GRADE guidelines: 13. Preparing Summary of Findings tables and evidence profiles—continuous outcomes

Presenting continuous outcomes in Summary of Findings tables presents particular challenges to interpretation. When each study uses the same outcome measure, and the units of that measure are intuitively interpretable (e.g., duration of hospitalization, duration of symptoms), presenting differences in means is usually desirable. When the natural units of the outcome measure are not easily interpretable, choosing a threshold to create a binary outcome and presenting relative and absolute effects become a more attractive alternative.When studies use different measures of the same construct, calculating summary measures requires converting to the same units of measurement for each study. The longest standing and most widely used approach is to divide the difference in means in each study by its standard deviation and present pooled results in standard deviation units (standardized mean difference). Disadvantages of this approach include vulnerability to varying degrees of heterogeneity in the underlying populations and difficulties in interpretation. Alternatives include presenting results in the units of the most popular or interpretable measure, converting to dichotomous measures and presenting relative and absolute effects, presenting the ratio of the means of intervention and control groups, and presenting the results in minimally important difference units. We outline the merits and limitations of each alternative and provide guidance for meta-analysts and guideline developers.     

Algorithms for identification of Guillain–Barré Syndrome among adolescents in claims databases

Background

Health insurance claims databases can provide data for studies of vaccine-related Guillain–Barre’ Syndrome (GBS), but not all patients with a diagnostic ICD-9-CM code for GBS have the disease. The objective of this study was to evaluate the positive predictive values (PPVs) of claims-based algorithms for identifying GBS cases in 4 claims database environments.

Methods

Potential cases were adolescents ages 11–21 with at least one claim for GBS (ICD-9-CM code 357.0). Medical record reviews by a panel of 3 neurologists were conducted for case confirmation. Claims data considered for inclusion in the case-ascertainment algorithm included coding position, physician specialty, visit type, diagnostic tests. PPVs were used to assess the contribution of study factors in predicting case status.

Results

Among 361 individuals with a GBS diagnosis code, 106 were confirmed overall (PPV = 0.29), varying from 0.24 to 0.56 across the 4 sites. Requiring the GBS code to be associated with a neurologist visit (PPV = 0.53) or to be in a primary position on an inpatient claim (0.56) improved the performance. A composite algorithm including a primary inpatient GBS code and a neurologist visit associated with any GBS code gave the highest PPV (0.70). Incorporating claims for diagnostic testing had little impact on the PPV. Findings were generally similar across study sites.

Conclusions

Algorithms were able to identify GBS cases better than the single occurrence of the diagnostic code for GBS, and these algorithms may perform similarly in different claims environments.     

User- and Message-Level Correlates of Endorsement and Engagement for HIV-Related Messages on Twitter: Cross-sectional Study

BackgroundYouth and young adults continue to experience high rates of HIV and are also frequent users of social media. Social media platforms such as Twitter can bolster efforts to promote HIV prevention for these individuals, and while HIV-related messages exist on Twitter, little is known about the impact or reach of these messages for this population.ObjectiveThis study aims to address this gap in the literature by identifying user and message characteristics that are associated with tweet endorsement (favorited) and engagement (retweeted) among youth and young men (aged 13-24 years).MethodsIn a secondary analysis of data from a study of HIV-related messages posted by young men on Twitter, we used model selection techniques to examine user and tweet-level factors associated with tweet endorsement and engagement.ResultsTweets from personal user accounts garnered greater endorsement and engagement than tweets from institutional users (aOR 3.27, 95% CI 2.75-3.89; P<.001). High follower count was associated with increased endorsement and engagement (aOR 1.05, 95% CI 1.04-1.06; P<.001); tweets that discussed STIs garnered lower endorsement and engagement (aOR 0.59, 95% CI 0.47-1.74; P<.001).ConclusionsFindings suggest practitioners should partner with youth to design and disseminate HIV prevention messages on social media, incorporate content that resonates with youth audiences, and work to challenge stigma and foster social norms conducive to open conversation about sex, sexuality, and health.     

BrainAGE and regional volumetric analysis of a Buddhist monk: a longitudinal MRI case study

ABSTRACT

Yongey Mingyur Rinpoche (YMR) is a Tibetan Buddhist monk, and renowned meditation practitioner and teacher who has spent an extraordinary number of hours of his life meditating. The brain-aging profile of this expert meditator in comparison to a control population was examined using a machine learning framework, which estimates “brain-age” from brain imaging. YMR’s brain-aging rate appeared slower than that of controls suggesting early maturation and delayed aging. At 41 years, his brain resembled that of a 33-year-old. Specific regional changes did not differentiate YMR from controls, suggesting that the brain-aging differences may arise from coordinated changes spread throughout the gray matter.     

SSEP and F-wave studies in acute inflammatory demyelinating polyradiculoneuropathy

Somatosensory evoked potentials (SSEPs) and F-wave responses were compared after tibial (PTN) and median (MN) nerve stimulation in patients with acute inflammatory demyelinating polyradiculoneuropathy (AIDP). Nineteen patients were evaluated within 2 weeks of AIDP onset. Each had F-wave and PTN-SSEP studies; 18 had MN-SSEP studies. Ten patients had absent or prolonged MN-SSEP latencies, whereas 16 had abnormal MN F-wave studies. Seventeen patients had an abnormal PTN-SSEP while 12 had abnormal PTN F waves. Every patient with an abnormal MN-SSEP also had an abnormal PTN-SSEP. The most common PTN-SSEP findings were an absent or prolonged latency of N8 or N22. All patients had either an abnormal MN F wave or PTN-SSEP.     

