This study sought to determine the cardiovascular physiologic correlates of sleep-disordered breathing (SDB) in American-style football (ASF) participants using echocardiography, vascular applanation tonometry, and peripheral arterial tonometry. Forty collegiate ASF participants were analyzed at pre- and postseason time points with echocardiography and vascular applanation tonometry. WatchPAT (inclusive of peripheral arterial tonometry) used to assess for SDB was then performed at the postseason time point. Twenty-two of 40 (55%) ASF participants demonstrated SDB with an apnea-hypopnea index (pAHI) ≥5. ASF participants with SDB were larger (109 ± 20 vs 92 ± 14 kg, p = 0.004) and more likely linemen position players (83% vs 50%, p = 0.03). Compared with those without SDB, ASF participants with SDB demonstrated relative impairments in left ventricular diastolic and vascular function as reflected by lower lateral e′ (14 ± 3 vs 17 ± 3 cm/s, p = 0.007) and septal e′ (11 ± 2 vs 13 ± 2 cm/s, p = 0.009) tissue velocities and higher pulse wave velocity (5.4 ± 0.9 vs 4.8 ± 0.5 m/s, p = 0.02). In the total cohort, there were significant positive correlations between pAHI and pulse wave velocity (r = 0.42, p = 0.008) and inverse correlations between pAHI and the averaged e′ tissue velocities (r = −0.42, p = 0.01). In conclusion, SDB is highly prevalent in youthful collegiate ASF participants and associated with relative impairments in cardiac and vascular function. Targeted efforts to identify youthful populations with SDB, including ASF participants, and implement SDB treatment algorithms, represent important future clinical directives.

Prediction models aim to use available data to predict a health state or outcome that has not yet been observed. Prediction is primarily relevant to clinical practice, but is also used in research, and administration. While prediction modeling involves estimating the relationship between patient factors and outcomes, it is distinct from casual inference. Prediction modeling thus requires unique considerations for development, validation, and updating. This document represents an effort from editors at 31 respiratory, sleep, and critical care medicine journals to consolidate contemporary best practices and recommendations related to prediction study design, conduct, and reporting. Herein, we address issues commonly encountered in submissions to our various journals. Key topics include considerations for selecting predictor variables, operationalizing variables, dealing with missing data, the importance of appropriate validation, model performance measures and their interpretation, and good reporting practices. Supplemental discussion covers emerging topics such as model fairness, competing risks, pitfalls of "modifiable risk factors", measurement error, and risk for bias. This guidance is not meant to be overly prescriptive; we acknowledge that every study is different, and no set of rules will fit all cases. Additional best practices can be found in the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines, to which we refer readers for further details.

BACKGROUND: The prevalence of optic nerve and retinal vascular changes within the obstructive sleep apnea (OSA) population are not well-known, although it has been postulated that optic nerve ischemic changes and findings related to an elevated intracranial pressure may be more common in OSA patients. We prospectively evaluated the ocular fundus in unselected patients undergoing overnight diagnostic polysomnography (PSG). METHODS: Demographic data, medical/ocular history, and nonmydriatic fundus photographs were prospectively collected in patients undergoing PSG at our institution and reviewed for the presence of optic disc edema for which our study was appropriately powered a priori. Retinal vascular changes were also evaluated. OSA was defined using the measures of both sleep-disordered breathing and hypoxia. RESULTS: Of 250 patients evaluated in the sleep center, fundus photographs were performed on 215 patients, among whom 127 patients (59%) had an apnea/hypopnea index (AHI) ≥15 events per hour, including 36 with severe OSA. Those with AHI <15 served as the comparison group. None of the patients had optic disc edema (95% confidence interval [CI]: 0%-3%). There was no difference in rates of glaucomatous appearance or pallor of the optic disc among the groups. Retinal arteriolar changes were more common in severe OSA patients (odds ratio: 1.09 per 5 unit increase in AHI; 95% CI, 1.02-1.16; P = 0.01), even after controlling for mean arterial blood pressure. CONCLUSIONS: We did not find an increased prevalence of optic disc edema or other optic neuropathies in our OSA population. However, retinal vascular changes were more common in patients with severe OSA, independent of blood pressure.

