Clinical Decision Support for Improved Neonatal Care: The Development of a Machine Learning Model for the Prediction of Late-onset Sepsis and Necrotizing Enterocolitis.

OBJECTIVE: To develop an artificial intelligence-based software system for predicting late-onset sepsis (LOS) and necrotizing enterocolitis (NEC) in infants admitted to the neonatal intensive care unit (NICU). STUDY DESIGN: Single-center, retrospective cohort study, conducted in the NICU of the Antwerp University Hospital. Continuous monitoring data of 865 preterm infants born at <32 weeks gestational age, admitted to the NICU in the first week of life, were used to train an XGBoost machine learning (ML) algorithm for LOS and NEC prediction in a cross-validated setup. Afterward, the model's performance was assessed on an independent test set of 148 patients (internal validation). RESULTS: The ML model delivered hourly risk predictions with an overall sensitivity of 69% (142/206) for all LOS/NEC episodes and 81% (67/83) for severe LOS/NEC episodes. The model showed a median time gain of ≤10 hours (IQR, 3.1-21.0 hours), compared with historical clinical diagnosis. On the complete retrospective dataset, the ML model made 721 069 predictions, of which 9805 (1.3%) depicted a LOS/NEC probability of ≥0.15, resulting in a total alarm rate of <1 patient alarm-day per week. The model reached a similar performance on the internal validation set. CONCLUSIONS: Artificial intelligence technology can assist clinicians in the early detection of LOS and NEC in the NICU, which potentially can result in clinical and socioeconomic benefits. Additional studies are required to quantify further the effect of combining artificial and human intelligence on patient outcomes in the NICU.

Cerebral oxygen saturation and autoregulation during hypotension in extremely preterm infants.

BACKGROUND: The impact of the permissive hypotension approach in clinically well infants on regional cerebral oxygen saturation (rScO2) and autoregulatory capacity (CAR) remains unknown. METHODS: Prospective cohort study of blinded rScO2 measurements within a randomized controlled trial of management of hypotension (HIP trial) in extremely preterm infants. rScO2, mean arterial blood pressure, duration of cerebral hypoxia, and transfer function (TF) gain inversely proportional to CAR, were compared between hypotensive infants randomized to receive dopamine or placebo and between hypotensive and non-hypotensive infants, and related to early intraventricular hemorrhage or death. RESULTS: In 89 potentially eligible HIP trial patients with rScO2 measurements, the duration of cerebral hypoxia was significantly higher in 36 hypotensive compared to 53 non-hypotensive infants. In 29/36 hypotensive infants (mean GA 25 weeks, 69% males) receiving the study drug, no significant difference in rScO2 was observed after dopamine (n = 13) compared to placebo (n = 16). Duration of cerebral hypoxia was associated with early intraventricular hemorrhage or death.  Calculated TF gain (n = 49/89) was significantly higher reflecting decreased CAR in 16 hypotensive compared to 33 non-hypotensive infants. CONCLUSIONS: Dopamine had no effect on rScO2 compared to placebo in hypotensive infants. Hypotension and cerebral hypoxia are associated with early intraventricular hemorrhage or death. IMPACT: Treatment of hypotension with dopamine in extremely preterm infants increases mean arterial blood pressure, but does not improve cerebral oxygenation. Hypotensive extremely preterm infants have increased duration of cerebral hypoxia and reduced cerebral autoregulatory capacity compared to non-hypotensive infants. Duration of cerebral hypoxia and hypotension are associated with early intraventricular hemorrhage or death in extremely preterm infants. Since systematic treatment of hypotension may not be associated with better outcomes, the diagnosis of cerebral hypoxia in hypotensive extremely preterm infants might guide treatment.

Application of NPE in the assessment of a patent ductus arteriosus.

