Predicting Responses to Mechanical Ventilation for Preterm Infants with Acute Respiratory Illness using Artificial Neural Networks

Infants born prematurely are particularly susceptible to respiratory illness due to underdeveloped lungs, which can often result in fatality. Preterm infants in acute stages of respiratory illness typically require mechanical ventilation assistance, and the efficacy of the type of mechanical ventilation and its delivery has been the subject of a number clinical studies. With recent advances in machine learning approaches, particularly deep learning, it may be possible to estimate future responses to mechanical ventilation in real‐time, based on ventilation monitoring up to the point of analysis. In this work, recurrent neural networks are proposed for predicting future ventilation parameters due to the highly nonlinear behavior of the ventilation measures of interest and the ability of recurrent neural networks to model complex nonlinear functions. The resulting application of this particular class of neural networks shows promise in its ability to predict future responses for different ventilation modes. Towards improving care and treatment of preterm newborns, further development of this prediction process for ventilation could potentially aid in important clinical decisions or studies to improve preterm infant health

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

Environmental And Genetic Risk Factors For Bronchopulmonary Dysplasia In Neonates

We hypothesized that early intubation would decrease the hazard of BPD and BPD/death in premature infants regardless of need for reintubation. Specific aims included assessing rates of BPD and BPD/death in infants first extubated between day of life (DOL)1-3 versus 4-7, 8+ and impact of re-intubation. We included infants with gestational age ≤28weeks, birth weight ≤1000g, and intubation on DOL1. Proportional hazards regression modeled time to BPD and BPD/death, adjusting for potential confounders. Of 262 infants, 101 (38.55%), 41 (15.65%) and 120 (45.80%) were extubated between DOL1-3, 4-7, and 8+, respectively. Extubation between DOL4-7 versus DOL1-3 was associated with an increased hazard of developing BPD (HR 1.7; 95%CI 1.0-2.8; p\u3c0.05). Extubation on DOL 8+ was associated with a significantly increased hazard compared to extubation between DOL1-3 (16.9; 10.5-27.1; \u3c0.0001) or DOL4-7 (10.0; 6.1-16.3; \u3c 0.0001). Similar results were noted with BPD/death. Re-intubation did not affect BPD and BPD/death. Delaying extubation beyond the first 3 and 7 days was associated with an increased risk of BPD and BPD/death. Re-intubation did not impact outcomes

INTERMITTENT HYPOXEMIA IN PRETERM INFANTS

Intermittent hypoxemia (IH) is defined as episodic drops in oxygen saturation (SpO2). Virtually all preterm infants have IH events. Extremely preterm infants have hundreds of IH events per day. The extent of IH is not apparent clinically as accurately documenting cardiorespiratory events for day-to-day patient care management is challenging. High resolution pulse oximeters with 2 second averaging time are currently the ideal methods to measure IH. We have developed novel methods and processes to accurately and efficiently calculate an IH profile that reflects to spectrum of the problem. The natural progression of IH is dynamic. There is low incidence of IH in the few 2 weeks of life, followed by a progressive increase until peak IH at 4-5 week after which IH plateaus. Multiple factors place preterm infants at high risk for increased IH. These factors include respiratory immaturity, lung disease, and anemia. We also show that preterm infants prenatally exposed to opioids or inflammation (due to maternal chorioamnionitis) have increased IH measures compared to unexposed infants. Interestingly, the increased IH in the exposed groups persists beyond the immediate postnatal period. Brief episodes of oxygen desaturations may seem clinically insignificant; however, these events may have a cumulative effect on neonatal outcomes. There is mounting evidence from both animal models and clinical studies suggesting that IH is associated with injury and poor outcomes such as impaired growth, retinopathy of prematurity and neurodevelopmental impairment. In addition data from neonatal animal models and adults with obstructive sleep apnea suggest that IH is pro inflammatory itself. We demonstrate in this document for the first time in preterm infants that IH is associated with increased serum inflammatory marker, C-reactive protein. Finally, a valuable experience throughout this process is working with a talented and dedicated multidisciplinary team. We are a solid example of the value of team science during this new era of clinical and translational research. Our respiratory control research program is one of handful programs nationwide able to perform such high-fidelity studies related to cardiorespiratory events in preterm infants. We will continue to tackle complex questions involving health of infants

