Adherence, persistence and treatment switching in psoriasis

Abstract

Aim: This study aims to investigate drug utilization patterns in the treatment of psoriasis (PsO) from 1 to 5 years in a real-life setting with Adalimumab (Ada), Etanercept (Eta), Ustekinumab (Ust), Golimumab (Gol), Ixekizumab (Ixe), Secukinumab (Sec) and Apremilast (Apr). Materials & methods: Data from an observational study were used to calculate adherence using the Proportion of Days Covered (PDC) method and persistence. Results & conclusion: Treatment adherence was found to be good for all the drugs studied across all years of analysis, while persistence was suboptimal, showing a marked decrease from the third year of study onward. In the treatment of PsO, greater attention needs to be paid to treatment persistence.

Plain language summary

This summary explains that when a patient follows their doctor's medication instructions and continues using the same medication over time to treat a condition like psoriasis, they can expect safer and more effective outcomes. This study examined these aspects to assess how different medications perform over the long term and to explore ways to improve their prescription. The findings highlight that the main issue is not so much in following instructions but in continuing to use the same medication throughout the treatment duration. Raising awareness among healthcare professionals about these issues is crucial to help patients maintain consistent therapy over time and improve their care pathway.

Plain language summary

Summary points.

• Adherence and persistence are deemed critical for the safety and effectiveness of chronic therapies such as psoriasis. This multicentric study explored adherence, persistence and therapy switching in psoriasis patients over 1 to 5 years of treatment.

• Treatment adherence across all studied drugs and years consistently exceeded 80%, underscoring the safety of these therapies.

• Patients showed treatment persistence up to the second year, which then gradually declined through the fifth year.

• Comparing naive and experienced patients, naive patients consistently demonstrated higher persistence.

• Regarding therapy switching, most patients did not switch to another therapy, indicating a need for further investigation.

• For patients with psoriasis undergoing home treatment with biologic and targeted synthetic disease-modifying antirheumatic drugs, adherence is not a concern.

• Long-term therapies face a persistence issue, highlighting a need for healthcare providers to focus on improving patient care pathways.

• Real-life studies present a valuable opportunity for direct drug comparisons with the same indications, offering insights to optimize psoriasis treatment.

Psoriasis (PsO) is a chronic multisystemic inflammatory disease of the skin and joints, associated with a multitude of comorbidities such as cardiovascular diseases, diabetes, dyslipidemia, liver diseases and depression [1–3].

Treatment options include a variety of topical therapies, phototherapy, traditional systemic agents and biologics [4]. Among all therapeutic options, biologics are the most effective choice [5,6], including tumor necrosis factor (TNF) antagonists adalimumab (Ada), etanercept (Eta), infliximab (Inf), certolizumab (Cer); as well as interleukin 12/23 inhibitors ustekinumab (Ust), interleukin 17 inhibitors secukinumab (Sec), ixekizumab (Ixe); Brodalumab [7] and bimekizumab [8] interleukin 23 inhibitors guselkumab (Gus), risankizumab, tildrakizumab, the phosphodiesterase inhibitor apremilast (Apr) [9]; and Janus Kinase inhibitors, such as tofacitinib [10]. Oral therapy includes, in addition to methotrexate, acitretin and cyclosporine, Apr [11]. This non-biologic phosphodiesterase-4 inhibitor has shown efficacy in the treatment of PsO [12].

Patients with moderate-to-severe psoriasis often require long-term or even lifelong systemic treatment. Achieving long-term disease control through the continued administration of a single systemic drug is highly desirable for these patients. However, discontinuation or switching of systemic drugs is common due to the failure of primary or secondary treatment or adverse events [13–15]. Treatment persistence, defined as the time from the start to the discontinuation of a single drug [16], can be used as an indicator of therapeutic success [17]. The objectives of this study were to analyze adherence, persistence and switching of Ada, Eta, Gus, Ixe, Sec, Ust and Apr in the treatment of PsO.

