The 2021 European Alliance of Associations for Rheumatology/American College of Rheumatology points to consider for diagnosis and management of autoinflammatory type I interferonopathies: CANDLE/PRAAS, SAVI and AGS.

OBJECTIVE: Autoinflammatory type I interferonopathies, chronic atypical neutrophilic dermatosis with lipodystrophy and elevated temperature/proteasome-associated autoinflammatory syndrome (CANDLE/PRAAS), stimulator of interferon genes (STING)-associated vasculopathy with onset in infancy (SAVI) and Aicardi-Goutières syndrome (AGS) are rare and clinically complex immunodysregulatory diseases. With emerging knowledge of genetic causes and targeted treatments, a Task Force was charged with the development of 'points to consider' to improve diagnosis, treatment and long-term monitoring of patients with these rare diseases. METHODS: Members of a Task Force consisting of rheumatologists, neurologists, an immunologist, geneticists, patient advocates and an allied healthcare professional formulated research questions for a systematic literature review. Then, based on literature, Delphi questionnaires and consensus methodology, 'points to consider' to guide patient management were developed. RESULTS: The Task Force devised consensus and evidence-based guidance of 4 overarching principles and 17 points to consider regarding the diagnosis, treatment and long-term monitoring of patients with the autoinflammatory interferonopathies, CANDLE/PRAAS, SAVI and AGS. CONCLUSION: These points to consider represent state-of-the-art knowledge to guide diagnostic evaluation, treatment and management of patients with CANDLE/PRAAS, SAVI and AGS and aim to standardise and improve care, quality of life and disease outcomes.

The 2021 European Alliance of Associations for Rheumatology/American College of Rheumatology Points to Consider for Diagnosis and Management of Autoinflammatory Type I Interferonopathies: CANDLE/PRAAS, SAVI, and AGS.

OBJECTIVE: Autoinflammatory type I interferonopathies, chronic atypical neutrophilic dermatosis with lipodystrophy and elevated temperature/proteasome-associated autoinflammatory syndrome (CANDLE/PRAAS), stimulator of interferon genes (STING)-associated vasculopathy with onset in infancy (SAVI), and Aicardi-Goutières syndrome (AGS) are rare and clinically complex immunodysregulatory diseases. With emerging knowledge of genetic causes and targeted treatments, a Task Force was charged with the development of "points to consider" to improve diagnosis, treatment, and long-term monitoring of patients with these rare diseases. METHODS: Members of a Task Force consisting of rheumatologists, neurologists, an immunologist, geneticists, patient advocates, and an allied health care professional formulated research questions for a systematic literature review. Then, based on literature, Delphi questionnaires, and consensus methodology, "points to consider" to guide patient management were developed. RESULTS: The Task Force devised consensus and evidence-based guidance of 4 overarching principles and 17 points to consider regarding the diagnosis, treatment, and long-term monitoring of patients with the autoinflammatory interferonopathies, CANDLE/PRAAS, SAVI, and AGS. CONCLUSION: These points to consider represent state-of-the-art knowledge to guide diagnostic evaluation, treatment, and management of patients with CANDLE/PRAAS, SAVI, and AGS and aim to standardize and improve care, quality of life, and disease outcomes.

Learning unsupervised contextual representations for medical synonym discovery.

OBJECTIVES: An important component of processing medical texts is the identification of synonymous words or phrases. Synonyms can inform learned representations of patients or improve linking mentioned concepts to medical ontologies. However, medical synonyms can be lexically similar ("dilated RA" and "dilated RV") or dissimilar ("cerebrovascular accident" and "stroke"); contextual information can determine if 2 strings are synonymous. Medical professionals utilize extensive variation of medical terminology, often not evidenced in structured medical resources. Therefore, the ability to discover synonyms, especially without reliance on training data, is an important component in processing training notes. The ability to discover synonyms from models trained on large amounts of unannotated data removes the need to rely on annotated pairs of similar words. Models relying solely on non-annotated data can be trained on a wider variety of texts without the cost of annotation, and thus may capture a broader variety of language. MATERIALS AND METHODS: Recent contextualized deep learning representation models, such as ELMo (Peters et al., 2019) and BERT, (Devlin et al. 2019) have shown strong improvements over previous approaches in a broad variety of tasks. We leverage these contextualized deep learning models to build representations of synonyms, which integrate the context of surrounding sentence and use character-level models to alleviate out-of-vocabulary issues. Using these models, we perform unsupervised discovery of likely synonym matches, which reduces the reliance on expensive training data. RESULTS: We use the ShARe/CLEF eHealth Evaluation Lab 2013 Task 1b data to evaluate our synonym discovery method. Comparing our proposed contextualized deep learning representations to previous non-neural representations, we find that the contextualized representations show consistent improvement over non-contextualized models in all metrics. CONCLUSIONS: Our results show that contextualized models produce effective representations for synonym discovery. We expect that the use of these representations in other tasks would produce similar gains in performance.