Xiangya School of Nursing, Central South University, Changsha, Hunan, China.
College of Applied Technology, Hunan Open University, Changsha, Hunan, China.
Front Cell Infect Microbiol. 2023 Jun 2;13:1179369. doi: 10.3389/fcimb.2023.1179369. eCollection 2023.
According to the Global Tuberculosis Report for three consecutive years, tuberculosis (TB) is the second leading infectious killer. Primary pulmonary tuberculosis (PTB) leads to the highest mortality among TB diseases. Regretfully, no previous studies targeted the PTB of a specific type or in a specific course, so models established in previous studies cannot be accurately feasible for clinical treatments. This study aimed to construct a nomogram prognostic model to quickly recognize death-related risk factors in patients initially diagnosed with PTB to intervene and treat high-risk patients as early as possible in the clinic to reduce mortality.
We retrospectively analyzed the clinical data of 1,809 in-hospital patients initially diagnosed with primary PTB at Hunan Chest Hospital from January 1, 2019, to December 31, 2019. Binary logistic regression analysis was used to identify the risk factors. A nomogram prognostic model for mortality prediction was constructed using R software and was validated using a validation set.
Univariate and multivariate logistic regression analyses revealed that drinking, hepatitis B virus (HBV), body mass index (BMI), age, albumin (ALB), and hemoglobin (Hb) were six independent predictors of death in in-hospital patients initially diagnosed with primary PTB. Based on these predictors, a nomogram prognostic model was established with high prediction accuracy, of which the area under the curve (AUC) was 0.881 (95% confidence interval [Cl]: 0.777-0.847), the sensitivity was 84.7%, and the specificity was 77.7%.Internal and external validations confirmed that the constructed model fit the real situation well.
The constructed nomogram prognostic model can recognize risk factors and accurately predict the mortality of patients initially diagnosed with primary PTB. This is expected to guide early clinical intervention and treatment for high-risk patients.
根据连续三年的《全球结核病报告》，结核病（TB）是第二大传染性杀手。原发性肺结核（PTB）在结核病疾病中导致的死亡率最高。遗憾的是，以前没有研究针对特定类型或特定病程的 PTB，因此以前研究中建立的模型不能准确地适用于临床治疗。本研究旨在构建列线图预后模型，以便快速识别初诊为 PTB 的患者的死亡相关风险因素，以便在临床上尽早干预和治疗高危患者，从而降低死亡率。
我们回顾性分析了 2019 年 1 月 1 日至 2019 年 12 月 31 日期间在湖南省胸科医院初诊为原发性 PTB 的 1809 例住院患者的临床资料。采用二元逻辑回归分析识别风险因素。使用 R 软件构建用于死亡率预测的列线图预后模型，并使用验证集进行验证。
单因素和多因素逻辑回归分析显示，饮酒、乙型肝炎病毒（HBV）、体重指数（BMI）、年龄、白蛋白（ALB）和血红蛋白（Hb）是初诊为原发性 PTB 的住院患者死亡的六个独立预测因素。基于这些预测因素，建立了一个具有较高预测准确性的列线图预后模型，其中曲线下面积（AUC）为 0.881（95%置信区间[Cl]：0.777-0.847），灵敏度为 84.7%，特异性为 77.7%。内部和外部验证证实，所构建的模型拟合实际情况良好。
构建的列线图预后模型可以识别风险因素并准确预测初诊为原发性 PTB 的患者的死亡率。这有望指导高危患者的早期临床干预和治疗。
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