Tenacious sleep nerd with over 20 years of research and data analysis experience.
Scientist known for successfully planning and executing multifaceted clinical research projects. A high level thinker with exceptional strategic skills and the rare ability to effectively improve collaborations between diverse stakeholders. Extensive experience in analyzing data, publishing manuscripts, and statistical programming.
Currently a PhD student in the Epidemiology & Clinical Research program at the department of Health Research and Policy at Stanford. Working to improve our understanding of the genetic basis of sleep and sleep disorders by creating a large cohort and making the data freely available to the scientific community.
Current Role at Stanford
Senior Manager of Clinical Research
Responsibilities included leading technical initiatives, writing grants/proposals, performing statistical analysis and power calculations for grant applications/publications, designing research studies/clinical trials, developing budgets, negotiating contracts, and actively building successful collaborations with internal and third party organizations.
•Project Director for the Stanford Technology Analytics and Genomics in Sleep (STAGES) study: a prospective study that will sleep related data on 30,000 sleep clinic patients (age 13 and up) including genetic and phenotypic data. Managed the study at the strategic level to ensure that the project progresses on time and on budget. Chair the Operations Committee, coordinated the development of the data management portal, provided high-level oversight for data collection, and managed the databases and servers for and secure storage and sharing.
• Project Director for the Alliance Sleep Questionnaire (ASQ): an online questionnaire that uses complex, branching logic to identify potential sleep disorders. Partnered with stakeholders from 5 sites to develop the ASQ’s content, conducted the pilot study, managed deployment, and integrated the new tool into the core workflow at Stanford’s Sleep Clinic. Responsible for the ongoing management of the ASQ (monitoring data acquisition and integrity, assessing data quality, developing scoring algorithms, and performing data analysis). To date, the ASQ has been completed by over 10,000 people, is critical for >10 research studies (including STAGES, Google baseline pilot), and has become standard of care at the Stanford Sleep Medicine Center.
• Helped department secure >40 million dollars in funding by directing the submission of 15 grant applications. Grants included an 18 million dollar family foundation grant to build a prospective cohort of 30k sleep clinic patients and a 7.85 million dollar NIH P01 grant to research the genetic, neurobiological, and immunological basis of type 1 narcolepsy.
• Database Architect/Administrator for the Stanford Sleep Cohort and Narcolepsy Cohort. Created system to link clinical, research, and sleep study data on >40,000 individuals including biological data on >5,000 narcolepsy cases and >15,000 controls. Optimized data security and operational effectiveness by providing technical expertise and developing both the schema and data dictionaries.
• Implemented and managed Stanford’s Multi-site PSG Triple Re-Score Project. Authored manual of operations for the Stanford Site, developed a partnership with Philips Respironics to streamline data-export of >500 studies, hired/managed scoring techs, produced final dataset, and provided regular updates to the project’s steering committee.
• Program and Technical Director of Stanford’s Accredited Sleep Technologist Education Program. Developed A-STEP’s course curriculum (including speakers and materials), managed all administrative requirements (enrollment and record keeping), and presented lectures on various topics ranging from sleep scoring to patient hook ups.
• Provided operational oversight for 8 clinical studies with sample sizes ranging from 40 to over 8,000.
• Authored/co-authored 30 manuscripts and abstracts published in scientific journals.
• Composed/managed >40 department active IRB protocols (involved updates/renewals, adherence to regulations, and coordination of inter institutional agreements with >25 collaborators).
Professional Affiliations and Activities
Member, World Sleep Society (2019 - Present)
Member, Sleep Research Society (2010 - Present)
Member, California Sleep Society (2007 - Present)
Member, American Association of Sleep Technologists (1997 - Present)
Education & Certifications
Master of Science, Stanford University, EPIDM-MS (2013)
RPSGT, Board of Registered Polysomnographic Technologists, Sleep Technologist #2157 (1998)
RST, American Board of Sleep Medicine, Registered Sleep Technologist #2791 (2012)
Bachelor of Arts, University of California San Diego, Psychology (1995)
DEVELOPMENT OF COMPLEX DATA PLATFORM FOR THE STANFORD TECHNOLOGY ANALYTICS AND GENOMICS IN SLEEP (STAGES) STUDY
OXFORD UNIV PRESS INC. 2019
View details for Web of Science ID 000471071001102
Non-invasive machine learning estimation of effort differentiates sleep-disordered breathing pathology.
