Table_1_A nomogram for predicting mortality of patients initially diagnosed with primary pulmonary tuberculosis in Hunan province, China: a retrospective study.xlsx

ObjectiveAccording 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.MethodsWe 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.ResultsUnivariate 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.ConclusionThe 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.

本研究旨在构建一个预后评分图模型，以迅速识别初诊原发性肺结核（PTB）患者死亡相关风险因素，以便在临床早期干预和治疗高风险患者，从而降低死亡率。研究对湖南胸科医院2019年1月1日至2019年12月31日期间初诊为原发性PTB的1809名住院患者的临床数据进行回顾性分析。采用二元逻辑回归分析识别风险因素。利用R软件构建了基于这些预测因子的死亡率预后评分图模型，并使用验证集进行验证。结果显示，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）是初诊原发性PTB住院患者死亡的六个独立预测因子。基于这些预测因子建立的预后评分图模型具有较高的预测准确性，其曲线下面积（AUC）为0.881（95%置信区间[CI]：0.777-0.847），敏感性为84.7%，特异性为77.7%。内部和外部验证均证实所构建的模型与实际情况拟合良好。结论：所构建的预后评分图模型能够识别风险因素，并准确预测初诊原发性PTB患者的死亡率，这有望指导临床对高风险患者进行早期干预和治疗。