In many preterm infants, the ductus arteriosus remains patent beyond the first few days of life. This prolonged patency is associated with numerous adverse outcomes, but the extent to which these adverse outcomes are attributable to the hemodynamic consequences of ductal patency, if at all, has not been established. Different treatment strategies have failed to improve short-term outcomes, with a paucity of data on the correct diagnostic and pathophysiological assessment of the patent ductus arteriosus (PDA) in association with long-term outcomes. Echocardiography is the selected method of choice for detecting a PDA, assessing the impact on the preterm circulation and monitoring treatment response. PDA in a preterm infant can result in pulmonary overcirculation and systemic hypoperfusion, Therefore, echocardiographic assessment should include evaluation of PDA characteristics, indices of pulmonary overcirculation with left heart loading conditions, and indices of systemic hypoperfusion. In this review, we provide an evidence-based overview of the current and emerging ultrasound measurements available to identify and monitor a PDA in the preterm infant. We offer indications and limitations for using Neonatologist Performed Echocardiography to optimize the management of a neonate with a PDA.

Early treatment versus expectative management of patent ductus arteriosus in preterm infants: a multicentre, randomised, non-inferiority trial in Europe (BeNeDuctus trial).

BACKGROUND: Much controversy exists about the optimal management of a patent ductus arteriosus (PDA) in preterm infants, especially in those born at a gestational age (GA) less than 28 weeks. No causal relationship has been proven between a (haemodynamically significant) PDA and neonatal complications related to pulmonary hyperperfusion and/or systemic hypoperfusion. Although studies show conflicting results, a common understanding is that medical or surgical treatment of a PDA does not seem to reduce the risk of major neonatal morbidities and mortality. As the PDA might have closed spontaneously, treated children are potentially exposed to iatrogenic adverse effects. A conservative approach is gaining interest worldwide, although convincing evidence to support its use is lacking. METHODS: This multicentre, randomised, non-inferiority trial is conducted in neonatal intensive care units. The study population consists of preterm infants (GA < 28 weeks) with an echocardiographic-confirmed PDA with a transductal diameter > 1.5 mm. Early treatment (between 24 and 72 h postnatal age) with the cyclooxygenase inhibitor (COXi) ibuprofen (IBU) is compared with an expectative management (no intervention intended to close a PDA). The primary outcome is the composite of mortality, and/or necrotising enterocolitis (NEC) Bell stage ≥ IIa, and/or bronchopulmonary dysplasia (BPD) defined as the need for supplemental oxygen, all at a postmenstrual age (PMA) of 36 weeks. Secondary outcome parameters are short term sequelae of cardiovascular failure, comorbidity and adverse events assessed during hospitalization and long-term neurodevelopmental outcome assessed at a corrected age of 2 years. Consequences regarding health economics are evaluated by cost effectiveness analysis and budget impact analysis. DISCUSSION: As a conservative approach is gaining interest, we investigate whether in preterm infants, born at a GA less than 28 weeks, with a PDA an expectative management is non-inferior to early treatment with IBU regarding to the composite outcome of mortality and/or NEC and/or BPD at a PMA of 36 weeks. TRIAL REGISTRATION: This trial is registered with the Dutch Trial Register NTR5479 (registered on 19 October 2015), the registry sponsored by the United States National Library of Medicine Clinicaltrials.gov NCT02884219 (registered May 2016) and the European Clinical Trials Database EudraCT 2017-001376-28 .

Decreased Variability and Low Values of Perfusion Index on Day One Are Associated with Adverse Outcome in Extremely Preterm Infants.

OBJECTIVE: To develop new quantitative features for the Perfusion Index signal recorded continuously over the first 24 hours of life in a cohort of extremely low gestational age newborns and to assess the association of these features with normal and adverse short-term outcome. STUDY DESIGN: A cohort study of extremely low gestational age newborns. Adverse outcome was defined as early mortality before 72 hours of life, acquired severe periventricular-intraventricular hemorrhage, or severe cystic leukomalacia. Perfusion Index values were obtained from the plethysmographic signal of a pulse oximeter. Perfusion Index signals were separated into low-frequency (trend) and high-frequency (detrend) components. Three features were extracted during four 6-hour epochs: mean of the trend component (mean-trend), SD of the trend component (SD-trend), and SD of the detrend component (SD-detrend). The SD features represent long-term variability (SD-trend) and short-term variability (SD-detrend) of the Perfusion Index. A mixed-effects model was fitted to each feature. RESULTS: Ninety-nine infants were included in the analysis. Quadratic-time mixed-effects models provided the best fit for all 3 features. The mean-trend component was lower for the adverse outcome compared with the normal outcome group with a difference of 0.142 Perfusion Index (P = .001). SD-detrend component was also lower for the adverse compared with the normal outcome group, although this difference of 0.031 Perfusion Index/days2 was dependent on time (P < .001). CONCLUSION: Low values and reduced short-term variability of Perfusion Index on day 1 are associated with adverse outcome.

Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants.

OBJECTIVE: To test the potential utility of applying machine learning methods to regional cerebral (rcSO2) and peripheral oxygen saturation (SpO2) signals to detect brain injury in extremely preterm infants. STUDY DESIGN: A subset of infants enrolled in the Management of Hypotension in Preterm infants (HIP) trial were analysed (n = 46). All eligible infants were <28 weeks' gestational age and had continuous rcSO2 measurements performed over the first 72 h and cranial ultrasounds performed during the first week after birth. SpO2 data were available for 32 infants. The rcSO2 and SpO2 signals were preprocessed, and prolonged relative desaturations (PRDs; data-driven desaturation in the 2-to-15-min range) were extracted. Numerous quantitative features were extracted from the biosignals before and after the exclusion of the PRDs within the signals. PRDs were also evaluated as a stand-alone feature. A machine learning model was used to detect brain injury (intraventricular haemorrhage-IVH grade II-IV) using a leave-one-out cross-validation approach. RESULTS: The area under the receiver operating characteristic curve (AUC) for the PRD rcSO2 was 0.846 (95% CI: 0.720-0.948), outperforming the rcSO2 threshold approach (AUC 0.593 95% CI 0.399-0.775). Neither the clinical model nor any of the SpO2 models were significantly associated with brain injury. CONCLUSION: There was a significant association between the data-driven definition of PRDs in rcSO2 and brain injury. Automated analysis of PRDs of the cerebral NIRS signal in extremely preterm infants may aid in better prediction of IVH compared with a threshold-based approach. Further investigation of the definition of the extracted PRDs and an understanding of the physiology underlying these events are required.

Application and potential of artificial intelligence in neonatal medicine.

Neonatal care is becoming increasingly complex with large amounts of rich, routinely recorded physiological, diagnostic and outcome data. Artificial intelligence (AI) has the potential to harness this vast quantity and range of information and become a powerful tool to support clinical decision making, personalised care, precise prognostics, and enhance patient safety. Current AI approaches in neonatal medicine include tools for disease prediction and risk stratification, neurological diagnostic support and novel image recognition technologies. Key to the integration of AI in neonatal medicine is the understanding of its limitations and a standardised critical appraisal of AI tools. Barriers and challenges to this include the quality of datasets used, performance assessment, and appropriate external validation and clinical impact studies. Improving digital literacy amongst healthcare professionals and cross-disciplinary collaborations are needed to harness the full potential of AI to help take the next significant steps in improving neonatal outcomes for high-risk infants.

Hypotension in Preterm Infants (HIP) randomised trial.

OBJECTIVE: To determine whether restricting the use of inotrope after diagnosis of low blood pressure (BP) in the first 72 hours of life affects survival without significant brain injury at 36 weeks of postmenstrual age (PMA) in infants born before 28 weeks of gestation. DESIGN: Double-blind, placebo-controlled randomised trial. Caregivers were masked to group assignment. SETTING: 10 sites across Europe and Canada. PARTICIPANTS: Infants born before 28 weeks of gestation were eligible if they had an invasive mean BP less than their gestational age that persisted for ≥15 min in the first 72 hours of life and a cerebral ultrasound free of significant (≥ grade 3) intraventricular haemorrhage. INTERVENTION: Participants were randomly assigned to saline bolus followed by either a dopamine infusion (standard management) or placebo (5% dextrose) infusion (restrictive management). PRIMARY OUTCOME: Survival to 36 weeks of PMA without severe brain injury. RESULTS: The trial terminated early due to significant enrolment issues (7.7% of planned recruitment). 58 infants were enrolled between February 2015 and September 2017. The two groups were well matched for baseline variables. In the standard group, 18/29 (62%) achieved the primary outcome compared with 20/29 (69%) in the restrictive group (p=0.58). Additional treatments for low BP were used less frequently in the standard arm (11/29 (38%) vs 19/29 (66%), p=0.038). CONCLUSION: Though this study lacked power, we did not detect major differences in clinical outcomes between standard or restrictive approach to treatment. These results will inform future studies in this area. TRIAL REGISTRATION NUMBER: NCT01482559, EudraCT 2010-023988-17.

