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.

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 lengthL≥ 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.

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.

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.

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.

A New Algorithm for Automatically Calculating Noise, Spatial Resolution, and Contrast Image Quality Metrics: Proof-of-Concept and Agreement With Subjective Scores in Phantom and Clinical Abdominal CT.

OBJECTIVES: The aims of this study were to develop a proof-of-concept computer algorithm to automatically determine noise, spatial resolution, and contrast-related image quality (IQ) metrics in abdominal portal venous phase computed tomography (CT) imaging and to assess agreement between resulting objective IQ metrics and subjective radiologist IQ ratings. MATERIALS AND METHODS: An algorithm was developed to calculate noise, spatial resolution, and contrast IQ parameters. The algorithm was subsequently used on 2 datasets of anthropomorphic phantom CT scans, acquired on 2 different scanners (n = 57 each), and on 1 dataset of patient abdominal CT scans (n = 510). These datasets include a range of high to low IQ: in the phantom dataset, this was achieved through varying scanner settings (tube voltage, tube current, reconstruction algorithm); in the patient dataset, lower IQ images were obtained by reconstructing 30 consecutive portal venous phase scans as if they had been acquired at lower mAs. Five noise, 1 spatial, and 13 contrast parameters were computed for the phantom datasets; for the patient dataset, 5 noise, 1 spatial, and 18 contrast parameters were computed. Subjective IQ rating was done using a 5-point Likert scale: 2 radiologists rated a single phantom dataset each, and another 2 radiologists rated the patient dataset in consensus. General agreement between IQ metrics and subjective IQ scores was assessed using Pearson correlation analysis. Likert scores were grouped into 2 categories, "insufficient" (scores 1-2) and "sufficient" (scores 3-5), and differences in computed IQ metrics between these categories were assessed using the Mann-Whitney U test. RESULTS: The algorithm was able to automatically calculate all IQ metrics for 100% of the included scans. Significant correlations with subjective radiologist ratings were found for 4 of 5 noise ( R2 range = 0.55-0.70), 1 of 1 spatial resolution ( R2 = 0.21 and 0.26), and 10 of 13 contrast ( R2 range = 0.11-0.73) parameters in the phantom datasets and for 4 of 5 noise ( R2 range = 0.019-0.096), 1 of 1 spatial resolution ( R2 = 0.11), and 16 of 18 contrast ( R2 range = 0.008-0.116) parameters in the patient dataset. Computed metrics that significantly differed between "insufficient" and "sufficient" categories were 4 of 5 noise, 1 of 1 spatial resolution, 9 and 10 of 13 contrast parameters for phantom the datasets and 3 of 5 noise, 1 of 1 spatial resolution, and 10 of 18 contrast parameters for the patient dataset. CONCLUSION: The developed algorithm was able to successfully calculate objective noise, spatial resolution, and contrast IQ metrics of both phantom and clinical abdominal CT scans. Furthermore, multiple calculated IQ metrics of all 3 categories were in agreement with subjective radiologist IQ ratings and significantly differed between "insufficient" and "sufficient" IQ scans. These results demonstrate the feasibility and potential of algorithm-determined objective IQ. Such an algorithm should be applicable to any scan and may help in optimization and quality control through automatic IQ assessment in daily clinical practice.

A Continuous Late-Onset Sepsis Prediction Algorithm for Preterm Infants Using Multi-Channel Physiological Signals From a Patient Monitor.

The aim of this study is to develop an explainable late-onset sepsis (LOS) prediction algorithm using continuous multi-channel physiological signals that can be applied to a patient monitor for preterm infants in a neonatal intensive care unit (NICU). The algorithm uses features on heart rate variability (HRV), respiration, and motion, based on electrocardiogram (ECG) and chest impedance (CI). In this study, 127 preterm infants were included, of whom 59 were bloodculture-proven LOS patients and 68 were control patients. Features in 24 hours before the onset of sepsis (LOS group), and an age-matched onset time point (control group) were extracted and fed into machine learning classifiers with gestational age and birth weight. We compared the prediction performance of several well-known classifiers using features from different signal channels (HRV, respiration, and motion) individually as well as their combinations. The prediction performance was evaluated using the area under the receiver-operating-characteristics curve (AUC). The best performance was achieved by an extreme gradient boosting classifier combining features from all signal channels, with an AUC of 0.88, a positive predictive value of 0.80, and a negative predictive value of 0.83 during the 6 hours preceding LOS onset. This feasibility study demonstrates the complementary predictive value of motion information in addition to cardiorespiratory information for LOS prediction. Furthermore, visualization of how each feature in the individual patient impacts the algorithm decision strengthen its interpretability. In clinical practice, it is important to motivate clinical interventions and this visualization method can help to support the clinical decision.