Investigating the Use of Support Vector Machine Classification on Structural Brain Images of Preterm–Born Teenagers as a Biological Marker

Preterm birth has been shown to induce an altered developmental trajectory of brain structure and function. With the aid support vector machine (SVM) classification methods we aimed to investigate whether MRI data, collected in adolescence, could be used to predict whether an individual had been born preterm or at term. To this end we collected T1–weighted anatomical MRI data from 143 individuals (69 controls, mean age 14.6y). The inclusion criteria for those born preterm were birth weight ≤ 1500g and gestational age < 37w. A linear SVM was trained on the grey matter segment of MR images in two different ways. First, all the individuals were used for training and classification was performed by the leave–one–out method, yielding 93% correct classification (sensitivity = 0.905, specificity = 0.942). Separately, a random half of the available data were used for training twice and each time the other, unseen, half of the data was classified, resulting 86% and 91% accurate classifications. Both gestational age (R = –0.24, p<0.04) and birth weight (R = –0.51, p < 0.001) correlated with the distance to decision boundary within the group of individuals born preterm. Statistically significant correlations were also found between IQ (R = –0.30, p < 0.001) and the distance to decision boundary. Those born small for gestational age did not form a separate subgroup in these analyses. The high rate of correct classification by the SVM motivates further investigation. The long–term goal is to automatically and non–invasively predict the outcome of preterm–born individuals on an individual basis using as early a scan as possible

Comparison of oxygen supplementation in very preterm infants: Variations of oxygen saturation features and their application to hypoxemic episode based risk stratification

BackgroundOxygen supplementation is commonly used to maintain oxygen saturation (SpO2) levels in preterm infants within target ranges to reduce intermittent hypoxemic (IH) events, which are associated with short- and long-term morbidities. There is not much information available about differences in oxygenation patterns in infants undergoing such supplementations nor their relation to observed IH events. This study aimed to describe oxygenation characteristics during two types of supplementation by studying SpO2 signal features and assess their performance in hypoxemia risk screening during NICU monitoring.Subjects and methodsSpO2 data from 25 infants with gestational age &lt;32 weeks and birthweight &lt;2,000 g who underwent a cross over trial of low-flow nasal cannula (NC) and digitally-set servo-controlled oxygen environment (OE) supplementations was considered in this secondary analysis. Features pertaining to signal distribution, variability and complexity were estimated and analyzed for differences between the supplementations. Univariate and regularized multivariate logistic regression was applied to identify relevant features and develop screening models for infants likely to experience a critically high number of IH per day of observation. Their performance was assessed using area under receiver operating curves (AUROC), accuracy, sensitivity, specificity and F1 scores.ResultsWhile most SpO2 measures remained comparable during both supplementations, signal irregularity and complexity were elevated while on OE, pointing to more volatility in oxygen saturation during this supplementation mode. In addition, SpO2 variability measures exhibited early prognostic value in discriminating infants at higher risk of critically many IH events. Poincare plot variability at lag 1 had AUROC of 0.82, 0.86, 0.89 compared to 0.63, 0.75, 0.81 for the IH number, a clinical parameter at observation times of 30 min, 1 and 2 h, respectively. Multivariate models with two features exhibited validation AUROC &gt; 0.80, F1 score &gt; 0.60 and specificity &gt;0.85 at observation times ≥ 1 h. Finally, we proposed a framework for risk stratification of infants using a cumulative risk score for continuous monitoring.ConclusionAnalysis of oxygen saturation signal routinely collected in the NICU, may have extensive applications in inferring subtle changes to cardiorespiratory dynamics under various conditions as well as in informing clinical decisions about infant care

Artificial Intelligence for the prediction of weaning readiness outcome in a multi-centrical clinical cohort of mechanically ventilated patients

