Diagnostic Value of Model-Based Iterative Algorithm in Tuberculous Pleural Effusion

Abstract

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 (x2 = 14.74, 25.22, and 76.45, P < 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).

Significance of adenosine deaminase in diagnosing tuberculous pleurisy

“…The TB fluid is usually clear but may be opalescent as in case of bacterial pleurisy tečnost je obično bistra, ali može biti i zamućena kao u slučaju bakterijskog pleuritisa praćenog parapneumoničnim izlivom sa pneumonijom [5]. Na osnovu C-reaktivnog proteina, amilaze ili moždanog natriuretskog peptida iz uzorka pleuralnog izliva mogu se diferencirati različiti tipovi izliva [6,7]. Uz antibiotsku terapiju (fluorohinolonima), izliv može parcijalno ili potpuno regredirati u slučaju bakterijskog parapneumoničnog izliva, što odlaže postavljanje prave dijagnoze do trenutka dok se izliv ponovo ne javi (Slika 1).…”

ADA as main biochemical marker in patients with tuberculous effusion

“…Tuberculous pleurisy is hard to diagnose due to slow course of disease and lack of specificity in symptoms and diagnostic methods. Because of that is need multidisciplinary approach [9] . Symptoms are usually acute or subacute: 75% of patients have chest pain, 70% cough, 85% night sweats, 50% dyspnea and more than half with fever [9] .…”

“…Because of that is need multidisciplinary approach [9] . Symptoms are usually acute or subacute: 75% of patients have chest pain, 70% cough, 85% night sweats, 50% dyspnea and more than half with fever [9] . So, symptoms are not typical and need other diagnostic tools.…”

“…Better approach is thoracentesis. Tuberculous pleural effusion is an exudate, colorless or straw (yellowish) or sometimes can also be bloody [9] . Neutrophils are dominant in the initial 24 h of the inflammatory response, macrophages are in peak at 96 h after the onset of inflammation, and T lymphocytes are dominant in the subsequent inflammatory response, gradually forming pleural granulomas [9] .…”

Retracted: Diagnostic Value of Model‐Based Iterative Algorithm in Tuberculous Pleural Effusion

“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”