Use and Waste of Reconstituted Whole Blood Exchange Transfusions: An 11-year National Observational Study.

OBJECTIVES: To identify indications for exchange transfusions, assess the use and waste of exchange transfusion products (ie, reconstituted whole blood exchange transfusions), and determine nationwide distribution and prevalence of these transfusions in the Netherlands. STUDY DESIGN: All 9 neonatal intensive care units and 15 non-neonatal intensive care unit hospitals participated in this retrospective, observational, cohort study. We retrieved data on the indications for and use of all exchange transfusion products ordered by participating centers over an 11-year period. RESULTS: A total of 574 patients for whom 1265 products were ordered were included for analyses. Severe ABO (32.6%) and non-ABO (25.2%) immune hemolysis and subsequent hyperbilirubinemia were the most frequent indications. Rare indications were severe leukocytosis in Bordetella pertussis (2.1%) and severe anemia (1.5%). Approximately one-half of all ordered products remained unused. In 278 of 574 neonates (48.4%), ≥1 products were not used, of which 229 (82.7%) were due to the resolving of severe hyperbilirubinemia with further intensification of phototherapy. The overall prevalence of neonates who received an exchange transfusion was 14.6:100 000 liveborn neonates. CONCLUSIONS: A considerable proportion of products remained unused, and annually a limited number of patients are treated with an exchange transfusion in the Netherlands, highlighting the rarity of the procedure in the Netherlands.

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.

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.

Neurodevelopmental outcome at 5.5 years in Dutch preterm infants born at 24-26 weeks' gestational age: the EPI-DAF study.

OBJECTIVE: After lowering the Dutch threshold for active treatment from 25 to 24 completed weeks' gestation, survival to discharge increased by 10% in extremely preterm live born infants. Now that this guideline has been implemented, an accurate description of neurodevelopmental outcome at school age is needed. DESIGN: Population-based cohort study. SETTING: All neonatal intensive care units in the Netherlands. PATIENTS: All infants born between 240/7 and 266/7 weeks' gestation who were 5.5 years' corrected age (CA) in 2018-2020 were included. MAIN OUTCOME MEASURES: Main outcome measure was neurodevelopmental outcome at 5.5 years. Neurodevelopmental outcome was a composite outcome defined as none, mild or moderate-to-severe impairment (further defined as neurodevelopmental impairment (NDI)), using corrected cognitive score (Wechsler Preschool and Primary Scale of Intelligence Scale-III-NL), neurological examination and neurosensory function. Additionally, motor score (Movement Assessment Battery for Children-2-NL) was assessed. All assessments were done as part of the nationwide, standardised follow-up programme. RESULTS: In the 3-year period, a total of 632 infants survived to 5.5 years' CA. Data were available for 484 infants (77%). At 5.5 years' CA, most cognitive and motor (sub)scales were significantly lower compared with the normative mean. Overall, 46% had no impairment, 36% had mild impairment and 18% had NDI. NDI-free survival was 30%, 49% and 67% in live born children at 24, 25 and 26 weeks' gestation, respectively (p<0.001). CONCLUSIONS: After lowering the threshold for supporting active treatment from 25 to 24 completed weeks' gestation, a considerable proportion of the surviving extremely preterm children did not have any impairment at 5.5 years' CA.

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.

Opportunities and Challenges of Prognostic Models for Extremely Preterm Infants.

Predicting the short- and long-term outcomes of extremely preterm infants remains a challenge. Multivariable prognostic models might be valuable tools for clinicians, parents, and policymakers for providing accurate outcome estimates. In this perspective, we discuss the opportunities and challenges of using prognostic models in extremely preterm infants at population and individual levels. At a population level, these models could support the development of guidelines for decisions about treatment limits and may support policy processes such as benchmarking and resource allocation. At an individual level, these models may enhance prenatal counselling conversations by considering multiple variables and improving transparency about expected outcomes. Furthermore, they may improve consistency in projections shared with parents. For the development of prognostic models, we discuss important considerations such as predictor and outcome measure selection, clinical impact assessment, and generalizability. Lastly, future recommendations for developing and using prognostic models are suggested. Importantly, the purpose of a prognostic model should be clearly defined, and integrating these models into prenatal counselling requires thoughtful consideration.

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.

Rapid exome sequencing as a first-tier test in neonates with suspected genetic disorder: results of a prospective multicenter clinical utility study in the Netherlands.