Quando un paziente soffre di insufficienza respiratoria acuta, viene praticata la ventilazione meccanica (VM) finché questa non riesce a respirare di nuovo in autonomia. Il medico di Terapia Intensiva verifica ogni giorno se la VM può essere interrotta. Questo screening consiste in una prima fase, il Readiness Test (RT), che è composta da vari parametri clinici. Se questo test ha esito positivo, si sottopone il paziente a 30 minuti di respirazione spontanea (SBT). Se anche l'SBT viene superato con successo, la VM viene interrotta. Al contrario, se l’RT o l’SBT falliscono, il paziente rimane in VM e verrà rivalutato il giorno successivo. Quindi ogni giorno possono verificarsi tre scenari mutuamente esclusivi: l’SBT non verrà tentato, l’SBT fallirà o l’SBT avrà successo (portando quindi all’estubazione del paziente). Il modello di intelligenza artificiale sviluppato, è progettato per dedurre fin dalle prime ore del mattino quale dei tre scenari si verificherà probabilmente nel corso della giornata, partendo dai dati clinici del paziente, dalle informazioni raccolte nel diario clinico dei giorni precedenti e dall'intera storia di registrazione minuto-per-minuto dei vari parametri del ventilatore meccanico, provenienti da uno studio osservazionale retrospettivo multicentrico, condotto in Italia nel corso di 27 mesi. Questi dati vengono elaborati con un approccio di Deep Learning, attraverso una topologia di rete neurale multi-sorgente, alimentata da architetture ricorrenti multiple. Gli iper-parametri sono ottimizzati per selezionare il modello desiderato attraverso la convalida incrociata, riservando 36 pazienti su 182 per testare le prestazioni finali del modello su una serie di metriche, tra cui uno score personalizzato progettato per evidenziare l'impatto clinico. Il modello di intelligenza artificiale finale mostra un'accuratezza del 79% [74, 83%], uno score personalizzato di 0,01 [-0,04, 0,05], un MCC di 0,28 [0,17, 0,39], ottenendo un punteggio migliore rispetto agli altri modelli di confronto, tra cui XG Boost, addestrato solo sui dati clinici giornalieri del giorno precedente, che ha avuto un'accuratezza del 61% [56%, 66%], un MCC di 0,14 [0,06, 0,2] e uno score personalizzato di -0,05 [-0,08, -0,01]. Complessivamente, il modello di intelligenza artificiale è in grado di approssimare bene l'attuale gestione clinica giorno per giorno, fornendo suggerimenti al mattino presto. Inoltre, c'è ancora spazio per migliorare l'utilità clinica del modello considerando ulteriori dati di addestramento personalizzati.When someone suffers from acute respiratory failure, mechanical ventilation (MV) is performed until they can breathe on their own again. The doctor checks every day whether the MV can be stopped. This screening consists of a first phase, the Readiness Testing (RT), which includes various clinical parameters. If this test is successful, 30 minutes of spontaneous breathing (SBT) is attempted. If also the SBT is passed successfully, the VM is stopped. On the contrary, if RT or SBT fails, the patient will be re-evaluated the next day. So, every day three mutually exclusive scenarios may happen: SBT will not be attempted, SBT will fail, or SBT will succeed. Our artificial intelligence model is designed to infer early in the morning which of the three scenarios will probably occur during the day, starting from the patient's clinical data, from the information collected in the previous day’s clinical diary, and from whole minute-by-minute recording history of the various parameters of the mechanical ventilator, coming from a retrospective observational multi-centrical study, conducted in Italy over a course of 27 months. Those data are processed with a deep learning approach, through a multi-source neural network topology, powered by multiple recurrent architectures. Hyper-parameters are optimized to select the purposed model through cross-validation, setting aside 36 out of 182 patients for testing final model performance over a variety of metrics, including a custom score designed to highlight clinical impact. The final AI model had an accuracy of 79% [74, 83%], a custom score of 0.01 [-0.04, 0.05], a MCC of 0.28 [0.17, 0.39], scoring better than the other comparison models, including XG Boost that was trained on daily and baseline clinical data of the previous day only, which had an accuracy of 61% [56%, 66%], a MCC of 0.14 [0.06, 0.2] and a custom score of -0.05 [-0.08, -0.01]. Overall, AI model could approximate well what is the current clinical management throughout day-by-day providing suggestions early in the morning. Moreover, there are still space to improve the model clinical utility considering additional tailored training data