Background: The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information. Main body: While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for "Emergency ML." Throughout the patient care pathway, there are opportunities for MLsupported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models. Conclusion: This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume.
Impaired sleep for hospital patients is an all too common reality. Sleep disruptions due to unnecessary overnight vital sign monitoring are associated with delirium, cognitive impairment, weakened immunity, hypertension, increased stress, and mortality. It is also one of the most common complaints of hospital patients while imposing additional burdens on healthcare providers. Previous efforts to forgo overnight vital sign measurements and improve patient sleep used providers’ subjective stability assessment or utilized an expanded, thus harder to retrieve, set of vitals and laboratory results to predict overnight clinical risk. Here, we present a model that incorporates past values of a small set of vital signs and predicts overnight stability for any given patient-night. Using data obtained from a multi-hospital health system between 2012 and 2019, a recurrent deep neural network was trained and evaluated using ~2.3 million admissions and 26 million vital sign assessments. The algorithm is agnostic to patient location, condition, and demographics, and relies only on sequences of five vital sign measurements, a calculated Modified Early Warning Score, and patient age. We achieved an area under the receiver operating characteristic curve of 0.966 (95% confidence interval [CI] 0.956–0.967) on the retrospective testing set, and 0.971 (95% CI 0.965–0.974) on the prospective set to predict overnight patient stability. The model enables safe avoidance of overnight monitoring for ~50% of patient-nights, while only misclassifying 2 out of 10,000 patient-nights as stable. Our approach is straightforward to deploy, only requires regularly obtained vital signs, and delivers easily actionable clinical predictions for a peaceful sleep in hospitals.
AbstractQuantitative descriptions of the morphology and structure of peripheral nerves is central in the development of bioelectronic devices interfacing the nerves. While histological procedures and microscopy techniques yield high-resolution detailed images of individual axons, automated methods to extract relevant information at the single-axon level are not widely available. We implemented a segmentation algorithm that allows for subsequent feature extraction in immunohistochemistry (IHC) images of peripheral nerves at the single fiber scale. These features include short and long cross-sectional diameters, area, perimeter, thickness of surrounding myelin and polar coordinates of single axons within a nerve or nerve fascicle. We evaluated the performance of our algorithm using manually annotated IHC images of 27 fascicles of the swine cervical vagus; the accuracy of single-axon detection was 82%, and of the classification of fiber myelination was 89%.
Afferent and efferent fibers in the vagus travel inside nerve fascicles and form branches to innervate organs and regulate organ functions. The organization of fibers and fascicles in the vagus trunk, with respect to the functions they mediate and the organs they innervate, remains largely unknown. Accordingly, it is unknown whether that anatomical organization can be leveraged by bioelectronic devices for function- and organ-specific neuromodulation. To characterize the microscopic functional anatomy of the vagus we developed a pipeline consisting of micro-computed tomography-based morphometry of fascicles and quantitative immunohistochemistry of single fibers. We found that, in the swine vagus, fascicles form clusters specific to afferent and efferent functions, increasingly separated in the rostral direction, and other clusters specific to the innervated organs, including larynx, lungs and heart, increasingly separated in the caudal direction. Large myelinated and small unmyelinated efferents have small counts, cover large area of the nerve, and are limited to specific fascicles; small unmyelinated afferents have large counts, cover small area, and are uniformly distributed across fascicles. To test whether fiber populations can be selectively modulated, we developed a multi-contact cuff electrode that observes the fascicular vagus anatomy. Targeting of different nerve sub-sections evokes compound action potentials from different fiber types and elicits differential organ responses, including laryngeal muscle contraction, cough reflex, and changes in breathing and heart rate. Our results indicate that vagus fibers are spatially clustered according to functions they mediate and organs they innervate and can be differentially modulated by spatially selective nerve stimulation.
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