Materials & methods

A retrospective, observational, non-interventional, multicenter pharmacological study was conducted, involving nine hospital pharmacies across Italy. The maximum analysis period considered was from January 2011 to December 2020. The drugs analyzed included Ada, Eta, Gus, Sec, Ust, Ixe and Apr. For the purpose of analysis, the following information was recorded: anonymized patient demographic details, drug, therapeutic indication, start and end dates of therapy and the date and quantity of drug dispensed. The use of these data enabled the calculation of:

• Adherence to treatment as the Proportion of Days Covered (PDC), in other words, the number of days covered by therapy over the persistence of treatment [18];

• Persistence, calculated as the difference in days between the first and last dispensation of the same drug [19];

• Switching between the drugs under study with the same indication.

Criterion for discontinuation

To identify patients who, as of 31 December 2020, were still on treatment at the date of the last dispensation (b), the ideal pharmacological coverage period (c) + 90 days was added. The pharmacological coverage period was calculated by multiplying the total dose dispensed to the patient by the standard dosage as per the technical data sheet. If (d) >31st December 2020, the patient was considered to be still in therapy; otherwise, they were marked as having discontinued (Figure 1). Figure 1 illustrates the patient selection process who were still under treatment as of the cut-off date, 31 December 2020. This distinction is crucial for accurately calculating persistence but must be contextualized within the specific period being analyzed. For instance, a patient commencing therapy in 2012 and discontinuing in 2014, as of the cut-off date of 31 December 2020, would not be in therapy. However, having undergone treatment for 2 years, 2012 to 2014, they would be considered persistent in a 1-year analysis but not in a 3-year analysis. This rationale underpins the distinct analyses performed for various years.

Figure 1.

Computation of patients discontinuing.

The cut-off date to identify discontinuing patients was set at 31st December 2020, the last useful reporting date. Patients with a continuation date beyond 31 December 2020 were considered still in therapy; otherwise, they were marked as having discontinued.

Selection of the patient cohort, 1 – 2 – 3 – 4 – 5 years of analysis

This study analyzed the adherence and persistence to treatment of patients with PsO, stratifying the analysis cohorts into 1, 2, 3, 4 and 5 years, respectively. The criteria for selecting patients were as follows: persistence >365 days or 730 or 1095 or 1460 or 1825 and discontinuation <365 days or 730 or 1095 or 1825. The various combinations select the patient cohort considered in the analyses. For example, patients in 1-year analysis were those with persistence >365 and discontinuation <365.

Naive patients to treatment

The identification of treatment-naive patients was carried out by selecting a reference period of 365 days relative to the minimum date provided by the analysis report. Specifically, the requested data covered a timeline from 1 January 2011 to 31 December 2020, such that the reference period encompassed the entire year of 2011. Patients who were present in the data both after 1 January 2012 and in 2011 were considered to be already undergoing treatment, while all others were defined as naive. The analysis was conducted on these naive patients.

Experienced patients

Experienced patients are those who have been treated with at least one of the drugs under study, which are biologics, and who represent the treatment lines subsequent to the first one.

Statistical analysis

A descriptive analysis was conducted. Categorical variables were summarized using frequency and percentage. Continuous variables were presented using mean and standard deviation (SD) or median and interquartile range (IQR) as the range where required and appropriate. The difference between variables was calculated using the non-parametric ‘Mann-Whitney’ test. The persistence analysis was graphed as a Kaplan-Meier curve, and the log-rank test was applied for statistical significance. All analyses were carried out using GraphPad Prism, Version 10.1.1 (270), 21 November 2023 for Mac OS X, GraphPad Software, CA, USA, www.graphpad.com.

Ethics approval

Information strictly required for the purpose of the study was collected in an anonymized manner. Informed consent was not required, as this was an observational, non-interventional retrospective study. The study was authorized by the ethics committee of each participating centre under the code Ada_Eta_Bio2021.

Results

During the analysis period, data from 1620 patients with PsO were collected. Of these, 1347 used the drugs under study as first-line treatment. The initial count of patients included in the analysis was 1620, divided into 1347 naive and 368 experienced patients. The drug utilization analysis was primarily conducted on naive patients in the first line, stratified across various years of treatment (Figure 2). Figure 2 presents the analysis scheme and the division into treatment lines. As a result, six treatment lines were identified, describing patients with switches from 1 to 5.

