Wang Sihua, Gu Ruohua, Ren Pengfei, Chen Yu, Wu Di, Li Linlin
The Third People's Hospital of Henan Province and Henan Hospital for Occupational Diseases, Zhengzhou, Henan Province, China.
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China.
Front Public Health. 2025 Jan 15;12:1515867. doi: 10.3389/fpubh.2024.1515867. eCollection 2024.
This study aims to identify risk factors associated with tuberculosis-specific mortality (TSM) in older adult patients with pulmonary tuberculosis (TB) and to develop a competing risk nomogram for TSM prediction.
We conducted a retrospective cohort study and randomly selected 528 older adult pulmonary TB patients hospitalized in designated hospitals in Henan Province between January 2015 and December 2020. The cumulative incidence function (CIF) was calculated for both TSM and non-tuberculosis-specific mortality (non-TSM). A Fine and Gray proportional subdistribution hazards model and a competing risk nomogram were developed to predict TSM in older adult patients.
The 5-year cumulative incidence functions (CIFs) for TSM and non-TSM were 9.7 and 9.4%, respectively. The Fine and Gray model identified advanced age, retreatment status, chest X-rays (CXR) cavities, and hypoalbuminemia as independent risk factors for TSM. The competing risk nomogram for TSM showed good calibration and excellent discriminative ability, achieving a concordance index (c-index) of 0.844 (95% confidence interval [CI]: 0.830-0.857).
The Fine and Gray model provided an accurate evaluation of risk factors associated with TSM. The competing risk nomogram, developed using the Fine and Gray model, provided accurate and personalized predictions of TSM.
本研究旨在确定老年肺结核患者结核病特异性死亡率(TSM)的相关危险因素,并开发一种用于TSM预测的竞争风险列线图。
我们进行了一项回顾性队列研究,随机选取了2015年1月至2020年12月期间在河南省指定医院住院的528例老年肺结核患者。计算了TSM和非结核病特异性死亡率(非TSM)的累积发病率函数(CIF)。开发了一个Fine and Gray比例子分布风险模型和一个竞争风险列线图,以预测老年患者的TSM。
TSM和非TSM的5年累积发病率函数(CIF)分别为9.7%和9.4%。Fine and Gray模型确定高龄、复治状态、胸部X线(CXR)空洞和低白蛋白血症为TSM的独立危险因素。TSM的竞争风险列线图显示出良好的校准和出色的判别能力,一致性指数(c指数)为0.844(95%置信区间[CI]:0.830 - 0.857)。
Fine and Gray模型对与TSM相关的危险因素进行了准确评估。使用Fine and Gray模型开发的竞争风险列线图对TSM进行了准确且个性化的预测。