Department of Respiratory and Critical Care Medicine, Hebei Chest Hospital, Shijiazhuang, Hebei 050000, China.
Department 5 of Tuberculosis, Hebei Chest Hospital, Shijiazhuang, Hebei 050000, China.
J Healthc Eng. 2022 Feb 9;2022:7845767. doi: 10.1155/2022/7845767. eCollection 2022.
Although there are several diagnostic modalities for tuberculous pleurisy, there is still a lack of easy, cost-effective, and rapid methods for confirming the diagnosis. In order to facilitate clinicians to diagnose patients with tuberculous pleurisy at an early stage, help patients to obtain treatment early, and reduce lung damage, it is hoped that new techniques will be available in the future to help diagnose tuberculous pleurisy rapidly in the clinic. To this end, this paper investigates the problem of bidirectional consistency based on event-triggered iterative learning. Firstly, a dynamic linearized data model of TB pleurisy intelligent system is established using compact-form dynamic linearization method, and a parameter estimation algorithm of TB pleurisy data model is proposed; then, based on this data model, an output observer and a dead zone controller are designed, and an event-triggered distributed model-free iterative learning bidirectional consistency control strategy is constructed by combining with signal graph theory. In this paper, 112 patients with pleural effusion were collected, including 76 patients with confirmed or clinically diagnosed tuberculous pleural effusion and 36 patients with nontuberculous pleural effusion. Pleural effusion T-SPOT.TB, blood T-SPOT.TB, pleural effusion Xpert MTB/RIF, and pleural effusion adenosine deaminase (ADA) tests were performed before treatment in the included patients. The sensitivity of pleural effusion T-SPOT.TB was higher than that of peripheral blood T-SPOT.TB (76.32%, 58/76), pleural effusion Xpert MTB/RIF (65.79%, 50/76), and pleural effusion ADA (28.95%, 22/76); the differences were statistically significant ( = 14.74, 25.22, and 76.45, < 0.01). The specificity of the Xpert MTB/RIF test for pleural effusion (100%, 36/36) was higher than that for pleural effusion T-SPOT.TB (77.78%, 28/36), peripheral blood T-SPOT.TB, and pleural effusion T-SPOT.TB. The sensitivity of the combined Xpert MTB/RIF test (64.47%, 49/76) was lower than that of the pleural effusion T-SPOT.TB alone (97.37%, 74/76).
虽然有几种诊断结核性胸膜炎的方法，但仍然缺乏简单、经济有效且快速的方法来确认诊断。为了方便临床医生早期诊断结核性胸膜炎患者，帮助患者尽早获得治疗，减少肺损伤，希望未来能有新技术帮助临床快速诊断结核性胸膜炎。为此，本文研究了基于事件触发迭代学习的双向一致性问题。首先，利用紧凑形式的动态线性化方法建立 TB 胸膜炎智能系统的动态线性化数据模型，并提出 TB 胸膜炎数据模型的参数估计算法；然后，基于该数据模型，设计了输出观测器和死区控制器，并结合信号图论，构建了事件触发分布式无模型迭代学习双向一致性控制策略。本文共收集了 112 例胸腔积液患者，其中确诊或临床诊断结核性胸腔积液 76 例，非结核性胸腔积液 36 例。纳入患者在治疗前均进行了胸腔积液 T-SPOT.TB、血 T-SPOT.TB、胸腔积液 Xpert MTB/RIF 和胸腔积液腺苷脱氨酶（ADA）检测。胸腔积液 T-SPOT.TB 的敏感性高于外周血 T-SPOT.TB（76.32%，58/76）、胸腔积液 Xpert MTB/RIF（65.79%，50/76）和胸腔积液 ADA（28.95%，22/76）；差异有统计学意义（ = 14.74、25.22 和 76.45， < 0.01）。胸腔积液 Xpert MTB/RIF 试验的特异性（100%，36/36）高于胸腔积液 T-SPOT.TB（77.78%，28/36）、外周血 T-SPOT.TB 和胸腔积液 T-SPOT.TB。联合 Xpert MTB/RIF 试验的敏感性（64.47%，49/76）低于胸腔积液 T-SPOT.TB 单独试验（97.37%，74/76）。
