2007 |
Kushida, Clete A |
U01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Apples: Apnea Positive Pressure Long-Term Efficacy Study
DESCRIPTION (provided by applicant): Nasal continuous positive airway pressure (CPAP) therapy is in widespread use as the primary treatment for the obstructive sleep apnea syndrome (OSAS), a sleep-related breathing disorder affecting more than 15 million Americans. The therapeutic effectiveness of CPAP in providing significant, stable, and long-term neurocognitive or other functional benefits to patients with OSAS has not been systematically investigated. The revised proposed study is a randomized, blinded, sham-controlled, multi-center trial of CPAP therapy. The principal aims of the study are: 1) to assess the long-term effectiveness of CPAP therapy on neurocognitive function, mood, sleepiness, and quality of life by administering tests of these indices to subjects randomly assigned to active or sham CPAP; 2) to identify specific neurocognitive deficits associated with OSAS in a large, heterogeneous subject population; 3) to determine which deficits in neurocognitive function in OSAS subjects are reversible and most sensitive to the effects of CPAP; 4) to develop a composite multivariate outcome measure from the results of this study that can be used to assess the clinical effectiveness of CPAP in improving neurocognitive function, mood, sleepiness, and quality of life; and 5) to use functional magnetic resonance imaging to compare cortical activation before and after CPAP therapy, and to assess whether this change is associated with improvement in specific neurocognitive task performance. The primary endpoint of this proposed study is the effect of 6 months of CPAP treatment on neurocognitive function. A total of 1050 subjects (525 per treatment group) will be enrolled from the patient populations at five sites (Stanford; U of Arizona; Harvard; St. Luke's Hospital, MO; St. Mary's Hospital, WA). This study will advance our knowledge of the major, most debilitating, clinically relevant OSAS-associated conditions, and, by scientifically establishing the effectiveness of CPAP therapy, should greatly improve access for the countless victims now denied treatment.
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0.958 |
2016 — 2017 |
Kushida, Clete A |
R21Activity Code Description: To encourage the development of new research activities in categorical program areas. (Support generally is restricted in level of support and in time.) |
Predictive Adherence Modeling (Pam) Study
A major problem for both clinicians and patients is patient adherence. In the field of sleep medicine, patients with obstructive sleep apnea (OSA) have variable adherence to the gold standard treatment for this condition: continuous positive airway pressure (CPAP) therapy. The proposed Predictive Adherence Modeling (PAM) Study will use two large OSA datasets [the NHLBI-supported Apnea Positive Pressure Long-term Efficacy Study (APPLES) and the AHRQ-supported Comparative Outcomes Management with Electronic Data Technology (COMET) Study] and three NIDA-supported datasets, to accomplish three specific aims: (1) To construct a general, calibration-based approach for deriving prognostic definitions of adherence. The goal is to develop this approach by using adherence to continuous positive airway pressure for patients with obstructive sleep apnea as a testbed. (2) To develop a predictive model for adherence. Continuous measures of adherence (e.g., mean hours of adherence per night), will be used so that the outcome is kept at full resolution and highest information content, which maximizes opportunities for predictive models to distinguish among patients of differing behaviors. Adherence will also be operationalized as a multivariate outcome and predictive-modeling methods for multivariate outcomes will be used, in addition to modern regularized methods that will allow sifting through extensive lists of candidate predictors. The project will include methods that are specially designed to explore predictive interactions, such as regression trees, and we will allow for nonlinear predictors through use of various spline basis expansions, tree-based methods, and neural net technology. Ensemble methods will be employed, such as boosting, wherein many different regression models are fit and then combined to capitalize on their collective ability to predict outcome, and there will be correction for overfitting through use of validation techniques. Using these methods will allow the team to identify predictive models that are more robust, in that predictive performance will be sustained in other data sets. Further, the preceding techniques will be combined in order to construct models that optimize prediction of adherence. Finally, existing statistical methodology will be extended and adapted to the specific problem of adherence prediction, developing new statistical technology as needed. (3) To build a suite of statistical tools that will facilitate development of predictive models of adherence in any field of medicine. The plan is to develop a suite of statistical tools that will facilitate development of predictive models of adherence in any field of medicine, which will include three essential elements: (a) A description of the statistical methods contained within the suite in language accessible to non-statistician medical professionals. (b) A user-friendly package of code will be provided for the suite of statistical tools. This code will be provided in two languages, SAS® and the freeware R. (c) The code will include a number of visualization tools to facilitate interpretation and utilization of predictive models by clinical practitioners.
