Analysis of Anakinra Therapy for the Deficiency of Interleukin-1 Receptor Antagonist through Clinical Evidence.

BACKGROUND: Deficiency of interleukin-1 receptor antagonist (DIRA) is a rare life-threatening autosomal recessive autoinflammatory disease with symptoms including but not limited to osteomyelitis, periostitis, and systemic inflammation. DIRA is developed from the loss-of-function biallelic mutations of the IL1RN gene that encodes IL-1 receptor antagonist (IL-1RA), leading to the unchecked pro-inflammatory signaling and subsequent systemic inflammation. Thus, anakinra as the recombinant IL-1RA has become the primary drug to treat DIRA. Although anakinra has been effective for the complete remission of DIRA, it has also shown various side effects. To confirm the efficacy and safety issues associated with DIRA treatment, we conducted a literature review and secondary data analysis to enhance our understanding on this important topic. METHODS: Through comprehensive literature search, we have identified 15 papers with 25 patients studied. The demographic, clinical, and genetic data were extracted, followed by statistical analysis to support the physiological mechanisms of anakinra treatment. RESULTS: Through the literature review and data analysis, it was found that 88% of patients had complete clinical remission of DIRA upon continual treatment with anakinra; patients had a mean improvement of Hemoglobin (+3.18 g/dL), Erythrocyte Sedimentation Rate (-53.4 mm/h), and C-reactive Protein (-135.45 mg/L) levels, suggesting that the improvement of hematopoietic function and inflammation is a mechanism for anakinra treatment. Various genetic variants were also identified from the patient data that cause the loss of function of IL-1RA, providing real patient genomic data to support the anakinra treatment. CONCLUSIONS: Considering the inconsistency and certain variations from clinical research influenced by specific conditions, this review along with the data analysis confirms the efficacy and safety of anakinra treatment for DIRA.

Accuracy of generative artificial intelligence models in differential diagnoses of familial Mediterranean fever and deficiency of Interleukin-1 receptor antagonist.

With the increasing development of artificial intelligence, large language models (LLMs) have been utilized to solve problems in natural language processing tasks. More recently, LLMs have shown unique potential in numerous applications within medicine but have been particularly investigated for their ability in clinical reasoning. Although the diagnostic accuracy of LLMs in forming differential diagnoses has been reviewed in general internal medicine applications, much is unknown in autoinflammatory disorders. From the nature of autoinflammatory diseases, forming a differential diagnosis is challenging due to the overlapping symptoms between disorders and even more difficult without genetic screening. In this work, the diagnostic accuracy of the Generative Pre-Trained Transformer Model-4 (GPT-4), GPT-3.5, and Large Language Model Meta AI (LLaMa) were evaluated in clinical vignettes of Deficiency of Interleukin-1 Receptor Antagonist (DIRA) and Familial Mediterranean Fever (FMF). We then compared these models to a control group including one internal medicine physician. It was found that GPT-4 did not significantly differ in correctly identifying DIRA and FMF patients compared to the internist. However, the physician maintained a significantly higher accuracy than GPT-3.5 and LLaMa 2 for either disease. Overall, we explore and discuss the unique potential of LLMs in diagnostics for autoimmune diseases.