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. Clinical data and cardiorespiratory signals were acquired before extubation. Primary outcome was prediction of extubation success. Automated analysis of cardiorespiratory signals, development of clinical and cardiorespiratory features, and a 2-stage Clinical Decision-Balanced Random Forest classifier were used. A leave-one-out cross-validation was done. Performance was analyzed by ROC curves and determined by balanced accuracy. An exploratory analysis was performed for extubations before 7 days of age. RESULTS: A total of 241 infants were included and 44 failed (18%) extubation. The classifier had a balanced accuracy of 73% (sensitivity 70% [95% CI: 63%, 76%], specificity 75% [95% CI: 62%, 88%]). As an additional clinical-decision tool, the classifier would have led to an increase in extubation success from 82% to 93% but misclassified 60 infants who would have been successfully extubated. In infants extubated before 7 days of age, the classifier identified 16/18 failures (specificity 89%) and 73/105 infants with success (sensitivity 70%). CONCLUSIONS: Machine learning algorithms may improve a balanced prediction of extubation outcomes, but further refinement and validation is required. IMPACT: A machine learning-derived predictive model combining clinical data with automated analyses of individual cardiorespiratory signals may improve the prediction of successful extubation and identify infants at higher risk of failure with a good balanced accuracy. Such multidisciplinary approach including medicine, biomedical engineering and computer science is a step forward as current tools investigated to predict extubation outcomes lack sufficient balanced accuracy to justify their use in future trials or clinical practice. Thus, this individualized assessment can optimize patient selection for future trials of extubation readiness by decreasing exposure of low-risk infants to interventions and maximize the benefits of those at high risk.

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. NCPAP and HFNC were applied randomly for 45 min each, while ribcage and abdominal movements, electrocardiogram, oxygen saturation, and fraction of inspired oxygen (FiO2) were recorded. Respiratory signals were analyzed using an automated method, and differences between NCPAP and HFNC features and changes in FiO2 were analyzed. RESULTS: A total of 30 infants with median [interquartile range] gestational age of 27 weeks [25.7, 27.9] and birth weight of 930 g [780, 1090] were studied. Infants were extubated at 5 days [2, 13] of life with 973 g [880, 1170] and three failed (10%). No differences in cardiorespiratory behavior were noted, except for longer respiratory pauses (9.2 s [5.0, 11.5] vs. 7.3 s [4.6, 9.3]; p = 0.04) and higher FiO2 levels (p = 0.02) during HFNC compared to NCPAP. CONCLUSIONS: In extremely preterm infants studied shortly after extubation, the use of HFNC was associated with longer respiratory pauses and higher FiO2 requirements.

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. A variety of objective measures have been proposed to better define the optimal time for extubation, but none have proven clinically useful. In a pilot study, investigators from this group have shown promising results from sophisticated, automated analyses of cardiorespiratory signals as a predictor of extubation readiness. The aim of this study is to develop an automated predictor of extubation readiness using a combination of clinical tools along with novel and automated measures of cardiorespiratory behavior, to assist clinicians in determining when extremely preterm infants are ready for extubation. METHODS: In this prospective, multicenter observational study, cardiorespiratory signals will be recorded from 250 eligible extremely preterm infants with birth weights ≤1250 g immediately prior to their first planned extubation. Automated signal analysis algorithms will compute a variety of metrics for each infant, and machine learning methods will then be used to find the optimal combination of these metrics together with clinical variables that provide the best overall prediction of extubation readiness. Using these results, investigators will develop an Automated system for Prediction of EXtubation (APEX) readiness that will integrate the software for data acquisition, signal analysis, and outcome prediction into a single application suitable for use by medical personnel in the neonatal intensive care unit. The performance of APEX will later be prospectively validated in 50 additional infants. DISCUSSION: The results of this research will provide the quantitative evidence needed to assist clinicians in determining when to extubate a preterm infant with the highest probability of success, and could produce significant improvements in extubation outcomes in this population. TRIAL REGISTRATION: Clinicaltrials.gov identifier: NCT01909947 . Registered on July 17 2013. Trial sponsor: Canadian Institutes of Health Research (CIHR).

