Implementation of Machine Learning Pipelines for Clinical Practice: Development and Validation Study.

Background: Artificial intelligence (AI) technologies, such as machine learning and natural language processing, have the potential to provide new insights into complex health data. Although powerful, these algorithms rarely move from experimental studies to direct clinical care implementation.

Objective: We aimed to describe the key components for successful development and integration of two AI technology-based research pipelines for clinical practice.

Standardizing electronic health record ventilation data in the pediatric long-term mechanical ventilator-dependent population.

Background: Sharing data across institutions is critical to improving care for children who are using long-term mechanical ventilation (LTMV). Mechanical ventilation data are complex and poorly standardized. This lack of data standardization is a major barrier to data sharing.

Automated prediction of extubation success in extremely preterm infants: the APEX multicenter study.

Background: Extremely preterm infants are frequently subjected to mechanical ventilation. Current prediction tools of extubation success lacks accuracy.

Methods: Multicenter study including infants with birth weight ≤1250 g undergoing their first extubation attempt.

Assessment of Extubation Readiness Using Spontaneous Breathing Trials in Extremely Preterm Neonates.

Importance: Spontaneous breathing trials (SBTs) are used to determine extubation readiness in extremely preterm neonates (gestational age ≤28 weeks), but these trials rely on empirical combinations of clinical events during endotracheal continuous positive airway pressure (ET-CPAP).

Objectives: To describe clinical events during ET-CPAP and to assess accuracy of comprehensive clinical event combinations in predicting successful extubation compared with clinical judgment alone.

Design, Setting, And Participants: This multicenter diagnostic study used data from 259 neonates seen at 5 neonatal intensive care units from the prospective Automated Prediction of Extubation Readiness (APEX) study from September 1, 2013, through August 31, 2018.

Cardiorespiratory behavior of preterm infants receiving continuous positive airway pressure and high flow nasal cannula post extubation: randomized crossover study.

Background: Nasal continuous positive airway pressure (NCPAP) and high flow nasal cannula (HFNC) are modes of non-invasive respiratory support commonly used after extubation in extremely preterm infants. However, the cardiorespiratory physiology of these infants on each mode is unknown.

Methods: Prospective, randomized crossover study in infants with birth weight ≤1250 g undergoing their first extubation attempt.

The effects of nasal continuous positive airway pressure and high flow nasal cannula on heart rate variability in extremely preterm infants after extubation: A randomized crossover trial.

Background: NCPAP and High flow nasal cannula (HFNC) are common modes of non-invasive respiratory support used after extubation. Heart rate variability (HRV) has been demonstrated as a marker of well-being in neonates and differences in HRV were described in preterm infants receiving respiratory care. The objective was to investigate the effects of NCPAP and HFNC on HRV after extubation.

Undersampling and Bagging of Decision Trees in the Analysis of Cardiorespiratory Behavior for the Prediction of Extubation Readiness in Extremely Preterm Infants.

Extremely preterm infants often require endotracheal intubation and mechanical ventilation during the first days of life. Due to the detrimental effects of prolonged invasive mechanical ventilation (IMV), clinicians aim to extubate infants as soon as they deem them ready.Unfortunately, existing strategies for prediction of extubation readiness vary across clinicians and institutions, and lead to high reintubation rates.

The Impact of Time Interval between Extubation and Reintubation on Death or Bronchopulmonary Dysplasia in Extremely Preterm Infants.

Objective: To explore the relation between time to reintubation and death or bronchopulmonary dysplasia (BPD) in extremely preterm infants.

Study Design: This was a subanalysis from an ongoing multicenter observational study. Infants with birth weight ≤1250 g, requiring mechanical ventilation, and undergoing their first elective extubation were prospectively followed throughout hospitalization.

Patterns of reintubation in extremely preterm infants: a longitudinal cohort study.

BackgroundThe optimal approach for reporting reintubation rates in extremely preterm infants is unknown. This study aims to longitudinally describe patterns of reintubation in this population over a broad range of observation windows following extubation.MethodsTiming and reasons for reintubation following a first planned extubation were collected from infants with birth weight ≤1,250 g.

Heart Rate Variability in Extremely Preterm Infants Receiving Nasal CPAP and Non-Synchronized Noninvasive Ventilation Immediately After Extubation.

Introduction: There is a paucity of studies comparing the physiological effects of nasal CPAP or non-synchronized noninvasive ventilation (ns-NIV) during the postextubation phase in preterm infants. Heart rate variability (HRV) can identify system instability before clinical or laboratory signs of deterioration. Thus, we sought to investigate any differences in HRV between those modes.

APEX_SCOPE: A graphical user interface for visualization of multi-modal data in inter-disciplinary studies.

In multi-disciplinary studies, different forms of data are often collected for analysis. For example, APEX, a study on the automated prediction of extubation readiness in extremely preterm infants, collects clinical parameters and cardiorespiratory signals. A variety of cardiorespiratory metrics are computed from these signals and used to assign a cardiorespiratory pattern at each time.

A semi-Markov chain approach to modeling respiratory patterns prior to extubation in preterm infants.

After birth, extremely preterm infants often require specialized respiratory management in the form of invasive mechanical ventilation (IMV). Protracted IMV is associated with detrimental outcomes and morbidities. Premature extubation, on the other hand, would necessitate reintubation which is risky, technically challenging and could further lead to lung injury or disease.

Prediction of Extubation readiness in extremely preterm infants by the automated analysis of cardiorespiratory behavior: study protocol.

Background: Extremely preterm infants (≤ 28 weeks gestation) commonly require endotracheal intubation and mechanical ventilation (MV) to maintain adequate oxygenation and gas exchange. Given that MV is independently associated with important adverse outcomes, efforts should be made to limit its duration. However, current methods for determining extubation readiness are inaccurate and a significant number of infants fail extubation and require reintubation, an intervention that may be associated with increased morbidities.

Automated ongoing data validation and quality control of multi-institutional studies.

This paper addresses the problem of ensuring the validity and quality of data in ongoing multi-disciplinary studies where data acquisition spans several geographical sites. It describes an automated validation and quality control procedure that requires no user supervision and monitors data acquired from different locations before analysis. The procedure is illustrated for the Automated Prediction of Extubation readiness (APEX) project in preterm infants, where acquisition of clinical and cardiorespiratory data occurs at 6 sites using different equipment and personnel.

Correlation of clinical parameters with cardiorespiratory behavior in successfully extubated extremely preterm infants.

Extremely preterm infants (gestational age ≤ 28 weeks) often require EndoTracheal Tube-Invasive Mechanical Ventilation (ETT-IMV) to survive. Clinicians wean infants off ETT-IMV as early as possible using their judgment and clinical information. However, assessment of extubation readiness is not accurate since 20 to 40% of preterm infants fail extubation.

Feature selection and oversampling in analysis of clinical data for extubation readiness in extreme preterm infants.

We present an approach for the analysis of clinical data from extremely preterm infants, in order to determine if they are ready to be removed from invasive endotracheal mechanical ventilation. The data includes over 100 clinical features, and the subject population is naturally quite small. To address this problem, we use feature selection, specifically mutual information, in order to choose a small subset of informative features.

Organizational principles of cloud storage to support collaborative biomedical research.

This paper describes organizational guidelines and an anonymization protocol for the management of sensitive information in interdisciplinary, multi-institutional studies with multiple collaborators. This protocol is flexible, automated, and suitable for use in cloud-based projects as well as for publication of supplementary information in journal papers. A sample implementation of the anonymization protocol is illustrated for an ongoing study dealing with Automated Prediction of EXtubation readiness (APEX).