Search

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.

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

Copas’ method corrects a pooled estimate from an aggregated data meta-analysis for publication bias. Its performance has been studied for one particular mechanism of publication bias. We show through simulations that Copas’ method is not robust against other realistic mechanisms. This questions the usefulness of Copas’ method, since publication bias mechanisms are typically unknown in practice.

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 r

A bivariate zero-inflated Poisson control chart for rare events in an industrial process was presented in the paper of Fatahi et al. (2012). They used a specific copula to address the dependency between the bivariate counts. The copula that they provided did not satisfy the criteria for a copula when the correlation parameter is negative and they did not calculate the upper control limit for the sum of events correctly. We provide proper copula’s and upper control limits and also discuss their choice of parameter estimators. We illustrate our results with simulations.

Vital signs are used in Neonatal Intensive Care Units (NICUs) to monitor the state of multiple patients at once. Alarms are triggered if a vital sign is below/above a predefined threshold. Numerous alarms sound each hour which could translate into an overload for the medical team, known as alarm fatigue. Yet many of these alarms do not require immediate clinical action of the caregivers. In this paper we automatically detect moments that need an immediate response (i.e. interaction with the patient) of the medical team in NICUs by using caregiver response to the patient, which is based on the interpretation of vital signs and of nonverbal cues (e.g. movements) delivered by patients. The ultimate goal of such approach is to reduce the overload of alarms while maintaining the patient safety. We use features extracted from the electrocardiogram (ECG) and pulse oxymetry (SpO2) sensors of the patient, as most unplanned interactions between patient and caregivers are due to deteriorations. Since in our unit an alarm can only be paused or silenced manually at the bedside, we used this information as a prior for caregiver response. We also propose different labeling schemes for classification, each representative of a possible interaction scenario within the nature of our problem. We accomplished a general detection of caregiver response with a mean AUC of 0.82. We also show that when trained only with stable and truly deteriorating (critical state) samples, the classifiers can better learn the difference between alarms that need no immediate response and those that do. In addition, we present an analysis of the posterior probabilities over time for different labeling schemes, and use it to speculate about the reasons behind some failure cases.

Een stepped wedge cluster gerandomiseerde design is een speciale vorm van gerandomiseerd klinisch onderzoek. De basis van dit klinische studieontwerp is dat de interventie stapsgewijs in de diverse clusteren wordt gestart, en dat het moment van starten door het lot (dus random) wordt bepaald. Dit ontwerp is populair onder epidemiologen en medische onderzoekers vanwege de flexibiliteit en efficiëntie. Echter, zoals wel vaker zijn de voor- en nadelen van dit design in de praktijk meestal niet zo duidelijk als in theorie. Het plannen en uitvoeren van een stepped wedge design is uitdagend. In vergelijking met het klassieke parallel group design, moeten er meer keuzes worden bij het opzetten van een stepped wedge design studie. Daarnaast kunnen niet alle standaard methoden voor data-analyse direct toegepast worden op gegevens die zijn verzameld in het stepped wedge design, en voor bepaalde problemen in de analyse is nog geen oplossing bekend. Het doel van dit proefschrift, was het onderzoeken van praktische problemen die men tegen kan komen bij het uitvoeren van een stepped wedge design studie aan de hand de CEA-Watch trial, zoals de praktische implementatie van het ontwerp en de toepassingen van statistische methoden voor een stepped wedge design. Het geheel in aanmerking genomen maakt duidelijk dat analyse van data verkregen met een stepped wedge designed studie geen simpele opdracht is. Toch is het stepped wedge design een veelbelovend design dat goede mogelijkheden biedt voor verdere uitbreiding om ook pure controle- en behandelarmen op te nemen en generalisatie naar een complexere situatie., A stepped wedge cluster randomized design is a special form of randomized clinical trial design. The basis of this clinical study design is that the intervention is introduced one by one in different clusters, and the moment of starting is determined by chance (i.e., random). This design is popular among epidemiologists and medical researchers because of its flexibility and efficiency. As in reality, the benefits and drawbacks of the design are usually not as clear cut as in theory. Planning and conducting a stepped wedge design is challenging. There are relatively more factors to configure in the stepped wedge design compared to the classic parallel group design. In addition, not all standard methods for data analysis can be directly applied to data collected within the stepped wedge design and certain issues remained open. The aim of this thesis, motivated by a real life example of the CEA-Watch trial, was to investigate and demonstrate the practical issues of conducting a stepped wedge design such as the implication of applying a stepped wedge design for a study and the applications of frequently used statistical methods for a stepped wedge design. Overall, it is not a trivial task to analyze data from a stepped wedge design. Nevertheless, the stepped wedge design is a very promising trial design option that has a lot of potentials to be further extended and generalized to more complicated situations.

Coronavirus disease 2019 (COVID-19) is an unprecedented and fast evolving pandemic, which has caused a large number of critically ill patients and deaths globally. It is an acute public health crisis leading to overloaded critical care capacity. Timely prediction of the clinical outcome (death/survival) of hospital-admitted COVID-19 patients can provide early warnings to clinicians, allowing improved allocation of medical resources. In a recently published paper, an interpretable machine learning model was presented to predict the mortality of COVID-19 patients with blood biomarkers, where the model was trained and tested on relatively small data sets. However, the model or performance stability was not explored and assessed. By re-analyzing the data, we reveal that the reported mortality prediction performance was likely over-optimistic and its uncertainty was underestimated or overlooked, with a large variability in predicting deaths.