Manifestation of rheumatoid arthritis after transsphenoidal surgery in a patient with acromegaly

Machine learning-based algorithms applied to drug prescriptions and other healthcare services in the Sicilian claims database to identify acromegaly as a model for the earlier diagnosis of rare diseases

Acromegaly is a rare disease characterized by a diagnostic delay ranging from 5 to 10 years from the symptoms' onset. The aim of this study was to develop and internally validate machine-learning algorithms to identify a combination of variables for the early diagnosis of acromegaly. This retrospective population-based study was conducted between 2011 and 2018 using data from the claims databases of Sicily Region, in Southern Italy. To identify combinations of potential predictors of acromegaly diagnosis, conditional and unconditional penalized multivariable logistic regression models and three machine learning algorithms (i.e., the Recursive Partitioning and Regression Tree, the Random Forest and the Support Vector Machine) were used, and their performance was evaluated. The random forest (RF) algorithm achieved the highest Area under the ROC Curve value of 0.83 (95% CI 0.79-0.87). The sensitivity in the test set, computed at the optimal threshold of predicted probabilities, ranged from 28% for the unconditional logistic regression model to 69% for the RF. Overall, the only diagnosis predictor selected by all five models and algorithms was the number of immunosuppressants-related pharmacy claims. The other predictors selected by at least two models were eventually combined in an unconditional logistic regression to develop a meta-score that achieved an acceptable discrimination accuracy (AUC = 0.71, 95% CI 0.66-0.75). Findings of this study showed that data-driven machine learning algorithms may play a role in supporting the early diagnosis of rare diseases such as acromegaly.

Is every joint symptom related to acromegaly?

Acromegaly is a chronic endocrinopathy characterized by the hypersecretion of growth hormone and insulin-like growth factor-1. Musculoskeletal pain is a frequent problem encountered in acromegaly and is associated with a reduction in the quality of life. In this study, the presence of inflammatory and degenerative rheumatologic diseases in acromegaly has been retrospectively evaluated. Forty patients with acromegaly who were in remission according to laboratory findings, but still having joint and back pain problems, were referred to rheumatology outpatient clinic and all the patients were examined by clinical, radiological, and laboratory data. Mean age was 47.1 years (22-75). When the radiological data were evaluated, erosions of the left sacroiliac joint were found by means of magnetic resonance imaging in 1 patient and degenerative joint changes were observed in 24 patients by X-ray imaging. However, there was no pathology in the radiological data of 15 patients. Laboratory data revealed antinuclear antibody positivity (3 nucleolar and 1 homogeneous) in 4 patients, rheumatoid factor positivity in 4, and cyclic citrullinated peptide positivity only in 1 patient. The diagnosis of an inflammatory rheumatologic disease, including rheumatoid arthritis, ankylosing spondylitis, or undifferentiated connective tissue disease was made in 3 patients. Besides, the diagnosis of diffuse idiopathic skeletal hyperostosis was also established in 6 patients. While degenerative joint diseases were frequently observed in our group similar to the literature, inflammatory rheumatologic diseases were also found in three patients. Distinguishing these two diseases is important because response to medical treatment is dramatically better in inflammatory diseases than in degenerative pathologies. A multidisciplinary approach is imperative for appropriate management of these patients.