Figure 2.

Flowchart analysis.

Of the 1620 patients analyzed, 1347 were in the first line of treatment. From these, 14 minors, 40 on Brodalumab, 11 on Certolizumab, 6 on Dimethyl fumarate and 17 on Golimumab were excluded due to low sample sizes. A further 175 patients were excluded due to persistence of less than 1 year, thereby categorizing the patients based on their minimum treatment duration.

The drug utilization analysis was performed on both the first line of treatment and subsequent lines. For patients on the first line, separate analyses were conducted for 1 to 5 years of treatment. Table 1 shows the number of patients, age, gender and adherence to treatment both as an average and as a percentage of patients with levels above 80%, divided by drug and year of analysis. Most patients used Ust, followed by Ada, Eta and Sec. The analysis of Gus was limited to only 1 year of treatment as its use in therapy started in 2019, as did Apr, introduced in 2017, and on a reduced number of patients.

Table 1.

Patient characteristics and adherence data over time.

Class	TNFi		IL23i	IL12/23i	IL17i		PDEi
Drug	Ada	Eta	Gus	Ust	Ixe	Sec	Apr	All
1 year	261	210	61	270	67	170	45	1084
Age, median (min–max)	50 (19–84)	55 (18–90)	55 (21–83)	49 (19–83)	51 (23–82)	50 (19–92)	58 (18–88)	<0.0001
Female (n, %)	93, 36	67, 32	20, 33	84, 31	21, 31	63, 37	17, 38
Adherence 1st Y (mean ± SD)	0.89 ± 0.15	0.89 ± 0.16	0.96 ± 0.07	0.93 ± 0.12	0.97 ± 0.07	0.93 ± 0.12	0.90 ± 0.16	<0.0001
% pat with Adh >0.8	80	80	93	89	94	89	87
2 years	250	204	-	258	40	136	34	922
Age, median (min–max)	50 (20–84)	55 (18–84)		49 (19–83)	51 (23–80)	50 (19–92)	65 (18–89)
Female (n, %)	88, 35	64, 31		80, 31	12, 30	51, 37	14, 41
Adherence 1st Y (mean ± SD)	0.89 ± 0.15	0.89 ± 0.16		0.93 ± 0.12	1.00 ± 0.01	0.94 ± 0.11	0.90 ± 0.17	<0.0001
Adherence 2nd Y (mean ± SD)	0.93 ± 0.13	0.91 ± 0.13		0.93 ± 0.01	0.96 ± 0.05	0.93 ± 0.11	0.82 ± 0.20	<0.0001
% pat with Adh >0.8 (1st Y)	79	80		89	30	91	88
% pat with Adh >0.8 (2nd Y)	86	85		90	14	92	83
3 years	244	197	-	243	24	106	-	814
Age, median (min–max)	50 (20–84)	55 (18–84)		49 (19–83)	52 (23–74)	51 (20–92)
Female (n, %)	87, 36	64, 32		77, 32	6, 25	43, 41	-
Adherence 1st Y (mean ± SD)	0.89 ± 0.15	0.89 ± 0.16		0.93 ± 0.13	0.99 ± 0.01	0.94 ± 0.11		<0.0001
Adherence 2nd Y (mean ± SD)	0.92 ± 0.13	0.91 ± 0.14		0.94 ± 0.09	0.95 ± 0.06	0.93 ± 0.09		<0.0001
Adherence 3nd Y (mean ± SD)	0.92 ± 0.14	0.91 ± 0.15		0.94 ± 0.08	0.54 ± 0.12	0.93 ± 0.10		<0.0001
% pat with Adh >0.8 (1st Y)	79	81		88	42	90
% pat with Adh >0.8 (2nd Y)	86	86		91	17	92
% pat with Adh >0.8 (3nd Y)	87	79		92	0	94
4 years	222	187	-	196	19	85	-	709
Age, median (min-max)	50 (20–84)	54 (18–84)		49 (19–83)	51 (23–74)	49 (20–92)		0,0093
Female (n, %)	78, 35	60, 32		63, 32	3, 16	38, 44
Adherence 1st Y (mean ± SD)	0.89 ± 0.15	0.89 ± 0.16		0.92 ± 0.13	0.98 ± 0.05	0.94 ± 0.11		<0.0001
Adherence 2nd Y (mean ± SD)	0.90 ± 0.15	0.89 ± 0.14		0.93 ± 0.09	0.93 ± 0.07	0.93 ± 0.09		ns
Adherence 3nd Y (mean ± SD)	0.90 ± 0.15	0.91 ± 0.15		0.93 ± 0.09	0.81 ± 0.27	0.91 ± 0.13		ns
Adherence 4nd Y (mean ± SD)	0.90 ± 0.15	0.88 ± 0.15		0.93 ± 0.09	0.91 ± 0.02	0.82 ± 0.15		0.0292
% pat with Adh >0.8 (1st Y)	78	77		86	100	88
% pat with Adh >0.8 (2nd Y)	65	83		90	100	91
% pat with Adh >0.8 (3nd Y)	84	77		88	67	87
% pat with Adh >0.8 (4nd Y)	83	81		90	100	58
5 years	216	160	-	178	-	77		631
Age, median (min–max)	50 (20–84)	53 (18–84)		49 (20–83)		50 (20–92)		ns
Female (n, %)	75, 35	51, 32		55, 31		33, 43
Adherence 1st Y (mean ± SD)	0.89 ± 0.15	0.88 ± 0.17		0.92 ± 0.14		0.94 ± 0.11		0.007
Adherence 2nd Y (mean ± SD)	0.90 ± 0.15	0.88 ± 0.15		0.93 ± 0.09		0.94 ± 0.08		ns
Adherence 3nd Y (mean ± SD)	0.90 ± 0.15	0.88 ± 0.17		0.93 ± 0.08		0.93 ± 0.14		ns
Adherence 4nd Y (mean ± SD)	0.91 ± 0.14	0.87 ± 0.17		0.93 ± 0.09		0.86 ± 0.22		ns
Adherence 5nd Y (mean ± SD)	0.89 ± 0.15	0.85 ± 0.14		0.93 ± 0.07		0.94 ± 0.04		0.001
% pat with Adh >0.8 (1st Y)	79	79		85		88
% pat with Adh >0.8 (2nd Y)	82	80		91		91
% pat with Adh >0.8 (3nd Y)	87	78		88		94
% pat with Adh >0.8 (4nd Y)	82	79		89		75
% pat with Adh >0.8 (5nd Y)	81	72		93		100

