Cutaneous Opportunistic Mycobacterial and Fungal Infections in Adult-Onset Immunodeficiency Due to Anti-Interferon-Gamma Autoantibodies-Decoding Skin Involvement Patterns

成人起病的抗干扰素-γ自身抗体免疫缺陷患者的皮肤机会性分枝杆菌和真菌感染——解读皮肤受累模式

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Abstract

BACKGROUND: Adult-onset immunodeficiency (AOID) due to anti-interferon-gamma autoantibodies (AIGA) predisposes patients to multiple opportunistic infections (OIs), including cutaneous involvement. However, data specifically addressing cutaneous OIs in AOID remain limited. This study aimed to characterize and compare clinical and laboratory features, with a focus on cutaneous OIs caused by mycobacteria and fungi. METHODS: A retrospective chart review was conducted on AOID patients with cutaneous OIs in Chiang Mai and Khon Kaen University Hospital from January 2000 to December 2020. Cases with infection due to Mycobacterium species-both Mycobacterium tuberculosis complex and non-tuberculous mycobacteria-and fungal pathogens were included. Clinical characteristics and laboratory parameters were analyzed, and a multivariate predictive model was developed to differentiate between cutaneous mycobacterial and fungal infections. RESULTS: A total of 61 AOID patients with 70 cutaneous infectious episodes were identified. Among these, fifty episodes were due to Mycobacterium infection. Lesions involving the neck with concurrent lymphadenopathy were more suggestive of Mycobacterium infection. In contrast, fungal infections were associated with generalized or truncal involvement, anemia, neutrophilia, lower monocyte percentage, hyperglobulinemia, and higher levels of aspartate aminotransferase. A multivariate model incorporating these variables achieved excellent discriminatory performance (area under the receiver operating characteristic curve: 0.94; 95% confidence interval: 0.87-0.99). CONCLUSIONS: Cutaneous lesions involving the neck area and lymphadenopathy are clinical clues that suggest cutaneous Mycobacterium OIs. Nevertheless, integration of clinical features with laboratory findings enables the development of a predictive model that can effectively differentiate between cutaneous mycobacterial and fungal infections. This predictive model may aid in selecting appropriate empirical antimicrobial therapy, particularly when microbiological confirmation is pending or inconclusive despite high clinical suspicion.

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