An In-Depth look at Sleep Spindles
With the help of an enormous data set, Shaun Purcell, PhD, strives to uncover the factors that correlate with sleep spindle patterns.
Sleep spindles are short bursts of brain activity primarily present during stage-two, non-rapid eye movement sleep. Measured by electroencephalogram (EEG), they typically last between .5 and 2 seconds, recur throughout sleep and are called spindles due to their waxing and waning shape in the EEG. While the brain circuitry involved in creating these spindles is relatively well understood, the function of sleep spindles has not clearly been identified. In a new study published in Nature Communications, BWH researchers examined the sleep records of 11,360 individuals, ages 4 to 97, to identify sleep spindle characteristics and properties that could be used in future genetic studies.
“Spindles are of interest because they are believed to underlie core processes of memory consolidation during sleep. Also, certain patient groups, including those with schizophrenia, have shown differences in their spindle activity, raising the possibility that spindles are brain-based biomarkers for certain types of disease,” said Shaun Purcell, PhD, of the BWH Department of Psychiatry, and lead author of this research.
Using the National Sleep Research Resource (NSRR), a massive online collection of sleep records run by BWH researchers, Purcell was able to study the spindle patterns of a sample size approximately one thousand times larger than most previous studies on sleep spindles. Purcell and his team found that within individuals, sleep spindles are stable over time and “fingerprint-like.” Much like fingerprints, when compared among individuals, spindle patterns are distinct and variable. The large sample size in this research allowed his team to identify certain factors that correlate to the variations seen between individuals. They identified associations in sleep spindle pattern with characteristics such as age, sex and medication usage. They also found that spindle patterns are highly heritable within families and influenced by genetics.
By identifying the variables associated with specific sleep spindle patterns, Purcell hopes to advance future genetic studies and perhaps use these bursts of brain activity, along with other features of the sleeping brain, as a tool in medicine. “Our findings will lead to an increased understanding of the genetic architecture of sleep spindles and their relation to behavioral and health outcomes,” said Purcell. “If spindles, or other aspects of sleep, are causally related to disease, it could have both diagnostic and therapeutic implications.”
This research was funded by the National Institute of Mental Health, the Stanley Center of the Broad Institute, and the National Heart, Lung, and Blood Institute (through their support of the National Sleep Research Resource).
Paper Cited: Purcell SM et al. “Characterizing Sleep Spindles in 11,630 Individuals from the National Sleep Research Resource” Nature Communications DOI: 10.1038/ncomms15930
Kidney Disease Risk Score Can Be Built Into Patients’ Electronic Health Records
More than 26 million Americans have chronic kidney disease (CKD). Primary care physicians who take care of these patients can help reduce the risk of complications and death if they recognize the progression of kidney failure early, but this is often difficult to do – deterioration can be rapid and more than one laboratory test may be needed to accurately predict a patient’s risk. A new electronic health record (EHR) tool could help physicians quickly and accurately flag patients that should be referred to a nephrologist. Designed by Brigham and Women’s Hospital investigators, this tool draws upon recent research that has identified several tests that can be used to calculate an individual’s risk score. Now, an automatic calculator can be built into EHRs and displayed prominently for a physician to see when they open a patient’s record. The tool was piloted at ten North Shore Physicians Group clinics this year, and a paper detailing the design and implementation of the application appear online this week in The Journal of the American Medical Informatics Association.
“Retrospective studies of patients who have had to go on dialysis show that being referred to a nephrologist just a few months earlier can have major benefits,” said corresponding author Lipika Samal, MD, MPH, a clinician investigator in the Division of General Internal Medicine. “We want to make it as easy as possible for a physician to quickly access and track a patient’s risk. This tool automatically calculates and displays a risk score within the health record, making it easier for a physician to spot disease progression and take action.”
The new clinical decision support tool calculates and displays kidney failure risk based on criteria identified from a large cohort study conducted by Canadian researchers (Tangri et al., 2011). Predictive risk factors that go into the calculation include serum and urine tests that collected during routine care. If test results for any of these predictive measures have not been collected and are not in a patient’s record at the time of a visit, the tool will display a recommendation to order the tests. Otherwise, the tool will display a five-year kidney disease risk score, and if the risk is high, a recommendation for a referral to a nephrologist.
The tool was deployed outside of the EHR in a way that would allow it to be used with different EHRs by utilizing interoperability standards called continuity of care documents (CCDs). The tool extracted the necessary tests from this interoperable document.
The research team validated the tool in 255 patients and subsequently deployed it to 10 primary care clinics. The team made improvements and updates to the tool based on feedback from physicians. In the course of the pilot, they processed more than half a million CCDs to diagnose CKD and to generate risk scores for patients with CKD.
Because of the interoperable nature of the tool, the team sees an opportunity to deploy this single application across multiple EHRs. They plan to implement it in eCare at BWH later this year. Samal also envisions applications for clinical decision support tools beyond CKD.
“One of the positive things about EHRs is that there is now a wealth of data that can be used to help us better predict an individual’s risk, especially for chronic and progressive diseases, like CKD,” said Samal. “We have the opportunity to use EHRs to improve patient care– tools like this one can help us seize that opportunity.”
This work was supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under award no. K23DK097187.
Paper cited: Samal L et al. “Implementation of a scalable, web-based, automated clinical decision support risk-prediction tool for chronic kidney disease using C-CDA and application programming interfaces” Journal of the American Medical Informatics Association DOI: 10.1093/jamia/ocx065