OBJECTIVE: Obstructive sleep-disordered breathing (SDB) events, unlike central events, are associated with increased respiratory effort. Esophageal pressure (Pes) monitoring is the gold standard for measuring respiratory effort, but it is typically poorly tolerated because of its invasive nature. The objective was to investigate whether machine learning can be applied to routinely collected non-invasive, polysomnography (PSG) measures to accurately model peak negative Pes. Approach: 1119 patients from the Stanford Sleep Clinic with PSGs containing Pes served as the sample. The selected non-invasive PSG signals included nasal pressure, oral airflow, thoracoabdominal effort, and snoring. A long short-term memory (LSTM) neural network was implemented to achieve a context-based mapping between the non-invasive features and the Pes values. A hold-out dataset served as a prospective validation of the algorithm without needing to undertake a costly new study with the impractically invasive Pes. Main results: The median difference between the measured and predicted Pes was 0.61 cmH2O with an interquartile range (IQR) of 2.99 cmH2O and 5th and 95th percentiles of -5.85 cmH2O and 5.47 cmH2O, respectively. The model performed well when compared to actual esophageal pressure signal (rhomedian=0.581, p=0.01; IQR = 0.298; rho5% = 0.106; rho95% = 0.843). Significance: A significant difference in predicted Pes was shown between normal breathing and all obstructive SDB events; whereas, central apneas did not significantly differ from normal breathing. The developed system may be used as a tool for quantifying respiratory effort from the existing clinical practice of PSG without the need for Pes, improving characterization of SDB events as obstructive or not. .
View details for PubMedID 30736016
Factors associated with fatigue in patients with insomnia.
Journal of psychiatric research
2019; 117: 24–30
Although fatigue is common in insomnia, the clinical associates of fatigue in patients with insomnia are largely unknown. We aimed to investigate the clinical associates of fatigue in patients with insomnia. Patients visiting the Stanford Sleep Medicine Center completed the Insomnia Severity Index (ISI), Insomnia Symptom Questionnaire (ISQ), the Fatigue Severity Scale (FSS), the Epworth Sleepiness Scale (ESS), and the Patient Health Questionnaire (PHQ-9). Among 6367 patients, 2024 were diagnosed with insomnia (age 43.06 ± 15.19 years; 1110 women and 914 men) according to the ISI and the ISQ. Insomnia patients with severe fatigue (n = 1306) showed higher insomnia symptoms, daytime sleepiness, depression and longer habitual sleep duration than those without severe fatigue (n = 718). Higher insomnia symptoms, daytime sleepiness and depressive symptoms, and longer habitual sleep duration, independently predicted higher fatigue scores. Among insomnia patients with daytime sleepiness (ESS≥10), only habitual sleep duration and depression predicted fatigue scores. The interaction between insomnia severity and daytime sleepiness significantly predicted the severity of fatigue. Depression was a significant mediator between insomnia and fatigue. For 598 insomnia patients undergoing overnight polysomnography (PSG), no significant correlations were found between fatigue and any PSG parameters. The current study suggests that managing insomnia or depression may reduce the fatigue of insomnia patients, whereas arbitrary efforts to prolong sleep duration may worsen their fatigue.