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. OBJECTIVE: We aimed to describe current ventilator data in the electronic health record (EHR) and propose a framework for standardizing these data using a common data model (CDM) across multiple populations and sites. METHODS: We focused on a cohort of patients with LTMV dependence who were weaned from mechanical ventilation (MV). We extracted and described relevant EHR ventilation data. We identified the minimum necessary components, termed "Clinical Ideas," to describe MV from time of initiation to liberation. We then utilized existing resources and partnered with informatics collaborators to develop a framework for incorporating Clinical Ideas into the PEDSnet CDM based on the Observational Medical Outcomes Partnership (OMOP). RESULTS: We identified 78 children with LTMV dependence who weaned from ventilator support. There were 25 unique device names and 28 unique ventilation mode names used in the cohort. We identified multiple Clinical Ideas necessary to describe ventilator support over time: device, interface, ventilation mode, settings, measurements, and duration of ventilation usage per day. We used Concepts from the SNOMED-CT vocabulary and integrated an existing ventilator mode taxonomy to create a framework for CDM and OMOP integration. CONCLUSION: The proposed framework standardizes mechanical ventilation terminology and may facilitate efficient data exchange in a multisite network. Rapid data sharing is necessary to improve research and clinical care for children with LTMV dependence.

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. METHODS: We summarized the approach, results, and key learnings from the implementation of the following two systems implemented at a large, tertiary care children's hospital: (1) epilepsy surgical candidate identification (or epilepsy ID) in an ambulatory neurology clinic; and (2) an automated clinical trial eligibility screener (ACTES) for the real-time identification of patients for research studies in a pediatric emergency department. RESULTS: The epilepsy ID system performed as well as board-certified neurologists in identifying surgical candidates (with a sensitivity of 71% and positive predictive value of 77%). The ACTES system decreased coordinator screening time by 12.9%. The success of each project was largely dependent upon the collaboration between machine learning experts, research and operational information technology professionals, longitudinal support from clinical providers, and institutional leadership. CONCLUSIONS: These projects showcase novel interactions between machine learning recommendations and providers during clinical care. Our deployment provides seamless, real-time integration of AI technology to provide decision support and improve patient care.

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. We present an approach using Random Forest classifiers for the analysis of cardiorespiratory variability to predict extubation readiness. We address the issue of data imbalance by employing random undersampling of examples from the majority class before training each Decision Tree in a bag. By incorporating clinical domain knowledge, we further demonstrate that our classifier could have identified 71% of infants who failed extubation, while maintaining a success detection rate of 78%.

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. In such a situation, exploratory analysis requires a visualization tool capable of displaying these different types of acquired and computed signals in an integrated environment. Thus, we developed APEX_SCOPE, a graphical tool for the visualization of multi-modal data comprising cardiorespiratory signals, automated cardiorespiratory metrics, automated respiratory patterns, manually classified respiratory patterns, and manual annotations by clinicians during data acquisition. This MATLAB-based application provides a means for collaborators to view combinations of signals to promote discussion, generate hypotheses and develop features.

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. We present an approach to modeling respiratory patterns of infants who succeeded extubation and those who required reintubation which relies on Markov models. We compare the use of traditional Markov chains to semi-Markov models which emphasize cross-pattern transitions and timing information, and to multi-chain Markov models which can concisely represent non-stationarity in respiratory behavior over time. The models we developed expose specific, unique similarities as well as vital differences between the two populations.

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. We have identified more than 40 problems with clinical information and 25 possible problems with the cardiorespiratory signals. Our validation and quality control procedure identifies these problems in an ongoing manner so that they can be timely addressed and corrected throughout this long-term collaborative study.

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. We extended our work in automated prediction of extubation readiness by examining correlations of automated cardiorespiratory features to clinical parameters in successfully extubated infants. Only a few features, mainly those related to variability of breathing synchrony, had any consistent correlation with clinical parameters, namely gestational age, day of life at extubation, and bicarbonate. We conclude that the automated cardiorespiratory features likely provide different information additional to clinical practice.

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).