Association between pain, arthropathy and health-related quality of life in patients suffering from acromegaly. A cross-sectional study
Notice bibliographique
Résumé
INTRODUCTION: Despite successful therapy, acromegalic patients have reduced health-related quality of life (HRQoL) compared to healthy controls. Finding predictors of poor HRQoL can be crucial to improving these patients' global health state. Aim: The primary objective of the study was to find out predictors of HRQoL. Secondary objectives were: (I) to determine correlations with AcroQoL subscales, and (II) to identify predictors for subscales. MATERIALS AND METHODS: In this cross-sectional study conducted in 2019 at the Messina Policlinic Hospital, 45 acromegalic patients were assessed at the Physical and Rehabilitative Medicine Ambulatory. During routine outpatient clinic attendances, the following questionnaires were administered: Acromegaly Quality of Life Questionnaire (AcroQoL), Patient-Assessed Acromegaly Symptom Questionnaire (PASQ), and Western Ontario and McMaster Universities Arthritis Index (WOMAC). We furthermore included the following variables obtained by medical record review: age, BMI, disease duration, previous surgery (Yes/No), previous radiotherapy (Yes/No), use of GH lowering medications (Yes/No), hypertension (Yes/No), diabetes mellitus (Yes/No), and biochemical control of the disease (Yes/No): immunoradiometric assays were employed to serum GH and IGF-1 measurements to identify biochemical control of the disease. Correlation between outcome measures and AcroQoL has been performed. Pearson's r was calculated for continuous data following normal distribution (AcroQoL, PASQ, AcroQoL-B, AcroQoL-R, WOMAC-P), while Spearman's rank order correlation was calculated for non-normally distributed data (WOMAC, WOMAC-F, WOMAC-S, AcroQoL-P) and point-biserial correlation for binary variables (biochemically controlled disease, use of GH lowering medications, radiotherapy, surgery). The same correlation analysis was performed for the AcroQoL subscales. Multiple linear regression with backwards, stepwise analysis was used to assess the influence on AcroQoL of correlated variables. RESULTS: AcroQoL was strongly negatively correlated with PASQ (r=-0.700, p<0.001) and negatively correlated with WOMAC [rs (43)=-0.530, p<0.001] and among WOMAC subscales with WOMAC-Physical fitness [rs (43)=-0.518, p<0.001] WOMAC-Pain [r (43)=-0.428, p=0.003], WOMAC-Stiffness [rs (43)=-0.393, p=0.007], and radiotherapy [r (43) =-0.314, p=0.035]. After univariate stepwise regression, PASQ was the strongest independent predictor of AcroQoL, with R2 of 0.392 [F (1,43)=27.695, p<0.001]. CONCLUSIONS: This study shows that the severity of painful symptoms is the most important predictor of HRQoL in patients with acromegaly; at the same time, acromegalic arthropathy leads to pain and to a variable amount of functional impairment, exerting great impact on the patient's perception of his health status. Measure of the progression of arthropathy and symptomatic management could lead to a great HRQoL benefit.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».