View details for DOI 10.1016/j.jpsychires.2019.06.021
View details for PubMedID 31272015
- Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy NATURE COMMUNICATIONS 2018; 9
Sleep disorders, depression and anxiety are associated with adverse safety outcomes in healthcare workers: A prospective cohort study
JOURNAL OF SLEEP RESEARCH
2018; 27 (6): e12722
The objective of the study was to determine if sleep disorder, depression or anxiety screening status was associated with safety outcomes in a diverse population of hospital workers. A sample of shift workers at four hospitals participated in a prospective cohort study. Participants were screened for five sleep disorders, depression and anxiety at baseline, then completed prospective monthly surveys for the next 6 months to capture motor vehicle crashes, near-miss crashes, occupational exposures and medical errors. We tested the associations between sleep disorders, depression and anxiety and adverse safety outcomes using incidence rate ratios adjusted for potentially confounding factors in a multivariable negative binomial regression model. Of the 416 hospital workers who participated, two in five (40.9%) screened positive for a sleep disorder and 21.6% screened positive for depression or anxiety. After multivariable adjustment, screening positive for a sleep disorder was associated with 83% increased incidence of adverse safety outcomes. Screening positive for depression or anxiety increased the risk by 63%. Sleep disorders and mood disorders were independently associated with adverse outcomes and contributed additively to risk. Our findings suggest that screening for sleep disorders and mental health screening can help identify individuals who are vulnerable to adverse safety outcomes. Future research should evaluate sleep and mental health screening, evaluation and treatment programmes that may improve safety.
View details for PubMedID 30069960
Periodic limb movements in sleep: Prevalence and associated sleepiness in the Wisconsin Sleep Cohort.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
2018; 129 (11): 2306–14
OBJECTIVES: Periodic limb movements in sleep (PLMS) are thought to be prevalent in elderly populations, but their impact on quality of life remains unclear. We examined the prevalence of PLMS, impact of age on prevalence, and association between PLMS and sleepiness.METHODS: We identified limb movements in 2335 Wisconsin Sleep Cohort polysomnograms collected over 12 years. Prevalence of periodic limb movement index (PLMI) ≥15 was calculated at baseline (n = 1084). McNemar's test assessed changes in prevalence over time. Association of sleepiness and PLMS evaluated using linear mixed modeling and generalized estimating equations. Models adjusted for confounders.RESULTS: Prevalence of PLMI ≥15 at baseline was 25.3%. Longitudinal prevalence increased significantly with age (p = 2.97 * 10-14). Sleepiness did not differ significantly between PLMI groups unless stratified by restless legs syndrome (RLS) symptoms. The RLS+/PLM+ group was sleepier than the RLS+/PLM- group. Multiple Sleep Latency Test trended towards increased alertness in the RLS-/PLM+ group compared to RLS-/PLM-.CONCLUSIONS: A significant number of adults have PLMS and prevalence increased with age. No noteworthy association between PLMI category and sleepiness unless stratified by RLS symptoms.SIGNIFICANCE: Our results indicate that RLS and PLMS may have distinct clinical consequences and interactions that can help guide treatment approach.
View details for PubMedID 30243181
Diagnostic value of sleep stage dissociation as visualized on a 2-Dimensional sleep state space in human narcolepsy.
Journal of neuroscience methods
Type 1 narcolepsy (NT1) is characterized by symptoms believed to represent Rapid Eye Movement (REM) sleep stage dissociations, occurrences where features of wake and REM sleep are intermingled, resulting in a mixed state. We hypothesized that sleep stage dissociations can be objectively detected through the analysis of nocturnal Polysomnography (PSG) data, and that those affecting REM sleep can be used as a diagnostic feature for narcolepsy.A Linear Discriminant Analysis (LDA) model using 38 features extracted from EOG, EMG and EEG was used in control subjects to select features differentiating wake, stage N1, N2, N3 and REM sleep. Sleep stage differentiation was next represented in a 2D projection. Features characteristic of sleep stage differences were estimated from the residual sleep stage probability in the 2D space. Using this model we evaluated PSG data from NT1 and non-narcoleptic subjects. An LDA classifier was used to determine the best separation plane.This method replicates the specificity/sensitivity from the training set to the validation set better than many other methods.Eight prominent features could differentiate narcolepsy and controls in the validation dataset. Using a composite measure and a specificity cut off 95% in the training dataset, sensitivity was 43%. Specificity/sensitivity was 94%/38% in the validation set. Using hypersomnia subjects, specificity/sensitivity was 84%/15%. Analyzing treated narcoleptics the specificity/sensitivity was 94%/10%.Sleep stage dissociation can be used for the diagnosis of narcolepsy. However the use of some medications and presence of undiagnosed hypersomnolence patients impacts the result.
View details for DOI 10.1016/j.jneumeth.2017.02.004
View details for PubMedID 28219726
Breathing Disturbances Without Hypoxia Are Associated With Objective Sleepiness in Sleep Apnea.