The introduction of rapid exome sequencing (rES) for critically ill neonates admitted to the neonatal intensive care unit has made it possible to impact clinical decision-making. Unbiased prospective studies to quantify the impact of rES over routine genetic testing are, however, scarce. We performed a clinical utility study to compare rES to conventional genetic diagnostic workup for critically ill neonates with suspected genetic disorders. In a multicenter prospective parallel cohort study involving five Dutch NICUs, we performed rES in parallel to routine genetic testing for 60 neonates with a suspected genetic disorder and monitored diagnostic yield and the time to diagnosis. To assess the economic impact of rES, healthcare resource use was collected for all neonates. rES detected more conclusive genetic diagnoses than routine genetic testing (20% vs. 10%, respectively), in a significantly shorter time to diagnosis (15 days (95% CI 10-20) vs. 59 days (95% CI 23-98, p < 0.001)). Moreover, rES reduced genetic diagnostic costs by 1.5% (€85 per neonate). CONCLUSION:  Our findings demonstrate the clinical utility of rES for critically ill neonates based on increased diagnostic yield, shorter time to diagnosis, and net healthcare savings. Our observations warrant the widespread implementation of rES as first-tier genetic test in critically ill neonates with disorders of suspected genetic origin. WHAT IS KNOWN: • Rapid exome sequencing (rES) enables diagnosing rare genetic disorders in a fast and reliable manner, but retrospective studies with neonates admitted to the neonatal intensive care unit (NICU) indicated that genetic disorders are likely underdiagnosed as rES is not routinely used. • Scenario modeling for implementation of rES for neonates with presumed genetic disorders indicated an expected increase in costs associated with genetic testing. WHAT IS NEW: • This unique prospective national clinical utility study of rES in a NICU setting shows that rES obtained more and faster diagnoses than conventional genetic tests. • Implementation of rES as replacement for all other genetic tests does not increase healthcare costs but in fact leads to a reduction in healthcare costs.

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.

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.

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.

Short-Term Outcome after Repeated Less Invasive Surfactant Administration: A Retrospective Cohort Study.

INTRODUCTION: Less invasive surfactant administration (LISA) to preterm infants is associated with decreased risk for death or BPD. After LISA, a considerable proportion requires a second dose of surfactant because of ongoing respiratory distress syndrome, raising a clinical dilemma between intubation or performing a repeated LISA (re-LISA) procedure. We aim to assess efficacy of re-LISA in avoiding subsequent nasal continuous positive airway pressure failure (need for intubation in the first 72 h of life; CPAP-F), to identify factors associated with subsequent CPAP-F, and to compare short-term outcomes following re-LISA to surfactant retreatment by endotracheal intubation and mechanical ventilation. METHODS: This was an observational retrospective study in two Dutch NICUs. Inclusion criterion was infants with gestational age <32 0/7 weeks requiring a second surfactant dose. Multivariate logistic regression analysis was performed. RESULTS: Of 209 infants requiring second surfactant dose, 132 received re-LISA. Subsequent CPAP-F was observed in 56 (42%) infants and was associated with extreme prematurity (OR 2.6, 95% CI: 1.2-5.8) and FiO2>0.5 (OR 5.4, 95% CI: 2.0-14.7). Infants receiving re-LISA had a lower risk of death or BPD compared to infants intubated for the second surfactant dose (OR 0.4, 95% CI: 0.2-0.9). Infants with CPAP-F after re-LISA had similar outcomes compared to those intubated for second surfactant dose. CONCLUSION: Re-LISA is effective in reducing CPAP-F and is associated with lower risk of death or BPD compared to retreatment via an endotracheal tube. Infants failing CPAP after re-LISA have similar outcomes compared to intubated infants. These findings support the use of re-LISA in preterm infants with ongoing RDS.

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.

Preterm Birth is Associated with Lower Academic Attainment at Age 12 Years: A Matched Cohort Study by Linkage of Population-Based Datasets.

