A New Digital Preoperative Planning Method for Total Hip Arthroplasties

Preoperative templating is an important part of a THA. The ability to accurately determine magnification of the hip on the radiograph and apply identical magnification to the radiograph and template will improve accuracy of preoperative templating of THA. We designed a templating method using a new way of determining the hip magnification with a linear relationship between magnification of the hip and the reference object on top of the pubis symphysis; the relationship was determined on 50 radiographs. We then compared our method with two other templating methods: an analog method assuming an average hip magnification of 15% and a digital method determining the hip magnification with a one-to-one relationship between the reference object and the hip. All methods were reproducible. Uniform undersizing occurred when templating with the digital method based on the one-to-one relationship; the analog method best predicted the implanted prosthesis size, closely followed by our new digital templating method; the new method will be particularly applicable for preoperative THA when analog methods are replaced by digital method

Predicting neonatal sepsis using features of heart rate variability, respiratory characteristics and ECG-derived estimates of infant motion

This study in preterm infants was designed to characterize the prognostic potential of several features of heart rate variability (HRV), respiration, and (infant) motion for the predictive monitoring of late-onset sepsis (LOS). In a neonatal intensive care setting, the cardiorespiratory waveforms of infants with blood-culture positive LOS were analyzed to characterize the prognostic potential of 22 features for discriminating control from sepsis-state, using the Naïve Bayes algorithm. Historical data of the subjects acquired from a period sufficiently before the clinical suspicion of LOS was used as control state, whereas data from the 24 h preceding the clinical suspicion of LOS were used as sepsis state (test data). The overall prognostic potential of all features was quantified at three-hourly intervals for the period corresponding to test data by calculating the area under the receiver operating characteristics curve. For the 49 infants studied, features of HRV, respiration, and movement showed characteristic changes in the hours leading up to the clinical suspicion of sepsis, namely, an increased propensity toward pathological heart rate decelerations, increased respiratory instability, and a decrease in spontaneous infant activity, i.e., lethargy. While features characterizing HRV and respiration can be used to probe the state of the autonomic nervous system, those characterizing movement probe the state of the motor system-dysregulation of both reflects an increased likelihood of sepsis. By using readily interpretable features derived from cardiorespiratory monitoring, opportunities for pre-emptively identifying and treating LOS can be developed.</p

Detecting central apneas using multichannel signals in premature infants

Objective. Monitoring of apnea of prematurity, performed in neonatal intensive care units by detecting central apneas (CAs) in the respiratory traces, is characterized by a high number of false alarms. A two-step approach consisting of a threshold-based apneic event detection algorithm followed by a machine learning model was recently presented in literature aiming to improve CA detection. However, since this is characterized by high complexity and low precision, we developed a new direct approach that only consists of a detection model based on machine learning directly working with multichannel signals. Approach. The dataset used in this study consisted of 48 h of ECG, chest impedance and peripheral oxygen saturation extracted from 10 premature infants. CAs were labeled by two clinical experts. 47 features were extracted from time series using 30 s moving windows with an overlap of 5 s and evaluated in sets of 4 consecutive moving windows, in a similar way to what was indicated for the two-step approach. An undersampling method was used to reduce imbalance in the training set while aiming at increasing precision. A detection model using logistic regression with elastic net penalty and leave-one-patient-out cross-validation was then tested on the full dataset. Main results. This detection model returned a mean area under the receiver operating characteristic curve value equal to 0.86 and, after the selection of a FPR equal to 0.1 and the use of smoothing, an increased precision (0.50 versus 0.42) at the expense of a decrease in recall (0.70 versus 0.78) compared to the two-step approach around suspected apneic events. Significance. The new direct approach guaranteed correct detections for more than 81% of CAs with length L≥ 20 s, which are considered among the most threatening apneic events for premature infants. These results require additional verifications using more extensive datasets but could lead to promising applications in clinical practice. </p

FlexEvent:going beyond Case-Centric Exploration and Analysis of Multivariate Event Sequences

