Cardiorespiratory monitoring with a wireless and nonadhesive belt measuring diaphragm activity in preterm and term infants: A multicenter non-inferiority study.

INTRODUCTION: We determined if the heart rate (HR) monitoring performance of a wireless and nonadhesive belt is non-inferior compared to standard electrocardiography (ECG). Secondary objective was to explore the belt's respiratory rate (RR) monitoring performance compared to chest impedance (CI). METHOD: In this multicenter non-inferiority trial, preterm and term infants were simultaneously monitored with the belt and conventional ECG/CI for 24 h. HR monitoring performance was estimated with the HR difference and ability to detect cardiac events compared to the ECG, and the incidence of HR-data loss per second. These estimations were statistically compared to prespecified margins to confirm equivalence/non-inferiority. Exploratory RR analyses estimated the RR trend difference and ability to detect apnea/tachypnea compared to CI, and the incidence of RR-data loss per second. RESULTS: Thirty-nine infants were included. HR monitoring with the belt was non-inferior to the ECG with a mean HR difference of 0.03 beats per minute (bpm) (standard error [SE] = 0.02) (95% limits of agreement [LoA]: [-5 to 5] bpm) (p < 0.001). Second, sensitivity and positive predictive value (PPV) for cardiac event detection were 94.0% (SE = 0.5%) and 92.6% (SE = 0.6%), respectively (p ≤ 0.001). Third, the incidence of HR-data loss was 2.1% (SE = 0.4%) per second (p < 0.05). The exploratory analyses of RR showed moderate trend agreement with a mean RR-difference of 3.7 breaths/min (SE = 0.8) (LoA: [-12 to 19] breaths/min), but low sensitivities and PPV's for apnea/tachypnea detection. The incidence of RR-data loss was 2.2% (SE = 0.4%) per second. CONCLUSION: The nonadhesive, wireless belt showed non-inferior HR monitoring and a moderate agreement in RR trend compared to ECG/CI. Future research on apnea/tachypnea detection is required.

Jointly pooling aggregated effect sizes and their standard errors from studies with continuous clinical outcomes.

The DerSimonian-Laird (DL) weighted average method for aggregated data meta-analysis has been widely used for the estimation of overall effect sizes. It is criticized for its underestimation of the standard error of the overall effect size in the presence of heterogeneous effect sizes. Due to this negative property, many alternative estimation approaches have been proposed in the literature. One of the earliest alternative approaches was developed by Hardy and Thompson (HT), who implemented a profile likelihood instead of the moment-based approach of DL. Others have further extended this likelihood approach and proposed higher-order likelihood inferences (e.g., Bartlett-type corrections). In addition, corrections factors for the estimated DL standard error, like the Hartung-Knapp-Sidik-Jonkman (HKSJ) adjustment, and the restricted maximum likelihood (REML) estimation have been suggested too. Although these improvements address the uncertainty in estimating the between-study variance better than the DL method, they all assume that the true within-study standard errors are known and equal to the observed standard errors of the effect sizes. Here, we will treat the observed standard errors as estimators for the within-study variability and we propose a bivariate likelihood approach that jointly estimates the overall effect size, the between-study variance, and the potentially heteroskedastic within-study variances. We study the performance of the proposed method by means of simulation, and compare it to DL (with and without HKSJ), HT, their higher-order likelihood methods, and REML. Our proposed approach seems to have better or similar coverages compared to the other approaches and it appears to be less biased in the case of heteroskedastic within-study variances when this heteroskedasticty is correlated with the effect size.

Interevent-time distribution and aftershock frequency in non-stationary induced seismicity.

The initial footprint of an earthquake can be extended considerably by triggering of clustered aftershocks. Such earthquake-earthquake interactions have been studied extensively for data-rich, stationary natural seismicity. Induced seismicity, however, is intrinsically inhomogeneous in time and space and may have a limited catalog of events; this may hamper the distinction between human-induced background events and triggered aftershocks. Here we introduce a novel Gamma Accelerated-Failure-Time model for efficiently analyzing interevent-time distributions in such cases. It addresses the spatiotemporal variation and quantifies, per event, the probability of each event to have been triggered. Distentangling the obscuring aftershocks from the background events is a crucial step to better understand the causal relationship between operational parameters and non-stationary induced seismicity. Applied to the Groningen gas field in the North of the Netherlands, our model elucidates geological and operational drivers of seismicity and has been used to test for aftershock triggering. We find that the hazard rate in Groningen is indeed enhanced after each event and conclude that aftershock triggering cannot be ignored. In particular we find that the non-stationary interevent-time distribution is well described by our Gamma model. This model suggests that 27.0(± 8.5)% of the recorded events in the Groningen field can be attributed to triggering.