2017; 40 (11)
To determine whether defining two subtypes of sleep-disordered breathing (SDB) events-with or without hypoxia-results in measures that are more strongly associated with hypertension and sleepiness.A total of 1022 participants with 2112 nocturnal polysomnograms from the Wisconsin Sleep Cohort were analyzed with our automated algorithm, developed to detect breathing disturbances and desaturations. Breathing events were time-locked to desaturations, resulting in two indices-desaturating (hypoxia-breathing disturbance index [H-BDI]) and nondesaturating (nonhypoxia-breathing disturbance index [NH-BDI]) events-regardless of arousals. Measures of subjective (Epworth Sleepiness Scale) and objective (2981 multiple sleep latency tests from a subset of 865 participants) sleepiness were analyzed, in addition to clinically relevant clinicodemographic variables. Hypertension was defined as BP ≥ 140/90 or antihypertensive use.H-BDI, but not NH-BDI, correlated strongly with SDB severity indices that included hypoxia (r ≥ 0.89, p ≤ .001 with 3% oxygen-desaturation index [ODI] and apnea hypopnea index with 4% desaturations). A doubling of desaturation-associated events was associated with hypertension prevalence, which was significant for ODI but not H-BDI (3% ODI OR = 1.06, 95% CI = 1.00-1.12, p < .05; H-BDI OR 1.04, 95% CI = 0.98-1.10) and daytime sleepiness (β = 0.20 Epworth Sleepiness Scale [ESS] score, p < .0001; β = -0.20 minutes in MSL on multiple sleep latency test [MSLT], p < .01). Independently, nondesaturating event doubling was associated with more objective sleepiness (β = -0.52 minutes in MSL on MSLT, p < .001), but had less association with subjective sleepiness (β = 0.12 ESS score, p = .10). In longitudinal analyses, baseline nondesaturating events were associated with worsening of H-BDI over a 4-year follow-up, suggesting evolution in severity.In SDB, nondesaturating events are independently associated with objective daytime sleepiness, beyond the effect of desaturating events.
View details for PubMedID 29029253
- Prescription and Non-Prescription Medications for Sleep-Related Clinical Trials Sleep (Abstract suppl.) 2016; 39: A375
- Validation of the Alliance Sleep Questionnaire (ASQ) Obstructive Sleep Apnea (OSA) Module in Sleep Disordered Patients Sleep (Abstract suppl.) 2016; 39: A124
- T Cell Receptor Sequencing in Narcolepsy Sleep (Abstract suppl.) 2016; 39
- Characterization of the Stanford Narcolepsy Database Sleep (Abstract suppl.) 2016; 39: A246
Sleep-stage transitions during polysomnographic recordings as diagnostic features of type 1 narcolepsy.
2015; 16 (12): 1558-1566
Type 1 narcolepsy/hypocretin deficiency is characterized by excessive daytime sleepiness, sleep fragmentation, and cataplexy. Short rapid eye movement (REM) latency (≤15 min) during nocturnal polysomnography (PSG) or during naps of the multiple sleep latency test (MSLT) defines a sleep-onset REM sleep period (SOREMP), a diagnostic hallmark. We hypothesized that abnormal sleep transitions other than SOREMPs can be identified in type 1 narcolepsy.Sleep-stage transitions (one to 10 epochs to one to five epochs of any other stage) and bout length features (one to 10 epochs) were extracted from PSGs. The first 15 min of sleep were excluded when a nocturnal SOREMP was recorded. F(0.1) measures and receiver operating characteristic curves were used to identify specific (≥98%) features. A data set of 136 patients and 510 sex- and age-matched controls was used for the training. A data set of 19 cases and 708 sleep-clinic patients was used for the validation.(1) ≥5 transitions from ≥5 epochs of stage N1 or W to ≥2 epochs of REM sleep, (2) ≥22 transitions from ≥3 epochs of stage N2 or N3 to ≥2 epochs of N1 or W, and (3) ≥16 bouts of ≥6 epochs of N1 or W were found to be highly specific (≥98%). Sensitivity ranged from 16% to 30%, and it did not vary substantially with and without medication or a nocturnal SOREMP. In patients taking antidepressants, nocturnal SOREMPs occurred much less frequently (16% vs. 36%, p < 0.001).Increased sleep-stage transitions notably from ≥2.5 min of W/N1 into REM are specifically diagnostic for narcolepsy independent of a nocturnal SOREMP.
