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Drug-Induced Sleep Endoscopy (DISE) with Target Controlled Infusion (TCI) and Bispectral Analysis in Obstructive Sleep Apnea
Published on: December 6, 2016
Systemic immune-inflammation index and systemic inflammation response index in obstructive sleep apnoea: A systematic
R K B de Souza1, V R B de Menezes1, J L G C Monteiro2
1Department of Oral and Maxillofacial Surgery, University of Pernambuco - School of Dentistry (UPE/FOP), Recife, Brazil.
Abstract:
Obstructive sleep apnoea (OSA) involves the repetitive collapse of the upper airway, leading to sleep fragmentation, hypoxaemia, hypercapnia, and increased sympathetic activity, with potential physiological consequences including increased oxidative stress, endothelial dysfunction, metabolic dysregulation, a tendency towards coagulation, and chronic systemic inflammation. The purpose of this systematic review was to determine whether the systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) are reliable markers for detecting systemic inflammation in patients diagnosed with OSA, and whether they can predict its severity. Searches were conducted in the PubMed, Embase, Web of Science, Scopus, and LILACS (BVS) databases. Additional searches in the grey literature and manual searches were also performed. After selecting studies according to the eligibility criteria, 13 were included in this review. The meta-analysis comparing the SII index between patients with OSA and those without (controls) showed a statistically significant difference (mean difference 75.50, 95% confidence interval 8.24-142.76; P = 0.03), although certainty of the evidence was low. The comparison of the SIRI index between patients with severe and mild OSA also showed a significant difference (mean difference 1.25, 95% confidence interval 0.03-2.47; P = 0.04), with low certainty of the evidence. The indices studied, which can be calculated via a simple and low-cost routine test, appear to be possible indicators of systemic inflammation in these patients and seem to predict the severity of the disease. This suggests their potential use for diagnosis of the disease and treatment monitoring.
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