Long‐term 52‐week trends in apremilast safety outcomes for treatment of psoriasis in clinical practice: a multicentre, retrospective case series
Notice bibliographique
Résumé
Dear Editor, Apremilast has demonstrated a favourable safety profile for the treatment of psoriasis based on its pivotal randomized controlled trials (RCTs), ESTEEM 1 and 2.1 2 One‐year safety data suggested that apremilast did not increase the exposure‐adjusted incidence of adverse events (AEs) during long‐term treatment.1 2 Furthermore, approximately half of the reported nausea and diarrhoea AEs resolved within 1 month of treatment in the RCTs.1 2 Our coauthors previously reported the real‐world 16‐week safety outcomes of apremilast in patients with psoriasis.3 However, there are scant data evaluating safety trends in real‐world practice beyond 16 weeks. The objective of the current study was to report the long‐term safety data of our patient cohort during weeks 16–52 of apremilast treatment and to evaluate trends in reported AEs. The study was approved by the research ethics board at Sunnybrook Health Sciences Centre (190‐2016) and Women's College Hospital (2016‐0072‐E). We conducted a multicentre, retrospective chart review at two academic hospitals in Toronto, Canada. The charts were identified by generating a list of all patients ever prescribed apremilast at both hospitals using the electronic health record databases. In addition, the regional field nurse case manager responsible for the apremilast patient support programme also provided a list of patients prescribed apremilast that was used to verify our hospital records. Adult patients (age ≥ 18 years) diagnosed with psoriasis and using apremilast for ≥ 16 weeks were included. Patients who discontinued apremilast during weeks 0–16 of treatment or were lost to follow‐up were excluded. Safety data (reported AEs) were recorded during weeks 0–16 and weeks 16–52 of treatment. New‐onset AEs after week 16 were reported during weeks 16–52. Ongoing AEs that started during weeks 0–16 and persisted beyond week 16 were not counted again in weeks 16–52. All patients were asked about AEs at every visit and the physicians were expected to record any AEs that were new or ongoing from a previous visit. If an AE was not re‐recorded in the chart at a follow‐up visit, it was assumed that the AE had resolved. Patients who discontinued treatment during weeks 16–52 due to AEs were recorded and their reported AEs during weeks 16–52 were included in the study outcomes. Safety data from weeks 16–52 were compared with data from weeks 0–16. McNemar's exact test was used to compare proportions, and a matched‐pairs t‐test was used to compare means between the two time periods. Bonferroni correction was applied to all P‐values generated by McNemar's exact test to account for multiple testing. P‐values < 0·05 were considered significant. In total 131 patients met the inclusion criteria. Seventy (60·3%) were male, the mean age was 50·6 ± 11·3 years and the mean disease duration was 18·2 ± 11·8 years. Comorbidities and safety outcomes are summarized in Table 1 (full data available on request). Of the 131 patients, 30·5% reported AEs during weeks 16–52, and this was significantly lower than the 53·4% of patients experiencing AEs during weeks 0–16 (P < 0·001). Furthermore, the average number of reported AEs per patient was significantly lower during long‐term treatment beyond 16 weeks (weeks 0–16: 1·2 ± 1·5; weeks 16–52: 0·4 ± 0·8; P < 0·001). Accordingly, common AEs during weeks 0–16 were reported in lower proportions during weeks 16–52, including a significantly lower proportion of reported diarrhoea (P = 0·004). Of the 61 patients who did not report AEs during weeks 0–16, only 10 (16%) developed new AEs during long‐term treatment. Therefore, for a patient who did not experience an AE during weeks 0–16, the probability of developing an AE during weeks 16–52 was 16·3% (95% confidence interval 7·0–25·6). In total, 11 patients (8·4%) withdrew from treatment due to AEs during weeks 16–52: at week 20 (n = 5), week 28 (n = 1), week 32 (n = 1) and week 44 (n = 4). There were no reports of tuberculosis reactivation, new‐onset malignancy or serious opportunistic infections. Characteristics of the study cohort and safety outcomes of patients treated with apremilast (n = 131 throughout) AE, adverse event. aIn at least 20 patients. bIn at least nine patients. Baseline data remained the same throughout the 52‐week follow‐up period for the 131 patients who were followed. Characteristics of the study cohort and safety outcomes of patients treated with apremilast (n = 131 throughout) AE, adverse event. aIn at least 20 patients. bIn at least nine patients. Baseline data remained the same throughout the 52‐week follow‐up period for the 131 patients who were followed. These long‐term, real‐world safety outcomes are consistent with the apremilast RCTs.1 2 Our data suggest that a smaller proportion of patients experience AEs during weeks 16–52 of apremilast treatment compared with weeks 0–16 (P < 0·001). The decrease in AEs during weeks 16–52 can be partly explained because individuals who withdrew from treatment due to AEs during weeks 16–52 may have had further AEs during follow‐up that were not registered. In other words, an individual who dropped out at week 20 could have experienced more AEs if they had continued until week 52. The decrease in AEs may also be partly attributed to limitations of the study. Given the retrospective nature of the data, the quality of the data is not optimal and the heterogeneity of providers and site‐specific management may have influenced the proportion of AEs being recorded throughout the study. In summary, this real‐world study of safety outcomes in patients with psoriasis using apremilast therapy suggests that the proportion of reported AEs decreases during weeks 16–52 of treatment. Hence, physicians should consider encouraging patients to tolerate AEs during the first 16 weeks of apremilast treatment, as they may resolve over time. Funding sources: The Canadian Association of Psoriasis Patients funded and supported this project. Conflicts of interest: J.Y. has been a speaker, consultant and investigator for AbbVie, Allergan, Amgen, Astellas, Boehringer Ingelheim, Celgene, Centocor, Coherus, Dermira, Eli Lilly, Forward, Galderma, GSK, Janssen, LEO Pharma, Medimmune, Merck, Novartis, Pfizer, Regeneron, Roche, Sanofi Genzyme, Takeda, UCB, Valeant and Xenon. N.H.S. has been a consultant and/or speaker for AbbVie, Actelion, Amgen, Celgene, Genzyme, Hospira, Janssen, Janssen Biotech, Lilly, Novartis, Sanofi and Takeda. S.W. has been a consultant for AbbVie, Amgen, Celgene, Janssen and Lilly. J.R.G. and A.I. have no conflicts of interest to declare.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».