Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care Journals

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

Autonomic Nervous System and Sleep: Order and Disorder

This comprehensive book addresses all elements of the autonomic nervous system (ANS) and sleep interaction, as well as ANS alterations in sleep and how these impact primary and comorbid sleep dysfunction. It meets the market need for a comprehensive text that deals with ANS changes in sleep and how these impact various neurological, medical, and primary sleep disorders. Organized into three parts, the book begins with a review of the foundational bodily systems that participate in coordination of ANS activity with other homeostatic responses such as respiration, cardiovascular reflexes, and responses to stress. Part two then examines methods of laboratory evaluation and the why, when, how of interpreting heart rate variability in sleep. To conclude, the final section of the book broadly covers the many clinical aspects of ANS, including insomnia, restless leg syndrome, sleep apnea, sleep related epilepsy, and acute autonomic neuropathy. Autonomic Nervous System and Sleep enhances the reader\u27s understanding of the pathophysiology of various disorders, and explains how to apply this profound understanding is important to new lines of therapy to improve morbidity.https://scholarship.shu.edu/faculty-publications/1004/thumbnail.jp

What can we learn from eye movements distribution during sleep?

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Fasciculations masquerading as minipolymyoclonus in bulbospinal muscular atrophy

Minipolymyoclonus has been described in both anterior horn cell disorders and central nervous system degenerative conditions. While its etiology remains unclear and speculative, a central generator has been previously proposed. We describe a case of bulbospinal muscular atrophy (Kennedy′s disease), where minipolymyoclonus-like movements corresponded to fasciculations in neurophysiological studies. Our novel finding suggests that the etiologies of minipolymyoclonus in central and peripheral nervous system disorders are distinct, despite outward clinical similarity. The term "minipolyfasciculations" may be more reflective of the underlying process causing minipolymyoclonus-like movements in lower motor neuron disorders

PREVALENCE AND FACTORS AFFECTING REM AND SLOW WAVE SLEEP REBOUND ON CPAP TITRATION STUDY IN PATIENTS WITH OBSTRUCTIVE SLEEP APNEA

Background. In patients with obstructive sleep apnea syndrome (OSAS) treatment with CPAP results in an increase of REM sleep and slow wave sleep, but there is limited information about the prevalence of REM rebound in patients with OSAS and possible factors related to the rebound. Objective. REM rebound (RR) and slow wave sleep rebound (SWSR) has been described as a frequent phenomenon that occurs during CPAP titration, but the quantity that qualify for RR has not been mentioned in literature. The objective of our study was to determine the prevalence of REM rebound and slow wave sleep rebound in our sleep disorders center, to attempt to define RR and look for factors that may affect RR and SWSR on the first night of CPAP titration. Materials and methods. We included patients who had both baseline polysomnogram (bPSG) and CPAP polysomnogram (cPSG) studies done in the same laboratory. We included 179 patients>18 years with Apnea hypopnea index (AHI)>10/hr on the baseline study, with an adequate CPAP titration study. We compared the percentages of REM sleep and slow wave sleep during bPSG and cPSG. We analyzed the frequency of presentation and looked for the factors affecting RR and SWSR. Results. 179 patients were enrolled (M/F:118/61), with a mean age of 48.6±4 for men, and 51.6±12.9 for women. The mean interval between the bPSG and cPSG was 45 days. The mean REM percentage during the bPSG was 15.55 percent and during cPSG study it was 21.57 percent. We took 6 percent as our differential point as the results became statistically significant at this point (p:0001). We therefore present our data by dividing our patients population with RR6%. The mean SWS percentage during the bPSG was 8.11±9.68 and during the cPSG was 13.17±10, with a p:0.35 which is not statistically significant. The multiple regression model showed that the variables that contribute more to the REM change are: REM sleep during bPSG (-0.56), bAHI (0.24) and the body mass index (0.081). Conclusions. We suggest that an increase greater than 6% in REM sleep should be considered REM rebound, since 6.15 percent was the statistically significant difference between bPSG REM sleep and cPSG. The prevalence of RR in our group was 46 percent and the variables that contribute more to RR are REM sleep during bPSG, AHI at baseline and body mass index