In many domains, multivariate event sequence data is collected focused around an entity (the case). Typically, each event has multiple attributes, for example, in healthcare a patient has events such as hospitalization, medication, and surgery. In addition to the multivariate events, also the case (a specific attribute, e.g., patient) has associated multivariate data (e.g., age, gender, weight). Current work typically only visualizes one attribute per event (label) in the event sequences. As a consequence, events can only be explored from a predefined case-centric perspective. However, to find complex relations from multiple perspectives (e.g., from different case definitions, such as doctor), users also need an event- and attribute-centric perspective. In addition, support is needed to effortlessly switch between and within perspectives. To support such a rich exploration, we present FlexEvent: an exploration and analysis method that enables investigation beyond a fixed case-centric perspective. Based on an adaptation of existing visualization techniques, such as scatterplots and juxtaposed small multiples, we enable flexible switching between different perspectives to explore the multivariate event sequence data needed to answer multi-perspective hypotheses. We evaluated FlexEvent with three domain experts in two use cases with sleep disorder and neonatal ICU data that show our method facilitates experts in exploring and analyzing real-world multivariate sequence data from different perspectives

Continuous sensing and quantification of body motion in infants:A systematic review

Abnormal body motion in infants may be associated with neurodevelopmental delay or critical illness. In contrast to continuous patient monitoring of the basic vitals, the body motion of infants is only determined by discrete periodic clinical observations of caregivers, leaving the infants unattended for observation for a longer time. One step to fill this gap is to introduce and compare different sensing technologies that are suitable for continuous infant body motion quantification. Therefore, we conducted this systematic review for infant body motion quantification based on the PRISMA method (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In this systematic review, we introduce and compare several sensing technologies with motion quantification in different clinical applications. We discuss the pros and cons of each sensing technology for motion quantification. Additionally, we highlight the clinical value and prospects of infant motion monitoring. Finally, we provide suggestions with specific needs in clinical practice, which can be referred by clinical users for their implementation. Our findings suggest that motion quantification can improve the performance of vital sign monitoring, and can provide clinical value to the diagnosis of complications in infants.</p

Continuous movement quantification in preterm infants using fiber mat:Advancing long-term monitoring in hospital settings

Movement patterns in preterm infants can offer crucial insights into their physiological state including maturational development and sleep. These patterns can also serve as early indicators for potential deteriorations, such as cerebral palsy, sepsis, and epilepsy. In this study, we investigated a novel 2-D optical fiber mat system for the automated monitoring of infant movement, thereby enhancing the efficiency and safety of neonatal care in both the neonatal intensive care unit (NICU) and neonatal medium care unit (NMCU). The 20 preterm infants admitted to both NICU and NMCU were enrolled in the study. They underwent monitoring for a duration of 2–5 h using both an optical fiber mat and a camera which provided valuable movement annotations. The signals from the fiber mat were quantified, selected, and then integrated into a consolidated movement signal. This signal was subsequently transformed into binary states, distinguishing between “movement” and “still” based on the distribution of the movement signal. The proposed fiber mat system achieved a mean [standard deviation (SD)] area under the receiver operating curve (AUC) of 0.91 (0.05), and an F-score of 0.73 (0.09), when compared with manually annotated video recordings. This study demonstrates the feasibility of continuous movement monitoring for preterm infants within hospital settings. It illustrates the promising potential to evolve into a predictive tool for monitoring patient deterioration through the fusion of physiological information in both hospital environments and within the comfort of homes

Characterising the motion and cardiorespiratory interaction of preterm infants can improve the classification of their sleep state

Aim: This study aimed to classify quiet sleep, active sleep and wake states in preterm infants by analysing cardiorespiratory signals obtained from routine patient monitors. Methods: We studied eight preterm infants, with an average postmenstrual age of 32.3 ± 2.4 weeks, in a neonatal intensive care unit in the Netherlands. Electrocardiography and chest impedance respiratory signals were recorded. After filtering and R-peak detection, cardiorespiratory features and motion and cardiorespiratory interaction features were extracted, based on previous research. An extremely randomised trees algorithm was used for classification and performance was evaluated using leave-one-patient-out cross-validation and Cohen's kappa coefficient. Results: A sleep expert annotated 4731 30-second epochs (39.4 h) and active sleep, quiet sleep and wake accounted for 73.3%, 12.6% and 14.1% respectively. Using all features, and the extremely randomised trees algorithm, the binary discrimination between active and quiet sleep was better than between other states. Incorporating motion and cardiorespiratory interaction features improved the classification of all sleep states (kappa 0.38 ± 0.09) than analyses without these features (kappa 0.31 ± 0.11). Conclusion: Cardiorespiratory interactions contributed to detecting quiet sleep and motion features contributed to detecting wake states. This combination improved the automated classifications of sleep states