Ada: Adalimumab; Adh: Adherence; Apr: Apremilast; Eta: Etanercept; Gus: Guselkumab; Ixe: Ixekizumab; Sec: Secukinumab; Ust: Ustekizumab.

The median age for all drugs under study was around 50 years, except for Apr, which had values of 58 and 65, respectively, with an analysis at 1 and 2 years. In all cases, 35% of those in treatment were women, identifying PsO as a predominantly male pathology. Figure 3 shows the treatment persistence of the drugs under study at 1, 2, 3, 4 and 5 years, respectively. Ust consistently recorded superior persistence across all 5 years. In the persistence analysis, 1 year into treatment, the percentages of patients who continued treatment were 90% for Ust, 85% for Ixe, 81% for Gus, 79% for Sec, 74% for Eta, 70% for Ada, and 42% for Apr (Figure 3A); at 2 years, 76% for Ust, 66% for Ixe, 61% for Eta, 56% for Sec, 54% for Ada and 23% for Apr (Figure 3B); at 3 years, 60% for Ust, 45% for Eta, 43% for Ada, 42% for Ixe, 35% for Sec and 3% for Apr (Figure 3C); at 4 years, 45% for Ust, 34% for Eta, 32% for Ada and 16% for Sec (Figure 3D); at 5 years, 36% for Ust, 27% for Ada, 22% for Eta and 6% for Sec (Figure 3E).