Predictive Intelligent Control of Oxygenation in Preterm Infants: A Two-Center Feasibility Study.

INTRODUCTION: Supplemental oxygen therapy is a mainstay of modern neonatal intensive care for preterm infants. However, both insufficient and excess oxygen delivery are associated with adverse outcomes. Automated or closed loop FiO2 control has been developed to keep SpO2 within a predefined target range more effectively. METHODS: The aim of this study was to investigate the feasibility of closed loop FiO2 control by Predictive Intelligent Control of Oxygenation (PRICO) on the Fabian ventilator in maintaining SpO2 within a target range (88/89-95%) in preterm infants on different modes of invasive and noninvasive respiratory support. In two tertiary neonatal intensive care units, preterm infants with an FiO2 >0.21 were included and received an 8 h nonblinded treatment period of closed loop FiO2 control by PRICO, flanked by two 8 h control periods of routine manual control (RMC1 and RMC2). RESULTS: 32 preterm infants were included (median gestational age 26 + 5 weeks [IQR 25 + 5-27 + 6], median birthweight 828 grams [IQR 704-930]). Six patients received invasive respiratory support, while 26 received noninvasive respiratory support (18 CPAP, 4 DuoPAP, and 4 nasal IMV). The time percentage within the SpO2 target range was increased with PRICO (74.4% [IQR 67.8-78.5]) compared to RMC1 (65.8% [IQR 51.1-77.8]; p = 0.011) and RMC2 (60.6% [IQR 56.2-66.6]; p < 0.001) with an estimated median difference of 6.0% (95% CI 1.2-11.5) and 9.8% (95% CI 6.0-13.0), respectively. CONCLUSION: In preterm infants on invasive and noninvasive respiratory supports, closed loop FiO2 control by PRICO compared to RMC is feasible and superior in maintaining SpO2 within target ranges.

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.

A Comparison of Video-based Methods for Neonatal Body Motion Detection.

Preterm infants in a neonatal intensive care unit (NICU) are continuously monitored for their vital signs, such as heart rate and oxygen saturation. Body motion patterns are documented intermittently by clinical observations. Changing motion patterns in preterm infants are associated with maturation and clinical events such as late-onset sepsis and seizures. However, continuous motion monitoring in the NICU setting is not yet performed. Video-based motion monitoring is a promising method due to its non-contact nature and therefore unobtrusiveness. This study aims to determine the feasibility of simple video-based methods for infant body motion detection. We investigated and compared four methods to detect the motion in videos of infants, using two datasets acquired with different types of cameras. The thermal dataset contains 32 hours of annotated videos from 13 infants in open beds. The RGB dataset contains 9 hours of annotated videos from 5 infants in incubators. The compared methods include background substruction (BS), sparse optical flow (SOF), dense optical flow (DOF), and oriented FAST and rotated BRIEF (ORB). The detection performance and computation time were evaluated by the area under receiver operating curves (AUC) and run time. We conducted experiments to detect motion and gross motion respectively. In the thermal dataset, the best performance of both experiments is achieved by BS with mean (standard deviation) AUCs of 0.86 (0.03) and 0.93 (0.03). In the RGB dataset, SOF outperforms the other methods in both experiments with AUCs of 0.82 (0.10) and 0.91 (0.05). All methods are efficient to be integrated into a camera system when using low-resolution thermal cameras.

Effect of Music Therapy Interventions on Heart Rate Variability in Premature Infants.

Premature infants are at risk of developing serious complications after birth. Communicative interventions performed in neonatal intensive care units (NICUs), such as music therapy interventions, can reduce the stress experienced by these infants and promote the development of their autonomic nervous system. In this study we investigated the effects of music therapy interventions, consisting of singing, humming, talking or rhythmic reading, on premature infants by investigating the effects on their heart rate variability (HRV). A total of 27 communicative intervention from 18 patients were included in this study. The NN-intervals were extracted from the ECG and the mean ± SEM values for the 6 different features (HR, SDNN, RMSSD, pNN50, pDec and SDDec) was investigated. Median feature values for the pre- and communicative intervention were compared using the Wilcoxon signed-rank test. An increase in values for the SDNN, RMSSD and pNN50 was found in the 20 minutes preceding the communicative intervention, when caregiving activities were performed, and was followed by an immediate decrease at the start of the intervention. Features' variability during the intervention appeared to be smaller than in the pre-communicative intervention, indicating improved autonomic regulation. This difference was, however, not statistically significant possibly due to different types of activities applied during the communicative intervention per patient.