OBJECTIVE: To compare academic attainment at age 12 years in preterm children born below 30 weeks of gestation with matched term-born peers, using standardized, nationwide and well-validated school tests. STUDY DESIGN: This population-based, national cohort study was performed by linking perinatal data from the nationwide Netherlands Perinatal Registry with educational outcome data from Statistics Netherlands and included 4677 surviving preterm children born at 250/7-296/7 weeks of gestational age and 366 561 controls born at 40 weeks of gestational age in 2000-2007. First, special education participation rate was calculated. Subsequently, all preterm children with academic attainment test data derived at age 12 years were matched to term-born children using year and month of birth, sex, parity, socioeconomic status, and maternal age. Total, language, and mathematics test scores and secondary school level advice were compared between these 2 groups. RESULTS: Children below 30 weeks of gestation had a higher special education participation rate (10.2% vs 2.7%, P < .001) than term-born peers. Preterm children had lower total (-0.37 SD; 95% CI -0.42 to -0.31), language (-0.21 SD; 95% CI -0.27 to -0.15), and mathematics (-0.45 SD; 95%CI -0.51 to -0.38) z scores, and more often a prevocational secondary school level advice (62% vs 46%, P < .001). CONCLUSIONS: A substantial proportion of children born before 30 weeks of gestation need special education at the end of elementary schooling. These children have significant deficits on all measures of academic attainment at age 12 years, especially mathematics, compared with matched term-born peers.

Two-year neurodevelopmental outcome in children born extremely preterm: the EPI-DAF study.

OBJECTIVE: In 2010, the Dutch practice regarding initiation of active treatment in extremely preterm infants was lowered from 25 completed weeks' to 24 completed weeks' gestation. The nationwide Extremely Preterm Infants - Dutch Analysis on Follow-up Study was set up to provide up-to-date data on neurodevelopmental outcome at 2 years' corrected age (CA) after this guideline change. Design: National cohort study. PATIENTS: All live born infants between 240/7 weeks' and 266/7 weeks' gestational age who were 2 years' CA in 2018-2020. MAIN OUTCOME MEASURE: Impairment at 2 years' CA, based on cognitive score (Bayley-III-NL), neurological examination and neurosensory function. RESULTS: 651 of 991 live born infants (66%) survived to 2 years' CA, with data available for 554 (85%). Overall, 62% had no impairment, 29% mild impairment and 9% moderate-to-severe impairment (further defined as neurodevelopmental impairment, NDI). The percentage of survivors with NDI was comparable for infants born at 24 weeks', 25 weeks' and 26 weeks' gestation. After multivariable analysis, severe brain injury and low maternal education were associated with higher odds on NDI. NDI-free survival was 48%, 67% and 75% in neonatal intensive care unit (NICU)-admitted infants at 24, 25 and 26 weeks' gestation, respectively. CONCLUSIONS: Lowering the threshold has not been accompanied by a large increase in moderate-to-severely impaired infants. Among live-born and NICU-admitted infants, an increase in NDI-free survival was observed from 24 weeks' to 26 weeks' gestation. This description of a national cohort with high follow-up rates gives an accurate description of the range of outcomes that may occur after extremely preterm birth.

Toddler motor performance and intelligence at school age in preterm born children: A longitudinal cohort study.

BACKGROUND: Current knowledge regarding differences in verbal intelligence scores (VIQ) and performance intelligence scores (PIQ) in preterm born children is limited. As early motor performance may be essential for developing later visual-perceptual and visual-motor skills, early motor performance may be associated with PIQ. AIMS: To evaluate whether in preterm born children motor performance at two years was associated with PIQ at eight years. METHODS: Single-centre cohort study including 88 children born <30 weeks' gestation between 2007 and 2011, who completed the Bayley Scales of Infant and Toddler Development-III (BSID-III) at two years and the Wechsler Intelligence Scale for Children-III-NL (WISC-III-NL) at eight years. Outcome measurements (mean (SD)) were gross and fine motor performance based on the BSID-III, and PIQ and VIQ based on the WISC-III-NL. Linear regression analysis was performed to evaluate the association between motor performance at two years and PIQ at eight years. RESULTS: At two years, mean BSID-III gross motor scaled score was 9.0 (SD 3.0) and fine motor score was 11.5 (SD 2.3). At eight years, mean PIQ was 94.9 (SD 13.5) and mean VIQ 101.8 (SD 13.7). A one-point increase in fine motor scaled score was associated with 1.7 points (95% CI 0.5-2.8) increase in PIQ. Gross motor scaled score was not associated with PIQ. CONCLUSIONS: Fine motor performance in toddlerhood was related to PIQ at school age, with lower scores indicating a lower PIQ. Early assessment of fine motor performance may be beneficial in identifying children at risk for lower performance intelligence.

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.

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

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