Machine Learning to Support Hemodynamic Intervention in the Neonatal Intensive Care Unit.

Hemodynamic support in neonatal intensive care is directed at maintaining cardiovascular wellbeing. At present, monitoring of vital signs plays an essential role in augmenting care in a reactive manner. By applying machine learning techniques, a model can be trained to learn patterns in time series data, allowing the detection of adverse outcomes before they become clinically apparent. In this review we provide an overview of the different machine learning techniques that have been used to develop models in hemodynamic care for newborn infants. We focus on their potential benefits, research pitfalls, and challenges related to their implementation in clinical care.

Monitoring Circulation During Transition in Extreme Low Gestational Age Newborns: What's on the Horizon?

Echocardiography and near-infrared spectroscopy have significantly changed our view on hemodynamic transition of the extreme preterm infant. Instead of focusing on maintaining an arbitrary target value of blood pressure, we aim for circulatory well-being by a comprehensive holistic assessment of markers of cardiovascular instability. Most of these clinical and biochemical indices are influenced by transition itself and remain poor discriminators to identify patients with a potential need for therapeutic intervention. At the same time, the evolution in data capturing and storage has led to a change in our approach to monitor vital parameters. Continuous trend monitoring has become more and more relevant. By using signal extraction methods, changes in trends over time can be quantified. In this review, we will discuss the impact of these innovations on the current monitoring practices and explore some of the potential benefits these techniques may have in improving real-time detection of extreme low birth weight infants at risk for morbidity related to impaired hemodynamic transition.

Early regional myocardial dysfunction in young patients with Duchenne muscular dystrophy.

BACKGROUND: In young patients (aged <12 years) with Duchenne muscular dystrophy (DMD), cardiac systolic function is generally described to be within the normal range. Recent studies have suggested the presence of subclinical dysfunction in these young patients using cardiac magnetic resonance imaging, tissue Doppler measurements, and myocardial velocity gradients. The aim of this study was to further assess regional myocardial function in a young group of patients with DMD using myocardial velocity and deformation imaging. METHODS: Thirty-two patients with DMD (mean age, 7.9 years; range, 3-12 years) and 29 age-matched normal controls were studied with echocardiography. Standard echocardiographic measurements of left ventricular (LV) systolic and diastolic function were performed. Myocardial velocity and deformation data, including peak systolic and early and late diastolic myocardial velocities, peak systolic strain rate (SR), and peak systolic strain (epsilon), were calculated in the radial direction in the inferolateral LV wall and in the longitudinal direction in the interventricular septum, the LV anterolateral wall, and the right ventricular (RV) free wall. RESULTS: Higher heart rates and increased LV end-systolic dimensions were seen in patients with DMD compared with controls. Significant decreases in radial and longitudinal peak systolic SR, peak systolic epsilon, and peak systolic and early diastolic myocardial velocities were found in the LV inferolateral and anterolateral walls in patients with DMD. No significant differences in longitudinal function could be found in the interventricular septum or in the RV free wall. CONCLUSION: In young patients with DMD who have global normal systolic function, reductions in systolic deformation parameters as well as reduced early diastolic myocardial velocities can be detected in the anterolateral and inferolateral LV walls. The prognostic significance of these findings warrants further longitudinal follow-up.