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Enregistrement W2898564948 · doi:10.1111/bjd.17347

Pyoderma gangrenosum and its impact on quality of life: a multicentre, prospective study

2018· letter· en· W2898564948 sur OpenAlexaff
Arvin Ighani, Dalal Almutairi, Ayat Rahmani, Adam V. Weizman, Vincent Piguet, Afsáneh Alavi

Notice bibliographique

RevueBritish Journal of Dermatology · 2018
Typeletter
Langueen
DomaineMedicine
ThématiqueAutoimmune and Inflammatory Disorders
Établissements canadiensMount Sinai HospitalWomen's College HospitalUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésPyoderma gangrenosumMedicineDermatologyQuality of life (healthcare)Prospective cohort studyPyodermaIntensive care medicineSurgeryInternal medicineDisease

Résumé

récupéré en direct d'OpenAlex

Dear Editor, Pyoderma gangrenosum (PG) is a rare, inflammatory skin condition characterized by painful ulcers. Despite its chronic course, there is scant research evaluating quality of life (QoL) in patients with PG. Furthermore, there are no validated QoL instruments specifically designed for PG. The objective of this study was to evaluate the impact of PG on QoL and to summarize the clinical features of PG in our cohort. We conducted a multicentre, prospective study in a tertiary academic centre and a community‐based dermatology clinic in Toronto, Canada. Inclusion criteria for patients were: (i) diagnosis of PG by a dermatologist and (ii) age ≥ 18 years. Patients from both institutions were assessed using identical inclusion criteria. All cases of PG were biopsy proven, except if the patient had peristomal PG or deferred biopsy. Patients completed an enrolment questionnaire developed by wound care experts from the faculty of the institutions where the study was conducted. Patients completed the validated Dermatology Life Quality Index (DLQI) to assess QoL. In a comprehensive review assessing the DLQI, its validity, reliability and responsiveness to change were well documented in several skin diseases. However, only its responsiveness has been documented specifically for PG and there are scant data analysing its validity and reliability in this disease.1 The mean DLQI scores of patients stratified by site of onset, number of flares and underlying comorbidities were compared using a two‐tailed independent‐samples t‐test. Fifty patients were included for analysis. Their clinical features, demographic data and DLQI scores are summarized in Table 1 (breakdown of DLQI by item available on request). The most common subtype of PG in our cohort was ulcerative (70%) and the most frequently reported comorbidities were inflammatory bowel disease (IBD) (30%), rheumatoid arthritis (RA) (16%) and diabetes (14%). The mean reported DLQI score was 14·9 ± 8·0, which is at the high end of scores reported in other studies, ranging from 8·4 to 15.2,3,4 Our high DLQI scores may be partly explained because some of our patients were evaluated at an academic tertiary‐care centre. This centre manages complex patients whose QoL may have been impacted by their various comorbidities, like diabetes, which can impact wound healing. Summary of clinical features, demographic data and quality‐of‐life scores for patients with pyoderma gangrenosum (PG) (n = 50) DLQI, Dermatology Life Quality Index. Summary of clinical features, demographic data and quality‐of‐life scores for patients with pyoderma gangrenosum (PG) (n = 50) DLQI, Dermatology Life Quality Index. Clinical symptoms, such as self‐reported pain (7·5 ± 3·1 on a 10‐point scale), are likely major contributors to the high DLQI scores in our patients, as previous studies demonstrated an association between pain and decreased health‐related QoL in peristomal PG.5 The DLQI question pertaining to pain also generated the highest mean DLQI subscore (2·1 ± 1·0 out of 3) among all 10 survey items. There were no significant differences in DLQI scores when patients were stratified by (i) site of disease onset: lower extremities vs. other (P = 0·73); (ii) number of flares: zero or one vs. at least two (P = 0·26); (iii) presence or absence of IBD (P = 0·97); or (iv) presence or absence of RA (P = 0·52). Furthermore, QoL may be influenced by the coexistence of depression. In a study conducted by Binus et al., depression was documented at higher rates for patients with PG (21%) than in the general population, and 14% of these patients (14 of 103) were diagnosed with major depression after the onset of their PG.6 In our study, 10% of patients reported comorbid depression, with 4% of patients developing depression after their PG diagnosis, 4% with previously documented depression prior to PG diagnosis, and 2% who could not recall whether their depression started before or after their PG diagnosis. Overall, these findings suggest that PG has a severely negative impact on QoL, highlighting the psychosocial and emotional burden of these patients. Notably, 14% of patients reported diabetes and 8% reported hypertension, with an average cohort body mass index (BMI) of 31·7 ± 8·9 kg m−2 (obesity class I). Other investigators have also noticed the large proportion of comorbid diabetes and obesity in their patients with PG.6,7,8 Al Ghazal et al. reported 25·5–28·6% of patients having diabetes, with 32·6% of patients being obese.7,8 Comparably, Binus et al. reported 28·2% of patients having diabetes, with an average BMI of 30·6 kg m−2.6 Metabolic comorbidities may partly reflect the impact of systemic corticosteroid treatment in patients with PG, as 56% of our patients have been previously treated with these agents. However, the majority of them were treated with only short‐term systemic corticosteroids (< 3 months). In conclusion, this prospective multicentre study of 50 patients with PG indicates that PG has a severely negative impact on patients’ QoL. A validated QoL assessment tool, specifically for patients with PG, would assist in monitoring treatment outcomes and tracking patient improvement during follow‐up visits. Future studies should aim to identify key features that can be used in a PG‐specific QoL survey and evaluate the impact of therapies on patient QoL. We would like to thank Professor Andrew Finlay for his helpful comments during the preparation of this manuscript. Funding sources: none. Conflicts of interest: none 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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,018
Tête enseignante GPT0,317
Écart entre enseignants0,299 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations22
Publié2018
Routes d'admission1
Résumé présentnon

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