The Effects of a New Wireless Non-Adhesive Cardiorespiratory Monitoring Device on the Skin Conditions of Preterm Infants.

AIM: The aim of our study was to investigate skin conditions when wearing and removing a novel wireless non-adhesive cardiorespiratory monitoring device for neonates (Bambi-Belt) compared to standard adhesive electrodes. STUDY DESIGN: This was a prospective study including preterm neonates requiring cardiorespiratory monitoring. Besides standard electrodes, the infants wore a Bambi Belt for 10 consecutive days. Their skin conditions were assessed using Trans Epidermal Water Loss (TEWL) and the Neonatal Skin Condition Score (NSCS) after daily belt and standard electrode removal. The ∆TEWL was calculated as the difference between the TEWL at the device's location (Bambi-Belt/standard electrode) and the adjacent control skin location, with a higher ∆TEWL indicating skin damage. RESULTS: A total of 15 infants (gestational age (GA): 24.1-35.6 wk) were analyzed. The ΔTEWL significantly increased directly after electrode removal (10.95 ± 9.98 g/m2/h) compared to belt removal (5.18 ± 6.71 g/m2/h; F: 8.73, p = 0.004) and after the washout period (3.72 ± 5.46 g/m2/h vs. 1.86 ± 3.35 g/m2/h; F: 2.84, p = 0.09), although the latter did not reach statistical significance. The TEWL was not influenced by prolonged belt wearing. No significant differences in the NSCS score were found between the belt and electrode (OR: 0.69, 95% CI [0.17, 2.88], p = 0.6). CONCLUSION: A new wireless non-adhesive device for neonatal cardiorespiratory monitoring was well tolerated in preterm infants and may be less damaging during prolonged wearing.

Comparison of analysis methods and design choices for treatment-by-period interaction in unidirectional switch designs: a simulation study.

BACKGROUND: Due to identifiability problems, statistical inference about treatment-by-period interactions has not been discussed for stepped wedge designs in the literature thus far. Unidirectional switch designs (USDs) generalize the stepped wedge designs and allow for estimation and testing of treatment-by-period interaction in its many flexible design forms. METHODS: Under different forms of the USDs, we simulated binary data at both aggregated and individual levels and studied the performances of the generalized linear mixed model (GLMM) and the marginal model with generalized estimation equations (GEE) for estimating and testing treatment-by-period interactions. RESULTS: The parallel group design had the highest power for detecting the treatment-by-period interactions. While there was no substantial difference between aggregated-level and individual-level analysis, the GLMM had better point estimates than the marginal model with GEE. Furthermore, the optimal USD for estimating the average treatment effect was not efficient for treatment-by-period interaction and the marginal model with GEE required a substantial number of clusters to yield unbiased estimates of the interaction parameters when the correlation structure is autoregressive of order 1 (AR1). On the other hand, marginal model with GEE had better coverages than GLMM under the AR1 correlation structure. CONCLUSION: From the designs and methods evaluated, in general, parallel group design with a GLMM is, preferred for estimation and testing of treatment-by-period interaction in a clustered randomized controlled trial for a binary outcome.

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.

How did governmental interventions affect the spread of COVID-19 in European countries?

BACKGROUND: To reduce the transmission of the severe acute respiratory syndrome coronavirus 2 in its first wave, European governments have implemented successive measures to encourage social distancing. However, it remained unclear how effectively measures reduced the spread of the virus. We examined how the effective-contact rate (ECR), the mean number of daily contacts for an infectious individual to transmit the virus, among European citizens evolved during this wave over the period with implemented measures, disregarding a priori information on governmental measures. METHODS: We developed a data-oriented approach that is based on an extended Susceptible-Exposed-Infectious-Removed (SEIR) model. Using the available data on the confirmed numbers of infections and hospitalizations, we first estimated the daily total number of infectious-, exposed- and susceptible individuals and subsequently estimated the ECR with an iterative Poisson regression model. We then compared change points in the daily ECRs to the moments of the governmental measures. RESULTS: The change points in the daily ECRs were found to align with the implementation of governmental interventions. At the end of the considered time-window, we found similar ECRs for Italy (0.29), Spain (0.24), and Germany (0.27), while the ECR in the Netherlands (0.34), Belgium (0.35) and the UK (0.37) were somewhat higher. The highest ECR was found for Sweden (0.45). CONCLUSIONS: There seemed to be an immediate effect of banning events and closing schools, typically among the first measures taken by the governments. The effect of additionally closing bars and restaurants seemed limited. For most countries a somewhat delayed effect of the full lockdown was observed, and the ECR after a full lockdown was not necessarily lower than an ECR after (only) a gathering ban.