The photobiology of the human circadian clock

In modern society, the widespread use of artificial light at night disrupts the suprachiasmatic nucleus (SCN), which serves as our central circadian clock. Existing models describe excitatory responses of the SCN to primarily blue light, but direct measures in humans are absent. The combination of state-of-the-art neuroimaging techniques and custom-made MRI compatible light-emitting diode devices allowed to directly measure the light response of the SCN. In contrast to the general expectation, we found that blood oxygen level–dependent (BOLD) functional MRI signals in the SCN were suppressed by light. The suppressions were observed not only in response to narrowband blue light (λ_max: 470 nm) but remarkably, also in response to green (λ_max: 515 nm) and orange (λ_max: 590 nm), but not to violet light (λ_max: 405 nm). The broadband sensitivity of the SCN implies that strategies on light exposure should be revised: enhancement of light levels during daytime is possible with wavelengths other than blue, while during nighttime, all colors are potentially disruptive.

Due to the Earth’s rotation around its axis, many organisms developed an internal clock to anticipate the predictable changes in the environment that occur every 24 h, including the daily light–dark cycle. In mammals, this clock is located in the suprachiasmatic nucleus (SCN), located in the hypothalamus directly above the optic chiasm (1, 2). The SCN receives information from the retina regarding ambient light levels via intrinsically photosensitive retinal ganglion cells (ipRGCs), thus synchronizing its internal clock to the external light–dark cycle. ipRGCs contain the photopigment melanopsin, which is maximally sensitive to blue light, with a peak response to 480-nm light (3, 4). In addition, ipRGCs also receive input from rod cells and cone cells (5–7). The three cone cell subtypes in the human retina respond maximally to 420-nm, 534-nm, and 563-nm light, while rod cells respond maximally to 498-nm light (8). In rodents, input from cone cells renders the SCN sensitive to a broad spectrum of wavelengths (9), while rod cells mediate the SCN’s sensitivity to low-intensity light (10, 11). Recently, these findings in rodents were proposed to translate to humans (12), suggesting that the human clock is not only sensitive to blue light, but may also be sensitive to other colors.In humans, circadian responses to light are generally measured indirectly (e.g., by measuring melatonin levels or 24-h behavioral rhythms). These indirect measures revealed that circadian responses to light in humans are most sensitive to blue light (13–16); however, green light has also been found to contribute to circadian phase shifting and changes in melatonin to a larger extent than would have been predicted based solely on the melanopsin response, suggesting that rods and/or cones may also provide functional input to the circadian system in humans (17). Despite this indirect evidence suggesting that several colors can affect the human circadian clock, this has never been measured directly due to technical limitations. Thus, current guidelines regarding the use of artificial light are based solely on the clock’s sensitivity to blue light. For example, blue light is usually filtered out in electronic screens during the night (18, 19), and blue-enriched light is used by night shift workers to optimize their body rhythm for achieving maximum performance (20–22).The ability to directly image the human SCN in vivo has been severely limited due to its small size and the relatively low spatial resolution provided by medical imaging devices. Previous functional MRI (fMRI) studies using 3-Tesla (3T) scanners were restricted to recording the “suprachiasmatic area,” which encompasses a large part of the hypothalamus and thus includes many other potentially light-sensitive nuclei (23–25). To overcome this limitation, we used a 7T MRI scanner, which can provide images with sufficiently high spatial resolution to image small brain nuclei (26) such as the SCN. Here, we applied colored light stimuli to healthy volunteers using a custom-designed MRI-compatible light-emitting diode (LED) device designed to stimulate specific photoreceptors while measuring SCN activity using fMRI. Using analytical approaches, we then identified the SCN’s response, the smallest brain nucleus that has so far been imaged. We found that the human SCN responds to a broad range of wavelengths (i.e., blue, green and orange light). Surprisingly, we also found that the blood oxygen level–dependent (BOLD) fMRI signal at the SCN is actually suppressed—not activated—by light.     

Changes in proteoglycan aggregates during cartilage mineralization

Summary The dimensions of proteoglycan aggregates, aggregated monomers, and nonaggregated monomers, and the proportion of aggregated monomers found in the different zones of bovine rib growth plate have been defined by the electron microscopic monolayer technique. Growth plates were divided into the following 1 mm thick transverse slices; the hypertrophic zone, the lower proliferative zone, the upper proliferative zone, a transitional zone, and epiphyseal cartilage. Proteoglycans prepared by associative extraction followed by equilibrium density gradient centrifugation under associative conditions were examined by electron microscopy. Proteoglycan aggregate size decreased sharply in the lower proliferative and hypertrophic zones, as indicated by decreases in hyaluronate filament length and in the number of monomers per aggregate. Aggregated proteoglycan monomers did not show evidence of proteolytic degradation. Nonaggregated monomers were shorter than aggregated monomers, but their mean length did not decrease in the lower proliferative and hypertrophic zones. However, the proportion of nonaggregated monomers increased in these zones. Thus, before the cartilage matrix mineralized in the lower proliferative zone and as the cartilage matrix began to mineralize in the hypertrophic zone, proteoglycan aggregate size decreased and the proportion of aggregated monomers decreased. These changes in matrix proteoglycans may be one of the events that allow cartilage mineralization.