The effect of apnea length on vital parameters in apnea of prematurity - Hybrid observations from clinical data and simulation in a mathematical model.

Apnea of prematurity (AOP) is a critical condition for preterm infants which can lead to several adverse outcomes. Despite its relevance, mechanisms underlying AOP are still unclear. In this work we aimed at improving the understanding of AOP and its physiologic responses by analyzing and comparing characteristics of real infant data and model-based simulations of AOP. We implemented an existing algorithm to extract apnea events originating from the central nervous system from a population of 26 premature infants (1248 h of data in total) and investigated oxygen saturation (SpO2) and heart rate (HR) of the infants around these events. We then extended a previously developed cardio-vascular model to include the lung mechanics and gas exchange. After simulating the steady state of a preterm infant, which successfully replicated results described in previous literature studies, the extended model was used to simulate apneas with different lengths caused by a stop in respiratory muscles. Apneas identified by the algorithm and simulated by the model showed several similarities, including a far deeper decrease in SpO2, with the minimum reached later in time, in case of longer apneas. Results also showed some differences, either due to how measures are performed in clinical practice in our neonatal intensive care unit (e.g. delayed detection of decline in SpO2 after apnea onset due to signal averaging) or to the limited number of very long apneas (≥80 s) identified in our dataset.

Quantifying the Sensitivity of Target Dose on Intrafraction Displacement in Intracranial Stereotactic Radiosurgery.

PURPOSE: Mask-immobilized stereotactic radiosurgery (SRS) using a gating window is an emerging technology. However, the amount of intracranial tumor motion that can be tolerated during treatment while satisfying clinical dosimetric goals is unknown. The purpose of this study was to quantify the sensitivity of target dose to tumor motion. METHODS AND MATERIALS: In clinical SRS plans, where a nose marker was tracked as surrogate for target motion, translational and rotational target movements were simulated using nose-marker displacements of ±0.5 mm, ±1.0 mm, or ±1.5 mm. The effect on minimum dose to 99% of the target (D99) and percent target coverage by prescription dose was quantified using mixed-effect modeling with variables: displacement, target volume, and location. RESULTS: The effect on dose metrics is statistically larger for translational displacements compared with rotational displacements, and the effect of pitch rotations is statistically larger compared with yaw rotations. The mixed-effect model for translations showed that displacement and target volume are statistically significant variables, for rotation the variable target distance to rotation axis is additionally significant. For mean target volume (12.6 cc) and translational nose-marker displacements of 0.5 mm, 1.0 mm, and 1.5 mm, D99 decreased by 2.2%, 7.1%, and 13.0%, and coverage by 0.4%, 1.8%, and 4.4%, respectively. For mean target volume, mean distance midpoint-target to pitch axis (7.6cm), and rotational nose-marker displacement of 0.5 mm, 1.0 mm, and 1.5 mm, D99 decreased by 1.0%, 3.6%, and 6.9%, and coverage by 0.2%, 0.8%, and 1.9%, respectively. For rotational yaw axis displacement, mean distance midpoint-target axis (4.2cm), D99 decreased by 0.3%, 1.2%, and 2.5%, and coverage by 0.1%, 0.2%, and 0.5%, respectively. CONCLUSIONS: Simulated target displacements showed that sensitivity of tumor dose to motion depends on both target volume and target location. Suggesting that patient- and target-specific thresholds may be implemented for optimizing the balance between dosimetric plan accuracy and treatment prolongation caused by out-of-tolerance motion.

Body Motion Detection in Neonates Based on Motion Artifacts in Physiological Signals from a Clinical Patient Monitor.

