Diagnostic Accuracy of Oxygen Desaturation Index for Sleep-Disordered Breathing in Patients With Diabetes

Dysmetabolism and Sleep Fragmentation in Obstructive Sleep Apnea Patients Run Independently of High Caffeine Consumption

Daytime hypersomnolence, the prime feature of obstructive sleep apnea (OSA), frequently leads to high coffee consumption. Nevertheless, some clinicians ask for patients’ caffeine avoidance. Caffeinated drinks are sometimes associated with more severe OSA. However, these effects are not consensual. Here we investigated the effect of caffeine consumption on sleep architecture and apnea/hypopnea index in OSA. Also, the impact of caffeine on variables related with dysmetabolism, dyslipidemia, and sympathetic nervous system (SNS) dysfunction were investigated. A total of 65 patients diagnosed with OSA and 32 without OSA were included after given written informed consent. Polysomnographic studies were performed. Blood was collected to quantify caffeine and its metabolites in plasma and biochemical parameters. 24 h urine samples were collected for catecholamines measurement. Statistical analyses were performed by SPSS: (1) non-parametric Mann-Whitney test to compare variables between controls and OSA; (2) multivariate logistic regression testing the effect of caffeine on sets of variables in the 2 groups; and (3) Spearmans’ correlation between caffeine levels and comorbidities in patients with OSA. As expected OSA development is associated with dyslipidemia, dysmetabolism, SNS dysfunction, and sleep fragmentation. There was also a significant increase in plasma caffeine levels in the OSA group. However, the higher consumption of caffeine by OSA patients do not alter any of these associations. These results showed that there is no apparent rationale for caffeine avoidance in chronic consumers with OSA.

Development and validation of a simple clinical nomogram for predicting obstructive sleep apnea

Obstructive sleep apnea is the most common type of sleep breathing disorder. Therefore, the purpose of our research is to construct and verify an objective and easy-to-use nomogram that can accurately predict a patient's risk of obstructive sleep apnea. In this study, we retrospectively collected the data of patients undergoing polysomnography at the Sleep Medicine Center of the First Affiliated Hospital of Guangzhou Medical University. Participants were randomly assigned to a training cohort (50%) and a validation cohort (50%). Logistic regression and Lasso regression models were used to reduce data dimensions, select factors and construct the nomogram. C-index, calibration curve, decision curve analysis and clinical impact curve analysis were used to evaluate the identification, calibration and clinical effectiveness of the nomogram. Nomograph validation was performed in the validation cohort. The study included 1035 people in the training cohort and 1078 people in the validation cohort. Logistic and Lasso regression analysis identified age, gender, diastolic blood pressure, body mass index, neck circumference and Epworth Sleepiness Scale as the predictive factors included in the nomogram. The training cohort (C-index = 0.741) and validation cohort (C-index = 0.745) had better identification and calibration effects. The areas under the curve of the nomogram and STOP-Bang were 0.741 (0.713-0.767) and 0.728 (0.700-0.755), respectively. Decision curve analysis and clinical impact curve analysis showed that the nomogram is clinically useful. We have established a concise and practical nomogram that will help doctors better determine the priority of patients referred to the sleep centre.

Diagnosis of Obstructive Sleep Apnea in Patients with Associated Comorbidity

Obstructive sleep apnea (OSA) is a heterogeneous disease with many physiological implications. OSA is associated with a great diversity of diseases, with which it shares common and very often bidirectional pathophysiological mechanisms, leading to significantly negative implications on morbidity and mortality. In these patients, underdiagnosis of OSA is high. Concerning cardiorespiratory comorbidities, several studies have assessed the usefulness of simplified screening tests for OSA in patients with hypertension, COPD, heart failure, atrial fibrillation, stroke, morbid obesity, and in hospitalized elders.The key question is whether there is any benefit in the screening for the existence of OSA in patients with comorbidities. In this regard, there are few studies evaluating the performance of the various diagnostic procedures in patients at high risk for OSA. The purpose of this chapter is to review the existing literature about diagnosis in those diseases with a high risk for OSA, with special reference to artificial intelligence-related methods.