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Medication-induced central sleep apnea (CSA) is one of the eight categories of causes of CSA but in the absence of awareness and careful history may be misclassified as primary CSA. While opioids are a well-known cause of respiratory depression and CSA, non-opioid medications including sodium oxybate, baclofen, valproic acid, gabapentin, and ticagrelor are less well-recognized. Opioids-induced respiratory depression and CSA are mediated primarily by µ-opioid receptors, which are abundant in the pontomedullary centers involved in breathing. The non-opioid medications, sodium oxybate, baclofen, valproic acid, and gabapentin, act upon brainstem gamma-aminobutyric acid (GABA) receptors, which co-colonize with µ-opioid receptors and mediate CSA. The pattern of ataxic breathing associated with these medications is like that induced by opioids on polysomnogram. Finally, ticagrelor also causes periodic breathing and CSA by increasing central chemosensitivity and ventilatory response to carbon dioxide. Given the potential consequences of CSA and the association between some of these medications with mortality, it is critical to recognize these adverse drug reactions, particularly because discontinuation of the offending agents has been shown to eliminate CSA., (© The Author(s) 2024. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Rationale: Determining whether an individual has obstructive or central sleep apnea is fundamental to selecting the appropriate treatment. Objectives: Here we derive an automated breath-by-breath probability of obstruction, as a surrogate of gold-standard upper airway resistance, using hallmarks of upper airway obstruction visible on clinical sleep studies. Methods: From five nocturnal polysomnography signals (airflow, thoracic and abdominal effort, oxygen saturation, and snore), nine features were extracted and weighted to derive the breath-by-breath probability of obstruction (P obs ). A development and initial test set of 29 subjects (development = 6, test = 23) (New York, NY) and a second test set of 39 subjects (Solingen, Germany), both with esophageal manometry, were used to develop P obs and validate it against gold-standard upper airway resistance. A separate dataset of 114 subjects with 2 consecutive nocturnal polysomnographies (New York, NY) without esophageal manometry was used to assess the night-to-night variability of P obs . Measurements and Main Results: A total of 1,962,229 breaths were analyzed. On a breath-by-breath level, P obs was strongly correlated with normalized upper airway resistance in both test sets (set 1: cubic adjusted [adj.] R 2  = 0.87, P  < 0.001, area under the receiver operating characteristic curve = 0.74; set 2: cubic adj. R 2  = 0.83, P  < 0.001, area under the receiver operating characteristic curve = 0.7). On a subject level, median P obs was associated with the median normalized upper airway resistance (set 1: linear adj. R 2  = 0.59, P  < 0.001; set 2: linear adj. R 2  = 0.45, P  < 0.001). Median P obs exhibited low night-to-night variability [intraclass correlation(2, 1) = 0.93]. Conclusions: Using nearly 2 million breaths from 182 subjects, we show that breath-by-breath probability of obstruction can reliably predict the overall burden of obstructed breaths in individual subjects and can aid in determining the type of sleep apnea.

Background: Central sleep apnea (CSA) is common among patients with heart failure and has been strongly linked to adverse outcomes. However, progress toward improving outcomes for such patients has been limited. The purpose of this official statement from the American Thoracic Society is to identify key areas to prioritize for future research regarding CSA in heart failure. Methods: An international multidisciplinary group with expertise in sleep medicine, pulmonary medicine, heart failure, clinical research, and health outcomes was convened. The group met at the American Thoracic Society 2019 International Conference to determine research priority areas. A statement summarizing the findings of the group was subsequently authored using input from all members. Results : The workgroup identified 11 specific research priorities in several key areas: 1 ) control of breathing and pathophysiology leading to CSA, 2 ) variability across individuals and over time, 3 ) techniques to examine CSA pathogenesis and outcomes, 4 ) impact of device and pharmacological treatment, and 5 ) implementing CSA treatment for all individuals Conclusions: Advancing care for patients with CSA in the context of heart failure will require progress in the arenas of translational (basic through clinical), epidemiological, and patient-centered outcome research. Given the increasing prevalence of heart failure and its associated substantial burden to individuals, society, and the healthcare system, targeted research to improve knowledge of CSA pathogenesis and treatment is a priority.

Study Objectives: The clear discrimination of central and obstructive hypopneas is highly relevant to avoid misinterpretation and inappropriate treatment of complicated breathing patterns. Esophageal manometry is the accepted standard for the differentiation of the phenotypes of sleep apnea. However, it is limited in its use due to poor acceptance by patients and therefore rarely performed in routine clinical practice. Flattening of the inspiratory airflow curve, paradoxical breathing, arousal position, sleep stages, and breathing pattern at the end of the hypopnea can each give hints for the classification of hypopnea. The aim of this study was to evaluate a standardized algorithm combining these polysomnographic parameters for the discrimination of hypopneas in everyday practice., Methods: Polysomnography (PSG) and esophageal manometry were performed in 41 patients suspected of having sleep apnea (33 male, 52.3 ± 15.9 yr, body mass index 28.6 ± 4.5 kg/m(2)). Hypopneas were independently discriminated by blinded investigators based on esophageal pressure and the PSG-based algorithm. Only those hypopneas that could be differentiated with both methods were evaluated., Results: There were 1,175 of 1,837 hypopneas (64%) that could be defined by esophageal pressure, 1,812 (98.6%) by the PSG-based algorithm. Using esophageal pressure as a reference, the new algorithm correctly defined 76.9% of central and 60.5% of obstructive hypopneas. The overall accuracy was 68%. The isolated analysis of single PSG parameters revealed a lower accuracy compared with the combined algorithm., Conclusions: The PSG-based algorithm allows for discrimination of most hypopneas. It is advantageous in comparison with esophageal pressure because it is noninvasive and less impaired by artefacts. Therefore, it is a potentially helpful tool for sleep specialists., Citation: Randerath WJ; Treml M; Priegnitz C; Stieglitz S; Hagmeyer L; Morgenstern C. Evaluation of a noninvasive algorithm for differentiation of obstructive and central hypopneas. SLEEP 2013;36(3):363-368.