Motion patterns in newborns contain important information. Motion patterns change upon maturation and changes in the nature of motion may precede critical clinical events such as the onset of sepsis, seizures and apneas. However, in clinical practice, motion monitoring is still limited to observations by caregivers. In this study, we investigated a practical yet reliable method for motion detection using routinely used physiological signals in the patient monitor. Our method calculated motion measures with a continuous wavelet transform (CWT) and a signal instability index (SII) to detect gross-motor motion in 15 newborns using 40 hours of physiological data with annotated videos. We compared the performance of these measures on three signal modalities (electrocardiogram ECG, chest impedance, and photo plethysmography). In addition, we investigated whether their combinations increased performance. The best performance was achieved with the ECG signal with a median (interquartile range, IQR) area under receiver operating curve (AUC) of 0.92(0.87-0.95), but differences were small as both measures had a robust performance on all signal modalities. We then applied the algorithm on combined measures and modalities. The full combination outperformed all single-modal methods with a median (IQR) AUC of 0.95(0.91-0.96) when discriminating gross-motor motion from still. Our study demonstrates the feasibility of gross-motor motion detection method based on only clinically-available vital signs and that best results can be obtained by combining measures and vital signs.

Automatic Separation of Respiratory Flow from Motion in Thermal Videos for Infant Apnea Detection.

Both Respiratory Flow (RF) and Respiratory Motion (RM) are visible in thermal recordings of infants. Monitoring these two signals usually requires landmark detection for the selection of a region of interest. Other approaches combine respiratory signals coming from both RF and RM, obtaining a Mixed Respiratory (MR) signal. The detection and classification of apneas, particularly common in preterm infants with low birth weight, would benefit from monitoring both RF and RM, or MR, signals. Therefore, we propose in this work an automatic RF pixel detector not based on facial/body landmarks. The method is based on the property of RF pixels in thermal videos, which are in areas with a smooth circular gradient. We defined 5 features combined with the use of a bank of Gabor filters that together allow selection of the RF pixels. The algorithm was tested on thermal recordings of 9 infants amounting to a total of 132 min acquired in a neonatal ward. On average the percentage of correctly identified RF pixels was 84%. Obstructive Apneas (OAs) were simulated as a proof of concept to prove the advantage in monitoring the RF signal compared to the MR signal. The sensitivity in the simulated OA detection improved for the RF signal reaching 73% against the 23% of the MR signal. Overall, the method yielded promising results, although the positioning and number of cameras used could be further optimized for optimal RF visibility.

Comparing Ventilation Parameters for COVID-19 Patients Using Both Long-Term ICU and Anesthetic Ventilators in Times of Shortage.

In the first months of the COVID-19 pandemic in Europe, many patients were treated in hospitals using mechanical ventilation. However, due to a shortage of ICU ventilators, hospitals worldwide needed to deploy anesthesia machines for ICU ventilation (which is off-label use). A joint guidance was written to apply anesthesia machines for long-term ventilation. The goal of this research is to retrospectively evaluate the differences in measurable ventilation parameters between the ICU ventilator and the anesthesia machine as used for COVID-19 patients. In this study, we included 32 patients treated in March and April 2020, who had more than 3 days of mechanical ventilation, either in the regular ICU with ICU ventilators (Hamilton S1), or in the temporary emergency ICU with anesthetic ventilators (Aisys, GE). The data acquired during regular clinical treatment was collected from the Patient Data Management Systems. Available ventilation parameters (pressures and volumes: PEEP, Ppeak, Pinsp, Vtidal), monitored parameters EtCO2, SpO2, derived compliance C, and resistance R were processed and analyzed. A sub-analysis was performed to compare closed-loop ventilation (INTELLiVENT-ASV) to other ventilation modes. The results showed no major differences in the compared parameters, except for Pinsp. PEEP was reduced over time in the with Hamilton treated patients. This is most likely attributed to changing clinical protocol as more clinical experience and literature became available. A comparison of compliance between the 2 ventilators could not be made due to variances in the measurement of compliance. Closed loop ventilation could be used in 79% of the time, resulting in more stable EtCO2. From the analysis it can be concluded that the off-label usage of the anesthetic ventilator in our hospital did not result in differences in ventilation parameters compared to the ICU treatment in the first 4 days of ventilation.

Predictive Intelligent Control of Oxygenation (PRICO) in preterm infants on high flow nasal cannula support: a randomised cross-over study.