Optimisation of clinical workflow and monitor settings safely reduces alarms in the NICU.

AIM: To address alarm fatigue, a new alarm management system which ensures a quicker delivery of alarms together with waveform information on nurses' handheld devices was implemented and settings optimised. The effects of this clinical implementation on alarm rates and nurses' responsiveness were measured in an 18-bed single family rooms neonatal intensive care unit (NICU). METHODS: The technical implementation of the alarm management system was followed by clinical workflow optimisation. Alarms and vital parameters from October 2017 to December 2019 were analysed. Measures included monitoring alarms, nurses' response to alarms and time spent by patients in different saturation ranges. A survey among nurses was performed to evaluate changes in alarm rate and use of protocols. RESULTS: A significant reduction of monitoring alarms per patient days was detected after the optimisation phase (in particular for SpO2 ≤ 80%, P < .001). More time was spent by infants within the optimal peripheral oxygen saturation range (88% < SpO2 < 95%, P < .001). Results from the surveys showed that false alarms are less likely to cause an inappropriate response after the optimisation phase. CONCLUSION: The implementation of an alarm management solution and an optimisation programme can safely reduce the alarm burden inside of the NICU environment.

Optimal unidirectional switch designs.

Stepped wedge designs and delayed start designs can all be considered as special cases of the so-called unidirectional switch design. This paper provides optimal proportions of clusters that are allocated to switch patterns in a unidirectional switch design to minimize the asymptotic variance of the treatment effect estimator. This unique optimal design applies to certain cross-sectional and longitudinal variance component models. When the intraclass correlation coefficient is zero, the optimal unidirectional switch design coincides with the classic (cluster) parallel group design. The optimal unidirectional switch design is more efficient than the optimal stepped wedge design and delayed start designs. Compared with the uniform unidirectional switch design, the efficiency gain of the optimal unidirectional switch design can be substantial, but it depends on the intraclass correlation and the cluster size. We also showed that augmenting the optimal stepped wedge design with pure control pattern is more efficient than the optimal stepped wedge design. In addition, robust minimax design for unidirectional switch design, delayed start design, and stepped wedge design are provided.

Predictive performance of TPA testing for recurrent disease during follow-up after curative intent surgery for colorectal carcinoma.

BACKGROUND: The aim of the present study was to investigate the predictive performance of serial tissue polypeptide antigen (TPA) testing after curative intent resection for detection of recurrence of colorectal malignancy. METHODS: Serum samples were obtained in 572 patients from three different hospitals during follow-up after surgery. Test characteristics of serial TPA testing were assessed using a cut-off value of 75 U/L. The relation with American Joint Committee on Cancer stage and the potential additive value of tissue polypeptide antigen testing upon standard carcinoembryonic antigen (CEA) testing were investigated. RESULTS: The area under the receiver operating characteristic curve of TPA for recurrent disease was 0.70, indicating marginal usefulness as a predictive test. Forty percent of cases that were detected by CEA testing would have been missed by TPA testing alone, whilst most cases missed by CEA were also not detected by TPA testing. In the subpopulation of patients with stage III disease predictive performance was good (area under the curve 0.92 within 30 days of diagnosing recurrent disease). In this group of patients, 86% of cases that were detected by CEA were also detected by TPA. CONCLUSIONS: Overall, TPA is a relatively poor predictor for recurrent disease during follow-up. When looking at the specific subpopulation of patients with stage III disease predictive performance of TPA was good. However, TPA testing was not found to be superior to CEA testing in this specific subpopulation.

