The standard HIV diagnostic algorithm comprises sequential serological screening, confirmatory antibody testing, and nucleic acid confirmation when necessary. This multi-step process often causes diagnostic delays, particularly in cases with indeterminate or discordant results. Developing a Nomogram-based predictive tool to identify individuals at high risk of anti-HIV-1 negative or inconclusive antibody confirmation could significantly optimize testing efficiency. A total of 2,468 individuals with HIV reactive screening results were randomly divided into a training set (70%) and a validation set (30%). In the training set, the risk factors of anti-HIV-1 negative/inconclusive results were identified by univariate and multivariate logistic regression models to construct a predictive model, and its internal stability was robustly assessed via 1,000-resample bootstrap validation. In the validation set, the Hosmer-Lemeshow test and calibration curves were used to evaluate the consistency of the model, while the receiver operating characteristic (ROC) curve was utilized to assess its discriminative ability. Multivariate analysis identified Gender, Education, voluntary counseling and testing (VCT), and Retesting results as independent predictors. Internal bootstrap validation confirmed excellent model stability (optimism-corrected C-index = 0.862). Furthermore, the model demonstrated good predictive performance with high discrimination ability (area under the ROC curve AUC = 0.874, 95% CI: 0.826–0.922) and calibration accuracy (Hosmer-Lemeshow test, P = 0.098) in the validation set. The nomogram accurately identifies individuals at high risk of negative/inconclusive antibody confirmation, enabling a streamlined two-step testing pathway that bypasses Western blot(WB) for selected cases. This approach reduces diagnostic delay and resource utilization while maintaining accuracy, offering a practical strategy for optimizing HIV testing services in diverse settings. Not applicable.
Liao et al. (Sat,) studied this question.