Figure 3.

Persistence of treatment at 1, 2, 3, 4 and 5 years.

Persistence of treatment at (A) 1, (B) 2, (C) 3, (D) 4, (E) 5 years. Using Kaplan-Meier curves, the persistence of patients divided based on treatment duration as shown in Figure 2 is graphed. The graphs order the drugs based on treatment persistence.

In Table 2, through the analysis of treatment lines subsequent to the first, the history of the transition to subsequent therapies was reconstructed. Specifically, patients who did not transition to any other biological therapy (STOP_I), patients who were still in therapy based on the considered period (Ongoing), and patients who switched to another drug were identified with the name of the drug followed by the number II. Figure 4 graphed the treatment persistence among treatment-naive and experienced patients at 1, 2, 3, 4 and 5 years, respectively. In a direct comparison between naive and experienced patients, the persistence rates at 1 year were 77 and 67% respectively (Figure 4A), at 2 years 61 and 47% (Figure 4B), at 3 years 45 and 27% (Figure 4C), at 4 years 32 and 15% (Figure 4D), and at 5 years 24 and 9% (Figure 4E).

Table 2.

Switch therapy over time.

Switch in the 1st year of analysis
From →	Ada (261)		Eta (210)		Gus (61)		Ust (270)		Ixe (67)		Sec (170)		Apr (45)
To ↓	N	%	N	%	N	%	N	%	N	%	N	%	N	%
Ongoing	181	69	156	74	45	74	242	90	56	84	133	78	19	42
Stop I	39	15	24	15	16	26	14	5	8	12	27	16	18	40
Ada_II			8	5			3	1	1	1	2	1	1	2
Eta_II	4	1							1	1
Gus_II	1	1					3	1			1	1	2	4
Ust_II	8	1	10	6							2	1	1	2
Ixe_II	9	3	5	3			2	1			2	1	1	2
Sec_II	17	7	4	3			6	2					2	4
Apr_II	2	1	2	1					1	1
Gol_II			1	1
Bro_II											2	1	1	2
Cer_II											1	1

Switch in the 2nd year of analysis
From →	Ada (254)		Eta (205)		Gus (-)		Ust (259)		Ixe (43)		Sec (138)		Apr (35)
To ↓	N	%	N	%	N	%	N	%	N	%	N	%	N	%
Ongoing	137	54	127	62			198	76	28	65	77	56	8	23
Stop I	61	24	36	18			28	11	11	26	41	30	18	51
Ada_II			11	5			6	2	1	1	3	2	2	6
Eta_II	4	2					2	1	1	1
Gus_II	2	1					5	2	1	1	2	1	2	6
Ust_II	8	3	14	7							3	2	1	3
Ixe_II	18	7	7	3			7	3			6	4	1	3
Sec_II	22	9	7	3			11	4					2	6
Apr_II	2	1	2	1					1	1
Gol_II			1	1			2	1
Bro_II											5	4	1	3
Cer_II											1	1

Switch in the 3rd year of analysis
From →	Ada (248)		Eta (199)		Gus (-)		Ust (246)		Ixe (27)		Sec (110)		Apr (-)
To ↓	N	%	N	%	N	%	N	%	N	%	N	%	N	%
Ongoing	106	43	90	45			146	59	11	41	38	35
Stop I	79	32	51	26			57	23	12	44	48	44
Ada_II			13	7			7	3	1	4	4	4
Eta_II	6	2					3	1	1	4
Gus_II	2	1	1	1			8	3	1	4	2	2
Ust_II	10	4	16	8							3	3
Ixe_II	19	8	9	5			10	4			7	6
Sec_II	23	9	13	7			13	5
Apr_II	2	1	3	2					1	4	1	1
Gol_II			1	1			2	1
Bro_II	1	1	1	1							6	5
Cer_II											1	1

