Development of a Prediction Model of Disease Activity in Support of Clinical Practice – the Acrodat Experience

Abstract

principal components analysis and descending hierarchical classification. Results:Steps 1-3: Development and adaptation of a self-reported 44-item questionnaire on a 0-10 scale of fears and beliefs. 5 domains established a priori: origin of the disease, flares, treatments, disease progression and consequences of disease.Step 4: In the study of 226 patients (161 RA, 65 axSpA), all dimensions were ≥ 0.79 and ≤ 0.93, apart from the domain "origin of the disease" (0.67). Principal component analysis identified 6 axes (63.7% of variance explained) and the descending hierarchical analysis identified 9 clusters (63.7% of variance). 2 clusters were highly correlated (r2= 0.80) and combined into a single cluster. Based on the psychometric validation, the following domains were identified: fears of the disease progression/consequences (18 items) and fears about treatment (5); beliefs related to: psychological influence (2), genetic influence (2), physical influence (4), diet influence (4), lifestyle (3) and diverse opinions (4). ConClusions: This analysis confirms the internal consistency of the questionnaire developed. The scoring results should help physicians to understand their patients' perceptions of their disease better and eventually lead to improved adherence to treatment.

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“…Other tools in development combine biochemical and clinical parameters to measure disease activity, and they could be useful not only for diagnosis of acromegaly, but also for evaluating the effects of treatment [ 53 , 54 ]. The SAGIT instrument is a comprehensive clinician-reported outcome tool to assess the key features of acromegaly and thus assist endocrinologists managing acromegaly in practice, with promising results from a pilot study [ 53 ].…”

“…SAGIT combines signs and symptoms, associated comorbidities, GH levels, IGF-1 levels, and tumour profile. Finally, ACRODAT is a decision algorithm based on IGF-1 level (SD score), tumour status (change on magnetic resonance imaging), comorbidities (number and severity), signs and symptoms (Patient Acromegaly Symptom Questionnaire score), and health-related quality of life (scored on a disease-specific measure) [ 54 ]. In a modelling exercise performed for this score, biochemical and tumour statuses were shown to be the primary predictors of disease activity [ 54 ].…”