters including (see long version of the need for (see in long version of the guideline) During assessment of the severity of disease as HRQoL (e.g., using or The should be taken with a or with to In order to of and and a and are is for at least to treatment on or of the is (e.g., no during and up to three after end of effectiveness of is by patient about and at If patient further may be parameters including (see long version of the and in of for three after of therapy. as described above, is patients that is not for three after of therapy. 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