Lara J. Kanbar

Prediction of extubation readiness in extreme preterm infants based on measures of cardiorespiratory variability

The majority of extreme preterm infants require endotracheal intubation and mechanical ventilation (ETT-MV) during the first days of life to survive. Unfortunately this therapy is associated with adverse clinical outcomes and consequently, it is desirable to remove ETT-MV as quickly as possible. However, about 25% of extubated infants will fail and require re-intubation which is also associated with a 5-fold increase in mortality and a longer stay in the intensive care unit. Therefore, the ultimate goal is to determine the optimal time for extubation that will minimize the duration of MV and maximize the chances of success. This paper presents a new objective predictor to assist clinicians in making this decision. The predictor uses a modern machine learning method (Support Vector Machines) to determine the combination of measures of cardiorespiratory variability, computed automatically, that best predicts extubation readiness. Our results demonstrate that this predictor accurately classified infants who would 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. The other challenge we address is class imbalance, as there are many more babies that succeed extubation than those who fail. To handle this problem, we use SMOTE, an algorithm which creates synthetic examples of the minority class.

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).

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.

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. METHODS: 15 preterm infants with birthweight ≤1,250 g and undergoing their first extubation attempt were studied immediately after disconnection from mechanical ventilation. Electrocardiogram (ECG) recordings were obtained while on nasal CPAP and ns-NIV in a random order (30–60 min on each). Time and frequency domain analyses were used to calculate HRV from 5-min segments of ECG. RESULTS: 12 of 15 infants were analyzed (3 were excluded for low ECG quality): 7 successes and 5 failures. HRV parameters were higher during ns-NIV when compared to nasal CPAP, but differences were not statistically different. However, absolute and relative differences in HRV values (all time domain parameters) were significantly higher in infants who failed extubation during ns-NIV. CONCLUSIONS: Nasal CPAP or ns-NIV provided immediately postextubation did not affect HRV. Interestingly, in an exploratory analysis, changes in HRV did occur during ns-NIV in the subgroup of infants who failed extubation. Hence, changes in HRV as early as 2 h after extubation should be further explored in larger studies as a potential predictor of postextubation respiratory failure.

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. An algorithm was generated to discriminate between reintubations attributable to respiratory and non-respiratory causes. Frequency and cumulative distribution curves were constructed for each category using 24 h intervals. The ability of observation windows to capture respiratory-related reintubations while limiting non-respiratory reasons was assessed using a receiver operating characteristic curve.ResultsOut of 194 infants, 91 (47%) were reintubated during hospitalization; 68% for respiratory and 32% for non-respiratory reasons. Respiratory-related reintubation rates steadily increased from 0 to 14 days post-extubation before reaching a plateau. In contrast, non-respiratory reintubations were negligible in the first post-extubation week, but became predominant after 14 days. An observation window of 7 days captured 77% of respiratory-related reintubations while only including 14% of non-respiratory cases.ConclusionReintubation patterns are highly variable and affected by the reasons for reintubation and observation window used. Ideally, reintubation rates should be reported using a cumulative distribution curve over time.

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.