View details for DOI 10.1016/j.sleep.2015.06.007
View details for PubMedID 26299470
Design and validation of a periodic leg movement detector.
2014; 9 (12)
Periodic Limb Movements (PLMs) are episodic, involuntary movements caused by fairly specific muscle contractions that occur during sleep and can be scored during nocturnal polysomnography (NPSG). Because leg movements (LM) may be accompanied by an arousal or sleep fragmentation, a high PLM index (i.e. average number of PLMs per hour) may have an effect on an individual's overall health and wellbeing. This study presents the design and validation of the Stanford PLM automatic detector (S-PLMAD), a robust, automated leg movement detector to score PLM. NPSG studies from adult participants of the Wisconsin Sleep Cohort (WSC, n = 1,073, 2000-2004) and successive Stanford Sleep Cohort (SSC) patients (n = 760, 1999-2007) undergoing baseline NPSG were used in the design and validation of this study. The scoring algorithm of the S-PLMAD was initially based on the 2007 American Association of Sleep Medicine clinical scoring rules. It was first tested against other published algorithms using manually scored LM in the WSC. Rules were then modified to accommodate baseline noise and electrocardiography interference and to better exclude LM adjacent to respiratory events. The S-PLMAD incorporates adaptive noise cancelling of cardiac interference and noise-floor adjustable detection thresholds, removes LM secondary to sleep disordered breathing within 5 sec of respiratory events, and is robust to transient artifacts. Furthermore, it provides PLM indices for sleep (PLMS) and wake plus periodicity index and other metrics. To validate the final S-PLMAD, experts visually scored 78 studies in normal sleepers and patients with restless legs syndrome, sleep disordered breathing, rapid eye movement sleep behavior disorder, narcolepsy-cataplexy, insomnia, and delayed sleep phase syndrome. PLM indices were highly correlated between expert, visually scored PLMS and automatic scorings (r2 = 0.94 in WSC and r2 = 0.94 in SSC). In conclusion, The S-PLMAD is a robust and high throughput PLM detector that functions well in controls and sleep disorder patients.
View details for DOI 10.1371/journal.pone.0114565
View details for PubMedID 25489744
View details for PubMedCentralID PMC4260847
Effects of Continuous Positive Airway Pressure on Neurocognitive Function in Obstructive Sleep Apnea Patients: The Apnea Positive Pressure Long-term Efficacy Study (APPLES)
2012; 35 (12): 1593-U40
To determine the neurocognitive effects of continuous positive airway pressure (CPAP) therapy on patients with obstructive sleep apnea (OSA).The Apnea Positive Pressure Long-term Efficacy Study (APPLES) was a 6-month, randomized, double-blind, 2-arm, sham-controlled, multicenter trial conducted at 5 U.S. university, hospital, or private practices. Of 1,516 participants enrolled, 1,105 were randomized, and 1,098 participants diagnosed with OSA contributed to the analysis of the primary outcome measures.Active or sham CPAP MEASUREMENTS: THREE NEUROCOGNITIVE VARIABLES, EACH REPRESENTING A NEUROCOGNITIVE DOMAIN: Pathfinder Number Test-Total Time (attention and psychomotor function [A/P]), Buschke Selective Reminding Test-Sum Recall (learning and memory [L/M]), and Sustained Working Memory Test-Overall Mid-Day Score (executive and frontal-lobe function [E/F])The primary neurocognitive analyses showed a difference between groups for only the E/F variable at the 2 month CPAP visit, but no difference at the 6 month CPAP visit or for the A/P or L/M variables at either the 2 or 6 month visits. When stratified by measures of OSA severity (AHI or oxygen saturation parameters), the primary E/F variable and one secondary E/F neurocognitive variable revealed transient differences between study arms for those with the most severe OSA. Participants in the active CPAP group had a significantly greater ability to remain awake whether measured subjectively by the Epworth Sleepiness Scale or objectively by the maintenance of wakefulness test.CPAP treatment improved both subjectively and objectively measured sleepiness, especially in individuals with severe OSA (AHI > 30). CPAP use resulted in mild, transient improvement in the most sensitive measures of executive and frontal-lobe function for those with severe disease, which suggests the existence of a complex OSA-neurocognitive relationship.Registered at clinicaltrials.gov. Identifier: NCT00051363.Kushida CA; Nichols DA; Holmes TH; Quan SF; Walsh JK; Gottlieb DJ; Simon RD; Guilleminault C; White DP; Goodwin JL; Schweitzer PK; Leary EB; Hyde PR; Hirshkowitz M; Green S; McEvoy LK; Chan C; Gevins A; Kay GG; Bloch DA; Crabtree T; Demen WC. Effects of continuous positive airway pressure on neurocognitive function in obstructive sleep apnea patients: the Apnea Positive Pressure Long-term Efficacy Study (APPLES). SLEEP 2012;35(12):1593-1602.