The analysis of terminal endpoint events in stepped wedge designs.

The stepped wedge design is a unique clinical trial design that allows for a sequential introduction of an intervention. However, the statistical analysis is unclear when this design is applied in survival data. The time-dependent introduction of the intervention in combination with terminal endpoints and interval censoring makes the analysis more complicated. In this paper, a time-on-study scale discrete survival model was constructed. Simulations were conducted primarily to study the performance of our model for different settings of the stepped wedge design. Secondary, we compared our approach to continuous Cox proportional hazard model. The results show that the discrete survival model estimates the intervention effects unbiasedly. If the length of the censoring interval is increased, the precision of the estimates is decreased. Without left truncation and late entry, the number of steps improves the precision of the estimates, whereas in combination of left truncation and late entry, the number of steps decreases the precision. Given the same number of participants and clusters, a parallel group design has higher precision than a stepped wedge design. Copyright © 2016 John Wiley & Sons, Ltd.

Simulation models for aggregated data meta-analysis: Evaluation of pooling effect sizes and publication biases.

Simulation studies are commonly used to evaluate the performance of newly developed meta-analysis methods. For methodology that is developed for an aggregated data meta-analysis, researchers often resort to simulation of the aggregated data directly, instead of simulating individual participant data from which the aggregated data would be calculated in reality. Clearly, distributional characteristics of the aggregated data statistics may be derived from distributional assumptions of the underlying individual data, but they are often not made explicit in publications. This article provides the distribution of the aggregated data statistics that were derived from a heteroscedastic mixed effects model for continuous individual data and a procedure for directly simulating the aggregated data statistics. We also compare our simulation approach with other simulation approaches used in literature. We describe their theoretical differences and conduct a simulation study for three meta-analysis methods: DerSimonian and Laird method for pooling aggregated study effect sizes and the Trim & Fill and precision-effect test and precision-effect estimate with standard errors method for adjustment of publication bias. We demonstrate that the choice of simulation model for aggregated data may have an impact on (the conclusions of) the performance of the meta-analysis method. We recommend the use of multiple aggregated data simulation models to investigate the sensitivity in the performance of the meta-analysis method. Additionally, we recommend that researchers try to make the individual participant data model explicit and derive from this model the distributional consequences of the aggregated statistics to help select appropriate aggregated data simulation models.

Treatment Durability of Limited Fasciectomy versus Percutaneous Needle Fasciotomy for Dupuytren Disease.

BACKGROUND: Patients with Dupuytren disease (DD) are mostly surgically treated by percutaneous needle fasciotomy (PNF) or limited fasciectomy (LF), but data on time intervals to retreatment are lacking. The authors aimed to estimate the risk of retreatment within certain time periods after treatment with PNF and LF. METHODS: The authors used data of participants of a cohort study on the course of DD who were treated only with PNF or LF. The primary outcome measure was time to retreatment of DD. The authors included sex, age at first treatment, and having a first-degree relative with DD as confounders in our analysis. A bivariate gamma frailty model was applied to estimate the risk of retreatment within 1, 3, 5, 10, and 20 years after treatment with PNF and LF. RESULTS: The time to retreatment was significantly shorter after treatment with PNF than after LF (Wald test statistic, 7.56; P < 0.001). The estimated 10-year risk of retreatment for men who underwent their first treatment at a younger age and with a first-degree relative with DD was 97% after PNF and 32% after LF. The estimated 10-year risk for women who underwent their first treatment at an older age without a first-degree relative with DD was 20% after PNF and 6% after LF. CONCLUSIONS: The results show that the patients treated with PNF have a higher risk of retreatment. The results of this study could contribute to individualized information on the treatment durability in the future, which would improve patient counseling about the expected retreatment needs. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, III.

A customised down-sampling machine learning approach for sepsis prediction.