OBJECTIVES: To determine if a quality improvement intervention improves sleep and delirium/cognition. DESIGN: Observational, pre-post design. SETTING: A tertiary academic hospital in the United States. PATIENTS: 300 medical ICU patients. INTERVENTIONS: This medical ICU-wide project involved a "usual care" baseline stage, followed by a quality improvement stage incorporating multifaceted sleep-promoting interventions implemented with the aid of daily reminder checklists for ICU staff. MEASUREMENTS AND MAIN RESULTS: Primary ICU outcomes were perceived sleep quality and noise ratings (measured on a 0-100 scale using the valid and reliable Richards-Campbell Sleep Questionnaire) and delirium/coma-free days. Secondary outcomes included ICU and hospital length of stay and mortality. Post-ICU measures of cognition and perceived sleep quality were evaluated in an ICU patient subset. During the baseline and sleep quality improvement stages, there were 122 and 178 patients, respectively, with more than one night in the ICU, accounting for 634 and 826 patient-days. Within the groups, 78 (63.9%) and 83 (46.6%) patients received mechanical ventilation. Over the 826 patient-day quality improvement period, checklist item completion rates ranged from 86% to 94%. In multivariable regression analysis of the quality improvement vs. baseline stages, improvements in overall Richards-Campbell Sleep Questionnaire sleep quality ratings did not reach statistical significance, but there were significant improvements in daily noise ratings (mean ± SD: 65.9±26.6 vs. 60.5±26.3, p = 0.001), incidence of delirium/coma (odds ratio: 0.46; 95% confidence interval, 0.23-0.89; p = 0.02), and daily delirium/coma-free status (odds ratio: 1.64; 95% confidence interval, 1.04-2.58; p = 0.03). Improvements in secondary ICU outcomes and post-ICU outcomes did not reach statistical significance. CONCLUSIONS: An ICU-wide quality improvement intervention to improve sleep and delirium is feasible and associated with significant improvements in perceived nighttime noise, incidence of delirium/coma, and daily delirium/coma-free status. Improvement in perceived sleep quality did not reach statistical significance.

Critically ill patients frequently experience poor sleep, characterized by frequent disruptions, loss of circadian rhythms, and a paucity of time spent in restorative sleep stages. Factors that are associated with sleep disruption in the intensive care unit (ICU) include patient-ventilator dysynchrony, medications, patient care interactions, and environmental noise and light. As the field of critical care increasingly focuses on patients' physical and psychological outcomes following critical illness, understanding the potential contribution of ICU-related sleep disruption on patient recovery is an important area of investigation. This review article summarizes the literature regarding sleep architecture and measurement in the critically ill, causes of ICU sleep fragmentation, and potential implications of ICU-related sleep disruption on patients' recovery from critical illness. With this background information, strategies to optimize sleep in the ICU are also discussed.

Study Objectives: Peripheral arterial tonometry (PAT)–based technology represents a validated portable monitoring modality for the diagnosis of OSA. We assessed the diagnostic accuracy of PAT-based technology in a large point-of-care cohort of patients studied with concurrent polysomnography (PSG). Methods: During study enrollment, all participants suspected to have OSA and tested by in-laboratory PSG underwent concurrent PAT device recordings. Results: Five hundred concomitant PSG and WatchPat tests were analyzed. Median (interquartile range) PSG AHI was 18 (8–37) events/h and PAT AHI3% was 25 (12–46) events/h. Average bias was + 4 events/h. Diagnostic concordance was found in 42%, 41%, and 83% of mild, moderate, and severe OSA, respectively (accuracy = 53%). Among patients with PAT diagnoses of moderate or severe OSA, 5% did not have OSA and 19% had mild OSA; in those with mild OSA, PSG showed moderate or severe disease in 20% and no OSA in 30% of patients (accuracy = 69%). On average, using a 3% desaturation threshold, WatchPat overestimated disease prevalence and severity (mean + 4 events/h) and the 4% threshold underestimated disease prevalence and severity by −6 events/h. Conclusions: Although there was an overall tendency to overestimate the severity of OSA, a significant percentage of patients had clinically relevant misclassifications. As such, we recommend that patients without OSA or with mild disease assessed by PAT undergo repeat in-laboratory PSG. Optimized clinical pathways are urgently needed to minimize therapeutic decisions instituted in the presence of diagnostic uncertainty.

In this issue of the Journal of Clinical Sleep Medicine, the Sleep Apnea Definitions (SAD) task force of the American Academy of Sleep Medicine (AASM) redefines many of the respiratory rules found in the 2007 AASM Manual for the Scoring of Sleep and Associated Events. This task force labored over a two-year period, analyzing the recent literature pertaining to respiratory events and sensor technology, and striving to reach consensus on a new set of respiratory rules. The paper details some significant changes including a single definition of a hypopnea. In addition, there is now the option to score hypopneas as central or obstructive. There is also greater concordance between adult and pediatric scoring rules for various respiratory events. (Please refer to the forthcoming new version of the scoring manual for the final version of the rules for scoring respiratory events.)

