Recent Publications by CFE Educators

OBJECTIVES

Obstructive sleep apnea (OSA) is usually assessed at discrete and infrequent timepoints. Wearable consumer sleep technologies (CST) may allow for more granular and longitudinal assessments of OSA therapy responses and OSA-related symptoms.

METHODS

In this case series, we enrolled hypoglossal nerve stimulator (HGNS) patients who had an effective treatment response for an 8-week study using a wearable CST. Participants started with "HGNS-on," were randomized to turn off HGNS therapy during either week 4 or 5 ("HGNS-off"), followed by a return to therapy, "HGNS-resume." Participants completed validated symptom questionnaires assessing sleepiness, insomnia symptoms, functional status, and overall sleep health (Satisfaction, Alertness, Timing, Efficiency, and Duration, SATED) each week. CST metrics and survey scores were compared between HGNS treatment phases. Associations between CST metrics and survey scores were assessed.

RESULTS

Seven participants with a total of 304 nights of CST data showed no statistically significant changes in total sleep time (TST), wake time after sleep onset, or sleep efficiency (SE) across the study periods. During HGNS-off, survey scores indicated significantly worsened OSA-related symptom scores. Two participants had significantly higher heart rate variability (HRV) during HGNS-off (by 3.3 and 6.3 ms) when compared to HGNS active therapy periods. Amongst CST metrics, SATED scores correlated with TST (r = 0.434, p  0.0001), HRV (r = -0.486, p  0.0001), and SE (r = 0.320, = 0.0014). In addition, FOSQ-10 scores correlated with average HR during sleep (r = -0.489, p  0.001).

CONCLUSION

A 1-week HGNS therapy withdrawal period impacted OSA-related sleep symptoms. Sleep-related metrics measured by a wearable CST correlated with symptom scores indicating potential value in the use of CSTs for longitudinal sleep-tracking in OSA patients.

LEVEL OF EVIDENCE

4 Laryngoscope, 134:3406-3411, 2024.

For many cancer sites low-dose risks are not known and must be extrapolated from those observed in groups exposed at much higher levels of dose. Measurement error can substantially alter the dose-response shape and hence the extrapolated risk. Even in studies with direct measurement of low-dose exposures measurement error could be substantial in relation to the size of the dose estimates and thereby distort population risk estimates. Recently, there has been considerable attention paid to methods of dealing with shared errors, which are common in many datasets, and particularly important in occupational and environmental settings. In this paper we test Bayesian model averaging (BMA) and frequentist model averaging (FMA) methods, the first of these similar to the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, and both fairly recently proposed, against a very newly proposed modification of the regression calibration method, the extended regression calibration (ERC) method, which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. The quasi-2DMC with BMA method performs well when a linear model is assumed, but very poorly when a linear-quadratic model is assumed, with coverage probabilities both for the linear and quadratic dose coefficients that are under 5% when the magnitude of shared Berkson error is large (50%). For the linear model the bias is generally under 10%. However, using a linear-quadratic model it produces substantially biased (by a factor of 10) estimates of both the linear and quadratic coefficients, with the linear coefficient overestimated and the quadratic coefficient underestimated. FMA performs as well as quasi-2DMC with BMA when a linear model is assumed, and generally much better with a linear-quadratic model, although the coverage probability for the quadratic coefficient is uniformly too high. However both linear and quadratic coefficients have pronounced upward bias, particularly when Berkson error is large. By comparison ERC yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the quasi-2DMC with BMA or FMA methods, particularly for the linear-quadratic model. The bias of the predicted relative risk at a variety of doses is generally smallest for ERC, and largest for the quasi-2DMC with BMA and FMA methods (apart from unadjusted regression), with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between ERC and the other two methods. In general ERC performs best in the scenarios presented, and should be the method of choice in situations where there may be substantial shared error, or suspected curvature in the dose response.