Introduction: Patient distress may be associated with a reduced quality of life (QoL), poor adherence to treatment and lower satisfaction with medical care. Best practice guidance recommends that distress is assessed at key points within the patient-care pathway. The aim of this study was to develop a pituitary-specific distress assessment tool.
Method: Working with the Pituitary Foundation, a Wellbeing Survey was generated, comprising 36 pituitary-specific items, plus all 40 items from the Oncology Distress Thermometer (ODT). It was posted to all Pituitary Foundation members (n=2500), enclosed with Pit Life (the magazine of the Pituitary Foundation).
Results: completed surveys were returned. Respondents age ranged from 18 to 90 years (mean age 59.13±14.25); 60% of participants were female; with hypopituitarism the most commonly reported diagnosis (43%). Multivariate regression modelling was used to determine the symptom clusters associated with the various diagnoses reported by respondents. The final analysis generated a 39item problem-list for the PDT, comprising 32 symptoms, three practical problems, and four emotional concerns. In terms of symptom clusters per pituitary condition, Cushings disease recorded the largest symptom list (n=24 symptoms), while both prolactinoma and non-functioning tumour had the smallest lists (n=4 items). Only one third of ODT items (n=13) appear on the PDT.
Conclusions: The ODT is already widely and effectively used in oncology services as a structured way to enable patients and healthcare professionals to collaborate on finding options for dealing with some of the common concerns (practical, emotional, physical and psychological) patients experience. Many studies using disease-specific questionnaires have demonstrated distress in patients with pituitary disease, which may not be disclosed or discussed during regular consultations. The PDT offers a potential solution, as well as illustrating the need for the development of disease-specific distress thermometers and greater knowledge about specific symptom clusters as reported by patients.