Switch in the 4th year of analysis
From →	Ada (222)		Eta (187)		Gus (-)		Ust (196)		Ixe (20)		Sec (85)		Apr (-)
To ↓	N	%	N	%	N	%	N	%	N	%	N	%	N	%
Ongoing	71	32	64	34			88	45	3	15	13	15
Stop I	84	38	59	32			61	31	12	60	48	56
Ada_II			15	8			7	4	1	5	4	5
Eta_II	6	3					3	2	1	5
Gus_II	2	1	1	1			8	4	1	5	2	2
Ust_II	12	5	18	10							3	4
Ixe_II	19	9	9	5			11	6			7	8
Sec_II	25	11	16	9			15	8
Apr_II	2	1	3	2					1	5	1	1
Gol_II			1	1			2	1
Bro_II	1	1	1	1			1	1			6	7
Cer_II											1	1

Switch in the 5th year of analysis
From →	Ada (216)		Eta (160)		Gus (-)		Ust (178)		Ixe (-)		Sec (77)		Apr (-)
To ↓	N	%	N	%	N	%	N	%	N	%	N	%	N	%
Ongoing	59	27	35	22			64	36			5	6
Stop I	89	41	60	38			64	36			49	63
Ada_II			16	10			7	4			4	5
Eta_II	6	3					3	2
Gus_II	2	1	1	1			10	6			2	3
Ust_II	12	6	18	11							3	4
Ixe_II	20	9	9	6			11	6			7	9
Sec_II	25	12	16	10			16	9
Apr_II	2	1	3	2							1	1
Gol_II			1	1			2	1
Bro_II	1	1	1	1			1	1			6	8
Cer_II											1	1

Ada: Adalimumab; Apr: Apremilast; Bro: Brodalumab; Cer: Certolizumab; Eta: Etanercept; Gol: Golimumab; Gus: Guselkumab; Ixe: Ixekizumab; Sec: Secukinumab; Ust: Ustekizumab.

Figure 4.

Comparison of the persistence of naive patients with experienced patients over time.

(A) 1, (B) 2, (C) 3, (D) 4, (E) 5 years.

Discussion

Adherence to treatment for drugs used in PsO is a subject of great interest to researchers as it is considered an indicator of safety for long-term treatments [20]. However, current data do not provide a unanimous indication of adherence levels, with conflicting evidence [21]. Doshi et al. in a study on American patients, described adherence levels, calculated using the PDC method at 1 year, of 0.61 as an average among Ada, Eta, Inf and Ust [22], unlike Huang et al. who reported adherence values, calculated using the MPR method, of 95.3, 98.1, 89.4 and 70.8%, as the percentage of patients with adherence >0.8, respectively for Ust, Sec, Eta and Ada [23].

In a recent review, Piragine et al. concluded that in PsO, adherence levels are suboptimal with an average of 61% of patients adhering, with fluctuations from 48 to 73% [24]. In this study, adherence levels were always optimal in all the years considered and for all the drugs under study. It is important to consider that the results regarding treatment adherence are influenced by numerous variables, such as the analysis method used, the time period, the patient cohort and the type of healthcare system. For these reasons, there are numerous studies in the literature that do not provide homogeneous data. Furthermore, adherence to treatment, in addition to being an indicator of the safety of therapies, can represent an indicator of the quality of the service offered in terms of health. Thus, it is not unusual that, in a universalistic system like Italy, adherence levels are high because access to medication is particularly easy for patients with PsO compared with systems where the patient bears more of the cost of accessing medication. In fact, in Italy, a patient prescribed a biologic drug benefits from the therapy without any direct financial outlay. It is also true that in calculating adherence, the dispensation of the drug is considered, likely an assumption of patient intake, but this may not be the case. Indeed, there can be no certainty that the patient, once the drug is picked up at the pharmacy, administers it at home as indicated by the clinician and reported in the technical data sheet. From this perspective, the calculation of adherence with the pharmacy-refill method could overestimate the data.