OBJECTIVE: To investigate the efficacy of automated control of inspired oxygen (FiO2) by Predictive Intelligent Control of Oxygenation (PRICO) on the Fabian ventilator in maintaining oxygen saturation (SpO2) in preterm infants on high flow nasal cannula (HFNC) support. DESIGN: Single-centre randomised two-period crossover study. SETTING: Tertiary neonatal intensive care unit. PATIENTS: 27 preterm infants (gestational age (GA) <30 weeks) on HFNC support with FiO2 >0.25. INTERVENTION: A 24-hour period on automated FiO2-control with PRICO compared with a 24-hour period on routine manual control (RMC) to maintain a SpO2 level within target range of 88%-95% measured at 30 s intervals. MAIN OUTCOME MEASURES: Primary outcome: time spent within target range (88%-95%). SECONDARY OUTCOMES: time spent above and below target range, in severe hypoxia (SpO2 <80%) and hyperoxia (SpO2 >98%), mean SpO2 and FiO2 and manual FiO2 adjustments. RESULTS: 15 patients received PRICO-RMC and 12 RMC-PRICO. The mean time within the target range increased with PRICO: 10.8% (95% CI 7.6 to 13.9). There was a decrease in time below target range: 7.6% (95% CI 4.2 to 11.0), above target range: 3.1% (95% CI 2.9 to 6.2) and in severe hypoxia: 0.9% (95% CI 1.5 to 0.2). We found no difference in time spent in severe hyperoxia. Mean FiO2 was higher during PRICO: 0.019 (95% CI 0.006 to 0.030). With PRICO there was a reduction of manual adjustments: 9/24 hours (95% CI 6 to 12). CONCLUSION: In preterm infants on HFNC support, automated FiO2-control by PRICO is superior to RMC in maintaining SpO2 within target range. Further validation studies with a higher sample frequency and different ventilation modes are needed.

Towards Continuous Camera-Based Respiration Monitoring in Infants.

Aiming at continuous unobtrusive respiration monitoring, motion robustness is paramount. However, some types of motion can completely hide the respiration information and the detection of these events is required to avoid incorrect rate estimations. Therefore, this work proposes a motion detector optimized to specifically detect severe motion of infants combined with a respiration rate detection strategy based on automatic pixels selection, which proved to be robust to motion of the infants involving head and limbs. A dataset including both thermal and RGB (Red Green Blue) videos was used amounting to a total of 43 h acquired on 17 infants. The method was successfully applied to both RGB and thermal videos and compared to the chest impedance signal. The Mean Absolute Error (MAE) in segments where some motion is present was 1.16 and 1.97 breaths/min higher than the MAE in the ideal moments where the infants were still for testing and validation set, respectively. Overall, the average MAE on the testing and validation set are 3.31 breaths/min and 5.36 breaths/min, using 64.00% and 69.65% of the included video segments (segments containing events such as interventions were excluded based on a manual annotation), respectively. Moreover, we highlight challenges that need to be overcome for continuous camera-based respiration monitoring. The method can be applied to different camera modalities, does not require skin visibility, and is robust to some motion of the infants.

Prediction of Late-Onset Sepsis in Preterm Infants Using Monitoring Signals and Machine Learning.

OBJECTIVES: Prediction of late-onset sepsis (onset beyond day 3 of life) in preterm infants, based on multiple patient monitoring signals 24 hours before onset. DESIGN: Continuous high-resolution electrocardiogram and respiration (chest impedance) data from the monitoring signals were extracted and used to create time-interval features representing heart rate variability, respiration, and body motion. For each infant with a blood culture-proven late-onset sepsis, a Cultures, Resuscitation, and Antibiotics Started Here moment was defined. The Cultures, Resuscitation, and Antibiotics Started Here moment served as an anchor point for the prediction analysis. In the group with controls (C), an "equivalent crash moment" was calculated as anchor point, based on comparable gestational and postnatal age. Three common machine learning approaches (logistic regressor, naive Bayes, and nearest mean classifier) were used to binary classify samples of late-onset sepsis from C. For training and evaluation of the three classifiers, a leave-k-subjects-out cross-validation was used. SETTING: Level III neonatal ICU. PATIENTS: The patient population consisted of 32 premature infants with sepsis and 32 age-matched control patients. INTERVENTIONS: No interventions were performed. MEASUREMENTS AND MAIN RESULTS: For the interval features representing heart rate variability, respiration, and body motion, differences between late-onset sepsis and C were visible up to 5 hours preceding the Cultures, Resuscitation, and Antibiotics Started Here moment. Using a combination of all features, classification of late-onset sepsis and C showed a mean accuracy of 0.79 ± 0.12 and mean precision rate of 0.82 ± 0.18 3 hours before the onset of sepsis. CONCLUSIONS: Information from routine patient monitoring can be used to predict sepsis. Specifically, this study shows that a combination of electrocardiogram-based, respiration-based, and motion-based features enables the prediction of late-onset sepsis hours before the clinical crash moment.

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.