OBJECTIVE: Sepsis is a life-threatening condition in the ICU and requires treatment in time. Despite the accuracy of existing sepsis prediction models, insufficient focus on reducing alarms could worsen alarm fatigue and desensitisation in ICUs, potentially compromising patient safety. In this retrospective study, we aim to develop an accurate, robust, and readily deployable method in ICUs, only based on the vital signs and laboratory tests. METHODS: Our method consists of a customised down-sampling process and a specific dynamic sliding window and XGBoost to offer sepsis prediction. The down-sampling process was applied to the retrospective data for training the XGBoost model. During the testing stage, the dynamic sliding window and the trained XGBoost were used to predict sepsis on the retrospective datasets, PhysioNet and FHC. RESULTS: With the filtered data from PhysioNet, our method achieved 80.74% accuracy (77.90% sensitivity and 84.42% specificity) and 83.95% (84.82% sensitivity and 82.00% specificity) on the test set of PhysioNet-A and PhysioNet-B, respectively. The AUC score was 0.89 for both datasets. On the FHC dataset, our method achieved 92.38% accuracy (88.37% sensitivity and 95.16% specificity) and 0.98 AUC score on the test set of FHC. CONCLUSION: Our results indicate that the down-sampling process and the dynamic sliding window with XGBoost brought robust and accurate performance to give sepsis prediction under various hospital settings. The localisation and robustness of our method can assist in sepsis diagnosis in different ICU settings.

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.

Can body position be arrhythmogenic?

INTRODUCTION: Palpitations occurring in specific body positions are often reported by patients, but the effect of body position on arrhythmia has received little research attention. We hypothesize that resting body position can exert pro-arrhythmogenic effects in various ways. For example, lateral body position is known to increase change atrial and pulmonary vein dimensions. METHODS: This observational study capitalizes on overnight polysomnography (PSG) recordings from a tertiary sleep clinic. PSGs were retrieved based on any mention of cardiac arrhythmia in the clinical report, irrespective of primary sleep diagnosis or (cardiac) comorbidities. Every instance of atrial ectopy was annotated and subgroups with a homogenous rate of atrial ectopy were created based on the Dunn index. A generalized linear mixed-effects model using age, sex, gender, sleep stage and body position was used to analyse the total amount of atrial ectopy in each combination of sleep stage and body position. Backward elimination was then performed to select the best subset of variables for the model. Presence of a respiratory event was then added to the model for the subgroup with a high atrial ectopy rate. RESULTS: PSGs of 22 patients (14% female, mean age 61y) were clustered and analysed. Body position, sleep stage, age or sex did not have a significant effect on atrial ectopy in the subgroup with a low rate of atrial ectopy (N = 18). However, body position did significantly affect the rate of atrial ectopy in the subgroup with a high rate of atrial ectopy (N = 4; 18%). Respiratory events significantly altered the atrial ectopy rate in only three body positions across two patients. DISCUSSION: In each individual with a high rate of atrial ectopy, the rate of atrial ectopy was significantly higher in either left or right decubital or supine position. Increase in atrial wall stretch in lateral decubital position and obstructive respiratory events in positional sleep apnea are two possible pathophysiological mechanisms, while avoidance of a body position due to symptomatic atrial ectopy in that position is an important limitation. CONCLUSION: In a selected cohort of patients with a high rate of atrial ectopy during overnight polysomnography, the occurrence of atrial ectopy is related to resting body position.

Central apnea detection in premature infants using machine learning.