If adherence is an indicator of the safety of therapies, persistence to treatment is synonymous with efficacy [25,26]; indeed, especially in chronic long-term treatments, the longer a patient uses the same drug, the greater its effectiveness in real life. In this regard, this study, conducting an analysis over five different time horizons, has highlighted that, in the long term, biologic therapies tend to lose persistence, reaching up to a 70% loss by the fifth year (Figure 3E). This evidence is also supported by previous publications with a marked decrease in persistence from the second year of treatment [27]. The meta-analysis by Lin et al. reports average persistence values at 4 years, for naive patients, of 60% with a fluctuation of ± 10% for Ada, 50% with a variation of ± 10% for Eta and 90% for Ust [28]. The meta-analysis by Lin et al. reports average persistence values at 4 years, for naive patients, of 60% with a fluctuation of ± 10% for Ada, 50% with a variation of ± 10% for Eta and 90% for Ust [28]. These values are much higher than those reported in this study. To delve deeper into this aspect, an analysis was made, through therapy switches, of the transition to the second line (Table 2). In this analysis, three categories of behaviors are distinguished; starting from the drug in use, patients can: 1. Remain in therapy, 2. Switch to another therapy, 3. Lost to follow-up. While for the latter, it is not possible to precisely determine the reasons for treatment interruption, for those who switch to another therapy, it is plausible to define a failure or intolerance. Specifically, analyzing year by year (Table 2), the share of those lost to follow-up is always higher compared with the switch to the second line. In the 5th year, for example, for Ada there was a 41% lost to follow-up and a 31% switch to another therapy, for Sec, the same percentage was recorded for switching to another therapy 31% but a significant 63% lost to follow-up. For this reason, further study is necessary to better understand the real drug usage profile by investigating the reasons for loss to follow-up. It is understood that such a high profile of discontinuation suggests a greater approach to the patient in terms of strong management of the same. The last aspect considered is the comparison between naive patients and those experienced and, as reported in Figure 4, in all years of analysis, treatment persistence was higher and statistically significant in the naive compared with the experienced. This data underscores how important it is for the prescriber to make the most appropriate pharmacological choice and how crucial it is to maintain high treatment persistence, which avoids switches to other therapies and ensures a higher quality of life for the patient. Indeed, as the comparison data show, a patient who begins switches is destined for greater failure of the same. This aspect should be emphasized in the concept of treatment persistence in chronic therapies.

Limitations

This study has several limitations, primarily the number of some molecules studied and its purely observational and retrospective nature. Therefore, while the evidence produced provides interesting insights and focuses on the prescribing attitudes of clinicians and patient behaviors, it requires further exploration with subsequent analyses. The principal aims of this study are to ascertain adherence and persistence levels in individuals with PsO undergoing long-term treatment within a purely observational, retrospective framework focused on drug utilization. It is imperative to acknowledge the inherent limitations in quantifying these metrics. Given the absence of direct patient feedback, we presume that drug dispensation equates to its consumption/administration at home. Nevertheless, this assumption doesn't invariably hold, potentially skewing adherence evaluations. Yet, considering the broad temporal scope and substantial patient cohort, such discrepancies are deemed minimal.

Conclusion

The problem in treating PsO with biologic and nonbiologic drugs is the persistence of treatment, which leads patients to change therapy, exposing them to a greater likelihood of inefficacy. Especially in the long term, from the third year of treatment onward, there are higher discontinuation percentages characterizing all the drugs under study. Despite this, Ust has consistently recorded higher persistence values. In terms of adherence, however, values have always remained optimal over time, highlighting the safety of such therapies. The analysis of treatment switching has illuminated the care pathway for patients with Psoriasis (Pso), outlining therapeutic choices beyond the initial line of treatment. The observation that most patients discontinuing the first-line medication do not transition to any other therapy is unusual for chronic conditions like psoriasis. This warrants further investigation to understand the true care and assistance pathway for patients, particularly in light of the outcomes following the cessation of therapy.

Author contributions

Each authors contributed (a) to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND (b) drafting the work or revising it critically for important intellectual content; AND (c) final approval of the version to be published; AND (d) agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Financial disclosure

The authors have no financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Competing interests disclosure

All authors report no conflicts of interest, with the exception of Enrico Pasut, who provided consultancy services for Biogen in 2020. The authors have no other competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript apart from those disclosed.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The authors state that they have obtained appropriate institutional review board approval or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.