Combining Cardiorespiratory Signals and Video-Based Actigraphy for Classifying Preterm Infant Sleep States

The classification of sleep state in preterm infants, particularly in distinguishing between active sleep (AS) and quiet sleep (QS), has been investigated using cardiorespiratory information such as electrocardiography (ECG) and respiratory signals. However, accurately differentiating between AS and wake remains challenging; therefore, there is a pressing need to include additional information to further enhance the classification performance. To address the challenge, this study explores the effectiveness of incorporating video-based actigraphy analysis alongside cardiorespiratory signals for classifying the sleep states of preterm infants. The study enrolled eight preterm infants, and a total of 91 features were extracted from ECG, respiratory signals, and video-based actigraphy. By employing an extremely randomized trees (ET) algorithm and leave-one-subject-out cross-validation, a kappa score of 0.33 was achieved for the classification of AS, QS, and wake using cardiorespiratory features only. The kappa score significantly improved to 0.39 when incorporating eight video-based actigraphy features. Furthermore, the classification performance of AS and wake also improved, showing a kappa score increase of 0.21. These suggest that combining video-based actigraphy with cardiorespiratory signals can potentially enhance the performance of sleep-state classification in preterm infants. In addition, we highlighted the distinct strengths and limitations of video-based actigraphy and cardiorespiratory data in classifying specific sleep states

MRI in patients with a cerebral aneurysm clip; review of the literature and incident databases and recommendations for the Netherlands

Background: In the past ferromagnetic cerebral aneurysm clips that are contraindicated for Magnetic Resonance Imaging (MRI) have been implanted. However, the specific clip model is often unknown for older clips, which poses a problem for individual patient management in clinical care. Methods: Literature and incident databases were searched, and a survey was performed in the Netherlands that identified time periods at which ferromagnetic and non-ferromagnetic clip models were implanted. Considering this information in combination with a national expert opinion, we describe an approach for risk assessment prior to MRI examinations in patients with aneurysm clips. The manuscript is limited to MRI at 1.5 T or 3 T whole body MRI systems with a horizontal closed bore superconducting magnet, covering the majority of clinical Magnetic Resonance (MR) systems. Results: From the literature a list of ferromagnetic clip models was obtained. The risk of movement or rotation of the clip due to the main magnetic field in case of a ferromagnetic clip is the main concern. In the incident databases records of four serious incidents due to aneurysm clips in MRI were found. The survey in the Netherlands showed that from 2000 onwards, no ferromagnetic clips were implanted in Dutch hospitals. Discussion: Recommendations are provided to help the MR safety expert assessing the risks when a patient with a cerebral aneurysm clip is referred for MRI, both for known and unknown clip models. This work was part of the development of a guideline by the Dutch Association of Medical Specialists

Fractional anisotropy in white matter tracts of very-low-birth-weight infants

Background: Advances in neonatal intensive care have not yet reduced the high incidence of neurodevelopmental disability among very-low-birth-weight (VLBW) infants. As neurological deficits are related to white-matter injury, early detection is important. Diffusion tensor imaging (DTI) could be an excellent tool for assessment of white-matter injury. Objective: To provide DTI fractional anisotropy (FA) reference values for white-matter tracts of VLBW infants for clinical use. Materials and methods: We retrospectively analysed DTI images of 28 VLBW infants (26-32 weeks gestational age) without evidence of white-matter abnormalities on conventional MRI sequences, and normal developmental outcome (assessed at age 1-3 years). For DTI an echoplanar sequence with diffusion gradient (b = 1,000 s/mm2) applied in 25 non-collinear directions was used. We measured FA and apparent diffusion coefficient (ADC) of different white-matter tracts in the first 4 days of life. Results: A statistically significant correlation was found between gestational age and FA of the posterior limb of the internal capsule in VLBW infants (r = 0.495, P<0.01). Conclusion: Values of FA and ADC were measured in white-matter tracts of VLBW infants. FA of the pyramidal tracts measured in the first few days after birth is related to gestational age