BACKGROUND AND OBJECTIVE: Apnea of prematurity is one of the most common diagnosis in neonatal intensive care units. Apneas can be classified as central, obstructive or mixed. According to the current international standards, minimal fluctuations or absence of fluctuations in the chest impedance (CI) suggest a central apnea (CA). However, automatic detection of reduced CI fluctuations leads to a high number of central apnea-suspected events (CASEs), the majority being false alarms. We aim to improve automatic detection of CAs by using machine learning to optimize detection of CAs among CASEs. METHODS: Using an optimized algorithm for automated detection, all CASEs were detected in a population of 10 premature infants developing late-onset sepsis and 10 age-matched control patients. CASEs were inspected by two clinical experts and annotated as CAs or rejections in two rounds of annotations. A total of 47 features were extracted from the ECG, CI and oxygen saturation signals considering four 30 s-long moving windows, from 30 s before to 15 s after the onset of each CASE, using a moving step size of 5 s. Consecutively, new CA detection models were developed based on logistic regression with elastic net penalty, random forest and support vector machines. Performance was evaluated using both leave-one-patient-out and 10-fold cross-validation considering the mean area under the receiver-operating-characteristic curve (AUROC). RESULTS: The CA detection model based on logistic regression with elastic net penalty returned the highest mean AUROC when features extracted from all four time windows were included, both using leave-one-patient-out and 10-fold cross-validation (mean AUROC of 0.88 and 0.90, respectively). Feature relevance was found to be the highest for features derived from the CI. A threshold for the false positive rate in the mean receiver-operating-characteristic curve equal to 0.3 led to a high percentage of correct detections for all CAs (78.2%) and even higher for CAs followed by a bradycardia (93.4%) and CAs followed by both a bradycardia and a desaturation (95.2%), which are more critical for the well-being of premature infants. CONCLUSIONS: Models based on machine learning can lead to improved CA detection with fewer false alarms.

Multicentre paired non-inferiority study of the cardiorespiratory monitoring performance of the wireless and non-adhesive Bambi® belt measuring diaphragm activity in neonates: study protocol.

INTRODUCTION: Cardiorespiratory monitoring is used in the neonatal intensive care unit (NICU) to assess the clinical status of newborn infants and detect critical deteriorations in cardiorespiratory function. Currently, heart rate (HR) is monitored by electrocardiography (ECG) and respiration by chest impedance (CI). Disadvantages of current monitoring techniques are usage of wired adhesive electrodes which may damage the skin and hinder care. The Bambi® belt is a wireless and non-adhesive alternative that enables cardiorespiratory monitoring by measuring electrical activity of the diaphragm via transcutaneous electromyography. A previous study showed feasibility of the Bambi® belt and this study compares the belt performance to ECG and CI. METHODS AND ANALYSIS: This multicentre non-inferiority paired study will be performed in the NICU of the Máxima Medical Center (MMC) in Veldhoven and the Emma Children's Hospital, Amsterdam University Medical Centre (AmsterdamUMC) in Amsterdam, The Netherlands. 39 infants in different postmenstrual age groups (minimally 10 infants<30 weeks, between 30-32 weeks and >32 weeks) will be recruited. These infants will be monitored with the Bambi® belt in addition to standard ECG and CI for 24 hours. The primary outcome is the HR, studied with three criteria: (1) the limits of agreement of the HR measurements in terms of the second-to-second difference in the HR between the belt and standard ECG, (2) the detection of cardiac events consisting of bradycardia and tachycardia and (3) the quality of HR-monitoring. The secondary outcome is the respiratory rate (RR), studied with the criteria (1) agreement in RR-trend monitoring, (2) apnoea and tachypnoea detection and (3) reliable registrations. ETHICS AND DISSEMINATION: This protocol was approved by the Medical Ethical Committee of the MMC and the Central Committee for Human Research. The MMC started patient recruitment in July and the AmsterdamUMC in August 2021. The results will be presented at conferences and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NL9480.

Tests for publication bias are unreliable in case of heteroscedasticity.

Regression based methods for the detection of publication bias in meta-analysis have been extensively evaluated in literature. When dealing with continuous outcomes, specific hidden factors (e.g., heteroscedasticity) may interfere with the test statistics. In this paper we investigate the influence of residual heteroscedasticity on the performance of four tests for publication bias: the Egger test, the Begg-Mazumdar test and two tests based on weighted regression. In the presence of heteroscedasticity, the Egger test and the weighted regression tests highly inflate the Type I error rate, while the Begg-Mazumdar test deflates the Type I error rate. Although all three tests already have low statistical power, heteroscedasticity typically reduces it further. Our results in combination with earlier discussions on publication bias tests lead us to conclude that application of these tests on continuous treatment effects is not warranted.

