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Record W2898564948 · doi:10.1111/bjd.17347

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

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

Bibliographic record

VenueBritish Journal of Dermatology · 2018
Typeletter
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders
Canadian institutionsMount Sinai HospitalWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsPyoderma gangrenosumMedicineDermatologyQuality of life (healthcare)Prospective cohort studyPyodermaIntensive care medicineSurgeryInternal medicineDisease

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.317
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations22
Published2018
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