Faculty Bibliography

Monitoring of respiratory mechanics is required for guiding patient-specific mechanical ventilation settings in critical care. Many models of respiratory mechanics perform poorly in the presence of variable patient effort. Typical modelling approaches either attempt to mitigate the effect of the patient effort on the airway pressure waveforms, or attempt to capture the size and shape of the patient effort. This work analyses a range of methods to identify respiratory mechanics in volume controlled ventilation modes when there is patient effort. The models are compared using 4 Datasets, each with a sample of 30 breaths before, and 2-3 minutes after sedation has been administered. The sedation will reduce patient efforts, but the underlying pulmonary mechanical properties are unlikely to change during this short time. Model identified parameters from breathing cycles with patient effort are compared to breathing cycles that do not have patient effort. All models have advantages and disadvantages, so model selection may be specific to the respiratory mechanics application. However, in general, the combined method of iterative interpolative pressure reconstruction, and stacking multiple consecutive breaths together has the best performance over the Dataset. The variability of identified elastance when there is patient effort is the lowest with this method, and there is little systematic offset in identified mechanics when sedation is administered.

BACKGROUND:) tables, which fail to account for the inter- and intra-patient variability between and within patients. The benefits of higher or lower tidal volume, PEEP, and other settings are highly debated and no consensus has been reached. Moreover, clinicians implicitly account for patient-specific factors such as disease condition and progression as they manually titrate ventilator settings. Hence, care is highly variable and potentially often non-optimal. These conditions create a situation that could benefit greatly from an engineered approach. The overall goal is a review of ventilation that is accessible to both clinicians and engineers, to bridge the divide between the two fields and enable collaboration to improve patient care and outcomes. This review does not take the form of a typical systematic review. Instead, it defines the standard terminology and introduces key clinical and biomedical measurements before introducing the key clinical studies and their influence in clinical practice which in turn flows into the needs and requirements around how biomedical engineering research can play a role in improving care. Given the significant clinical research to date and its impact on this complex area of care, this review thus provides a tutorial introduction around the review of the state of the art relevant to a biomedical engineering perspective. DISCUSSION/CONCLUSIONS:This review presents the significant clinical aspects and variables of ventilation management, the potential risks associated with suboptimal ventilation management, and a review of the major recent attempts to improve ventilation in the context of these variables. The unique aspect of this review is a focus on these key elements relevant to engineering new approaches. In particular, the need for ventilation strategies which consider, and directly account for, the significant differences in patient condition, disease etiology, and progression within patients is demonstrated with the subsequent requirement for optimal ventilation strategies to titrate for patient- and time-specific conditions. CONCLUSION/CONCLUSIONS:Engineered, protective lung strategies that can directly account for and manage inter- and intra-patient variability thus offer great potential to improve both individual care, as well as cohort clinical outcomes.

Conventional text classification models make a bag-of-words assumption reducing text into word occurrence counts per document. Recent algorithms such as word2vec are capable of learning semantic meaning and similarity between words in an entirely unsupervised manner using a contextual window and doing so much faster than previous methods. Each word is projected into vector space such that similar meaning words such as "strong" and "powerful" are projected into the same general Euclidean space. Open questions about these embeddings include their utility across classification tasks and the optimal properties and source of documents to construct broadly functional embeddings. In this work, we demonstrate the usefulness of pre-trained embeddings for classification in our task and demonstrate that custom word embeddings, built in the domain and for the tasks, can improve performance over word embeddings learnt on more general data including news articles or Wikipedia.

Natural Language Processing (NLP) holds potential for patient care and clinical research, but a gap exists between promise and reality. While some studies have demonstrated portability of NLP systems across multiple sites, challenges remain. Strategies to mitigate these challenges can strive for complex NLP problems using advanced methods (hard-to-reach fruit), or focus on simple NLP problems using practical methods (low-hanging fruit). This paper investigates a practical strategy for NLP portability using extraction of left ventricular ejection fraction (LVEF) as a use case. We used a tool developed at the Department of Veterans Affair (VA) to extract the LVEF values from free-text echocardiograms in the MIMIC-III database. The approach showed an accuracy of 98.4%, sensitivity of 99.4%, a positive predictive value of 98.7%, and F-score of 99.0%. This experience, in which a simple NLP solution proved highly portable with excellent performance, illustrates the point that simple NLP applications may be easier to disseminate and adapt, and in the short term may prove more useful, than complex applications.

Complex medical data sometimes requires significant data preprocessing to prepare for analysis. The complexity can lead non-domain experts to apply simple filters of available data or to not use the data at all. The preprocessing choices can also have serious effects on the results of the study if incorrect decision or missteps are made. In this work, we present open-source data filters for an analysis motivated by understanding mortality in the context of sepsis- associated cardiomyopathy in the ICU. We report specific ICU filters and validations through chart review and graphs. These published filters reduce the complexity of using data in analysis by (1) encapsulating the domain expertise and feature engineering applied to the filter, by (2) providing debugged and ready code for use, and by (3) providing sensible validations. We intend these filters to evolve through pull requests and forks and serve as common starting points for specific analyses.

Asynchronous Events (AEs) during mechanical ventilation (MV) result in increased work of breathing and potential poor patient outcomes. Thus, it is important to automate AE detection. In this study, an AE detection method, Automated Logging of Inspiratory and Expiratory Non-synchronized breathing (ALIEN) was developed and compared between standard manual detection in 11 MV patients. A total of 5701 breaths were analyzed (median [IQR]: 500 [469-573] per patient). The Asynchrony Index (AI) was 51% [28-78]%. The AE detection yielded sensitivity of 90.3% and specificity of 88.3%. Automated AE detection methods can potentially provide clinicians with real-time information on patient-ventilator interaction.