Optimized Detection of Central Apneas Preceding Late-Onset Sepsis in Premature Infants.

In neonatal intensive care units, respiratory traces of premature infants developing late onset sepsis (LOS) may also show episodes of apneas. However, since clinical patient monitors often underdetect apneas, clinical experts are required to investigate patients' traces looking for these events. In this work we present a method to optimize an existing algorithm for central apnea (CA) detection and how we used it together with human annotations to investigate the occurrence of CAs preceding LOS.The algorithm was optimized by using a previously-annotated dataset consisting of 90 hours, extracted from 10 premature infants. This allowed to double precision (19.7% vs 9.3%, median values per patient) without affecting recall (90.5% vs 94.5%) compared to the original algorithm. This choice caused the missed identification of just 1 additional CA (4 vs 3) in the whole dataset. The optimized algorithm was then used to annotate a second dataset consisting of 480 hours, extracted from 10 premature infants diagnosed with LOS. Annotations were corrected by two clinical experts.A significantly higher number of CA annotations was found in the 6 hours prior to sepsis onset (p-value < 0.05). The use of the optimized algorithm followed by human annotations proved to be a suitable, time-efficient method to annotate CAs before sepsis in premature infants, enabling future use in large datasets.

Estimation of Heart Rate Directly from ECG Spectrogram in Neonate Intensive Care Units.

This paper presents a simple yet novel method to estimate the heart frequency (HF) of neonates directly from the ECG signal, instead of using the RR-interval signals as generally done in clinical practices. From this, the heart rate (HR) can be derived. Thus, we avoid the use of peak detectors and the inherent errors that come with them.Our method leverages the highest Power Spectral Densities (PSD) of the ECG, for the bins around the frequencies related to heart rates for neonates, as they change in time (spectrograms).We tested our approach with the monitoring data of 6 days for 52 patients in a Neonate Intensive Care Unit (NICU) and compared against the HR from a commercial monitor, which produced a sample every second. The comparison showed that 92.4% of the samples have a difference lower than 5bpm. Moreover, we obtained a median MAE (Mean Absolute Error) between subjects equal to 2.28 bpm and a median RMSE (Root Mean Square Error) equal to 5.82 bpm. Although tested for neonates, we hypothesize that this method can also be customized for other populations.Finally, we analyze the failure cases of our method and found a direct co-allocation of errors due to moments with higher PSD in the lower frequencies with the presence of critical alarms related to other physiological systems (e.g. desaturation).

Statistical methods for unidirectional switch designs: Past, present, and future.

Clinical trials may apply or use a sequential introduction of a new treatment to determine its efficacy or effectiveness with respect to a control treatment. The reasons for choosing a particular switch design have different origins. For instance, they may be implemented for ethical or logistic reasons or for studying disease-modifying effects. Large-scale pragmatic trials with complex interventions often use stepped wedge designs (SWDs), where all participants start at the control group, and during the trial, the control treatment is switched to the new intervention at different moments. They typically use cross-sectional data and cluster randomization. On the other hand, new drugs for inhibition of cognitive decline in Alzheimer's or Parkinson's disease typically use delayed start designs (DSDs). Here, participants start in a parallel group design and at a certain moment in the trial, (part of) the control group switches to the new treatment. The studies are longitudinal in nature, and individuals are being randomized. Statistical methods for these unidirectional switch designs (USD) are quite complex and incomparable, and they have been developed by various authors under different terminologies, model specifications, and assumptions. This imposes unnecessary barriers for researchers to compare results or choose the most appropriate method for their own needs. This paper provides an overview of past and current statistical developments for the USDs (SWD and DSD). All designs are formulated in a unified framework of treatment patterns to make comparisons between switch designs easier. The focus is primarily on statistical models, methods of estimation, sample size calculation, and optimal designs for estimation of the treatment effect. Other relevant open issues are being discussed as well to provide suggestions for future research in USDs.