View details for DOI 10.5665/sleep.2226
View details for PubMedID 23204602
- Polysomnographic Recording Procedures Fundamentals of Sleep Technology Lippincott Williams & Wilkins. 2012; 2nd: 325–339
- Patient Preparation Fundamentals of Sleep Technology Lippincott Williams & Wilkins. 2012; 2nd: 311–324
The Association between Obstructive Sleep Apnea and Neurocognitive Performance-The Apnea Positive Pressure Long-term Efficacy Study (APPLES)
2011; 34 (3): 303-U207
To determine associations between obstructive sleep apnea (OSA) and neurocognitive performance in a large cohort of adults.Cross-sectional analyses of polysomnographic and neurocognitive data from 1204 adult participants with a clinical diagnosis of obstructive sleep apnea (OSA) in the Apnea Positive Pressure Long-term Efficacy Study (APPLES), assessed at baseline before randomization to either continuous positive airway pressure (CPAP) or sham CPAP.Sleep and respiratory indices obtained by laboratory polysomnography and several measures of neurocognitive performance.Weak correlations were found for both the apnea hypopnea index (AHI) and several indices of oxygen desaturation and neurocognitive performance in unadjusted analyses. After adjustment for level of education, ethnicity, and gender, there was no association between the AHI and neurocognitive performance. However, severity of oxygen desaturation was weakly associated with worse neurocognitive performance on some measures of intelligence, attention, and processing speed.The impact of OSA on neurocognitive performance is small for many individuals with this condition and is most related to the severity of hypoxemia.
View details for Web of Science ID 000287917600010
View details for PubMedID 21358847
View details for PubMedCentralID PMC3041706
- Sleep disorders: a widely ignored pandemic. FOCUS: Journal for Respiratory Care & Sleep Medicine 2009; Jan/Feb (28)
- Patient Preparation Fundamentals of Sleep Technology Lippincott Williams & Wilkins. 2007; 1: 241–252
The Apnea Positive Pressure Long-term Efficacy Study (APPLES): Rationale, Design, Methods, and Procedures
JOURNAL OF CLINICAL SLEEP MEDICINE
2006; 2 (3): 288-300
To assess the size, time course, and durability of the effects of long-term continuous positive airway pressure (CPAP) therapy on neurocognitive function, mood, sleepiness, and quality of life in patients with obstructive sleep apnea.Randomized, double-blinded, 2-arm, sham-controlled, multicenter, long-term, intention-to-treat trial of CPAP therapy.Sleep clinics and laboratories at 5 university medical centers and community-based hospitals. Patients or Participants: Target enrollment is 1100 randomly assigned subjects across 5 clinical centers.Active versus sham (subtherapeutic) CPAP. Measurements and Results: A battery of conventional and novel tests designed to evaluate neurocognitive function, mood, sleepiness, and quality of life.The Apnea Positive Pressure Long-term Efficacy Study (APPLES) is designed to study obstructive sleep apnea and test the effects of CPAP through a comprehensive, controlled, and long-term trial in a large sample of subjects with obstructive sleep apnea.
View details for Web of Science ID 000209775800006