A Blood and Biochemical Indicator-Based Prognostic Model Predicting Latent Tuberculosis Infection: A Retrospective Study

基于血液和生化指标的潜伏性结核感染预测模型:一项回顾性研究

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Abstract

BACKGROUND AND OBJECTIVES: Abnormal blood and biochemical indicators could increase the risk of infectious diseases. However, the association between blood together with biochemical indicators and latent tuberculosis infection (LTBI) has not been well confirmed. MATERIALS AND METHODS: Our aim was to assess the role of blood and biochemical indicators in the risk of LTBI. We enrolled 965 freshmen who were originating from tuberculosis key areas of a college in Nanjing. We used logistic regression models, restricted cubic spline (RCS), and nomograms to evaluate the association between blood and biochemical indicators and LTBI. In addition, calibration curves were performed to evaluate the quality of the model. RESULTS: Among these 965 participants, 311 were diagnosed as LTBI according to TST. Multivariate models showed that the population with an eosinophils percentage around <0.5% (OR: 2.82, 95% CI: 1.39-5.74, p = 0.004) and 0.5-5% (OR: 2.78, 95% CI: 1.07-7.23, p = 0.036) were positively associated with LTBI. Elevated uric acid levels (OR: 1.01, 95% CI: 1.00-1.02, p = 0.047) were significantly associated with LTBI. In addition, participants with a history of tuberculosis exposure (OR: 3.26, 95% CI: 1.39-7.66) and a history of tuberculosis (OR: 10.92, 95% CI: 1.24-96.08) were also positively correlated with LTBI. CONCLUSIONS: Eosinophils percentage and uric acid are associated with LTBIs. Participants who have tuberculosis exposure history and tuberculosis history are the critical target population.

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