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0.958 |
2020 — 2021 |
Abosch, Aviva Halpern, Casey Harrison (co-PI) [⬀] Kushida, Clete A Thompson, John A (co-PI) [⬀] |
UH3Activity Code Description: The UH3 award is to provide a second phase for the support for innovative exploratory and development research activities initiated under the UH2 mechanism. Although only UH2 awardees are generally eligible to apply for UH3 support, specific program initiatives may establish eligibility criteria under which applications could be accepted from applicants demonstrating progress equivalent to that expected under UH2. |
Adaptive Neurostimulation to Restore Sleep in Parkinson's Disease: An Investigation of Stn Lfp Biomarkers in Sleep Dysregulation and Repair @ University of Nebraska Medical Center
Project Summary Parkinson?s disease (PD) is a neurodegenerative disorder that leads to both motor and non-motor symptoms. While there is as yet no cure for PD, medical and surgical therapies have been developed that effectively target the motor symptoms of PD. Non-motor symptoms are far more disabling for patients, precede the onset of motor symptoms by a decade, are more insidious in onset, have been less apparent to clinicians, and are less effectively treated. Sleep dysfunction is oftentimes the most burdensome of the non-motor symptoms?both to patients and to their caregivers?is pervasive in patients with PD, and includes sleep fragmentation, insomnia, excessive daytime sleepiness, REM behavioral disorder, and restless leg syndrome. There are limited options for treating sleep dysfunction in PD, and the mainstay of therapy is the use of agents that mask the sleep disturbance?such as the sedative-hypnotic drugs?without addressing the underlying mechanisms. Although much attention has been devoted to PD motor symptoms, sleep dysfunction in PD has largely been ignored. Sleep is vital to homeostasis, cognition, and nervous system repair, and the dysfunctional sleep accompanying PD adversely affects both motor and non-motor symptoms, resulting in diminished quality of life, impairments in mood and behavior, and increased morbidity and mortality. Patients with PD who demonstrate significant motor fluctuations and dyskinesia are considered for subthalamic nucleus (STN) deep brain stimulation (DBS) surgery. Although STN-DBS is routinely used to treat PD motor symptoms, several studies have reported that STN-DBS also provides benefit for sleep dysregulation through normalization of sleep architecture. Additionally, local field potentials recorded from STN DBS electrodes implanted for the treatment of PD, have led to the identification of unique spectral patterns in STN oscillatory activity that correlate with distinct sleep cycles, offering insight into sleep dysregulation. Building on this work, and in response to RFA-NS- 18-023, this proposal will leverage novel investigational DBS battery technology (RC+S Summit System; Medtronic) that allows the exploration of sleep biomarkers and prototyping of closed-loop stimulation algorithms, to test the hypothesis that STN?a highly interconnected node within the basal ganglia?contributes to the regulation and disruption of human sleep behavior and can be manipulated for therapeutic advantage. Specifically, in PD patients undergoing STN-DBS, we will determine whether STN oscillations correlate with sleep stage transitions, then construct and evaluate sensing and adaptive stimulation paradigms that allow ongoing sleep-stage identification, and induce through adaptive stimulation an increase in duration of sleep stages associated with restorative sleep. This work will lead to findings that address a currently unmet clinical need, and relevant to the mission of NINDS and the BRAIN Initiative, will evaluate the use of adaptive stimulation of the STN in PD patients for the treatment of sleep dysfunction.
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0.911 |