A Diagnostic Model for NTM Disease in HIV-Positive Patients: A Machine Learning-Based Analysis with Novel Inflammatory Markers

Longfen Li^1,2Chunjing Shi^1,2Xing Liu^1,2Wenming Li^1,2Yun Luo^1,2Huajie Zhang^1,2Ge Wang^1,2Yanhong Zhao^1,2Yuanqing Huang^1,2Juan Yang^1,2Jiao Yang^1,2Shengan Li^3*Lingjun Shen^1,2*

• ¹The Third People's Hospital of Kunming, Kunming, China
• ²Yunnan Provincial Infectious Disease Clinical Medicine Center, Yunnan Provincial, China
• ³Kunming Medical University, Kunming, China

Globally, the prevalence of nontuberculous mycobacterial (NTM) co-infection among HIV-positive patients is increasing. The diagnosis of HIV-positive patients co-infected with NTM relies on mycobacterial culture and identification, as well as molecular biology techniques. However, culture-based methods are technically challenging, time-consuming, and costly. Therefore, it is urgent to explore early diagnostic methods for HIV-positive patients co-infected with NTM. To address this issue, the present study had aimed to explore new approaches for the early diagnosis of NTM disease in HIV-positive patients. This study aimed to thoroughly investigate the potential value of novel inflammatory markers in the early diagnosis of nontuberculous mycobacterial (NTM) disease among HIV-positive patients using machine learning techniques, thereby providing a scientifically sound and clinically feasible diagnostic basis for the early identification of this condition in clinical practice.