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.

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.

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.

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.

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.

Quality assessment and response to less invasive surfactant administration (LISA) without sedation.

BACKGROUND: Although sedative premedication for endotracheal intubation is considered standard of care, less invasive surfactant administration (LISA) is often performed without sedative premedication. The aim of this study was to assess success rates, technical quality and vital parameters in LISA without sedative premedication. METHODS: Prospective observational study in 86 neonates <32 weeks' gestation. LISA was performed according to a standardized protocol without use of sedative premedication. Outcome measures were success rates of LISA attempts, reasons for failure and quality of technical conditions. In 37 neonates, heart rate and oxygen saturation levels from 20 min before until 30 min after start of LISA were collected. RESULTS: In 48% of LISAs the first attempt failed and in 34% quality of technical conditions was inadequate. The success rate was significantly correlated with quality of technical conditions and experience of the performer. Desaturations <80% occurred in 54% of patients while bradycardia <80/min did not occur. CONCLUSION: This study shows a relatively low success rate of the first attempt of LISA, frequent inadequacy of technical quality and frequent oxygen desaturations. These effects may be improved by the use of sedative premedication.

Changes in autonomic regulation due to Kangaroo care remain unaffected by using a swaddling device.

AIM: To investigate the effects of a swaddling device known as the Hugsy (Hugsy, Eindhoven, the Netherlands) towards improving autonomic regulation. This device can be used both in the incubator and during Kangaroo care to absorb parental scent and warmth. After Kangaroo care, these stimuli can continue to be experienced by infants, while in the incubator. Additionally, a pre-recorded heartbeat sound can be played. METHOD: Autonomic regulation was compared in preterm infants before, during and after Kangaroo care with and without the use of a swaddling device in a within-subject study carried out in a level III neonatal intensive care unit. Descriptive statistics and effect sizes were calculated corresponding to changes in heart rate, respiratory rate, oxygen saturation, temperature and heart rate variability on intervention versus control days. RESULTS: In this study of 20 infants with a median (interquartile range) gestational age of 28.4 (27-29.9) weeks, Kangaroo care was associated with a decrease in heart rate, respiratory rate and heart rate variability on both intervention and control days. There were no differences between intervention and control days. CONCLUSION: The use of an alternative swaddling device aimed at facilitating Kangaroo care did not enhance autonomic regulation, as measured by vital signs and heart rate variability.

Features of Heart Rate Variability Capture Regulatory Changes During Kangaroo Care in Preterm Infants.

OBJECTIVE: To determine whether heart rate variability (HRV) can serve as a surrogate measure to track regulatory changes during kangaroo care, a period of parental coregulation distinct from regulation within the incubator. STUDY DESIGN: Nurses annotated the starting and ending times of kangaroo care for 3 months. The pre-kangaroo care, during-kangaroo care, and post-kangaroo care data were retrieved in infants with at least 10 accurately annotated kangaroo care sessions. Eight HRV features (5 in the time domain and 3 in the frequency domain) were used to visually and statistically compare the pre-kangaroo care and during-kangaroo care periods. Two of these features, capturing the percentage of heart rate decelerations and the extent of heart rate decelerations, were newly developed for preterm infants. RESULTS: A total of 191 kangaroo care sessions were investigated in 11 preterm infants. Despite clinically irrelevant changes in vital signs, 6 of the 8 HRV features (SD of normal-to-normal intervals, root mean square of the SD, percentage of consecutive normal-to-normal intervals that differ by >50 ms, SD of heart rate decelerations, high-frequency power, and low-frequency/high-frequency ratio) showed a visible and statistically significant difference (P <.01) between stable periods of kangaroo care and pre-kangaroo care. HRV was reduced during kangaroo care owing to a decrease in the extent of transient heart rate decelerations. CONCLUSION: HRV-based features may be clinically useful for capturing the dynamic changes in autonomic regulation in response to kangaroo care and other changes in environment and state.

Identifying User Preferences for a Digital Educational Solution for Young Seniors With Diabetes.

The Eindhoven Diabetes Education Simulator project was initiated to develop an educational solution that helps diabetes patients understand and learn more about their diabetes. This article describes the identification of user preferences for the development of such solutions. Young seniors (aged 50-65 years) with type 2 diabetes were chosen as the target group because they are likely to have more affinity with digital devices than older people and because 88% of the Dutch diabetes population is >50 years of age. Data about the target group were gathered through literature research and interviews. The literature research covered data about their device use and education preferences. To gain insight into the daily life of diabetes patients and current diabetes education processes, 20 diabetes patients and 10 medical experts were interviewed. The interviews were analyzed using affinity diagrams. Those diagrams, together with the literature data, formed the basis for two personas and corresponding customer journey maps. Literature showed that diabetes prevalence is inversely correlated to educational level. Computer and device use is relatively low within the target group, but is growing. The interviews showed that young seniors like to play board, card, and computer games, with others or alone. Family and loved ones play an important role in their lives. Medical experts are crucial in the diabetes education of young senior diabetes patients. These findings are translated into a list of design aspects that can be used for creating educational solutions.

Fetal heart rate variability during pregnancy, obtained from non-invasive electrocardiogram recordings.