Background: The coexistence of obstructive sleep apnea (OSA) and central sleep apnea (CSA) and Cheyne-Stokes respiration (CSR) is common in patients with heart failure (HF). While CPAP improves CSA/CSR by about 50%, maximal suppression is crucial in improving clinical outcomes. Auto-servoventilation (ASV) effectively suppresses CSA/CSR in HF, but few trials have been performed in patients with coexisting OSA and CSA/CSR. Our objective was to evaluate a randomized, controlled trial to compare the efficacy of ASV and CPAP in reducing breathing disturbances and improving cardiac parameters in patients with HF and coexisting sleep-disordered breathing., Methods: Both modes were delivered using the BiPAP autoSV (Philips Respironics) over a 12-month period. Seventy patients (63 men, 66.3 ± 9.1 y, BMI 31.3 ± 6.0 kg/m(2)) had coexisting OSA and CSA/CSR, arterial hypertension, coronary heart disease, or cardiomyopathy and clinical signs of heart failure New York Heart Association classes II-III. Polysomnography, brain natriuretic peptide (BNP), spiroergometry, and echocardiography were performed at baseline and after 3 and 12 months of treatment., Results: Both modes of therapy significantly improved respiratory disturbances, oxygen desaturations, and arousals over the study period. ASV reduced the central apnea hypopnea index (baseline CPAP, 21.8 ± 11.7; ASV, 23.1 ± 13.2; 12 months CPAP, 10.7 ± 8.7; ASV, 6.1 ± 7.8, P < .05) and BNP levels (baseline CPAP, 686.7 ± 978.7 ng/mL; ASV, 537.3 ± 891.8; 12 months CPAP, 847.3 ± 1848.1; ASV, 230.4 ± 297.4; P < .05) significantly more effectively as compared with CPAP. There were no relevant differences in exercise performance and echocardiographic parameters between the groups., Conclusions: ASV improved CSA/CSR and BNP over a 12-month period more effectively than CPAP.

The automatic differentiation of obstructive and central respiratory events is a major challenge in the diagnosis of sleep-disordered breathing. Esophageal pressure (Pes) measurement is the gold-standard method to identify these events. This study presents a new classifier that automatically differentiates obstructive and central hypopneas with the Pes signal and a new approach for an automatic noninvasive classifier with nasal airflow. An overall of 28 patients underwent night polysomnography with Pes recording, and a total of 769 hypopneas were manually scored by human experts to create a gold-standard annotation set. Features were automatically extracted from the Pes signal to train and test the classifiers (discriminant analysis, support vector machines, and adaboost). After a significantly (p < 0.01) higher incidence of inspiratory flow limitation episodes in obstructive hypopneas was objectively, invasively assessed compared to central hypopneas, the feasibility of an automatic noninvasive classifier with features extracted from the airflow signal was demonstrated. The automatic invasive classifier achieved a mean sensitivity, specificity, and accuracy of 0.90 after a 100-fold cross validation. The automatic noninvasive feasibility study obtained similar hypopnea differentiation results as a manual noninvasive classification algorithm. Hence, both systems seem promising for the automatic differentiation of obstructive and central hypopneas.

Objective: The coexistence of obstructive (OSAS) and central sleep apnoea (CSA) and Cheyne-Stokes respiration (CSR) is common in patients with and without underlying heart diseases. CPAP has been shown to improve CSA/CSR by about 50%, but recent data suggest maximal suppression of CSA is important in improving clinical outcomes in heart failure patients. Adaptive servo-ventilation (ASV) effectively suppresses CSA/CSR in heart failure, but only few trials have considered patients with coexisting OSAS and CSA/CSR., Methods: Prospective, observational pilot study to evaluate the efficacy of a new ASV device, the BiPAP AutoSV, in 10 male consecutive patients with coexisting OSAS and CSA/CSR with and without heart failure over eight weeks. Six had stable heart failure., Measurements and Results: The total AHI improved from 48.9+/-20.6/h to 8.7+/-7.4, the obstructive AHI from 15.8+/-16.2/h to 2.6+/-2.5/h and the central AHI from 33.1+/-10.8/h to 6.1+/-5.9/h (all p<0.01). Furthermore, there was a significant improvement in sleep profile and respiratory related arousals. The six patients with cardiovascular disease, including three with congestive heart failure, showed similar improvements in all parameters., Conclusions: BiPAP AutoSV was effective in reducing all types of respiratory disturbances in coexisting OSAS and CSA/CSR with and without heart failure. Further studies comparing the long-term clinical efficacy of this device against CPAP are warranted.