Critically ill patients commonly experience poor sleep quality in the intensive care unit (ICU) because of various modifiable factors. To address this issue, an ICU-wide, multifaceted quality improvement (QI) project was undertaken to promote sleep in the Johns Hopkins Hospital Medical ICU (MICU). To supplement previously published results of this QI intervention, the present article describes the specific QI framework used to develop and implement this intervention, which consists of 4 steps: (a) summarizing the evidence to create a list of sleep-promoting interventions, (b) identifying and addressing local barriers to implementation, (c) selecting performance measures to assess intervention adherence and patient outcomes, and (d) ensuring that all patients receive the interventions through staff engagement and education and regular project evaluation. Measures of performance included daily completion rates of daytime and nighttime sleep improvement checklists and completion rates of individual interventions. Although long-term adherence and sustainability pose ongoing challenges, this model provides a foundation for future ICU sleep promotion initiatives.

Outside sleep laboratory settings, peripheral arterial tonometry (PAT, eg, WatchPat) represents a validated modality for diagnosing obstructive sleep apnea (OSA). We have shown before that the accuracy of home sleep apnea testing by WatchPat 200 devices in diagnosing OSA is suboptimal (50%-70%). In order to improve its diagnostic performance, we built several models that predict the main functional parameter of polysomnography (PSG), Apnea Hypopnea Index (AHI). Participants were recruited in our Sleep Center and underwent concurrent in-laboratory PSG and PAT recordings. Statistical models were then developed to predict AHI by using robust functional parameters from PAT-based testing, in concert with available demographic and anthropometric data, and their performance was confirmed in a random validation subgroup of the cohort. Five hundred synchronous PSG and WatchPat sets were analyzed. Mean diagnostic accuracy of PAT was improved to 67%, 81% and 85% in mild, moderate-severe or no OSA, respectively, by several models that included participants' age, gender, neck circumference, body mass index and the number of 4% desaturations/hour. WatchPat had an overall accuracy of 85.7% and a positive predictive value of 87.3% in diagnosing OSA (by predicted AHI above 5). In this large cohort of patients with high pretest probability of OSA, we built several models based on 4% oxygen desaturations, neck circumference, body mass index and several other variables. These simple models can be used at the point-of-care, in order to improve the diagnostic accuracy of the PAT-based testing, thus ameliorating the high rates of misclassification for OSA presence or disease severity.

Objectives: With over 2 million cases of acute respiratory failure in the United States per year, noninvasive ventilation has become a leading treatment modality, often supplanting invasive mechanical ventilation as the initial treatment of choice. Most acute respiratory failure patients use a full face (oronasal) mask with noninvasive ventilation, which is known to impair communication, but its popularity and benefit has led many providers to accept the communication impairment. Medical staff periodically remove masks to communicate with patients, but patients are often limited to short utterances and risk lung derecruitment upon removal of positive pressure. These problems can lead to noninvasive ventilation failure, which is often linked to worse outcomes than first initiating invasive mechanical ventilation and can lead to increased hospitalization costs. Data Sources: We searched MEDLINE and Google Scholar for "speech," "communication," "impairment," "failure," "complications," "NIPPV," "NIV," and "noninvasive ventilation." Study Selection: We included articles with patients in acute respiratory failure. We excluded articles for patients using noninvasive ventilation therapy for obstructive sleep apnea. Data Synthesis: Communication impairment has been associated with increasing noninvasive ventilation anxiety (odds ratio, 1.25). Of patients using noninvasive ventilation, 48% require early discontinuation, 22% refuse noninvasive ventilation, and 9% are ultimately intubated. Improvements to communication have been shown to reduce fear and anxiety in invasive mechanical ventilation patients. Analogous communication problems exist with effective solutions in other fields, such as fighter pilot masks, that can be easily implemented to enhance noninvasive ventilation patient care, increase adherence to noninvasive ventilation treatment, and improve patient outcomes. Conclusions: Communication impairment is an underappreciated cause of noninvasive ventilation complications and failure and requires further characterization. Analogous solutions-such as throat microphones and mask-based microphones-that can be easily implemented show potential as cost-effective methods to reduce noninvasive ventilation failure.