OBJECTIVE: Non-invasive spectral analysis of fetal heart rate variability is a promising new field of fetal monitoring. To validate this method properly, we studied the relationship between gestational age and the influence of fetal rest-activity state on spectral estimates of fetal heart rate variability. DESIGN: Prospective longitudinal study. SETTING: Tertiary care teaching hospital. POPULATION: Forty healthy women with an uneventful singleton pregnancy. METHODS: Non-invasive fetal electrocardiogram measurements via the maternal abdomen were performed at regular intervals between 14 and 40 weeks of gestation and processed to detect beat-to-beat fetal heart rate. Simultaneous ultrasound recordings were performed to assess fetal rest-activity state. MAIN OUTCOME MEASURES: Absolute and normalized power of fetal heart rate variability in the low (0.04-0.15 Hz) and high (0.4-1.5 Hz) frequency band were obtained, using Fourier Transform. RESULTS: 14% of all measurements and 3% of the total amount of abdominal data (330 segments) was usable for spectral analysis. During 21-30 weeks of gestation, a significant increase in absolute low and high frequency power was observed. During the active state near term, absolute and normalized low frequency power were significantly higher and normalized high frequency power was significantly lower compared with the quiet state. CONCLUSIONS: The observed increase in absolute spectral estimates in preterm fetuses was probably due to increased sympathetic and parasympathetic modulation and might be a sign of autonomic development. Further improvements in signal processing are needed before this new method of fetal monitoring can be introduced in clinical practice.

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.

Continuous prediction and clinical alarm management of late-onset sepsis in preterm infants using vital signs from a patient monitor.

BACKGROUND AND OBJECTIVE: Continuous prediction of late-onset sepsis (LOS) could be helpful for improving clinical outcomes in neonatal intensive care units (NICU). This study aimed to develop an artificial intelligence (AI) model for assisting the bedside clinicians in successfully identifying infants at risk for LOS using non-invasive vital signs monitoring. METHODS: In a retrospective study from the NICU of the Máxima Medical Center in Veldhoven, the Netherlands, a total of 492 preterm infants less than 32 weeks gestation were included between July 2016 and December 2018. Data on heart rate (HR), respiratory rate (RR), and oxygen saturation (SpO2) at 1 Hz were extracted from the patient monitor. We developed multiple AI models using 102 extracted features or raw time series to provide hourly LOS risk prediction. Shapley values were used to explain the model. For the best performing model, the effect of different vital signs and also the input type of signals on model performance was tested. To further assess the performance of applying the best performing model in a real-world clinical setting, we performed a simulation using four different alarm policies on continuous real-time predictions starting from three days after birth. RESULTS: A total of 51 LOS patients and 68 controls were finally included according to the patient inclusion and exclusion criteria. When tested by seven-fold cross-validations, the mean (standard deviation) area under the receiver operating characteristic curve (AUC) six hours before CRASH was 0.875 (0.072) for the best performing model, compared to the other six models with AUC ranging from 0.782 (0.089) to 0.846 (0.083). The best performing model performed only slightly worse than the model learning from raw physiological waveforms (0.886 [0.068]), successfully detecting 96.1 % of LOS patients before CRASH. When setting the expected alarm window to 24 h and using a multi-threshold alarm policy, the sensitivity metric was 71.6 %, while the positive predictive value was 9.9 %, resulting in an average of 1.15 alarms per day per patient. CONCLUSIONS: The proposed AI model, which learns from routinely collected vital signs, has the potential to assist clinicians in the early detection of LOS. Combined with interpretability and clinical alarm management, this model could be better translated into medical practice for future clinical implementation.

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.

Heart rate-mediated blood pressure control in preterm fetal sheep under normal and hypoxic-ischemic conditions.

BACKGROUND: The understanding of hypoxemia-induced changes in baroreflex function is limited and may be studied in a fetal sheep experiment before, during, and after standardized hypoxic conditions. METHODS: Preterm fetal lambs were instrumented at 102 d gestation (term: 146 d). At 106 d, intrauterine hypoxia--ischemia was induced by 25 min of umbilical cord occlusion (UCO). Baroreflex-related fluctuations were calculated at 30-min intervals during the first week after UCO by transfer function (cross-spectral) analysis between systolic blood pressure (SBP) and R-R interval fluctuations, estimated in the low-frequency (LF, 0.04-0.15 Hz) band. LF transfer gain (baroreflex sensitivity) and delay (s) reflect the baroreflex function. RESULTS: Baseline did not differ in LF transfer gain and delay between controls and the UCO group. In controls, LF gain showed postnatal increase. By contrast, LF gain gradually decreased in the UCO group, resulting in significantly lower values 4-7 d after UCO. In the UCO group, LF delay increased and differed significantly from controls. CONCLUSION: Our results show that intrauterine hypoxia-ischemia results in reduced baroreflex sensitivity over a period of 7 d, indicating limited efficacy to buffer BP changes by adapting heart rate. Cardiovascular dysregulation may augment already present cerebral damage after systemic hypoxia-ischemia in the reperfusion period.

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.

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.

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.

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.

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.

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.